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date: 25 February 2024

Climate and Health across Africafree

Climate and Health across Africafree

  • Benjamin F. ZaitchikBenjamin F. ZaitchikJohns Hopkins University


Humans have understood the importance of climate to human health since ancient times. In some cases, the connections appear to be obvious: a flood can cause drownings, a drought can lead to crop failure and hunger, and temperature extremes pose a risk of exposure. In other cases, the connections are veiled by complex or unobserved processes, such that the influence of climate on a disease epidemic or a conflict can be difficult to diagnose. In reality, however, all climate impacts on health are mediated by some combination of natural and human dynamics that cause individuals or populations to be vulnerable to the effects of a variable or changing climate.

Understanding and managing negative health impacts of climate is a global challenge. The challenge is greater in regions with high poverty and weak institutions, however, and Africa is a continent where the health burden of climate is particularly acute. Observed climate variability in the modern era has been associated with widespread food insecurity, significant epidemics of infectious disease, and loss of life and livelihoods to climate extremes. Anthropogenic climate change is a further stress that has the potential to increase malnutrition, alter the distribution of diseases, and bring more frequent hydrological and temperature extremes to many regions across the continent.

Skillful early warning systems and informed climate change adaptation strategies have the potential to enhance resilience to short-term climate variability and to buffer against negative impacts of climate change. But effective warnings and projections require both scientific and institutional capacity to address complex processes that are mediated by physical, ecological, and societal systems. Here the state of understanding climate impacts on health in Africa is summarized through a selective review that focuses on food security, infectious disease, and extreme events. The potential to apply scientific understanding to early warning and climate change projection is also considered.


  • Policy, Politics, and Governance
  • Risk Management and Adaptation
  • Future Climate Change Scenarios
  • Climate Impact: Human Health
  • Climate of Africa

Climate and Health in Africa

Africa is commonly described as a “climate-vulnerable” continent in which rainfall variability, hydrological extremes, and anthropogenic climate change have the potential to inflict significant harm on large populations (Doumbia, Jalloh, & Diouf, 2014). This description is grounded in the hard reality of modern history. Droughts have triggered massive economic loss, famine, displacement, and possibly armed conflict in regions across Africa: the western Sahel, the Horn of Africa, Darfur, and others. Floods exact a significant cost as well in river basins across the continent, leading to immediate loss of property and lives, and sometimes triggering crippling economic hardship and epidemics of waterborne and vector-borne disease. Meanwhile, a warming climate may be associated with changes in the range of infectious disease, loss of crop production and fisheries, associated undernutrition, increases in extreme events, and exposure to acute heat stress.

At the same time, quantifying, predicting, and projecting the full impact that climate has on human health is a daunting challenge. In part, this is because of inadequate data over much of the continent. Climate-monitoring networks are sparse, economic and agricultural records can be inconsistent and incomplete, and health outcomes data are limited. But the challenge runs deeper than data. Understanding the impacts of climate on health is fundamentally difficult in any context because the connections are highly mediated by physical, ecological, and sociological factors. In Africa, rapid economic growth, demographic change, frequent political instability, and environmental changes independent of climate (e.g., overgrazing, deforestation) make it particularly difficult to trace climate impact to health outcome through these mediating dynamics.

One way to conceptualize these processes is to distinguish between health impacts that are primarily physically mediated, those that depend on ecological as well as physical mediation, and those that are most strongly influenced by societal factors layered on physical and ecological conditions (Figure 1). This is an imperfect classification, as few health impacts fall neatly into one category and there is frequent interaction across mediating processes and health outcomes. Flood control infrastructure, for example, is part of the physical mediating environment, but its construction, maintenance, and operation are functions of societal factors. Nutritional outcomes are affected by infectious disease burden, crossing ecological and social categories. Nevertheless, the classification provides an entry point for dealing with complex climate–health dynamics. The model shown in Figure 1 is similar to the models used by the Intergovernmental Panel on Climate Change (IPCC) and other climate and health reviews and assessments (Balbus et al., 2016; McMichael, Woodruff, & Hales, 2006; Smith et al., 2014).

Figure 1. Climate anomalies and trends mediated by physical, ecological, and societal processes can cause diverse health impacts, requiring a health system response. All categories of mediating process include natural and human systems.

In Africa, each of the pathways linking climate to health has long been a study concern. Initially, this work was largely motivated by the tremendous health challenges faced by Europeans residing in the African colonies. Detailed records were kept of disease outbreaks along the Gold Coast (current Guinea Coast), for example. A connection was made between the seasons and disease occurrence. Medically, the tropical year was considered to have three divisions: diarrheal/dysenteric, fevers/malaria, and congestive and pulmonary. The first coincided with the hottest months and was considered to be relatively healthy. Despite considerable physical adaptations of the body to the excessive heat, fever was generally rare. The second, coinciding with the rainy season, was considered the unhealthiest, although heavy rains could diminish the occurrence of malaria by disrupting the stagnation of surface waters where mosquitoes breed. The third division, that of the northeast Harmattan winds of the cold season, also brought healthy conditions as well as a frequent break from the heat. However, dust and cold lead to congestive and pulmonary problems. Modern study of climate and disease in Africa goes well beyond consideration of the seasonality of disease and physiological adaptations. Links between year-to-year frequency of certain diseases and changes in climate have been established, as have predictive models of disease.

Africa is a remarkably diverse continent. Rather than attempt a comprehensive inventory of all climate and health issues, more useful are overviews of salient examples of physically, ecologically, and socially mediated health challenges found in various regions. These examples include the most significant climate–health phenomena in developing countries of sub-Saharan Africa, several of which are being impacted by climate change. Neither the health examples nor the methods used to study them are unique to Africa, but the picture they compose is clear: the impact that climate variability and change have on food security is the single greatest climate–health issue facing Africa. It affects the well-being of more people than any other climate-related health risk, and it either underlies or amplifies other health risks, ranging from disease susceptibility to violent conflict. Studies of food security do not fall clearly within the health field, as food production and prices are traditionally the domain of agricultural and economic research. But health is a primary outcome of interest in food security analysis, and any climate impact on nutrition via food security must be considered in studies of climate–health dynamics. Infectious disease is a second critical area of climate impact. Africa stands out both for a high burden of several pan-tropical diseases, including malaria and cholera, and for the diversity of neglected tropical diseases that affect significant populations. Finally, climate extremes exact a significant annual health toll and may increase under global warming. These extremes have physically mediated impacts—drownings, injuries, and heat stress—but they also have lasting ecologically and socially mediated impacts through disease dynamics and economic stability.

As a starting point, a review of the basic characteristics of prevailing climate and climate variability across Africa is in order. The review then proceeds through climate–health examples, beginning with food security and other socially mediated processes, since they have the largest total burden, and continuing with infectious diseases, which are generally thought of as ecologically mediated phenomena, and the physically mediated impacts of climate extremes. Where possible and relevant, the impacts of climate variability and climate change are treated separately in order to distinguish between the existing and emerging climate-attributable health burden. These categories are also tied to different applications literatures, as climate variability is the basis for risk monitoring and early warning, while climate change projections connect to climate change adaptation activities.

Climate Zones and Climate Variability

Africa includes a full spectrum of tropical and subtropical climate zones, ranging from humid rainforests that receive in excess of 3000-mm rainfall per year to hyper-arid deserts that receive less than 100 mm. The seasonality of rainfall follows the migration of the intertropical convergence zone (ITCZ) over most of the continent, with the northern tropics and subtropics, including the Sahel and Ethiopian Highlands, receiving most of their rain in boreal summer, the southern tropics and subtropics receiving rain in austral summer, and the equatorial regions receiving two rainy seasons per year (Figure 2a). Midlatitude dynamics influence rainfall north of the Sahara Desert, where the seasonality of rainfall responds to the intrusion of storm tracks in boreal fall, winter, and spring. South Africa is similarly influenced by midlatitude storm tracks.

Temperature also varies widely across the continent (Figure 2b). Lowland tropical areas are hot most of the year, while highlands experience substantially cooler temperatures. This is relevant both for direct thermal stress and for the range and incubation periods of infectious diseases. Malaria risk, for example, is influenced by temperature because of the sensitivities of vector ecology, the Plasmodium incubation period, and human behavior in response to low and high temperatures.

Prevailing climate is relevant for understanding the ecological ranges of diseases and the background probability of extreme events. But climate variability is what is most important for risk monitoring and prediction. Rainfall variability, to pick one particularly relevant example, is substantial across most of the continent and is particularly large in semiarid regions in the northern and southern subtropics and in the Horn of Africa (Figure 2c). In these regions, subseasonal, seasonal, annual, and multi-annual rainfall variability is associated with droughts that threaten food security and pastoral livelihoods, changes in dust generation relevant to respiratory health and infections, hydrological and ecological variability influencing vector ecology, and access to safe water sources needed to avoid exposure to waterborne illnesses.

Figure 2. (a) Mean annual precipitation and month of peak precipitation—saturation indicates annual amount and hue indicates month of peak (1 = January; 12 = December); (b) mean annual temperature; (c) coefficient of variation in annual precipitation; (d) annual rainfall anomaly in years with moderate to strong El Niño. (a), (c), and (d) use Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data (Funk et al., 2014), with a 1981–2015 period of record. (b) Uses Worldclim analysis (Hijmans, Cameron, Parra, Jones, & Jarvis, 2005), with a 1960–1990 period of record.

In this context, it is important to recognize that Africa is diverse in climate variability as well as in prevailing climate. The warm (El Niño) phase of the El Niño Southern Oscillation (ENSO), for example, is known to cause summertime drought in the Sahel and Ethiopian Highlands but can lead to extreme wet conditions in equatorial East Africa in fall (Figure 2d). Differences like these can be analyzed through objective climate regionalization, which distinguishes between regions on the basis of differences in patterns of variability (Badr, Zaitchik, & Dezfuli, 2015; Dezfuli, Zaitchik, & Gnanadesikan, 2015). Proper regionalization can be critical when analyzing climate variability and its application to prediction of climate-related health risks.

Global Climate Models (GCMs) consistently have projected an increase in annual average temperature over the course of the 21st century. Rainfall projections for Africa, however, are notoriously uncertain, with low model consensus on even the direction of precipitation change in many regions (Bhattacharjee & Zaitchik, 2015; Niang et al., 2014; Rowell, Senior, Vellinga, & Graham, 2016). Efforts are underway to narrow this uncertainty through informed selection of GCM projections and, ultimately, improvement in GCM dynamics. But the uncertainty range is still large enough to warrant caution when designing adaptation interventions specific to a particular change in rainfall patterns. The seasonality of precipitation and the frequency of extreme events are also changing in some regions, and both of these variables are highly relevant to food security and infectious disease dynamics.

Socially Mediated: Food Security


Malnutrition, resulting from both acute food shortages and chronic or seasonal undernourishment, is a significant health burden for many countries in Africa. Sub-Saharan Africa has the highest prevalence of hunger in the world, with undernourishment rates of approximately 25% (Food and Agriculture Organization of the United Nations [FAO], 2015). An estimated 220 million people are estimated to be calorie deficient. The problem is particularly acute in children: the percentage of children under 5 who are stunted exceeds 30% in sub-Saharan Africa, and Eastern and Southern Africa and West and Central Africa are the only two major developing regions in the world in which the number of stunted children increased between 1990 and 2014 (UNICEF, 2015). Stunting is a powerful indicator of health outcomes in general. Fifty-three percent of infectious disease-related deaths in developing countries are associated with stunting, and severe stunting causes a fourfold increase in a child’s risk of death (Black et al., 2008; Caulfield, de Onis, Blossner, & Black, 2004). Rates of low-birthweight infants, underweight children, wasting, and childhood deaths attributable directly or indirectly to undernutrition are all among the highest in the world (UNICEF, 2015).

For these reasons, food insecurity is perhaps the most pressing health issue in Africa. Its causes are a matter of considerable research and political debate. In a proximal sense, rates of food insecurity are tied directly to poverty (Food and Agriculture Organization of the United Nations [FAO], 2011). It has been demonstrated time and again that undernourishment is most prevalent in poorer households, and that over- and undernutrition frequently coexist at the same time within a single community. Lack of education also contributes to malnutrition, as ignorance of basic information on nutrition, sanitation, and disease prevention within a household has been shown to contribute significantly to negative nutritional outcomes (Food and Agriculture Organization of the United Nations [FAO], 2011). Poverty and lack of education, however, are a product of larger social and institutional drivers, including physical isolation due to lack of infrastructure, ineffective or corrupt governance, and sociocultural attitudes that can limit economic or educational opportunities (Bain et al., 2013).

Links between climate and food security, then, are complex and heavily mediated by local, national, and international social, political, and economic factors. Nevertheless, climate does have the potential to affect food security through multiple pathways. First, food availability is tied to the performance of rainfall-dependent crops and the potential for crop and livestock loss under extreme events. Food access is influenced both by local climate variability—which will impact the price of food in local markets and farmers’ ability to pay for it—and by global food price spikes that are associated with climate shocks in remote food basket regions (Lagi, Bar-Yam, Bertrand, & Bar-Yam, 2011; Wiggins, Keats, & Compton, 2010). Food utilization is also affected by climate, since the risk of spoilage and the availability of biomass energy for cooking are both climate sensitive, and the stability of food supply can be affected by climate variability via its impacts on availability, access, and utilization ((Ray, Gerber, MacDonald, & West, 2015).

