Quantifying Climatic and Socio-Economic Influences on Urban Malaria in Surat, India: A Modelling Study

Background: Cities are becoming increasingly important habitats for mosquito-borne infections. The pronounced heterogeneity of urban landscapes challenges our understanding of the spatio-temporal dynamics of these diseases, and of the influence of climate and socio-economic factors at different spatial scales. Here, we quantify this joint influence on malaria risk by taking advantage of an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, Northwest India. Methods: We analyzed 10 years of monthly falciparum cases resolved at three nested spatial resolutions (for 7 zones, 32 units and 478 workers units subdivisions, respectively). With a Bayesian hierarchical mixed model that incorporates effects of population density, poverty, humidity and temperature, we investigate the main drivers of spatio-temporal malaria risk at the intermediate scale of districts. The significance of covariates and the model fit is then examined at lower and higher resolutions. Findings: The spatial variation of urban malaria cases is strongly stationary in time, whereby locations exhibiting high and low yearly cases remain largely consistent across years. Local socio-economic variation can be summarized with two main principal components, representing poverty and population density respectively. The model that incorporates these two factors together with local temperature and global relative humidity, best explains monthly malaria patterns at the intermediate resolution. The effects of local temperature and population density remain significant at the finest spatial scale. We further identify the specific areas where such increased resolution improves model fit. Interpretation: Malaria risk patterns within the city are largely driven by fixed spatial structures, highlighting the key role of local climate conditions and social inequality. As a result, malaria elimination efforts in the Indian subcontinent can benefit from identifying, predicting and targeting disease hotspots within cities. Spatio-temporal statistical models for the mesoscale of administrative units can inform control efforts, and be complemented with bespoke plans in the identified areas where finer scale data could be of value