A framework for the study of zoonotic disease emergence and its drivers: spillover of bat pathogens as a case study

Wood, James L. N.; Leach, Melissa; Waldman, Linda; MacGregor, Hayley; Fooks, Anthony R.; Jones, Kate E.; Restif, Olivier; Dechmann, Dina; Hayman, David T. S.; Baker, Kate S.; Peel, Alison J.; Kamins, Alexandra O.; Fahr, Jakob; Ntiamoa-Baidu, Yaa; Suu-Ire, Richard; Breiman, Robert F.; Epstein, Jonathan H.; Field, Hume E.; Cunningham, Andrew A.

Contextual descriptions of key drivers identified for each locality should be constructed by drawing on existing environmental, social and historical literatures, including qualitative analyses, to track up from key processes identified in the human-bat interactions and bats and ecology themes to identify national and international influences on these. Optimally, this would be complemented by empirical modelling techniques [2, 108] to investigate the correlation of disease spillover Spatially explicit ecosystem data, such as land use, wildlife densities, livestock densities, human population densities, climate and socio-economic variables are increasingly available for such analyses [2, 108] . With a careful interpretation, empirical models can quantify the impact of different spatial drivers on risk of initial spillover and subsequent spread and at what spatial scales these may be important. On the basis of time series analyses, risk maps could be generated that predict spatio-temporally changing 'hot zones' for spillover, thus contributing to forecasting.