Of these links, food availability is by far the most extensively studied. Broadly speaking, wet periods lead to higher crop yields and greater national-scale food availability in most countries of sub-Saharan Africa (Buhaug, Benaminsen, Sjaastad, & Theisen, 2015). But this macroscale association does not apply everywhere. A number of location-specific studies have failed to find a correlation between rainfall totals and crop yield and have noted more nuanced relationships in which length of growing season, timing of the onset of rains, or the pattern of wet or dry conditions within the cropping season are more important predictors of yield (Adejuwon, 2006). Temperature can also be an important predictor of yields (Lobell, Schlenker, & Costa-Roberts, 2011), with higher temperatures in sub-Saharan Africa expected to lead to reduced yields of many major crops (Sultan et al., 2013). In the observed record, however, variability in rainfall has been both larger and more significant to crop performance than interannual variability in temperature. Ray et al. (2015) find significant sensitivity of maize yields to ENSO-related rainfall variability over much of Africa, but they note that complex interactions between rainfall and temperature influence yields in many regions. While most studies of climate and yield variability are performed at large spatial scale, local rainfall variability can be a critical determinant of nutritional outcomes in subsistence-based communities (Grace, Davenport, Funk, & Lerner, 2012). This is relevant to climate change projections, as studies that fail to consider changes in variability or fail to account for highly local variability could underestimate the risk that climate change poses to food availability (Thornton, Ericksen, Herrero, & Challinor, 2014).

Though less studied in the climate context, inadequate access to food and markets is a major risk factor for food security that can be influenced by climate conditions. Transport costs in sub-Saharan Africa tend to be high (Haile, 2005), and general limitations in transportation and communication infrastructure can reduce market integration (Brown et al., 2012). Climate can influence market isolation through its impacts on transportation, including extreme events that damage infrastructure and regular seasonal wet and dry periods that affect river transport and the functioning of low-quality roads. Economic isolation resulting from lack of market integration is, in turn, associated with greater food insecurity (Burgess & Donaldson, 2010). This signal is communicated through food prices, which can differ widely between markets within a single country. Isolated markets exhibit low price correlation with integrated markets and global commodity indices (Davenport & Funk, 2015). This could, in principle, insulate isolated rural communities from global price shocks, but it frequently has a negative impact, as local prices spike under low-production or high-demand conditions, harming food access for poorer members of the community. Meanwhile, urban poor populations face access constraints when global commodity prices spike (Cohen & Garrett, 2010; Crush & Fayne, 2010). Volatility in global food prices is still poorly understood, but climate shocks to major food-producing regions play a significant role, either through directly impacting food supply or through triggering speculative bubbles in food prices (Lagi et al., 2011). The combined effects of a local climate shock with food price shocks can be especially detrimental to food security; countries in sub-Saharan Africa are particularly vulnerable to this combination on account of low purchasing power and a net dependence on food imports (Felix & Romuald, 2009).

The influence of climate variability on food utilization is relatively understudied, though it is understood that climate is relevant to the availability of biomass energy and clean water for cooking. Postharvest food loss during storage is another factor that affects both food access (via market availability) and utilization (when stored in the home). Postharvest loss is a leading constraint on food security in Africa, as poor storage techniques and infrastructure can lead to large food loss (Godfray et al., 2010; Parfitt, Barthel, & Macnaughton, 2010); for example, smallholder farmers in Africa lose an estimated 14 to 36% of their maize grain postharvest (Tefera, 2012). Food storage is sensitive to ambient temperature, humidity, and rainfall, as well as to extreme events that can disrupt postharvest processes. This points to the potential for climate variability and change to affect rates of postharvest loss, and to the need for adaptation strategies that target improved storage systems for African farmers (Stathers, Lamboll, & Mvumi, 2013). Food utilization could also be impacted by changes in crop choice in response to climate change and, potentially, by projected increases in diarrheal disease leading to reduced ability to absorb nutrients (Felix & Romuald, 2009).

The importance of climate variability to food security in Africa has motivated the creation of numerous climate outlooks and food security forecasts (Patt, Ogallo, & Hellmuth, 2007). The Famine Early Warning System Network (FEWS NET) is perhaps the most established and influential of these programs (Brown, 2008; Funk & Verdin, 2010; Verdin, Funk, Senay, & Choularton, 2005). Recognizing the multiple pathways to food insecurity, FEWS NET issues food security outlooks and acute warnings for 29 countries in Africa through a combination of seasonal weather forecasts, crop stress modeling, food price monitoring and projection, satellite-based rainfall and vegetation anomaly analysis, extreme weather alerts, and ENSO tracking. This provides a suite of risk indicators that capture climate influence on crop production and prices, allowing for decision-relevant food insecurity forecasts (Brown, Funk, Galu, & Choularton, 2007). Applying these forecasts to effective interventions, however, remains a challenge in many countries (Funk, 2011).

There is significant concern that climate change will exacerbate food insecurity over much of Africa, though with significant regional variability (Adejuwon, 2006; Adhikari, Nejadhashemi, & Woznicki, 2015; Berg, de Noblet-Ducoudré, Sultan, Lengaigne, & Guimberteau, 2013; Schlenker & Lobell, 2010; Sultan et al., 2013). This concern is based largely on projected climate stress on crop production. Climate change is expected to have significant negative impacts on grain production in sub-Saharan Africa, including potentially dramatic loss of productivity in wheat (Nelson et al., 2009) and maize (Schlenker & Lobell, 2010) in the absence of significant adaptation activities. Production of certain key export crops could also fall, affecting income and food access (Adhikari et al., 2015), and rising temperatures and altered drought patterns could have impacts on livestock (Niang et al., 2014). Investment in climate smart agriculture does have the potential to offset some of these risks (Brown & Funk, 2008; Challinor, Wheeler, Garforth, Craufurd, & Kassam, 2007). Climate change may also alter the range of pests that impact both crops and livestock systems, but there is substantial uncertainty in these projections (Niang et al., 2014).

The impact of these risks on food production, coupled with other factors influencing access and utilization, could have significant impacts on nutrition. Averaged across sub-Saharan Africa, climate change is projected to cause higher food prices, lower food affordability, a decrease in calorie intake, and increased malnourishment (Ringler, Zhu, Cai, Koo, & Wang, 2010). Childhood anemia, undernutrition, and stunting are projected to increase (Jankowska, Lopez-Carr, Funk, Husak, & Chafe, 2012); indeed, some studies indicate that climate change will cause a greater number of children to become undernourished in Africa than in any other region of the world (Nelson et al., 2009; Phalkey, Aranda-Jan, Marx, Hofle, & Sauerborn, 2015). This climate stress could be large enough to offset and possibly outweigh expected reductions in stunting due to economic development (Lloyd, Kovats, & Chalabi, 2011). There is also the potential for a gender imbalance in the impact of climate change on undernutrition, as girls may disproportionately suffer from reduced food intake under conditions of scarcity (Bain et al., 2013).

Conflict and Migration

The vast majority of research on African food security and nutrition under climate change has assumed relatively stable social conditions. Researchers attempt to account for factors such as economic development, changing fertility rates, and urbanization when possible, but the general assumption is that the pathways through which climate affects nutrition are stationary. A complementary line of analysis is concerned with the impact that climate change could have on fundamental social and economic stability. The idea that climate change could lead to increased conflict and large-scale migration is much discussed in research and policy circles (Buhaug et al., 2015; Salehyan, 2014), and weather-driven shocks to food production are frequently invoked as a stress that could provoke these phenomena (Homer-Dixon, 1991; Koubi, Bernauer, Kalbhenn, & Spilker, 2012) (Figure 3). Projections of potential climate–scarcity–conflict dynamics are most often studied in the developing world, and many studies have focused on Africa (Brown, Hammill, & McLeman, 2007; Hendrix & Glaser, 2007).

Figure 3. Hypothesized climate–scarcity–conflict feedbacks. Flowchart is derived from similar figures in Brown et al., 2007; Buhaug et al., 2015; Homer-Dixon, 1991).

Empirical evidence, however, is limited and controversial. Reduced food access due to an increase in global food prices, for example, has been cited as a cause for widespread food riots in Africa in 2007–2008 and as a trigger for the “Arab Spring” unrest in North Africa in 2011 (Berazneva & Lee, 2013; Johnstone & Mazo, 2011; Sternberg, 2012), suggesting that food insecurity can lead to significant political instability. The strength of the link between climate and the global food commodity price spikes, however, is debated, as is the question of whether the food security of undernourished people was the actual reason for “food riots” in either 2007–2008 or 2011 (Sneyd, Legwegoh, & Fraser, 2013). Local climate variability leading to reduced food production in both farming and pastoral systems has also been identified as a driver of violent conflict in Africa, including the extended conflict and suspected genocide in Darfur that began in 2003 and the less widely reported but significant conflicts in Kenya, Ghana, Nigeria, and elsewhere. Again, the complex nature of climate–scarcity–conflict dynamics makes it difficult to draw systematic conclusions. In Darfur, for example, most analysis points to a combination of political and cultural triggers for conflict (Kevane & Gray, 2008; O’Fahey, 2006; Salih, Mohamed Abdel Rahim Mohamed, 2005), though a relatively direct climate trigger does appear to be plausible in other situations (Scheffran, Ide, & Schilling, 2014). A systematic analysis of climate variability, food prices, and violent conflict across sub-Saharan Africa found significant relationships between climate and food production but failed to find a robust link between this relationship and incidences of violent conflict (Buhaug et al., 2015). The study’s authors note, however, that their analysis did not consider the possibility that climate-mediated impacts on food prices influence conflict via international food prices, localized subnational disturbances, or chronic impacts of food production on development.

Large-scale migration can be both a response to experienced food insecurity and a cause for future food insecurity. As with conflict, relationships between climate, food security, migration and, ultimately, health outcomes are difficult to untangle, but the potential for climate shocks and long-term climate change to trigger migration has received significant attention (Black, Bennett, Thomas, & Beddington, 2011; Brown et al., 2007; McLeman & Smit, 2006; Reuveny, 2007). In Africa, there is evidence that patterns of voluntary and forced migration are influenced by climate stress. This includes within-region rural-to-urban migration and large-scale migration between regions (Barrios, Bertinelli, & Strobl, 2006; Greiner & Sakdapolrak, 2013; McMichael, Barnett, & McMichael, 2012). The drought in the Horn of Africa in 2011, in particular, highlights the complicated interplay between food security and political dynamics in the face of a climate shock. It is also a climate pattern that could become more frequent under climate change (Funk et al., 2008).

Voluntary migration can be viewed as an adaption to changing climate (Tacoli, 2009), though experience shows that both forced and voluntary migration in response to climate stress has a negative impact on food security and other health outcomes in vulnerable populations (McMichael et al., 2012; Toole, 2005). An applied research effort by CARE has sought to distinguish between climate-affected populations that use migration to flourish, those that migrate to maintain their level of well-being, those that migrate but suffer (“erosive migration”), and those that are trapped because they lack the capacity to migrate (Warner et al., 2012). In all cases, there is potential risk to food security, but risks are greatest for erosive migration and trapped populations.

Ecologically Mediated: Infectious Disease

Vector-borne, zoonotic, and waterborne diseases exact a major toll across Africa. According to World Health Organizaton (WHO) Global Health Observatory estimates, malaria alone was responsible for on the order of 400,000 (231,000–546,000) deaths per year across the continent in 2015. This is a significant improvement over the more than 750,000 (626,000–993,000) deaths per year attributed to malaria in 2000, but it still leaves portions of sub-Saharan Africa with the highest malaria death rates in the world. In total, infectious and parasitic disease in Africa caused over 3 million deaths in 2012. These diseases include a large number of neglected tropical diseases that receive little research and have not been targeted by large-scale interventions.

Many of these infectious and parasitic diseases are sensitive to climate conditions, on account of climate impacts on incubation period, vector ecology, parasite range and reproduction rate, movement of animal hosts, or human behavior and physiological susceptibility.

Vector-Borne Disease

Malaria, on account of its enormous and widespread health burden, has received more attention than any other climate-sensitive disease. Multiple species of Anopheles mosquitoes serve as vectors across the continent, with An. gambiae s.s., An. arabiensis, and An. funestus responsible for the majority of transmission (Sinka et al., 2010). Notably, the relatively deadly Plasmodium falciparum malaria parasite is the most prevalent form of malaria in sub-Saharan Africa (Figure 4).

Figure 4. Mean rate of observed clinical Plasmodium falciparium malaria cases per person per year for the year 2015 (Bhatt et al., 2015), overlain on a map of population centers derived from the Gridded Population of the World v4 database, 2010 count data (Center for International Earth Science Information Network—CIESIN—Columbia University, 2016).

When describing risk profiles, epidemiologists typically distinguish between endemic and epidemic malaria regions. The definition of these categories is more complicated than is generally acknowledged (Hay, Smith, & Snow, 2008) and is the subject of some debate. In general terms, however, one can understand that endemic areas are characterized by “stable” transmission rates, with transmission occurring year-round (holoendemic) or seasonally with high regularity and prevalence rates (hyperendemic). Rates of lower endemicity have unstable transmission that can be highly sensitive to climate variability and other disturbance. Regions with unstable transmission are variously defined as having low endemicity (mesoendemic to hypoendemic categories) or as being epidemic zones in which outbreaks occur at irregular intervals. In these regions, acquired immunity is low, so malaria outbreaks can be severe and include significant mortality. From a climate perspective, epidemic malaria has been the focus of early warning systems, while the distribution of endemic malaria may be sensitive to long-term climate variability and change.

