Environmental factors are common forces driving infectious disease dynamics. We compared inter-annual and seasonal patterns of anthrax infections in two multi-host systems in southern Africa: Etosha National Park, Namibia, and Kruger National Park, South Africa. Using several decades of mortality data from each system, we assessed possible transmission mechanisms behind anthrax dynamics, examining 1) within- and between-species case correlations, and 2) associations between anthrax mortalities and environmental factors, specifically rainfall and the Normalized Difference Vegetation Index (NDVI). Anthrax cases in Kruger had wide inter-annual variation in case numbers, and large outbreaks seemed to follow roughly a decadal cycle. In contrast, outbreaks in Etosha were smaller in magnitude and occurred annually. In Etosha, the host species commonly affected remained consistent over several decades, although plains zebra (Equus quagga) became relatively more dominant. In Kruger, turnover of the main host species occurred after the 1990s, where the previously dominant host species, greater kudu (Tragelaphus strepsiceros), was replaced by impala (Aepyceros melampus). In both parks, anthrax infections showed two seasonal peaks, with each species having only one peak in a year. Zebra, springbok (Antidorcas marsupialis), wildebeest (Connochaetes taurinus) and impala cases peaked in wet seasons, while elephant (Loxodonta africana), kudu and buffalo (Syncerus caffer) cases peaked in dry seasons. For common host species shared between the two parks, anthrax mortalities peaked in the same season in both systems. Among host species with cases peaking in the same season, anthrax mortalities were mostly synchronized, which may imply similar transmission mechanisms or shared sources of exposure. Between seasons, outbreaks in one species may contribute to more cases in another species in the following season. Higher vegetation greenness was associated with more zebra and springbok anthrax mortalities in Etosha, but fewer elephant cases in Kruger. These results suggest that host behavioral responses to changing environmental conditions may affect anthrax transmission risk, with differences in transmission mechanisms leading to multi-host biseasonal outbreaks. This study reveals the dynamics and potential environmental drivers of anthrax in two savanna systems, providing a better understanding of factors driving biseasonal dynamics and outbreak variation among locations.

Animal mortality surveillance in Etosha National Park, Namibia and Kruger Naitonal Park, South Africa is opportunistic. Anthrax mortality in the datasets included anthrax confirmed cases from blood smear examination, bacterial culture or molecular diagnosis from blood swabs, as well as anthrax suspected cases diagnosed by symptoms (i.e., blood exudation) in cases where no samples were collected. We obtained anthrax mortality data from 1976 – 2014 in central Etosha and 1990 – 2015 in northern Kruger through the Etosha Ecological Institute and Office of the State Veterinarian in Kruger. We removed mortalities for which carcass discovery involved tracking the locations of scavengers with GPS (Global Positioning System) tags if the information was available, to better standardize surveillance efforts over time. We also inlcuded data for environmental fluctuations, including rainfall and NDVI (Normalized Difference Vegetation Index). Rainfall data were recorded at Okaukuejo station in central Etosha and Pafuri station in northern Kruger, and NDVI data were extracted from the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI_3g dataset. Anthrax case, rainfall, and NDVI data were binned into two intervals per month: days 1-15 and day 16 to the month's end.

These files include:

1. Anthrax mortalities and environmental variables in bimonthly intervals from central Etosha National Park 1976-2014

2. Anthrax mortalities and environmental variables in bimonthly intervals from northern Kruger National Park 1990-2015

3. Rainfall in bimonthly intervals from Pafuri station 1953-1991

4. R code for analyses (two scripts)