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. Accounting for differences in mobility by incorporating two fine-scale, density-dependent covariate layers eliminates differences in mixing but results in a doubling of the estimated transmission potential of the large urban district of Lahore. 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 interventions. We complement this analysis with estimates of the relationships between dengue transmission intensity and other putative environmental drivers thereof.

Original publication

DOI

10.1098/rsif.2015.0468

Type

Journal article

Journal

Journal of the Royal Society Interface

Publication Date

14/10/2015

Volume

12

Addresses

Department of Zoology, University of Oxford, Oxford OX1 3PS, UK moritz.kraemer@zoo.ox.ac.uk.

Keywords

Humans, Dengue Virus, Dengue, Likelihood Functions, Cities, Disease Outbreaks, Population Dynamics, Communicable Disease Control, Geography, Models, Theoretical, Rural Population, Urban Population, Pakistan