Considerable efforts have been made to develop climate-informed malaria early warning systems (MEWS) for epidemic zones of Africa, based on the observation that malaria vectors and the malaria parasite are sensitive to rainfall and temperature conditions (Craig, Snow, & Le Sueur, 1999), with a lag of weeks to months (Bomblies, Duchemin, & Eltahir, 2009; Teklehaimanot, Lipsitch, Teklehaimanot, & Schwartz, 2004). These MEWS-oriented studies tend to focus on single regions within Africa, and they rely on different datasets for both prediction and evaluation, which makes it difficult to draw broad conclusions across studies (Mabaso & Ndlovu, 2012). Nevertheless, the majority of studies have found some relationship between rainfall variability and malaria epidemics. In general, malaria epidemics are more likely to occur after periods of unusually heavy rainfall. The most likely explanation for this is that heavy rainfall in areas that are typically water limited expands the number of Anopheles breeding sites, particularly as waters recede and leave stagnant pools and puddles behind. This increases vector capacity and the probability of encounters between humans and vectors (Grover-Kopec et al., 2005; Patz et al., 2002). The association is not universal, however, as excess rainfall in some regions may have the effect of washing out breeding sites, breaking the association between rainfall and malaria risk (Molineaux, Wernsdorfer, & McGregor, 1988). This points to the need for ecosystem-specific (Githeko, Ogallo, Lemnge, Okia, & Ototo, 2014) and spatially specific (Arab, Jackson, & Kongoli, 2014) studies to support locally customized MEWS.

Beyond rainfall, studies have shown that temperature variability can also impact malaria transmission rates, with warm temperatures leading to malaria outbreaks in regions where low temperatures typically limit vector development, Plasmodium growth rates, or biting frequency (Lindsay & Martens, 1998; Molineaux et al., 1988). Other studies have made use of satellite-derived vegetation estimates—most commonly a normalized difference vegetation index (NDVI), a nondimensional parameter that is a proxy for vegetation coverage or health (Ceccato et al., 2007; Gomez-Elipe, Otero, Van Herp, & Aguirre-Jaime, 2007). These vegetation studies have sometimes found promising statistical associations, though the mechanism of association could be that vegetation and malaria are responding in parallel to a common rainfall forcing, rather than that malaria risk is actually mediated by vegetation.

Taking a broader climate perspective and seeking to extend the time horizon of MEWS, a number of studies have attempted to predict malaria epidemics as a function of large-scale climate modes such as ENSO. Since ENSO is a major driver of rainfall variability in parts of Africa, it can be employed as a predictor of rainfall-mediated epidemics at several months’ lead (Hashizume, Terao, & Minakawa, 2009; Lindsay, Bødker, Malima, Msangeni, & Kisinza, 2000; Mabaso, Kleinschmidt, Sharp, & Smith, 2007). Other seasonal prediction studies have applied dynamically based seasonal forecast systems to predict rainfall and temperature anomalies directly and have used those predictions to drive malaria models (Ceccato et al., 2007; Hoshen & Morse, 2004; Jones & Morse, 2010; Tompkins & Di Giuseppe, 2015). At a research level, many of these approaches to MEWS have shown promise, but to date few operational systems are in use.

From a climate change perspective, a number of studies have projected changes in the range of epidemic or endemic malaria across Africa. In the East African Highlands, mean warming is expected to lead to increased malaria risk (Ermert, Fink, & Paeth, 2013; Siraj et al., 2014). Diurnal and day-to-day temperature variability can also be quite important (Blanford et al., 2013; Paaijmans et al., 2014), however, suggesting that climate-based malaria risk projections must account for changes in variability and transient temperature extremes. There is also a possibility that warming will push the upper end of the malaria transmission optimum; Ryan et al. (2015) find that warming would be expected to increase the suitable area for malaria in Africa but to decrease and shift the most suitable areas for year-round transmission as temperatures in some currently holoendemic areas rise above known physiological optima. Projected changes in rainfall are highly uncertain for many parts of Africa, but in areas with projected decreases in precipitation—including the Sahel—combined drying and warming trends could lead to reduced malaria risk, or at least to a neutral climate change impact on transmission rates (Ermert et al., 2013; Yamana, Bomblies, & Eltahir, 2016).

Beyond malaria, extensive work has been done on early warning and climate change analysis for a number of other vector-borne infectious diseases in Africa. Rift Valley Fever (RVF), a potentially deadly vector-borne disease found in Eastern, Southern, and Western Africa, can be transmitted by several genera of mosquito and by other insect vectors. Work on RVF has shown that outbreaks are almost always preceded by prolonged periods of excessive rainfall in savannah ecosystems (Anyamba, Linthicum, Mahoney, Tucker, & Kelley, 2002; Davies, Linthicum, & James, 1985; Linthicum, Britch, & Anyamba, 2016), indicating that there is potential for early warning based on rainfall, or vegetation, or large-scale sea-surface temperature monitoring. Indeed, successful warning systems have been developed on the basis of these variables, with demonstrated predictive associations between ENSO and RVF outbreaks (Anyamba et al., 2009, 2012) and an operational system in place based on NDVI anomalies. The proposed mechanism underlying NDVI-based warning is that enhanced vegetation promotes survival of Aedes mosquitoes (Linthicum, Bailey, Davies, & Tucker, 1987; Tucker, Hielkema, & Roffey, 1985) and potentially other insect vectors as well. The details of this mechanism, however, are not fully described and could vary by insect species. Related work on RVF in South Africa has suggested that EWS can be developed using rainfall and soil moisture monitoring (Williams, Malherbe, Weepener, Majiwa, & Swanepoel, 2016), due to a similar proposed ecological mechanism. Bubonic plague, a tickborne zoonotic disease that is a health threat in East and Southern Africa, has also been found to show time-lagged relationships with rainfall variability (Moore et al., 2012), with possible applicability to EWS. In this case, the ecological mechanism could be related either to the population dynamics of the rodents that serve as hosts for the bacteria or to direct climate influence on tick survival and reproduction.

Waterborne Disease

Cholera, a waterborne disease transmitted by the Vibrio cholerae bacterium, is another climate-sensitive infectious disease that places a significant health burden on Africa. In 2014, Africa accounted for 55% of global suspected cholera cases reported to the WHO Global Health Observatory. In addition, the case fatality ratio for cholera in Africa is approximately 2%, which is double the threshold used by the WHO when assessing effective case management systems (Mengel, Delrieu, Heyerdahl, & Gessner, 2014). The actual number of cholera cases and deaths is difficult to judge on account of underreporting, but even the reported numbers suggest that the disease causes tens of thousands of deaths in Africa each year. In some regions, including the East African lakes and some coastal locations, the disease is present year round with regular seasonal peaks, while in others it appears in sporadic epidemics. One notable characteristic of cholera in Africa is that roughly three quarters of all reported cases occurred in inland regions (Rebaudet, Sudre, Faucher, & Piarroux, 2013b), with the largest number of cases found in the East African Great Lakes region and around Lake Chad. The disease burden is also significant in some coastal areas (Rebaudet, Sudre, Faucher, & Piarroux, 2013a).

Climate variability can affect V. cholerae through its influence on aquatic conditions, which are relevant to bacteria and to the plants and animals with which they associate. Climate—particularly heavy precipitation—also influences human behavior and systems relevant to cholera transmission. Wet conditions lead to outbreaks for multiple reasons, including the potential for the mixing of human waste with drinking water supplies during flooding. In many regions of Africa, cholera transmission is known to peak during the rainy season (Bompangue et al., 2009; Colombo, Francisco, Ferreira, Rubino, & Cappuccinelli, 1993; de Magny, Guégan, Petit, & Cazelles, 2007; Schaetti et al., 2009), and extreme precipitation events are often associated with epidemic outbreaks (de Magny et al., 2012; Guevart et al., 2006; Sasaki, Suzuki, Fujino, Kimura, & Cheelo, 2009). Dry conditions can also enhance transmission by limiting access to water for sanitation or to safe drinking water sources (Lawoyin, Ogunbodede, Olumide, & Onadeko, 1999; Tauxe, Holmberg, Dodin, Wells, & Blake, 1988). Warmer air temperatures have been associated with increased cholera risk in southeastern Africa, due to the relationship between air temperature and water temperature (Paz, 2009; Trærup, Ortiz, & Markandya, 2011).

These complex climate responses, combined with limited understanding of cholera ecology and reporting limitations, have made it difficult to establish a unifying framework for climate-based EWS or for projections of climate change impacts on cholera. Furthermore, cholera epidemics are ultimately a product of failed human infrastructure that allows for fecal–oral transmission of the disease. As such, the most damaging outbreaks often occur in cities or large refugee camps where climate might play some role as a trigger or stressor, but where epidemic dynamics are driven by nonclimate factors. Nevertheless, the relationship between climate variability and cholera outbreaks is robust and potentially predictive. El Niño conditions have been associated with increased cholera at continental scale (Griffith, Kelly-Hope, & Miller, 2006), with strong associations found in specific regions where El Niño leads to high rainfall and flooding (Nkoko et al., 2011; Olago et al., 2007). In general, El Niño events appear to shift the cholera burden to continental East Africa, primarily owing to shifts in rainfall and associated ecological parameters, but the details of the pattern are complex. A relationship between cholera episodes and Indian Ocean variability has also been found in parts of West Africa (de Magny et al., 2007), while outbreaks in Southern Africa have been associated with both large-scale (Paz, 2009) and local (Mendelsohn & Dawson, 2008) sea-surface temperatures (SSTs). Large-scale SST anomalies influence cholera risk through climate teleconnections, while local SST variability can alter coastal ecological systems, influencing V. cholerae concentrations relevant to coastal populations, as well as local weather.

The future of cholera in Africa most likely depends more on sanitation and health infrastructure than it does on climate change. Nevertheless, projections for wetter conditions in the East African Great Lakes region and for increased variability and frequency of hydrological extremes over much of the continent (Niang et al., 2014) do suggest the potential for more frequent climate triggers of cholera outbreaks.

Other Infectious Disease

Meningococcal meningitis is another climate-sensitive infectious disease that poses a significant health risk in Africa. The 21-country African “meningitis belt” stretching from Senegal to Ethoipia has the highest attack and fatality rates for bacterial meningitis in the world (Jusot et al., 2016). Infection causes inflammation of the membranes that cover the brain and spinal cord and can cause hearing loss, brain damage, and death. The fatality rate, when untreated, is 50% (WHO, 2015). Within the meningitis belt, disease risk follows a regular seasonal cycle, with transmission highest during the dry season. There is also significant interannual variability, with severe outbreaks occurring every 7 to 14 years (WHO, 2015). Variability appears to be associated with both temperature and dust load. Meningitis is transmitted from person to person through exchange of droplets of respiratory or throat secretions. Transmission may be elevated under dry and dusty conditions because damage to the nasopharyngeal mucosa increases susceptibility to infection; because heat and inhaled dust promote expression of bacterial virulence factors; and/or because of mediating social dynamics that are sensitive to weather conditions (Ceccato et al., 2014; Jusot et al., 2016; Pandya et al., 2015; WHO, 2015).

The climate influence on meningitis transmission in Africa has been reported for some time (Cheesbrough, Morse, & Green, 1995; Lapeyssonnie, 1963). A number of studies and initiatives have attempted to apply this knowledge to predictive modeling and EWS (Agier et al., 2013; Thomson et al., 2013). Studies have consistently found that elevated temperatures and dust load are associated with increased transmission and that these variables have predictive potential (Abdussalam, Monaghan, Dukić et al., 2014; Agier et al., 2012; Ceccato et al., 2014; Jusot et al., 2016; Oluwole, 2015). Relative humidity is also an important predictor (Abdussalam, Monaghan, Dukić et al., 2014; Pandya et al., 2015), and humidity-based models can skillfully predict the end of seasonal outbreaks (Pandya et al., 2015). Predicting the end of an outbreak is quite useful, as it can inform reactive vaccination campaigns. Interannual variability in meningitis cases has also been tied to large-scale climate variability, including ENSO and the Pacific Decadal Oscillation (Oluwole, 2015), due to the influence that these large-scale climate patterns have on temperature, moisture conditions, and winds. Warming under climate change may extend the transmission season and increase the total number of cases in the meningitis belt (Abdussalam, Monaghan, Steinhoff et al., 2014).

For a number of other high-profile diseases, climate is a proposed but uncertain driver of risk. Ebola, for example, has been associated with climate variability: outbreaks have been found to occur when wet years follow a period of several dry years (Tucker et al., 2002), possibly because an increase in the population of reservoir species. Transmission to humans is then thought to be more likely at the conclusion of the wet season, when there is a dramatic transition from wet to dry conditions that causes animals to crowd together around available resources (Pinzon et al., 2004). But the significance of this association is unknown, and the potential to use climate information to predict or project risk has not yet been realized.

A number of arboviruses transmitted by Aedes mosquitoes are present in Africa and are of global concern, including dengue, yellow fever, zika, and chikungunya. Within Africa, however, the health burden of severe dengue cases is poorly characterized (Jaenisch et al., 2014). This could well be due to underreporting, but it is also possible that disease ecology or human genetics have acted to limit its impact (Sierra, Kouri, & Guzmán, 2007). Like dengue, chikungunya and zika are transmitted by Aedes aegypti and Aedes albopictus mosquitoes. Both originated in Africa and are of great concern owing to their rapid spread into other parts of the world. Some projections suggest that Aedes aegypti and Aedes albopictus ranges in Africa could expand under climate change (Campbell et al., 2015), and urbanization is a risk factor for any disease transmitted by the urban-dwelling Aedes mosquitoes. However, before one can draw conclusions about potential changes due to climate change, more must be learned about the present climate sensitivity of dengue, zika, and chikungunya in Africa.

