Abstract
Macroscopic descriptions of populations commonly assume that encounters between individuals are well mixed; i.e., each individual has an equal chance of coming into contact with any other individual. Relaxing this assumption can be challenging though, due to the difficulty of acquiring detailed knowledge about the non-random nature of encounters. Here, we fitted a mathematical model of dengue virus transmission to spatial time series data from Pakistan and compared maximum-likelihood estimates of “mixing parameters” when disaggregating data across an urban-rural gradient. We show that dynamics across this gradient are subject not only to differing transmission intensities but also to differing strengths of nonlinearity due to differences in mixing. We furthermore show that neglecting spatial variation in mixing can lead to substantial underestimates of the level of effort needed to control a pathogen with vaccines or other control efforts. We complement this analysis with relevant contemporary environmental drivers of dengue.
