Abstract
AbstractSeasonal variation in human movement is globally ubiquitous and driven by a range of social, economic, and environmental factors. This temporal variability may also impact the spatial spread of infectious diseases by varying the likelihood of an introduction event in susceptible populations or the demographics of a population. In turn, infection (or infection risk) could modify individual behavior, creating heterogeneity in mobility patterns. Unfortunately, quantifying seasonal differences in travel patterns has largely been limited by data availability, particularly in low- and middle-income settings which have the greatest burden of many infectious diseases. This chapter reviews challenges and opportunities associated with quantifying, characterizing, and modeling seasonal mobility patterns. It then focuses on the need to integrate time-resolved mobility, demographic, and disease transmission data in infectious disease modeling frameworks to better inform epidemiological questions of interest and inform public health strategies.
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