This selective review of climate impact on infectious disease in Africa is not intended to be comprehensive. Rather, it provides an overview of observed and hypothesized climate links for representative vector-borne, waterborne, and directly transmitted diseases. The links are complex, on account of interacting ecological factors and disease dynamics, and they can be obscure owing to lack of relevant ecological data and underreporting or biased reporting of human cases. Nevertheless, robust climate associations with disease risk have been demonstrated for many diseases at the research level, and a number of EWS have been established. The ability to translate these EWS into decision-relevant information systems is an area of active work (Ceccato et al., 2014; Pandya et al., 2015; Thomson et al., 2014). Projections of future disease burden under climate change can be made on the basis of known disease sensitivities. Such projections can inform long-term planning for emerging health risks, evaluated relative to current conditions. At the same time, the rapid pace of social and economic change in most of Africa suggests that changing human conditions will be the largest driver of changes in infectious disease risk in coming decades.

Physically Mediated: Floods, Heat, and Dust

Physically mediated health impacts of climate are deceptively difficult to quantify. On the one hand, direct physical harm from a storm, flood, mudslide, or other acute extreme event is the most direct and therefore simplest form of climate impact to understand. On the other hand, vulnerability to these events is highly mediated by social and economic processes. Substandard housing, lack of access to early warnings, and inability or unwillingness to take protective action all play a role in determining the magnitude and distribution of physical health impacts during a climate extreme. This is true across the world, and it is notably the case in Africa on account of its large rural population, which can be isolated from information sources, and extensive poverty both in rural areas and in informal settlements in urban centers. Further, distinguishing the physically mediated impacts of a climate extreme from its broader ecologically and socially mediated impacts is difficult and not always useful. Extreme precipitation, for example, can lead to drowning and mudslides, but it also results in elevated risk of postinjury infection, diarrheal illness, and, in some cases, malnutrition due to crop loss, displacement, or other factors (Wang, Kanji, & Bandyopadhyay, 2009).


The difficulty of distinguishing climatic from social factors clearly applies in the case of floods. Floods can be responsible for more than 80% of annual natural disaster fatalities in Africa (Hoedjes et al., 2014), and the number of reported fatalities due to flooding has increased dramatically in Africa over the past half century, from fewer than 2,000 in the period 1950–1969 to over 14,000 in the period 1990–2009 (Di Baldassarre et al., 2010; Guha-Sapir, Below, & Hoyois, 2015). But numerous studies have shown that it is difficult to attribute variability or trends in the number of flood deaths to climate relative to other human factors, including demographic change, land-use change, and river management (Bates, Kundzewicz, Wu, & Palutikof, 2008; Blöschl & Montanari, 2010; Kundzewicz et al., 2005). In an analysis of flood trends in river basins across Africa, Di Baldassarre et al. (2010) found no significant trend in flood magnitude in Africa and concluded that the large trend in health impacts is dominated by changes in vulnerability patterns, most notably unplanned urbanization that has placed growing populations into informal settlements in floodprone cities such as Lusaka (Zambia), Dakar (Senegal), Alexandria (Egypt), and Ouagadougou (Burkina Faso).

At meteorological to interannual timescales, however, flood occurrence and the potential for health impacts are strongly related to rainfall variability. Studies have shown evidence of an ENSO signal on flood disasters across Africa (Li, Chai, Yang, & Li, 2016), and flood EWS ranging from daily to seasonal scale are an area of significant interest. Numerous examples of flood warning systems have been developed at national and river basin scale in Africa (Gumbricht, Wolski, Frost, & McCarthy, 2004; Haile, Tefera, & Rientjes, 2016; Thiemig et al., 2010; Trambauer et al., 2015). There is also interest in systems that could be generalized to continental scale and that could be used in countries that lack the hydrometeorological forecasting capacity to maintain their own flood systems (Jubach & Tokar, 2016). These systems employ satellite data and model-based forecasts to issue warnings ranging from flash flood timescales (Hoedjes et al., 2014) to medium-range hydrological forecasts (Thielen-del Pozo et al., 2015; Thiemig, Bisselink, Pappenberger, & Thielen, 2015), to seasonal flood risk outlooks (Braman et al., 2013). As with any climate services effort, the forecast is only one component of an effective information system; enabling policy environments and community engagement critical to the success of the system (Hellmuth, Moorhead, Thomson, & Williams, 2007; Jubach & Tokar, 2016).

Projections of flood casualties under changing climate conditions are highly uncertain. Floods themselves are difficult to project since they are influenced by land and water management as well as by meteorology (Field, 2012). Accounting for social and economic factors that drive vulnerability is also fraught with uncertainty. The one point in which there is some confidence is that increases in precipitation extremes would be expected to result in increased flood risk. Climate projections indicate that high-intensity rainfall will become more frequent over much of Africa, indicating that there is a potential for an increase in floods, all else being equal.


Temperature extremes—both heat waves and cold snaps—are also known to impact human health. With few exceptions, however, studies of the health burden of extreme temperatures have focused on the midlatitudes. Only in the face of climate change and a number of high-profile heat wave events in tropical countries has attention turned to the potential for temperature extremes, particularly high-temperature extremes, to pose a health risk in Africa. Health risks associated with high temperatures, including impacts on blood viscosity and cardiac output, are highest for the elderly and people who suffer from existing health conditions, and they can also impact children (Smith et al., 2014). The limited number of studies that have investigated the health impacts of temperature extremes in Africa have found that both high temperatures (Azongo, Awine, Wak, Binka, & Oduro, 2012; Diboulo et al., 2012; Egondi et al., 2012) and cold temperatures (Egondi et al., 2012; Egondi, Kyobutungi, & Rocklöv, 2015; Mrema, Shamte, Selemani, & Masanja, 2012) can have significant impacts on mortality and morbidity in Africa. In a systematic review of temperature impacts on mortality in sub-Saharan Africa, Amegah et al. (2016) found, with moderate confidence, that high temperatures cause an increase in all-cause mortality. However, they note that the literature is thin and that it is difficult to assess confidence in specific health impacts on asthma, respiratory disease, undernutrition, and most infectious diseases. A broader review of temperature impacts on health across the tropics came to similar conclusions (Burkart et al., 2014).

The risks of heat exposure may be greatest for populations living in urban informal settlements, as urban heat island effects can cause cities to be several degrees hotter than surrounding areas, and those living in informal settlements tend not to have access to climate-controlled environments or to rapid medical care (Egondi et al., 2012; Scovronick, Lloyd, & Kovats, 2015). Unusually cold temperatures can also pose a health risk to these populations (Egondi et al., 2015). Climate change projections point to an increase in the number of days with high Apparent Temperature (a humidity and wind speed-adjusted heat measure) across much of Africa, particularly in the East African Highlands, with potential consequences for health (Garland et al., 2015).


Finally, climate extremes and trends have the potential to impact dust emission and transport. Dust emission is sensitive to soil moisture and vegetation status, which are, in turn, influenced by rainfall and temperature, as well as by wind speed. Higher atmospheric dust load has been associated with relatively direct health impacts, such as transport accidents and respiratory illnesses, and with more highly mediated processes, such as allergies and disease risk (Goudie, 2009). There is significant interannual and decadal scale variability in dust load in dust-affected regions of Africa, including the Sahel and portions of Southern Africa, but long-term trends are either unclear or not fully explained (Goudie, 2014). Dust exposure has been linked to lung cancer in Northern Africa (Giannadaki, Pozzer, & Lelieveld, 2014) as well as to the meningitis outbreaks, as described in the preceding section on infectious diseases (see Ecologically mediated: Infectious Disease). African-sourced dust is also known to impact asthma, respiratory complaint, and cardiovascular disease in southern European countries (Karanasiou et al., 2012), suggesting that it likely has an impact on health in Africa as well, though targeted studies are still required (De Longueville, Hountondji, Henry, & Ozer, 2010; De Longueville, Ozer, Doumbia, & Henry, 2013).

Applying Climate Information for Health

Application of Early Warning Systems

The idea that observations and predictions of climate can be applied to anticipate disease is ancient (Sargent, 1982). The aspiration to predict or control floods and other climate disasters that bring physically mediated harm is perhaps even more ancient: Utnapishtim, the hero of the flood in the Epic of Gilgamesh, is granted eternal life after receiving early warning of the flood (albeit with help from the gods) and responding appropriately; Yu’s successful control of the catastrophic Great Flood of Gun-Yu in Chinese mythology leads to the establishment of the Xia dynasty. The importance of famine early warning has roots in the remarkably effective predictions (and governance!) found in the biblical story of Joseph and Pharaoh, which spared Egypt from famine and made it a place of refuge across the region.

In the modern era, conceptual disease early warning systems based on climate variability have been in existence for close to a century, including work in the 1920s by C. A. Gill on malaria in India and Leonard Rogers on multiple diseases in India and beyond (Gill, 1923; Rogers, 1923, 1925, 1926). These early studies recognized clear patterns between climate variability and disease incidence, and they also appreciated the need to consider climate variables in the context of other social and environmental factors: Gill’s malaria risk model, for example, included economic and epidemiological observations in addition to rainfall. Over the past decade, numerous disease early warning systems for Africa have been proposed, including several that disseminate risk estimates on a real-time basis (Thomson et al., 2014). Several of these systems are described in the infectious disease section of this review (see Ecologically mediated: Infectious Disease). Skillful systems for predicting crop yields have also existed for many decades, including work in India in the 1940s and 1950s that was endorsed by the Food and Agriculture Organization of the United Nations as a model for agricultural forecasts and subsequent statistically based objective yield forecast work by the United States Department of Agriculture in the 1960s and 1970s (Abreu & Riberas, 2008) that was transferred to a number of other countries. The effort to apply crop yield projections to food security outlooks gained momentum with the establishment of FEWS NET in the 1980s. Meanwhile, storm, flood, and heat-wave prediction has traditionally been handled by meteorological service agencies. Mature and operational systems have existed in developed countries and in high-profile cities and river basins throughout the world for some time.

Despite this long history, operational use of health early warning systems—particularly for highly mediated health risks like hunger and infectious disease—has been relatively limited across the world, and the problem is particularly acute in Africa. There are a number of reasons for this situation. In the case of disease early warning, implementation of skillful and effective systems depends on the presence of reliable data for both predictor variables (climate and mediating factors) and disease incidence. The lack of reliable, consistent, and available data records in most African countries, particularly for human health outcomes, makes it difficult to develop, implement, or assess proposed disease prediction systems. The WHO has also noted that most conceptual EWS have been developed for specific case studies, with limited budget and in an academic rather than an operational context. This makes them ill equipped for widespread operational use, and it also means that there is a shortage of established metrics for evaluating the accuracy and utility of proposed warning systems.

Institutional stovepipes are another recognized challenge, as meteorological services and health ministries have little history of collaboration. Health officials lack expertise in climate data and probabilistic forecasts and usually have no experience applying them to operational activities. Providers of climate information, meanwhile, have little exposure to decision making on health matters and may provide products that are not well suited for health risk monitoring or prediction. Efforts such as the establishment of the joint World Meteorological Organization–World Health Organization (WMO–WHO) Joint Office for Climate and Health and the broader initiative for a Global Framework for Climate Services (GFCS) are important steps to overcome these institutional barriers.

Ultimately, however, even a seamless and skillful forecasting system must be tied to a capacity for effective intervention. In the case of epidemic disease in many parts of Africa, communication systems, effective and realizable risk reduction techniques, and the ability to distribute vaccines and treatments in a timely manner must all be improved alongside the development of climate-informed early warning systems.

Similarly, in the case of food security, FEWS NET and other systems successfully predicted several damaging climate-driven food crises months in advance. This includes the 2010–2011 drought crisis in the Horn of Africa and the 2015 drought affecting Ethiopia. It is difficult to quantify the realized benefit that these predictions provided. Nevertheless, there is consensus that skillful early warning is not being translated to effective early action in the way that it should be (Bailey, 2012; Funk, 2011). A critical factor in this failure is the presence of perverse incentives: no single actor or institution is accountable for preventing a crisis, whereas every decision maker is held accountable for multiple competing priorities other than crisis prevention. Given that the prediction of a food crisis always comes with some uncertainty, the tendency is to delay action until it is absolutely required (Bailey, 2012).

This tendency is institutionalized by the fact that it is extremely difficult for humanitarian organizations to raise funds or devote resources on the basis of forecasts, since no emergency is yet underway. Crisis response institutions also tend to be risk averse to the possibility of “acting in vain”—allocating resources and warnings to a crisis that never materializes (Coughlan de Perez et al., 2015a). This aversion stems from both concrete financial risks and the potential for negative repercussions from donors and partner institutions in the case of a false alarm. It applies to any form of prediction-based health warning, but it has been most damaging in terms of lost opportunity in the area of food insecurity in Africa. The problem is amplified by the fact that food security in many countries of Africa is still viewed as a responsibility of the international aid community. This further diffuses responsibility and means that operational actors—a combination of UN agencies, nongovernmental organizations (NGOs), and partner government agencies from donor and recipient nations—must coordinate decision making and efforts. It also means that the political priorities of donor countries can be decisive and that well-meaning but cash-limited NGOs might need a crisis to materialize in order to motivate donations (Bailey, 2012; Fink & Redaelli, 2011; Olsen, Carstensen, & Høyen, 2003).

Improved application of climate information to health early warning in Africa, then, requires both improvements in prediction systems and changes in the way that predictions are used to inform action. In the area of improved prediction, there is a need for (1) improved data collection and dissemination, particularly in the area of health outcomes; (2) more complete integration of human systems into climate-based warnings; and (3) rigorous testing for predictive capability and value of information in the decision context.

