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. 2011 Jun 10;48(6):1333-1344.
doi: 10.1111/j.1365-2664.2011.02030.x.

Predictability of anthrax infection in the Serengeti, Tanzania

Affiliations

Predictability of anthrax infection in the Serengeti, Tanzania

Katie Hampson et al. J Appl Ecol. .

Abstract

Anthrax is endemic throughout Africa, causing considerable livestock and wildlife losses and severe, sometimes fatal, infection in humans. Predicting the risk of infection is therefore important for public health, wildlife conservation and livestock economies. However, because of the intermittent and variable nature of anthrax outbreaks, associated environmental and climatic conditions, and diversity of species affected, the ecology of this multihost pathogen is poorly understood.We explored records of anthrax from the Serengeti ecosystem in north-west Tanzania where the disease has been documented in humans, domestic animals and a range of wildlife. Using spatial and temporal case-detection and seroprevalence data from wild and domestic animals, we investigated spatial, environmental, climatic and species-specific associations in exposure and disease.Anthrax was detected annually in numerous species, but large outbreaks were spatially localized, mostly affecting a few focal herbivores.Soil alkalinity and cumulative weather extremes were identified as useful spatial and temporal predictors of exposure and infection risk, and for triggering the onset of large outbreaks.Interacting ecological and behavioural factors, specifically functional groups and spatiotemporal overlap, helped to explain the variable patterns of infection and exposure among species.Synthesis and applications. Our results shed light on ecological drivers of anthrax infection and suggest that soil alkalinity and prolonged droughts or rains are useful predictors of disease occurrence that could guide risk-based surveillance. These insights should inform strategies for managing anthrax including prophylactic livestock vaccination, timing of public health warnings and antibiotic provision in high-risk areas. However, this research highlights the need for greater surveillance (environmental, serological and case-detection-orientated) to determine the mechanisms underlying anthrax dynamics.

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Figures

Fig. 1
Fig. 1
Spatiotemporal patterns of anthrax infection in the Serengeti ecosystem. (a) Time series showing continuous detection of small numbers of cases in wildlife, interspersed with occasional large outbreaks. Probable cases are shown in black and suspect cases in grey. *549 ‘probable’ and 67 ‘suspect’ cases were detected. Black circles indicate hospital records, scaled according to numbers and gray squares indicate when livestock cases were reported (data quality too poor to quantify). (b) Spatial location of cases, showing outbreak areas. Villages shaded according to hospital reports from 1995 to 2008. Exact locations of carcasses retrieved during the 1998 (Sopa) outbreak were unavailable, so the shaded area demarcates the outbreak area. For the 2003 outbreak (crosses – Seronera), the locations of cases are randomized within a 10 km radius of the outbreak area, because exact locations were not available. Inset shows the location of buffalo carcasses during the 2009 outbreak in Maswa district. LGCA, Loliondo Game Control Area; NCA, Ngorongoro Conservation Area; SNP, Serengeti National Park.
Fig. 2
Fig. 2
Species-specific patterns of anthrax mortality in wildlife. Only the most commonly reported species are shown. Black and grey indicate probable and suspect cases respectively, as defined in the methods. *549 ‘probable’ and 67 ‘suspect’ impala cases were detected.
Fig. 3
Fig. 3
Maps showing (a) domestic dog seroprevalence and (b) buffalo and lion serostatus in relation to soil alkalinity. LGCA, Loliondo Game Control Area; NCA, Ngorongoro Conservation Area; SNP, Serengeti National Park.
Fig. 4
Fig. 4
Timing of anthrax outbreaks in relation to rainfall. Months in which outbreaks of anthrax occurred are indicated by dark grey bars. Outbreaks were defined as>10 cases including at least one confirmed by microscopy. The thick grey line denotes mean monthly rainfall (averaged across-outbreaks sites), and grey shading indicates the 95% confidence intervals. Monthly rainfall at each outbreak site (Sopa, Seronera, Naabi and Maswa) are shown by black lines.
Fig. 5
Fig. 5
Mean annual antibody levels in Serengeti lions vs. mean annual rainfall. The symbol size reflects the number of lions sampled and the line indicates the best fitting regression (R2 = 0.21, correlation coefficient =− 0.002, n = 20, P = 0.03).

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