Climate and vector borne disease
Many parasitic, viral, and bacterial diseases respond to variations in the climate whether through their geographic distribution, seasonality, inter-annual variability, or temporal and spatial trends. Detailed reviews of climate variables and the impact on pathogen and vector dynamics are available for a wide range of diseases [1, 2].
Known relationships of climate variability and change and the climate-sensitivity of most important infectious diseases causing considerable morbidity and mortality worldwide suggests the potential role of climate information in improving climate sensitive health outcomes [3]. Although many infectious diseases of humans are climate sensitive – those that are transmitted by arthropod (insect and tick) and snail vectors are particular important in lower and middle income countries [4]. They are therefore prioritized by the Tropical Disease Research [5] initiative of the World Health Organization and partners [5, 6].
Impact of climate on vector-borne disease transmission dynamics
Weather and climate conditions, as well as surface water availability, that can influence positively or negatively the transmission of arthropod-borne diseases include air and water temperature, rainfall, humidity, surface water and wind [7]. These conditions, may also manifest as extreme events causing flooding, drought, storms and heat/cold waves – impacting directly and indirectly on vector transmission dynamics. The direct impacts of climate on disease vectors are via adult survival and reproduction rates, the creation of breeding sites, and the development rates of the juvenile stage of the vector [8]. Pathogens transmitted to humans by insects and ticks spend part of their life cycle in their cold-blooded secondary (non-human) host where they are effectively at the temperature of the local micro-climate. Here the development rate of the pathogen (called the extrinsic incubation period) will slow down at lower temperatures increasing the probability that the insect/tick will not survive long enough for disease transmission to occur. Some interactions between vector/parasite and climate are relatively simple to model (e.g. the relationship between rainfall and breeding sites) but others are complex. For example, temperature interacts in multiple, sometimes opposing ways with different aspects of insect or pathogen biology. Despite this complexity, it is clear that, to varying degrees, climatic factors determine the geographic limitations of vector-borne diseases, their seasonal occurrence, year to year variability as well as medium and long term shifts in both geographic distribution and intensity of transmission.
In Africa, rainfall, humidity and temperature are major constraint to the development of vegetation, soils, water sources, agriculture and therefore the livelihoods of the continents diverse populations [9]. Understanding the spatial and temporal relationships of climate and environmental direct and indirect drivers of vector-borne disease transmission is important in order to benefit from climate information to better target current control activities or predict future challenges.
Temporal lags in observed climate and vector-borne diseases
The temporal dynamics of diseases transmitted by insects and ticks will lag factors such as rainfall, temperature and humidity by a number of months because of the many inbuilt delays to the transmission process [10]. For example, rainfall creates potential breeding sites for juvenile mosquito vectors, but newly laid eggs need time to mature as larvae and pupae before they emerge as adult mosquitoes capable of transmitting disease [11]. After emergence, the adult female mosquito needs to imbibe the pathogen (e.g. malaria parasite or dengue virus) from an infectious human host before transmitting it, after it completes its extrinsic incubation period, to another person [11]. In epidemic prone regions (such as semi-arid areas or highland areas bordering endemic zones), infection and immunity in the human host population are low at the beginning of the epidemic wave and therefore a number of blood meals, each separated by the days needed to complete the gonotrophic cycle, may be needed before a female mosquito encounters and infectious human host [11]. Further delays in the development of an epidemic result from the time taken between the human host being infected and being infectious – a process that takes place at the more or less consistent temperature of the human host. The result of these lags is that cumulative observed weather events and/or conditions may provide approximately 2–4 months warning of vector-borne disease outbreaks depending on local circumstances. Shorter lags usually occur in warmer environments where development rates of vector and parasite are faster. However warmer environments may be associated with drought which will likely (but not always) reduce vector breeding sites and adult mosquito survivorship. Understanding how climate drives disease transmission in a particular locale is a step towards using climate information to control disease [4].
Development of early warning systems (EWS)
If significant temporal relationships between the occurrence of specific climatic/environmental variables and human cases of vector-borne diseases are demonstrated, and an underlying mechanism is understood, then it is possible to consider the development of a climate-informed early warning systems [12]. EWS may help disease control services anticipate where and when outbreaks or increased transmission are likely to occur and react proactively to emerging changes in disease risk.
Disease early warning systems may be established based on epidemiological data alone. For instance, an unusual early seasonal rise in case numbers may trigger an epidemic alert for some diseases. These are often called “early detection systems” but in reality they are still providing early warning of likely increase in future cases [13]. Early warning can be extended using observed environmental or climatic data which may offer 2–3 months prior notice of likely changes in transmission risk. Early warning for climate sensitive diseases can be further extended by 3–6 months using seasonal climate forecasts [14].
Weather forecasts (< 2 weeks), on the other hand add little value to the prediction of a vector-borne disease epidemics. This is because they provide only a few additional days to early warning system that already have the potential for several months lead time just using observed climate or environmental data alone.