The need for improved data collection is widely recognized. Improved surveillance and monitoring systems for disease and undernutrition can be used to train better predictive models and to drive real-time risk prediction systems that use current incidence as one of the predictors of future burden (as is widely the case in epidemics modeling). Ancillary demographic information is also critical, as a disease case count is most meaningful if it is reported alongside estimates of population size, geographic distribution, and characteristics. The proliferation of cell phones and other communication technology has been pointed to as an opportunity area for collecting this information, though these systems need to be designed in a manner that encourages participation, ensures data quality, and does not depend on cutting-edge or data-intensive technologies that are presently inaccessible in many poor communities.

Integration of human systems into climate-based prediction is also important. While the incorporation of economic information was a feature of even the earliest disease prediction systems, and sophisticated systems like FEWS NET incorporate multiple forms of economic and demographic information as a matter of course, the majority of published disease prediction models for Africa still treat human social dynamics in a simplified manner, if they are addressed at all. It is more typical to see a correlation between rainfall or vegetation and case count, without consideration for livelihood patterns, access to health services, transportation networks, and other factors that can influence the magnitude of an impending epidemic and that could be relevant to preparedness and response efforts. Combining the best-available tools of climate analysis, including seasonal forecasts, remote sensing, and land data assimilation systems, with models of human settlement, mobility, and activity is both a substantial research challenge and an opportunity to better understand and predict health vulnerabilities.

Testing for the predictive skill and value of information represents its own set of challenges. The basic concept of evaluating models for out-of-sample predictive skill rather than just descriptive fit is straightforward from an analytical perspective, given sufficient data, even if it is not always practiced (Shortridge, Falconi, Zaitchik, & Guikema, 2015). It is critically important when proposing models for forecasts, since in-sample fit and predictive skill do not always correlate. Maximizing the value of information, however, requires collaborative design across disciplines. The value of information in an early warning system depends both on the accuracy, reliability, and time horizon of a prediction and on the information needs of decision makers. Seasonal climate forecasts, for example, are frequently disseminated in terms of terciles—above-average, average, or below-average rainfall. This is the primary approach used by the African Regional Climate Outlook Forums, even though it has long been recognized that the approach does not meet the needs of decision makers (Patt et al., 2007).

Collaborative design and evaluation of forecast products, communication of uncertainty in a meaningful way, and the use of “boundary organizations” specialized in spanning the worlds of climate information and decision making can be critical to the success of a forecast system (Buizer, Jacobs, & Cash, 2016). Working with decision makers to tie forecasts to specific actions is also critical. In this context, the use of “serious games” forecast-based decision-making exercises has proved successful in Africa and elsewhere when applied to health-relevant decision making in disaster preparedness and resource management (Suarez, Mendler de Suarez, Koelle, & Boykoff, 2014). This need for collaboration between climate information providers and decision makers is a pillar of the Climate Services movement (Hewitt, Mason, & Walland, 2012).

On the institutional side, there is a recognized need to establish decision-making processes that are capable of acting on forecast information. This includes changing the institutional cultures of accountability in a way that will encourage forecast-based decision making (Bailey, 2012), combined with a broader cultural change among donors, recipients, and operational institutions to be more accepting of false alarms. This second requirement is particularly challenging, as the negative impact of false alarms has been documented in a number of contexts (Dillon & Tinsley, 2008). Models of successful forecast-based action do exist, including the forecast-based financing approach for disaster preparedness (Coughlan de Perez et al., 2015a), use of climate-based food insecurity models within a weather index insurance scheme to speed delivery of famine relief (Chantarat, Barrett, Mude, & Turvey, 2007), and integrating climate forecast information into health programming at the local scale (Coughlan de Perez et al., 2015b). These models depend on effective and accepted financing mechanisms and outreach activities. Success stories, such as the Red Cross experience using seasonal forecasts to enhance flood preparedness in West Africa in 2008 (Braman et al., 2013), are causing these approaches to gain traction.

Climate Change Adaptation

Climate change is having significant impacts on health across the world (Smith et al., 2014). In Africa, projected impacts on crop production and food security (see Socially-Mediated: Food Security) are a cause for significant concern, and changing patterns of infectious disease (see Ecologically Mediated: Infectious Disease) and climate extremes (see Physically Mediated: Floods, heat, and dust) also require adaptive action (McMichael, 2013; Patz & Hatch, 2014). Public health, however, has received relatively little focus in National Adaptation Plan of Action (NAPA) reports developed by Least Developed Countries for the United Nations Framework Convention on Climate Change; these plans have tended to focus on agriculture and energy sectors, with less targeted attention to the health sector (Doumbia et al., 2014).

In general, health-oriented climate change adaptation plans are hindered by the large uncertainties present both in climate change projections for Africa and in quantitative understanding of the links between climate change and highly mediated health outcomes. It is not at all clear, for example, that ecologically or socially mediated health dynamics will remain the same in a changing climate or that statistical relationships found to be robust for interannual climate variability will apply over longer timescales (Patz, Campbell-Lendrum, Holloway, & Foley, 2005). These challenges apply across the world, but in Africa they are compounded by the fact that climate change is occurring in the presence of rapid economic development and demographic transformation. In this context, projecting the health impacts of future climate change requires coupled natural–human systems models that account for cross-sectoral interactions. Few such models exist for the health sector. Just as importantly, risk assessment and communication frameworks are required to apply the findings of these inherently uncertain projections to decisions on adaptation investment.

This is a daunting challenge, but there are a number of opportunities for research to meet the needs of application. The WHO has identified some of these opportunities in its Framework for Public Health Adaptation to Climate Change in the Africa Region (Regional Committee for Africa of the World Health Organization, 2011), which includes an eight-point adaptation action plan based on (1) risk and capacity assessment, (2) capacity building, (3) integrated health and environment surveillance, (4) social mobilization, (5) health-oriented environmental management, (6) scaling up existing public health activities, (7) strengthening cross-sectoral partnerships, and (8) enhanced research. The influence of this framework is still to be determined, and funds will be required for its implementation in most sub-Saharan African countries.

Spatial scale is another critical consideration. When evaluating health risks such as undernutrition, studies are required at the household scale, but these studies must be conducted in a manner that is scalable to large populations or, at a minimum, designed with an awareness of complementary studies and data-sharing standards that allow for meta-analysis across locally oriented studies (Phalkey et al., 2015). Placing health studies in ecological context is also important. Health statistics are most commonly aggregated to political units, but climate-related health burdens are, in many cases, better understood as a function of climate forcing imposed on natural and agricultural ecosystems. For this reason, ecosystem-based adaptation strategies offer an opportunity for translating research to adaptation action at scale (Doumbia et al., 2014).

The quantification and communication of risk under rapidly changing climatic and socioeconomic conditions is a grand challenge for all climate change adaptation strategies in developing countries. Tools such as Robust Decision Making (RDM) analysis have been applied successfully to characterize the robustness of infrastructure and adaptation strategies in the energy and environmental resources sectors (Lempert, 2011). RDM is founded on the principle that systems should be designed to be robust to uncertain future conditions rather than optimized for a specific existing or projected climate (Lempert, Groves, Popper, & Bankes, 2006). In the health sector in Africa, where there is deep uncertainty regarding the future dynamics of malnutrition, infectious disease, and ability to cope with climate extremes, frameworks like RDM offer an opportunity to identify risks that may be missed or understated in projections focused on mean climate projections rather than the range of potential outcomes.

Closing Remarks

There is no shortage of challenges for climate and health in Africa. Basic research is needed to understand fundamental processes, and applied research is needed to convert research findings into operational warning systems. Institutional will and capacity are needed to turn warnings into early action. Many of the key points have been summarized elsewhere, as noted in Applying Climate Information for Health (Hewitt et al., 2012; McMichael, 2013; Patz & Hatch, 2014; Regional Committee for Africa of the World Health Organization, 2011; Smith et al., 2014). In closing, there are three areas of need from the research perspective: data, collaboration, and coupled analysis.

Data is clearly a key challenge. In this area, there is opportunity for streamlined integration across disciplines and institutions engaged in climate, environmental, socioeconomic, and health data. There are also opportunities in merging data streams between rapidly advancing data collection tools in biophysical sciences, such as satellite-based remote sensing, household-level health monitoring through crowd-sourced data, improved digitally based survey techniques (Zimmerman, OlaOlorun, & Radloff, 2015), and enhanced collection and dissemination of traditionally collected health data. Interdisciplinary collaboration in both research and operations is another critical and oft-noted challenge. This includes the need to understand natural–human systems that are coupled across scales, to consider nutrition and disease interactions when evaluating climate–health interactions, and to recognize the persistent impacts that acute climate shocks have on populations via epidemics, physical displacement, and economic losses.

The need for improved collaboration holds across the spectrum from research to operations; one particularly important area for collaboration is in the design of prediction systems. Climate researchers often refer to the “cascade of uncertainty” that accompanies efforts to model the impacts of a climate signal on hydrology, ecology, social systems, and human health. At the same time, health experts are able to appreciate risks associated with drivers of health outcome, even in the absence of a deterministic model of health burden. Information on El Niño state, for example, can be sufficient to motivate early warning on hydrological extremes (Braman et al., 2013), and forecasts or projections of the vegetation state can inform preparation for potential epidemics of vector-borne disease, even if the full disease process cannot be modeled with predictive skill. Anticipation and reduction of the health burden attributable to climate in Africa may, in many cases, be better served by a collaborative model in which applications meet research-based understanding at an intermediate point, rather than on an expectation of explicit prediction of health outcome.

At the same time, improving our ability to simulate the coupled natural–human processes is a worthy and, ultimately, necessary research endeavor. Projections of long-term change in health risks and analysis of proposed interventions both require systems models that capture these dynamics. The science of coupled natural–human processes relevant to human health outcomes is still far from mature. While researchers have long recognized the need to account for socioeconomic variables in climate-based health predictions, process-based models that include human mobility, behavioral feedbacks on risk, and the simultaneous, interacting evolution of ecological conditions and human health and economic status are, at best, nascent research tools. The potential for these higher-order models to inform health planning and response under rapidly changing social and climate conditions is still unknown.


