Disentangling fine-scale effects of environment on malaria detection and infection to design risk-based disease surveillance systems in changing landscapes

Abstract Landscape changes have complex effects on malaria transmission, disrupting social and ecological systems determining the spatial distribution of risk. Within Southeast Asia, forested landscapes are associated with both increased malaria transmission and reduced healthcare access. Here, we adapt an ecological modelling framework to identify how local environmental factors influence the spatial distributions of malaria infections, diagnostic sensitivity and detection probabilities in the Philippines. Using convenience sampling of health facility attendees and Bayesian latent process models, we demonstrate how risk-based surveillance incorporating forest data increases the probability of detecting malaria foci over three-fold and enables estimation of underlying distributions of malaria infections. We show the sensitivity of routine diagnostics varies spatially, with the decreased sensitivity in closed canopy forest areas limiting the utility of passive reporting to identify spatial patterns of transmission. By adjusting for diagnostic sensitivity and targeting spatial coverage of health systems, we develop a model approach for how to use landscape data within disease surveillance systems. Together, this illustrates the essential role of environmental data in designing risk-based surveillance to provide an operationally feasible and cost-effective method to characterise malaria transmission while accounting for imperfect detection.