Oxygen isotopic analysis of some molluscan shells from fossil littoral deposits of Pleistocene age

Abstract

Newly available data sets present exciting opportunities to investigate how human population movement contributes to the spread of infectious diseases across large geographical distances. It is now possible to construct realistic models of infectious disease dynamics for the purposes of understanding global-scale epidemics. Nevertheless, a remaining unanswered question is how best to leverage the new data to parameterize models of movement, and whether one’s choice of movement model impacts modeled disease outcomes. We adapt three well-studied models of infectious disease dynamics, the SIR model; the SIS model; and the Ross-Macdonald model, to incorporate either of two candidate movement models. We describe the effect that the choice of movement model has on each disease model’s results, finding that in all cases there are parameter regimes where choosing one movement model instead of another has a profound impact on epidemiological outcomes. We further demonstrate the importance of choosing an appropriate movement model using the applied case of malaria transmission and importation on Bioko Island, Equatorial Guinea, finding that one model produces intelligible predictions of <i>R</i>₀ while the other produces nonsensical results. <h3>Significance Statement</h3> Newly available large-scale datasets of human population movement represent an opportunity to model how diseases spread between different locations. Combining infectious disease models with mechanistic models of host movement enables studies of how movement drives disease transmission and importation. Here we explore in what ways modeled epidemiological outcomes may be sensitive to the modeler’s choice of movement model structure. We use three different mathematical models of disease transmission to show how a model’s epidemiological predictions can change dramatically depending on the chosen host movement model. We find these different outcomes are robust to using the same data sources to parameterize each candidate model, which we illustrate using an example of real-world malaria transmission and importation in Bioko Island, Equatorial Guinea.