Urban malaria mapping: a new methodology to assess spatiotemporal trends

Objectives

This project proposes a novel spatio-temporal Bayesian hierarchical modeling approach where both individual and cross-sectional data are analyzed, considering alternative ways to address missing data. This project  on successful completion will deliver two outcomes.

1. First, from a methodological point of view, it will have a statistical approach that could be used in a range of applications that have spatial and temporal components, and that combine different types of data. It will expand the current literature on spatial epidemiology, and facilitate the use of spatial statistics in a variety of applications (public health being just one of them).
2. Second, from a public policy point of view, it will produce a comprehensive evaluation of the Urban Malaria Control Program (UMCP), and identify gaps in the program that could be improved, areas that impose challenges for vector control, and recommendations for program tuning and scaling-up. This outcome will directly impact current activities of the UMCP, and ultimately provide evidence for other cities considering control programs similar to the Tanzanian effort.