  • Abdussalam, A. F., Monaghan, A. J., Dukić, V. M., Hayden, M. H., Hopson, T. M., Leckebusch, G. C., & Thornes, J. E. (2014). Climate influences on meningitis incidence in northwest Nigeria. Weather, Climate, and Society, 6(1), 62–76.
  • Abdussalam, A. F., Monaghan, A. J., Steinhoff, D. F., Dukic, V. M., Hayden, M. H., Hopson, T. M., et al. (2014). The impact of climate change on meningitis in Northwest Nigeria: An assessment using CMIP5 climate model simulations. Weather, Climate, and Society, 6(3), 371–379.
  • Abreu, D. A., & Riberas, Z. T. (2008). General overview of the NASS objective yield and objective measurement programs. Washington, DC: U.S. Department of Agriculture, National Agricultural Statistics Service.
  • Adejuwon, J. (2006). Food security, climate variability and climate change in Sub Saharan West Africa. AIACC Final Reports: Project no. AF, 23.
  • Adhikari, U., Nejadhashemi, A. P., & Woznicki, S. A. (2015). Climate change and eastern Africa: A review of impact on major crops. Food and Energy Security, 4(2), 110–132.
  • Agier, L., Broutin, H., Bertherat, E., Djingarey, M. H., Lingani, C., Perea, W., & Hugonnet, S. (2013). Timely detection of bacterial meningitis epidemics at district level: A study in three countries of the African Meningitis Belt. Transactions of the Royal Society of Tropical Medicine and Hygiene, 107(1), 30–36.
  • Agier, L., Deroubaix, A., Martiny, N., Yaka, P., Djibo, A., & Broutin, H. (2012). Seasonality of meningitis in Africa and climate forcing: aerosols stand out. Journal of the Royal Society, Interface/the Royal Society, 10(79), 20120814.
  • Amegah, A. K., Rezza, G., & Jaakkola, J. J. (2016). Temperature-related morbidity and mortality in Sub-Saharan Africa: A systematic review of the empirical evidence. Environment International, 91, 133–149.
  • Anyamba, A., Chretien, J. P., Small, J., Tucker, C. J., Formenty, P. B., Richardson, J. H., et al. (2009). Prediction of a Rift Valley fever outbreak. Proceedings of the National Academy of Sciences of the United States of America, 106(3), 955–959.
  • Anyamba, A., Linthicum, K. J., Mahoney, R., Tucker, C. J., & Kelley, P. W. (2002). Mapping potential risk of Rift Valley fever outbreaks in African savannas using vegetation index time series data. Photogrammetric Engineering and Remote Sensing, 68(2), 137–145.
  • Anyamba, A., Linthicum, K. J., Small, J. L., Collins, K. M., Tucker, C. J., Pak, E. W., et al. (2012). Climate teleconnections and recent patterns of human and animal disease outbreaks. PLoS Neglected Tropical Diseases, 6(1), e1465.
  • Arab, A., Jackson, M. C., & Kongoli, C. (2014). Modelling the effects of weather and climate on malaria distributions in West Africa. Malaria Journal, 13.
  • Azongo, D. K., Awine, T., Wak, G., Binka, F. N., & Oduro, A. R. (2012). A time series analysis of weather variability and all-cause mortality in the Kasena-Nankana Districts of Northern Ghana, 1995–2010. Global Health Action, 5, 14–22.
  • Badr, H. S., Zaitchik, B. F., & Dezfuli, A. K. (2015). A tool for hierarchical climate regionalization. Earth Science Informatics, 8(4), 949–958.
  • Bailey, R. (2012). Famine early warning and early action: The cost of delay. Royal Institute of International Affairs. Available online.
  • Bain, L. E., Awah, P. K., Geraldine, N., Kindong, N. P., Siga, Y., Bernard, N., & Tanjeko, A. T. (2013). Malnutrition in Sub-Saharan Africa: Burden, causes and prospects. Pan African Medical Journal, 15(1).
  • Balbus, J., Crimmins, A., Gamble, J., Easterling, D., Kunkel, K., Saha, S., & Sarofim, M. (2016). Chapter 1: Climate change and human health. The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment (pp. 25–42). U.S. Global Change Research Program, Washington, DC.
  • Barrios, S., Bertinelli, L., & Strobl, E. (2006). Climatic change and rural–urban migration: The case of sub-Saharan Africa. Journal of Urban Economics, 60(3), 357–371.
  • Bates, B., Kundzewicz, Z., Wu, S., & Palutikof, J. (2008). Climate change and water. Technical paper of the Intergovernmental Panel on Climate Change. Geneva: IPCC Secretariat.
  • Berazneva, J., & Lee, D. R. (2013). Explaining the African food riots of 2007–2008: An empirical analysis. Food Policy, 39, 28–39.
  • Berg, A., de Noblet-Ducoudré, N., Sultan, B., Lengaigne, M., & Guimberteau, M. (2013). Projections of climate change impacts on potential C4 crop productivity over tropical regions. Agricultural and Forest Meteorology, 170, 89–102.
  • Bhatt, S., Weiss, D. J., Cameron, E., Bisanzio, D., Mappin, B., Dalrymple, U., et al. (2015). The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature, 526(7572), 207–211.
  • Bhattacharjee, P. S., & Zaitchik, B. F. (2015). Perspectives on CMIP5 model performance in the Nile River headwaters regions. International Journal of Climatology, 35(14), 4262–4275.
  • Black, R., Bennett, S. R., Thomas, S. M., & Beddington, J. R. (2011). Climate change: Migration as adaptation. Nature, 478(7370), 447–449.
  • Black, R. E., Allen, L. H., Bhutta, Z. A., Caulfield, L. E., De Onis, M., Ezzati, M., et al. (2008). Maternal and child undernutrition: Global and regional exposures and health consequences. The Lancet, 371(9608), 243–260.
  • Blanford, J. I., Blanford, S., Crane, R. G., Mann, M. E., Paaijmans, K. P., Schreiber, K. V., & Thomas, M. B. (2013). Implications of temperature variation for malaria parasite development across Africa. Scientific Reports, 3, 1300.
  • Blöschl, G., & Montanari, A. (2010). Climate change impacts—throwing the dice? Hydrological Processes, 24(3), 374–381.
  • Bomblies, A., Duchemin, J., & Eltahir, E. A. (2009). A mechanistic approach for accurate simulation of village scale malaria transmission. Malaria Journal, 8, 223.
  • Bompangue, D., Giraudoux, P., Piarroux, M., Mutombo, G., Shamavu, R., Sudre, B., et al. (2009). Cholera epidemics, war and disasters around Goma and Lake Kivu: an eight-year survey. PLoS Neglected Tropical Diseases, 3(5), e436.
  • Braman, L. M., van Aalst, M. K., Mason, S. J., Suarez, P., Ait-Chellouche, Y., & Tall, A. (2013). Climate forecasts in disaster management: Red Cross flood operations in West Africa, 2008. Disasters, 37(1), 144–164.
  • Brown, M., Funk, C., Galu, G., & Choularton, R. (2007). Earlier famine warning possible using remote sensing and models. Eos, 88, 381–382.
  • Brown, M., Tondel, F., Essam, T., Thorne, J., Mann, B., Leonard, K., et al. (2012). Country and regional staple food price indices for improved identification of food insecurity. Global Environmental Change, 22(3), 784–794.
  • Brown, M. E. (2008). Famine early warning systems and remote sensing data. Berlin & Heidelberg: Springer Science & Business Media.
  • Brown, M. E., & Funk, C. C. (2008). Food security under climate change. Science, 319(5863), 580–581.
  • Brown, O., Hammill, A., & McLeman, R. (2007). Climate change as the “new” security threat: Implications for Africa. International Affairs, 83(6), 1141–1154.
  • Buhaug, H., Benaminsen, T. A., Sjaastad, E., & Theisen, O. M. (2015). Climate variability, food production shocks, and violent conflict in sub-Saharan Africa. Environmental Research Letters, 10(12), 125015.
  • Buizer, J., Jacobs, K., & Cash, D. (2016). Making short-term climate forecasts useful: Linking science and action. Proceedings of the National Academy of Sciences of the United States of America, 113(17), 4597–4602.
  • Burgess, R., & Donaldson, D. (2010). Can openness mitigate the effects of weather shocks? Evidence from India’s famine era. American Economic Review, 100(2), 449–453.
  • Burkart, K., Khan, M. M., Schneider, A., Breitner, S., Langner, M., Kramer, A., & Endlicher, W. (2014). The effects of season and meteorology on human mortality in tropical climates: A systematic review. Transactions of the Royal Society of Tropical Medicine and Hygiene, 108(7), 393–401.
  • Campbell, L. P., Luther, C., Moo-Llanes, D., Ramsey, J. M., Danis-Lozano, R., & Peterson, A. T. (2015). Climate change influences on global distributions of dengue and chikungunya virus vectors. Philosophical Transactions of the Royal Society of London.Series B, Biological Sciences, 370(1665).
  • Caulfield, L. E., de Onis, M., Blossner, M., & Black, R. E. (2004). Undernutrition as an underlying cause of child deaths associated with diarrhea, pneumonia, malaria, and measles. American Journal of Clinical Nutrition, 80(1), 193–198.
  • Ceccato, P., Ghebremeskel, T., Jaiteh, M., Graves, P. M., Levy, M., Ghebreselassie, S., et al. (2007). Malaria stratification, climate, and epidemic early warning in Eritrea. American Journal of Tropical Medicine and Hygiene, 77(Suppl. 6), 61–68.
  • Ceccato, P., Trzaska, S., Pérez García-Pando, C., Kalashnikova, O., del Corral, J., Cousin, R., et al. (2014). Improving decision-making activities for meningitis and malaria. Geocarto International, 29(1), 19–38.
  • Center for International Earth Science Information Network—CIESIN—Columbia University. (2016). Gridded Population of the World, Version 4 (GPWv4): Population Count.
  • Challinor, A., Wheeler, T., Garforth, C., Craufurd, P., & Kassam, A. (2007). Assessing the vulnerability of food crop systems in Africa to climate change. Climatic Change, 83(3), 381–399.
  • Chantarat, S., Barrett, C. B., Mude, A. G., & Turvey, C. G. (2007). Using weather index insurance to improve drought response for famine prevention. American Journal of Agricultural Economics, 89(5), 1262–1268.
  • Cheesbrough, J., Morse, A., & Green, S. (1995). Meningococcal meningitis and carriage in western Zaire: a hypoendemic zone related to climate? Epidemiology and Infection, 114(01), 75–92.
  • Cohen, M. J., & Garrett, J. L. (2010). The food price crisis and urban food (in) security. Environment and Urbanization, 22(2), 467–482.
  • Colombo, M., Francisco, M., Ferreira, B., Rubino, S., & Cappuccinelli, P. (1993). The early stage of the recurrent cholera epidemic in Luanda, Angola. European Journal of Epidemiology, 9(5), 563–565.
  • Coughlan de Perez, E., Nerlander, L., Monasso, F., van Aalst, M., Mantilla, G., Muli, E., et al. (2015b). Managing health risks in a changing climate: Red Cross operations in East Africa and Southeast Asia. Climate and Development, 7(3), 197–207.
  • Coughlan de Perez, E., van den Hurk, B., van Aalst, M., Jongman, B., Klose, T., & Suarez, P. (2015a). Forecast-based financing: An approach for catalyzing humanitarian action based on extreme weather and climate forecasts. Natural Hazards and Earth System Science, 15(4), 895–904.
  • Craig, M., Snow, R., & Le Sueur, D. (1999). A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitology Today, 15(3), 105–111.
  • Crush, J. S., & Fayne, B. (2010). Pathways to insecurity: Urban food supply and access in Southern African cities African Food Security Urban Network (AFSUN).
  • Davenport, F., & Funk, C. (2015). Using time series structural characteristics to analyze grain prices in food insecure countries. Food Security, 7(5), 1055–1070.
  • Davies, F. G., Linthicum, K. J., & James, A. D. (1985). Rainfall and epizootic Rift Valley fever. Bulletin of the World Health Organization, 63(5), 941–943.
  • De Longueville, F., Hountondji, Y., Henry, S., & Ozer, P. (2010). What do we know about effects of desert dust on air quality and human health in West Africa compared to other regions? Science of the Total Environment, 409(1), 1–8.
  • De Longueville, F., Ozer, P., Doumbia, S., & Henry, S. (2013). Desert dust impacts on human health: an alarming worldwide reality and a need for studies in West Africa. International Journal of Biometeorology, 57(1), 1–19.
  • Dezfuli, A. K., Zaitchik, B. F., & Gnanadesikan, A. (2015). Regional atmospheric circulation and rainfall variability in south equatorial Africa. Journal of Climate, 28(2), 809–818.
  • Di Baldassarre, G., Montanari, A., Lins, H., Koutsoyiannis, D., Brandimarte, L., & Blöschl, G. (2010). Flood fatalities in Africa: From diagnosis to mitigation. Geophysical Research Letters, 37(22).
  • Diboulo, E., Sie, A., Rocklov, J., Niamba, L., Ye, M., Bagagnan, C., & Sauerborn, R. (2012). Weather and mortality: A 10 year retrospective analysis of the Nouna Health and Demographic Surveillance System, Burkina Faso. Global Health Action, 5, 6–13.
  • Dillon, R. L., & Tinsley, C. H. (2008). How near-misses influence decision making under risk: A missed opportunity for learning. Management Science, 54(8), 1425–1440.
  • Doumbia, S., Jalloh, A., & Diouf, A. (2014). Review of research and policies for climate change adaptation in the health sector in West Africa (No. 88). Brighton, U.K.: Future Agricultures Consortium.
  • Egondi, T., Kyobutungi, C., Kovats, S., Muindi, K., Ettarh, R., & Rocklöv, J. (2012). Time-series analysis of weather and mortality patterns in Nairobi’s informal settlements. Global Health Action, 5(19065), 23–32.
  • Egondi, T., Kyobutungi, C., & Rocklöv, J. (2015). Temperature variation and heat wave and cold spell impacts on years of life lost among the urban poor population of Nairobi, Kenya. International Journal of Environmental Research and Public Health, 12(3), 2735–2748.
  • Ermert, V., Fink, A. H., & Paeth, H. (2013). The potential effects of climate change on malaria transmission in Africa using bias-corrected regionalised climate projections and a simple malaria seasonality model. Climatic Change, 120(4), 741–754.
  • Felix, B., & Romuald, K. S. (2009). Do climatic shocks matter for food security in developing countries? Clermont-Ferrand, France: Centre d’Etudes et de Recherches sur le Développement International (CERDI).
