This forecast was produced using published data reporting:
The underlying model uses machine learning to fit three sub-models: the first two sub-models are described in the paper:
Basinski, AJ et al, 2020. Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus incidence in West Africa. BioRxiv.
These sub-models return the combined probability that, in a pixel with a specific set of environmental attributes, both M. natalensis and the Lassa virus are present. These sub-models use boosted classification trees developed in the R programming language, and the corresponding code is publicly available on github at this link.
The third sub-model returns the probability that a human habitation is occupied by M. natalensis, and is fit to rainfall patterns over the previous year using principal components regression. Specifically, in the third sub-model, monthly rainfall averages over a year are encoded into z-scores of two principal components. The principal components, in turn, describe annual rainfall patterns across West Africa. Lassa spillover risk, as depicted in the map, is proportional to the probability that a Lassa-infected rodent is present in a human habitation (product of the three sub-model outputs).