Sub-seasonal to seasonal (termed S2S) forecasts are currently an intense area of climate and weather research and may, in the future, provide additional predictability at the two week to two month time frame. Because of the short prediction time frame in Africa of weather forecasts (1–5 days) and the experimental nature of S2S forecasts neither are considered further here. However, as the science advances, opportunities for using S2S forecasts in vector disease control programmes may emerge.
Decadal (10–30 year) and long-term shifts in the climate may also impact on vector-borne diseases by changing their geographic range. In a recent study of warming in the East African highlands the authors calculated that an additional 6 million individuals now live in regions of Ethiopia that are above the temperature threshold for malaria transmission compared with 30 years ago; this change resulting from a slow upward shift in minimum temperature [15]. However, while decadal variations in the climate are increasingly understood to exist, our ability to predict such changes in an operational context is not currently developed and may yet prove impossible because of the strong stochastic character of the climate [16]. Trends in temperature, where decadal variations are weak, provide an indication of longer term climate changes.
The climate information regarding climate change timescale (> 50 years) are highly uncertain and beyond the normal decision timeframe of Ministries of health; they are considered here in the context of historical trends.
The African climate system and its drivers at multiple time-scales
The health and wellbeing of African populations is closely tied to their environment which is itself closely linked to the regional and local climate. An extreme range of climates span the continent, according to the Köppen-Geiger classification system (Fig. 1) [17]. Across the continent the climate varies from arid zones (including the Sahara, Somali-Chalbi and Kalahari deserts), steppe or semi-arid regions (e.g. Sahelian savannah) to humid tropical environments (Congo river basin). Humid subtropical climates are features found predominantly in southern Africa but also include areas in the Ethiopian highlands. In some regions these widely diverse climates co-exist within relatively small areas and rainfall amount and seasonality (for example) may change significantly over tens of kilometers [18]. The changes in seasons (particularly the rainy and dry seasons) is the dominant characteristic of regional climate and it consequently drives the seasonal pattern of human activities as well as vector-borne diseases across the continent. The large seasonal variations in rainfall that distinguish different climate zones is seen clearly in Fig. 2a–d –which indicates the fraction of mean annual rainfall that falls within 3 month seasons (December–February: DJF; March–May: MAM; June–August: JJA; September–November: SON). The Fig. 2b and d indicate that East Africa has a bimodal season while others, such as the Sahel (see Fig. 2c) have a single rainy season, more typical of monsoon behavior.
The most significant driver of seasonal temperature change across Africa (where proximity to the equator might suggest nearly constant year-round temperatures) is the monsoonal rains, in part related to the inter-tropical convergence zone defined previously. For instance, cloud cover at night will tend to increase minimum temperatures whereas cloud cover in the day time will tend to reduce maximum temperatures [19]. These different responses indicate that minimum and maximum temperatures are better treated as separate variables rather than combined as mean temperature.
Whereas weather is almost entirely governed by conditions in the atmosphere, the climate is substantially driven by slower processes, particularly in the major oceans. The climate at any location varies from its mean historical climate state on multiple time-scales, from annual to multi-decadal (10–30 years) to long-term climate change; the latter compatible with anthropogenic climate change signals. The magnitude of these variations and trends may enhance or decrease the climate suitability for different disease vectors and their pathogens.
Sea surface temperature variations in the Atlantic [20], Indian [21] and Pacific [22] oceans influence the African climate on different time scales. We consider three timescales of variability in the African climate that describe the past and provide some indication of the future. El Niño-Southern Oscillation (ENSO) is the most important driver of climate variability at seasonal-to-interannual timescales [23], a key source of climate predictability in Africa [24] (see Fig. 3) and relevant to the development of climate information services targeting health decision-makers [3]. It is important to recognize that ENSO (El Niño and La Niña) impact the climate (and thereby climate-sensitive health outcomes): (a) differently according to the variable of interest (e.g. rainfall, and minimum and maximum temperature), (b) at different spatial scales, (c) in some regions and not others, (d) in some seasons and not others, (e) often according to its strength, and sometimes in a non-linear fashion, (f) at varying periods (from 5 months to ~ two years), with both El Niño and La Niña events on occasions occurring in the same calendar year (e.g., 2010), (g) often substantially conditioned on the action of other climate drivers, such as the Indian Ocean Dipole [25].
Natural variations in the climate at 10–30 year time frames (decadal) have also been observed in Western, Eastern and Southern Africa and again may be specific to region and season. In Eastern Africa decadal rainfall variations are largely confined to the long rains which occur between March and May [26]. Where historical data is sufficient, long term trends in temperature and rainfall, consistent with climate change, may be established once the noise from shorter term natural variations in the climate have been removed. Unless the impact of the different timescales can be disentangled, there is considerable opportunity for confusion, with important implications for decision-making and potential maladaptation. For instance, climate change models have indicated that Eastern Africa will become wetter towards the end of the twenty-first century while the region has, since 1999, experienced an increased frequency of drought [27].
Here we aim to characterize the African climate – its variability, trends and potential predictability – and establish the relevance of this knowledge and current tools to operational vector-borne disease control efforts.