  • Field, C. B. (2012). Managing the risks of extreme events and disasters to advance climate change adaptation: Special report of the intergovernmental panel on climate change. Cambridge, U.K.: Cambridge University Press.
  • Fink, G., & Redaelli, S. (2011). Determinants of international emergency aid—humanitarian need only? World Development, 39(5), 741–757.
  • Food and Agriculture Organization of the United Nations [FAO]. (2011). The state of food insecurity in the world: How does international price volatility affect domestic economies and food security? Rome, Italy: Food and Agriculture Organization of the United Nations.
  • Food and Agriculture Organization of the United Nations [FAO]. (2015). The state of food insecurity in the world. Meeting the 2015 international hunger targets: Taking stock of uneven progress. Rome: Food and Agriculture Organization of the United Nations.
  • Funk, C. (2011). We thought trouble was coming. Nature, 476(7358), 7.
  • Funk, C., Dettinger, M. D., Michaelsen, J. C., Verdin, J. P., Brown, M. E., Barlow, M., & Hoell, A. (2008). Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proceedings of the National Academy of Sciences of the United States of America, 105(32), 11081–11086.
  • Funk, C., & Verdin, J. P. (2010). Real-time decision support systems: The famine early warning system network. Satellite rainfall applications for surface hydrology (pp. 295–320). New York: Springer.
  • Funk, C. C., Peterson, P. J., Landsfeld, M. F., Pedreros, D. H., Verdin, J. P., Rowland, J. D., et al. (2014). A quasi-global precipitation time series for drought monitoring. US Geological Survey Data Series, 832(4).
  • Garland, R. M., Matooane, M., Engelbrecht, F. A., Bopape, M. M., Landman, W. A., Naidoo, M., et al. (2015). Regional projections of extreme apparent temperature days in Africa and the related potential risk to human health. International Journal of Environmental Research and Public Health, 12(10), 12577–12604.
  • Giannadaki, D., Pozzer, A., & Lelieveld, J. (2014). Modeled global effects of airborne desert dust on air quality and premature mortality. Atmospheric Chemistry and Physics, 14(2), 957–968.
  • Gill, C. (1923). The prediction of malaria epidemics: With special reference to an actual forecast in 1921. Indian Journal of Medical Research, 10(4), 1136–1143.
  • Githeko, A. K., Ogallo, L., Lemnge, M., Okia, M., & Ototo, E. N. (2014). Development and validation of climate and ecosystem-based early malaria epidemic prediction models in East Africa. Malaria Journal, 13, 329.
  • Godfray, H. C., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., et al. (2010). Food security: The challenge of feeding 9 billion people. Science (New York), 327(5967), 812–818.
  • Gomez-Elipe, A., Otero, A., Van Herp, M., & Aguirre-Jaime, A. (2007). Forecasting malaria incidence based on monthly case reports and environmental factors in Karuzi, Burundi, 1997–2003. Malaria Journal, 6, 129.
  • Goudie, A. S. (2009). Dust storms: Recent developments. Journal of Environmental Management, 90(1), 89–94.
  • Goudie, A. S. (2014). Desert dust and human health disorders. Environment International, 63, 101–113.
  • Grace, K., Davenport, F., Funk, C., & Lerner, A. M. (2012). Child malnutrition and climate in sub-Saharan Africa: An analysis of recent trends in Kenya. Applied Geography, 35(1), 405–413.
  • Greiner, C., & Sakdapolrak, P. (2013). Rural–urban migration, agrarian change, and the environment in Kenya: A critical review of the literature. Population and Environment, 34(4), 524–553.
  • Griffith, D. C., Kelly-Hope, L. A., & Miller, M. A. (2006). Review of reported cholera outbreaks worldwide, 1995–2005. American Journal of Tropical Medicine and Hygiene, 75(5), 973–977.
  • Grover-Kopec, E., Kawano, M., Klaver, R. W., Blumenthal, B., Ceccato, P., & Connor, S. J. (2005). An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malaria Journal, 4, 6.
  • Guevart, E., Noeske, J., Solle, J., Essomba, J. M., Edjenguele, M., Bita, A., et al. (2006). Factors contributing to endemic cholera in Douala, Cameroon. [Determinants du cholera a Douala.] Medecine Tropicale: Revue Du Corps De Sante Colonial, 66(3), 283–291.
  • Guha-Sapir, D., Below, R., & Hoyois, P. (2015). EM-DAT: International disaster database. Brussels, Belgium: Catholic University of Louvain.
  • Gumbricht, T., Wolski, P., Frost, P., & McCarthy, T. (2004). Forecasting the spatial extent of the annual flood in the Okavango Delta, Botswana. Journal of Hydrology, 290(3), 178–191.
  • Haile, A. T., Tefera, F. T., & Rientjes, T. (2016). Flood forecasting in Niger-Benue basin using satellite and quantitative precipitation forecast data. International Journal of Applied Earth Observation and Geoinformation, 52, 475–484.
  • Haile, M. (2005). Weather patterns, food security and humanitarian response in sub-Saharan Africa. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360(1463), 2169–2182.
  • Hashizume, M., Terao, T., & Minakawa, N. (2009). The Indian Ocean Dipole and malaria risk in the highlands of western Kenya. Proceedings of the National Academy of Sciences of the United States of America, 106(6), 1857–1862.
  • Hay, S. I., Smith, D. L., & Snow, R. W. (2008). Measuring malaria endemicity from intense to interrupted transmission. The Lancet Infectious Diseases, 8(6), 369–378.
  • Hellmuth, M. E., Moorhead, A., Thomson, M. C., & Williams, J. (2007). Climate risk management in Africa: Learning from practice. New York: Columbia University, International Research Institute for Climate and Society.
  • Hendrix, C. S., & Glaser, S. M. (2007). Trends and triggers: Climate, climate change and civil conflict in sub-Saharan Africa. Political Geography, 26(6), 695–715.
  • Hewitt, C., Mason, S., & Walland, D. (2012). The global framework for climate services. Nature Climate Change, 2(12), 831–832.
  • Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., & Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25(15), 1965–1978.
  • Hoedjes, J. C., Kooiman, A., Maathuis, B. H., Said, M. Y., Becht, R., Limo, A., et al. (2014). A conceptual flash flood early warning system for Africa, based on terrestrial microwave links and flash flood guidance. ISPRS International Journal of Geo-Information, 3(2), 584–598.
  • Homer-Dixon, T. F. (1991). On the threshold: environmental changes as causes of acute conflict. International Security, 16(2), 76–116.
  • Hoshen, M. B., & Morse, A. P. (2004). A weather-driven model of malaria transmission. Malaria Journal, 3, 32.
  • Jaenisch, T., Junghanss, T., Wills, B., Brady, O. J., Eckerle, I., Farlow, A., et al. (2014). Dengue expansion in Africa-not recognized or not happening. Emerging Infectious Diseases, 20(10), e140487.
  • Jankowska, M. M., Lopez-Carr, D., Funk, C., Husak, G. J., & Chafe, Z. A. (2012). Climate change and human health: Spatial modeling of water availability, malnutrition, and livelihoods in Mali, Africa. Applied Geography, 33, 4–15.
  • Johnstone, S., & Mazo, J. (2011). Global warming and the Arab Spring. Survival, 53(2), 11–17.
  • Jones, A. E., & Morse, A. P. (2010). Application and validation of a seasonal ensemble prediction system using a dynamic malaria model. Journal of Climate, 23(15), 4202–4215.
  • Jubach, R., & Tokar, A. S. (2016). International severe weather and flash flood hazard early warning systems—Leveraging coordination, cooperation, and partnerships through a hydrometeorological project in Southern Africa. Water, 8(6), 258.
  • Jusot, J., Neill, D. R., Waters, E. M., Bangert, M., Collins, M., Moreno, L. B., et al. (2016). Airborne dust and high temperatures are risk factors for invasive bacterial disease. Journal of Allergy and Clinical Immunology.
  • Karanasiou, A., Moreno, N., Moreno, T., Viana, M., De Leeuw, F., & Querol, X. (2012). Health effects from Sahara dust episodes in Europe: Literature review and research gaps. Environment International, 47, 107–114.
  • Kevane, M., & Gray, L. (2008). Darfur: Rainfall and conflict. Environmental Research Letters, 3(3), 034006.
  • Koubi, V., Bernauer, T., Kalbhenn, A., & Spilker, G. (2012). Climate variability, economic growth, and civil conflict. Journal of Peace Research, 49(1), 113–127.
  • Kundzewicz, Z. W., Graczyk, D., Maurer, T., Pińskwar, I., Radziejewski, M., Svensson, C., & Szwed, M. (2005). Trend detection in river flow series: 1. Annual maximum flow/Détection de tendance dans des séries de débit fluvial: 1. Débit maximum annuel. Hydrological Sciences Journal, 50(5), 797–810.
  • Lapeyssonnie, L. (1963). Cerebrospinal meningitis in Africa. Bulletin of the World Health Organization, 28(Suppl.).
  • Lawoyin, T., Ogunbodede, N., Olumide, E., & Onadeko, M. (1999). Outbreak of cholera in Ibadan, Nigeria. European Journal of Epidemiology, 15(4), 365–368.
  • Lempert, R. (2011). Managing climate risks in developing countries with robust decision making. Washington, DC: World Resources Institute.
  • Lempert, R. J., Groves, D. G., Popper, S. W., & Bankes, S. C. (2006). A general, analytic method for generating robust strategies and narrative scenarios. Management Science, 52(4), 514–528.
  • Li, C., Chai, Y., Yang, L., & Li, H. (2016). Spatio-temporal distribution of flood disasters and analysis of influencing factors in Africa. Natural Hazards, 82(1), 721–731.
  • Lindsay, S. W., Bødker, R., Malima, R., Msangeni, H. A., & Kisinza, W. (2000). Effect of 1997–98 EI Niño on highland malaria in Tanzania. The Lancet, 355(9208), 989–990.
  • Lindsay, S. W., & Martens, W. J. (1998). Malaria in the African highlands: past, present and future. Bulletin of the World Health Organization, 76(1), 33–45.
  • Linthicum, K. J., Bailey, C. L., Davies, F. G., & Tucker, C. J. (1987). Detection of Rift Valley fever viral activity in Kenya by satellite remote sensing imagery. Science (New York), 235(4796), 1656–1659.
  • Linthicum, K. J., Britch, S. C., & Anyamba, A. (2016). Rift Valley fever: An emerging mosquito-borne disease. Annual Review of Entomology, 61, 395–415.
  • Lloyd, S. J., Kovats, R. S., & Chalabi, Z. (2011). Climate change, crop yields, and undernutrition: Development of a model to quantify the impact of climate scenarios on child undernutrition. Environmental Health Perspectives, 119(12), 1817.
  • Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production since 1980. Science (New York), 333(6042), 616–620.
  • Mabaso, M., & Ndlovu, N. (2012). Critical review of research literature on climate-driven malaria epidemics in sub-Saharan Africa. Public Health, 126(11), 909–919.
  • Mabaso, M. L., Kleinschmidt, I., Sharp, B., & Smith, T. (2007). El Niño Southern Oscillation (ENSO) and annual malaria incidence in Southern Africa. Transactions of the Royal Society of Tropical Medicine and Hygiene, 101(4), 326–330.
  • de Magny, G. C., Guégan, J., Petit, M., & Cazelles, B. (2007). Regional-scale climate-variability synchrony of cholera epidemics in West Africa. BMC Infectious Diseases, 7, 20.
  • de Magny, G. C., Thiaw, W., Kumar, V., Manga, N. M., Diop, B. M., Gueye, L., et al. (2012). Cholera outbreak in Senegal in 2005: Was climate a factor? PLoS One, 7(8), e44577.
  • McLeman, R., & Smit, B. (2006). Migration as an adaptation to climate change. Climatic Change, 76(1–2), 31–53.
  • McMichael, A. J. (2013). Globalization, climate change, and human health. New England Journal of Medicine, 368(14), 1335–1343.
  • McMichael, A. J., Woodruff, R. E., & Hales, S. (2006). Climate change and human health: Present and future risks. The Lancet, 367(9513), 859–869.
  • McMichael, C., Barnett, J., & McMichael, A. J. (2012). An III wind? Climate change, migration, and health. Environmental Health Perspectives, 120(5), 646.
  • Mendelsohn, J., & Dawson, T. (2008). Climate and cholera in KwaZulu-Natal, South Africa: The role of environmental factors and implications for epidemic preparedness. International Journal of Hygiene and Environmental Health, 211(1), 156–162.
  • Mengel, M. A., Delrieu, I., Heyerdahl, L., & Gessner, B. D. (2014). Cholera outbreaks in Africa. Cholera Outbreaks (pp. 117–144). New York: Springer.
  • Molineaux, L., Wernsdorfer, W., & McGregor, I. (1988). The epidemiology of human malaria as an explanation of its distribution, including some implications for its control. Malaria: Principles and practice of malariology (Vol. 2, pp. 913–998). London: Churchill Livingstone.
  • Moore, S. M., Monaghan, A., Griffith, K. S., Apangu, T., Mead, P. S., & Eisen, R. J. (2012). Improvement of disease prediction and modeling through the use of meteorological ensembles: Human plague in Uganda. PLoS One, 7(9), e44431.
  • Mrema, S., Shamte, A., Selemani, M., & Masanja, H. (2012). The influence of weather on mortality in rural Tanzania: a time-series analysis 1999–2010. Global Health Action, 5, 33–43.
  • Nelson, G. C., Rosegrant, M. W., Koo, J., Robertson, R., Sulser, T., Zhu, T., et al. (2009). Climate change: Impact on agriculture and costs of adaptation. Washington, DC: International Food Policy Research Institute.
  • Niang, I., Ruppel, O. C., Abdrabo, M. A., Essel, A., Lennard, C., Padgham, J., & Urquhart, P. (2014). Africa. In V. R. Barros, C. B. Field, D. J. Dokken, M. D. Mastrandrea, K. J. Mach, T. E. Bilir, et al. (Eds.), Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1199–1265). Cambridge, U.K.: Cambridge University Press.
  • Nkoko, D. B., Giraudoux, P., Plisnier, P., Mutombo Tinda, A., Piarroux, M., Sudre, B., et al. (2011). Dynamics of cholera outbreaks in Great Lakes region of Africa, 1978–2008. Emerging Infectious Diseases, 17(11), 2026–2034.
