Climate teleconnections and recent patterns of human and animal disease outbreaks

Abstract

Background: Recent clusters of outbreaks of mosquito-borne diseases (Rift Valley fever and chikungunya) in Africa and parts of the Indian Ocean islands illustrate how interannual climate variability influences the changing risk patterns of disease outbreaks. Although Rift Valley fever outbreaks have been known to follow periods of above-normal rainfall, the timing of the outbreak events has largely been unknown. Similarly, there is inadequate knowledge on climate drivers of chikungunya outbreaks. We analyze a variety of climate and satellite-derived vegetation measurements to explain the coupling between patterns of climate variability and disease outbreaks of Rift Valley fever and chikungunya.

Methods and findings: We derived a teleconnections map by correlating long-term monthly global precipitation data with the NINO3.4 sea surface temperature (SST) anomaly index. This map identifies regional hot-spots where rainfall variability may have an influence on the ecology of vector borne disease. Among the regions are Eastern and Southern Africa where outbreaks of chikungunya and Rift Valley fever occurred 2004-2009. Chikungunya and Rift Valley fever case locations were mapped to corresponding climate data anomalies to understand associations between specific anomaly patterns in ecological and climate variables and disease outbreak patterns through space and time. From these maps we explored associations among Rift Valley fever disease occurrence locations and cumulative rainfall and vegetation index anomalies. We illustrated the time lag between the driving climate conditions and the timing of the first case of Rift Valley fever. Results showed that reported outbreaks of Rift Valley fever occurred after ∼3-4 months of sustained above-normal rainfall and associated green-up in vegetation, conditions ideal for Rift Valley fever mosquito vectors. For chikungunya we explored associations among surface air temperature, precipitation anomalies, and chikungunya outbreak locations. We found that chikungunya outbreaks occurred under conditions of anomalously high temperatures and drought over Eastern Africa. However, in Southeast Asia, chikungunya outbreaks were negatively correlated (p<0.05) with drought conditions, but positively correlated with warmer-than-normal temperatures and rainfall.

Conclusions/significance: Extremes in climate conditions forced by the El Niño/Southern Oscillation (ENSO) lead to severe droughts or floods, ideal ecological conditions for disease vectors to emerge, and may result in epizootics and epidemics of Rift Valley fever and chikungunya. However, the immune status of livestock (Rift Valley fever) and human (chikungunya) populations is a factor that is largely unknown but very likely plays a role in the spatial-temporal patterns of these disease outbreaks. As the frequency and severity of extremes in climate increase, the potential for globalization of vectors and disease is likely to accelerate. Understanding the underlying patterns of global and regional climate variability and their impacts on ecological drivers of vector-borne diseases is critical in long-range planning of appropriate disease and disease-vector response, control, and mitigation strategies.

Figure 1. Summary correlation map between monthly NINO3.4 SST and rainfall anomalies, 1979–2008.

Correlation of sea surface temperatures and rainfall anomalies illustrate ENSO teleconnection patterns. There is a tendency for above (below) normal rainfall during El Niño (La Niña) events over East Africa (Southern Africa, Southeast Asia). Similar differential anomaly patterns were observed for other regions, especially within the global tropics. These extremes (above or below) in rainfall influence regional ecology and consequently dynamics of mosquito disease vector populations and patterns of mosquito-borne disease outbreaks.

Figure 2. Outbreak locations of chikungunya (2004–2006) and Rift Valley fever (2006–2009).

Symbols indicate distribution of recent outbreaks of chikungunya (2004–2006) shown by yellow dots and Rift Valley fever (2006–2009) shown by red, blue and green dots over eastern and southern Africa and the Indian Ocean islands.

Figure 3. Cumulative rainfall anomalies over Eastern Africa, October–December, 2005.

Negative rainfall anomalies correspond to the large-scale regional drought in Eastern Africa during October–December, 2005. Anomalies were calculated with reference to the 1995–2000 long term mean. Epicenters of chikungunya outbreaks during this period are shown by the four open black dots.

Figure 4. Cumulative rainfall anomalies and vegetation index anomalies for East Africa, Sudan and Southern Africa.

