Human footprint is associated with shifts in the assemblages of major vector-borne diseases

Abstract Predicting how increasing intensity of human–environment interactions affects pathogen transmission is essential to anticipate changing disease risks and identify appropriate mitigation strategies. Vector-borne diseases (VBDs) are highly responsive environmental changes, but such responses notoriously difficult isolate because depends on a suite ecological social in vectors hosts that may differ across species. Here we use the emerging tools cumulative pressure mapping machine learning better understand occurrence six medically important VBDs, differing ecology from sylvatic urban, respond multidimensional effects human pressure. We find not only footprint—an index pressure, incorporating built environments, energy transportation infrastructure, agricultural lands population density—an predictor VBD occurrence, there clear thresholds governing different VBDs. Across spectrum associated with lower including malaria, cutaneous leishmaniasis visceral leishmaniasis, give way high as dengue, chikungunya Zika. These heterogeneous VBDs highlight land-use transitions lead abrupt shifts infectious burdens public health needs.