  • O’Fahey, R. S. (2006). Conflict in Darfur: Historical and contemporary perspectives. Environmental degradation as a cause of conflict in Darfur (pp. 23–32). University for Peace, Switzerland.
  • Olago, D., Marshall, M., Wandiga, S. O., Opondo, M., Yanda, P. Z., Kangalawe, R., et al. (2007). Climatic, socio-economic, and health factors affecting human vulnerability to cholera in the Lake Victoria basin, East Africa. AMBIO: A Journal of the Human Environment, 36(4), 350–358.
  • Olsen, G. R., Carstensen, N., & Høyen, K. (2003). Humanitarian crises: What determines the level of emergency assistance? Media coverage, donor interests and the aid business. Disasters, 27(2), 109–126.
  • Oluwole, O. S. A. (2015). Climate regimes, El Niño-southern oscillation, and meningococcal meningitis epidemics. Frontiers in Public Health, 3, 187.
  • Paaijmans, K. P., Blanford, J. I., Crane, R. G., Mann, M. E., Ning, L., Schreiber, K. V., & Thomas, M. B. (2014). Downscaling reveals diverse effects of anthropogenic climate warming on the potential for local environments to support malaria transmission. Climatic Change, 125(3–4), 479–488.
  • Pandya, R., Hodgson, A., Hayden, M. H., Akweongo, P., Hopson, T., Forgor, A. A., et al. (2015). Using weather forecasts to help manage meningitis in the West African Sahel. Bulletin of the American Meteorological Society, 96(1), 103–115.
  • Parfitt, J., Barthel, M., & Macnaughton, S. (2010). Food waste within food supply chains: quantification and potential for change to 2050. Philosophical Transactions of the Royal Society of London.Series B, Biological Sciences, 365(1554), 3065–3081.
  • Patt, A. G., Ogallo, L., & Hellmuth, M. (2007). Sustainability: Learning from 10 years of climate outlook forums in Africa. Science (New York), 318(5847), 49–50.
  • Patz, J. A., Campbell-Lendrum, D., Holloway, T., & Foley, J. A. (2005). Impact of regional climate change on human health. Nature, 438(7066), 310–317.
  • Patz, J. A., & Hatch, M. J. (2014). Public health and global climate disruption. Public Health Review, 35(1), e23.
  • Patz, J. A., Hulme, M., Rosenzweig, C., Mitchell, T. D., Goldberg, R. A., Githeko, A. K., et al. (2002). Climate change (Communication arising): Regional warming and malaria resurgence. Nature, 420(6916), 627–628.
  • Paz, S. (2009). Impact of temperature variability on cholera incidence in southeastern Africa, 1971–2006. Ecohealth, 6(3), 340–345.
  • Phalkey, R. K., Aranda-Jan, C., Marx, S., Hofle, B., & Sauerborn, R. (2015). Systematic review of current efforts to quantify the impacts of climate change on undernutrition. Proceedings of the National Academy of Sciences of the United States of America, 112(33), E4522–E4529.
  • Pinzon, J. E., Wilson, J. M., Tucker, C. J., Arthur, R., Jahrling, P. B., & Formenty, P. (2004). Trigger events: Enviroclimatic coupling of Ebola hemorrhagic fever outbreaks. American Journal of Tropical Medicine and Hygiene, 71(5), 664–674.
  • Ray, D. K., Gerber, J. S., MacDonald, G. K., & West, P. C. (2015). Climate variation explains a third of global crop yield variability. Nature Communications, 6, 5989.
  • Rebaudet, S., Sudre, B., Faucher, B., & Piarroux, R. (2013a). Cholera in coastal Africa: A systematic review of its heterogeneous environmental determinants. Journal of Infectious Diseases, 208(Suppl. 1), S98–S106.
  • Rebaudet, S., Sudre, B., Faucher, B., & Piarroux, R. (2013b). Environmental determinants of cholera outbreaks in inland Africa: A systematic review of main transmission foci and propagation routes. Journal of Infectious Diseases, 208(Suppl. 1), S46–S54.
  • Regional Committee for Africa of the World Health Organization. (2011). Framework for public health adaptation to climate change in the African region. Regional Committee for Africa Sixty-First Session, Yamoussoukro, Côte d’Ivoire, 29.
  • Reuveny, R. (2007). Climate change-induced migration and violent conflict. Political Geography, 26(6), 656–673.
  • Ringler, C., Zhu, T., Cai, X., Koo, J., & Wang, D. (2010). Climate change impacts on food security in sub-Saharan Africa, Insights from comprehensive climate change scenarios, IFPRI Discussion Paper No. 1042. Washington, DC: International Food Policy Research Institute.
  • Rogers, L. (1923). The world incidence of leprosy in relation to meteorological conditions and its bearing on the probable mode of transmission. Transactions of the Royal Society of Tropical Medicine and Hygiene, 16(8), 440–464.
  • Rogers, L. (1925). Climate and disease incidence in India, with special reference to leprosy, phthisis, pneumonia and smallpox. Journal of State Medicine, 33, 501–510.
  • Rogers, L. (1926). Small-pox and climate in India: Forecasting of epidemics (No. 106). London: Medical Research Council Special Report Series.
  • Rowell, D. P., Senior, C. A., Vellinga, M., & Graham, R. J. (2016). Can climate projection uncertainty be constrained over Africa using metrics of contemporary performance? Climatic Change, 134(4), 621–633.
  • Ryan, S. J., McNally, A., Johnson, L. R., Mordecai, E. A., Ben-Horin, T., Paaijmans, K., & Lafferty, K. D. (2015). Mapping physiological suitability limits for malaria in Africa under climate change. Vector-Borne and Zoonotic Diseases, 15(12), 718–725.
  • Salehyan, I. (2014). Climate change and conflict: making sense of disparate findings. Political Geography, 43, 1–5.
  • Salih, Mohamed Abdel Rahim Mohamed. (2005). Understanding the conflict in Darfur. Centre of African Studies, University of Copenhagen.
  • Sargent, F. (1982). Hippocratic heritage: A history of ideas about weather and human health. Oxford: Pergamon Press.
  • Sasaki, S., Suzuki, H., Fujino, Y., Kimura, Y., & Cheelo, M. (2009). Impact of drainage networks on cholera outbreaks in Lusaka, Zambia. American Journal of Public Health, 99(11), 1982–1987.
  • Schaetti, C., Hutubessy, R., Ali, S. M., Pach, A., Weiss, M. G., Chaignat, C. L., & Khatib, A. M. (2009). Oral cholera vaccine use in Zanzibar: Socioeconomic and behavioural features affecting demand and acceptance. BMC Public Health, 9.
  • Scheffran, J., Ide, T., & Schilling, J. (2014). Violent climate or climate of violence? Concepts and relations with focus on Kenya and Sudan. International Journal of Human Rights, 18(3), 369–390.
  • Schlenker, W., & Lobell, D. B. (2010). Robust negative impacts of climate change on African agriculture. Environmental Research Letters, 5(1).
  • Scovronick, N., Lloyd, S., & Kovats, R. (2015). Climate and health in informal urban settlements. Environment and Urbanization, 27(2), 657–678.
  • Shortridge, J. E., Falconi, S. M., Zaitchik, B. F., & Guikema, S. D. (2015). Climate, agriculture, and hunger: Statistical prediction of undernourishment using nonlinear regression and data-mining techniques. Journal of Applied Statistics, 42(11), 2367–2390.
  • Sierra, B de la C, Kouri, G., & Guzmán, M. (2007). Race: A risk factor for dengue hemorrhagic fever. Archives of Virology, 152(3), 533–542.
  • Sinka, M. E., Bangs, M. J., Manguin, S., Coetzee, M., Mbogo, C. M., Hemingway, J., et al. (2010). The dominant Anopheles vectors of human malaria in Africa, Europe and the Middle East: occurrence data, distribution maps and bionomic précis. Parasites and Vectors, 3(1), 1.
  • Siraj, A. S., Santos-Vega, M., Bouma, M. J., Yadeta, D., Ruiz Carrascal, D., & Pascual, M. (2014). Altitudinal changes in malaria incidence in highlands of Ethiopia and Colombia. Science (New York), 343(6175), 1154–1158.
  • Smith, K. R., Woodward, A., Campbell-Lendrum, D., Chadee, D., Honda, Y., Liu, Q., et al. (2014). Human health: impacts, adaptation, and co-benefits. In V. R. Barros, C. B. Field, D. J. Dokken, M. D. Mastrandrea, K. J. Mach, T. E. Bilir, et al. (Eds.), Climate change 2014: Impacts, adaptation, and vulnerability. Part B: Regional aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 709–754). Cambridge, U.K.: Cambridge University Press.
  • Sneyd, L. Q., Legwegoh, A., & Fraser, E. D. (2013). Food riots: Media perspectives on the causes of food protest in Africa. Food Security, 5(4), 485–497.
  • Stathers, T., Lamboll, R., & Mvumi, B. M. (2013). Postharvest agriculture in changing climates: Its importance to African smallholder farmers. Food Security, 5(3), 361–392.
  • Sternberg, T. (2012). Chinese drought, bread and the Arab Spring. Applied Geography, 34, 519–524.
  • Suarez, P., Mendler de Suarez, J., Koelle, B., & Boykoff, M. (2014). Serious fun: Scaling up community based adaptation through experiential learning. Community-Based Adaptation to Climate Change: Scaling it Up (pp. 136–151). New York: Routledge.
  • Sultan, B., Roudier, P., Quirion, P., Alhassane, A., Muller, B., Dingkuhn, M., et al. (2013). Assessing climate change impacts on sorghum and millet yields in the Sudanian and Sahelian savannas of West Africa. Environmental Research Letters, 8(1).
  • Tacoli, C. (2009). Crisis or adaptation? Migration and climate change in a context of high mobility. Environment and Urbanization, 21(2), 513–525.
  • Tauxe, R. V., Holmberg, S. D., Dodin, A., Wells, J. V., & Blake, P. A. (1988). Epidemic cholera in Mali: High mortality and multiple routes of transmission in a famine area. Epidemiology and Infection, 100(02), 279–289.
  • Tefera, T. (2012). Post-harvest losses in African maize in the face of increasing food shortage. Food Security, 4(2), 267–277.
  • Thielen-del Pozo, J., Thiemig, V., Pappenberger, F., Revilla-Romero, B., Salamon, P., De Groeve, T., & Hirpa, F. (2015). The benefit of continental flood early warning systems to reduce the impact of flood disasters. (No. EUR 27533 EN). JRC Science Policy Report.
  • Thiemig, V., Bisselink, B., Pappenberger, F., & Thielen, J. (2015). A pan-African medium-range ensemble flood forecast system. Hydrology and Earth System Sciences, 19(8), 3365–3385.
  • Thiemig, V., Pappenberger, F., Thielen, J., Gadain, H., De Roo, A., Bodis, K., et al. (2010). Ensemble flood forecasting in Africa: A feasibility study in the Juba–Shabelle river basin. Atmospheric Science Letters, 11(2), 123–131.
  • Thomson, M. C., Firth, E., Jancloes, M., Mihretie, A., Onoda, M., Nickovic, S., et al. (2013). A climate and health partnership to inform the prevention and control of meningoccocal meningitis in sub-Saharan Africa: The MERIT initiative. Climate Science for Serving Society (pp. 459–484). New York: Springer.
  • Thomson, M. C., Mason, S., Platzer, B., Mihretie, A., Omumbo, J., Mantilla, G., et al. (2014). Climate and health in Africa. Earth Perspectives, 1(17),
  • Thornton, P. K., Ericksen, P. J., Herrero, M., & Challinor, A. J. (2014). Climate variability and vulnerability to climate change: a review. Global Change Biology, 20(11), 3313–3328.
  • Tompkins, A. M., & Di Giuseppe, F. (2015). Potential predictability of malaria in Africa using ECMWF monthly and seasonal climate forecasts. Journal of Applied Meteorology and Climatology, 54(3), 521–540.
  • Toole, M. (2005). Forced migrants: Refugees and internally displaced persons. Social Injustice and Public Health (p. 190204). Oxford: Oxford University Press.
  • Trærup, S. L., Ortiz, R. A., & Markandya, A. (2011). The costs of climate change: A study of cholera in Tanzania. International Journal of Environmental Research and Public Health, 8(12), 4386–4405.
  • Trambauer, P., Werner, M., Winsemius, H., Maskey, S., Dutra, E., & Uhlenbrook, S. (2015). Hydrological drought forecasting and skill assessment for the Limpopo river basin, Southern Africa. Hydrology and Earth System Sciences, 19(4), 1695–1711.
  • Tucker, C., Hielkema, J., & Roffey, J. (1985). The potential of satellite remote sensing of ecological conditions for survey and forecasting desert-locust activity. International Journal of Remote Sensing, 6(1), 127–138.
  • Tucker, C. J., Wilson, J. M., Mahoney, R., Anyamba, A., Linthicum, K., & Myers, M. F. (2002). Climatic and ecological context of the 1994–1996 Ebola outbreaks. Photogrammetric Engineering and Remote Sensing, 68(2), 147–152.
  • UNICEF. (2015). Levels and trends in child malnutrition: Key findings of the 2015 edition. United Nations Children’s Fund, World Health Organization, and World Bank Group.
  • Verdin, J., Funk, C., Senay, G., & Choularton, R. (2005). Climate science and famine early warning. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360(1463), 2155–2168.
  • Wang, L., Kanji, S., & Bandyopadhyay, S. (2009). The health impact of extreme weather events in sub-Saharan Africa. (World Bank Policy Research Working Paper Series No. 4979). Washington, DC: World Bank.
  • Warner, K., Afifi, T., Henry, K., Rawe, T., Smith, C., & De Sherbinin, A. (2012). Where the rain falls: Climate change, food and livelihood security, and migration. Bonn, Germany: Where the Rain Falls Project: CARE France and UNU-EHS.
  • WHO. (2015). Meningoccal meningitis Fact Sheet No141. Retrieved from
  • Wiggins, S., Keats, S., & Compton, J. (2010). What caused the food price spike of 2007/08? Lessons for world cereals markets. London: Food Prices Project Report, Overseas Development Institute.
  • Williams, R., Malherbe, J., Weepener, H., Majiwa, P., & Swanepoel, R. (2016). Anomalous high rainfall and soil saturation as combined risk Indicator of Rift Valley fever outbreaks, South Africa, 2008–2011. Emerging Infectious Diseases, 22(12), 1054–1062.
  • Yamana, T. K., Bomblies, A., & Eltahir, E. A. (2016). Climate change unlikely to increase malaria burden in West Africa. Nature Climate Change, 6, 1009–1013.
  • Zimmerman, L., OlaOlorun, F., & Radloff, S. (2015). Accelerating and improving survey implementation with mobile technology: Lessons from PMA2020 implementation in Lagos, Nigeria. Etude De La Population Africaine, 29(1), 1699.