Patterns of rainfall anomalies preceding outbreaks of Rift Valley fever in (A) East Africa: September–December, 2006, (C) Sudan: June–September, 2007, and (E) Southern Africa: October, 2007–January, 2008. Each outbreak was preceded by persistent and above-normal rain on the order of +200 mm for a period of ∼2–4 months (Fig. S2). This resulted in anomalous green-up of vegetation, creating ideal ecological conditions for the production of Aedes and Culex mosquito vectors that transmit Rift Valley fever virus to domestic animals and humans. Vegetation anomalies are shown for (B) East Africa: October, 2006–January 2007, (D) Sudan: July–September, 2007, and (F) Southern Africa: October, 2007–January, 2008. Rift Valley fever outbreaks are marked with yellow dots.

Figure 5. Spatial and temporal anomaly patterns of NDVI, SST in relation to RVF outbreaks.

Spatial and temporal anomaly patterns in normalized difference vegetation index for selected areas of South Africa (A: 29°E and 32.5°E, averaged from 23°S to 27°S), Sudan (B: 32.5°E and 34°E, averaged from 11°N to 15°N), Tanzania (C: 34°E and 37°E, averaged from 4.5°S to 8.5°S) and Kenya (D: 37°E and 42.5°E, averaged from 2°S to 2°N). Regions were plotted by geographic position west to east and represent areas with dense concentrations of Rift Valley fever cases. NDVI anomalies are depicted as percent departures from the 2002–2008 long-term mean, and show the response of vegetation to variations in rainfall. Periods shaded in green to purple indicate above-normal vegetation conditions associated with above-normal rainfall. Periods of persistent drought or below normal rainfall are shown in shades of yellow to red. Each Rift Valley fever outbreak was preceded by above-normal vegetation conditions resulting from persistent above-normal rainfall in the Horn of Africa and Sudan in 2006–2007. Chikungunya epidemics occurred over East Africa and Indian Ocean islands during the 2005–2006 drought period shown by negative NDVI anomalies from 2005–2006 [D: red boxed area]. Clusters of epidemics/epizootics of Rift Valley fever in East Africa (2006–2007) and Sudan (2007) occurred during the El Niño event of 2006–2007 when there were concurrent anomalously warmer WIO and Nino 3.4 SSTs. The transition to La Niña conditions in late 2007–early 2008 spatially shifted the area of above-normal rainfall and enhanced vegetation conditions to South Africa and Madagascar between February–March, 2008 and sporadically between February–March, 2009 in South Africa, leading to outbreaks of Rift Valley fever in these regions. This illustrates that spatial displacements in extreme rainfall and ecological conditions driven by large-scale climate mechanisms such as ENSO and regional circulation lead to spatial-temporal shifts in areas at risk for outbreaks of these mosquito-borne diseases.

Figure 6. Frequency distributions of chikungunya outbreak events in relation to temperature.

Frequency distributions of chikungunya outbreak events and 4-month cumulative temperature anomalies for East Africa (A), Central Africa (B), South Asia (C), and Southeast Asia (D). The 4-month anomaly threshold was used to represent periods of either cool temperatures or drought and extreme high temperatures The dashed line at zero depicts the 1979–2009 long-term mean temperature, with warmer-than-normal temperatures shown to the right (red) and cooler-than-normal temperatures shown to the left (blue) of the line. Cases shown to the right of the dashed line occurred during periods of elevated temperature with a persistence of 4 months.

Figure 7. Frequency distributions of chikungunya outbreak events in relation to precipitation.

Frequency distributions of chikungunya outbreak events and 4-month cumulative precipitation anomalies for East Africa (A), Central Africa (B), South Asia (C), and Southeast Asia (D). The 4-month anomaly threshold was used to represent periods of either persistent above-normal rainfall/wetness or persistent drought conditions. The dashed line at zero depicts the (1979–2009) long term rainfall, with greater-than-normal precipitation shown to the right (blue) and lower-than-normal precipitation shown to the left (red) of the line. Cases shown to the left of the dashed line occurred during periods of drought with a persistence of 4 months.