Local climate and social inequality drive spatio-temporal variation in malaria risk across a complex urban landscape

Rapid urbanization makes cities an increasingly important habitat for mosquito-borne infections. Although these diseases are both climate and poverty driven, the interaction of environmental and socio-economic factors at different spatial scales within cities remain poorly understood. We analyze 10 years of extensive surveillance dataset of falciparum malaria resolved at three different spatial resolutions for the city of Surat in Northwest India. The spatial pattern of malaria risk is found to be largely stationary in time. A Bayesian hierarchical mixed model that combines spatially explicit indicators of temperature, population density and poverty with interannual variability in humidity best explains and predicts these patterns. For the Indian subcontinent, which harbors a truly urban mosquito vector, malaria elimination should target disease hotspots within cities. We show that urban malaria risk patterns are strongly driven by fixed spatial structures, highlighting the key role of social and environmental inequality and the need for targeted control efforts.