Comparing metapopulation dynamics of infectious diseases under different models of human movement

Abstract

Newly available datasets 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 susceptible–infected–recovered model, the susceptible–infected–susceptible 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 R0, whereas the other produces nonsensical results.

An Algebraic Framework for Structured Epidemic Modeling

“…This feature is practical for (1) model formulation, in which parsimonious but empirically adequate models are found by testing different combinations and complexities of submodels, and (2) policy making, where it is important that policies be robust to variations in submodels and other modeling assumptions. Works such as [8] and [25] demonstrate the importance of testing multiple combinations of submodels. The categorical approach and its implementation in software disambiguates and assists this process.…”

“…A recurring theme in scientific modeling is the importance of stratified models, in which local dynamics are reproduced in multiple strata and strata interact according to a specified scheme. For example, Citron et al [8] compare stratified models defined by a choice of local epidemiological dynamics (SIR, SIS, or Ross-Macdonald) and a choice of stratification by location (the flux or simple trip models of metapopulation dynamics). Typed Petri nets offer a general methodology for stratifying models, which contrasts with the by-hand approach taken in [8].…”

CDC (Cindy and David’s Conversations) Game: Advising President to Survive Pandemic

“…Besides Allee effects, two other theories, i.e., metapopulation dynamics and tipping-point theories, are particularly important for supporting the dynamic zero-out policy. The metapopulation theory maintains that extinctions of local population are common events, and tipping point theory suggests that, at some critical points, population growth may transit to either outbreak or die-off from the tipping point (threshold) (Citron et al 2021). There are techniques for detecting tipping points by identifying some early warning signals (EWS), which can help to implement dynamic zero-out policy.…”

“…In the present study, we formulate the CDC-chief vs. president relationship as an evolutionary game to facilitate the analysis of their strategic interactions using a fictitious pair of players (Cindy as CDC chief, who represent the expertise authority of medical science, vs. David as President, the head of the decision-making body in general), and the CDC (Cindy and David's Conversations) game is apparently inspired by the classic Sir Philip Sydney (SPS) game and its extensions (Maynard-Smith 1991, Maynard-Smith & Harper 2003, Bergstrom & Lachmann 1997, 1998, Huttegger & Zollman 2010, Ma 2009, Ma et al 2010, Ma & Krings 2011, Whitmeyer 2020, Ma & Zhang 2021. We further take inspirations from classic ecological theories of metapopulation dynamics (Levins 1969, Citron et al 2021) and Allee effects (Allee 1932, Hilker et al 2009, in which the worldwide pandemic of COVID-19 can be abstracted as a dynamic system of metapopulation (of infected populations) consisting of approximately 200 local (country or regional) populations. The local populations are connected through migration/dispersal (such as international travels and trade logistics) and collectively form the global metapopulation, that is, metapopulaton is a population of local populations.…”

Reassessment of contact restrictions and testing campaigns against COVID-19 via spatio-temporal modeling

“…al. [ 40 ] consider metapopulation versions of an SIR, an SIS, and a Ross-Macdonald model integrating Eulerian movement (direct out-flux) and Lagrangian movement model (net out-flux and influx). The two movement models were analyzed to synthesize conditions under which one model can be superior against the other with respect to epidemiological outcomes.…”