Comparing Metapopulation Dynamics of Infectious Diseases under Different Models of Human Movement

Abstract

Newly available data sets present an exciting opportunity 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 R 0 while the other produces nonsensical results.Mathematical modeling | Infectious disease dynamics | Human population movement | Malaria

Spatial scales in human movement between reservoirs of infection

“…We note here that the human movement models we have studied are comparable to those developed in Refs. [27, 28] for vectorbourne diseases, which label the single daily journey models as so-called ‘Lagrangian’ mobility models and the multiple successive movement models as ‘Eulerian’. We consider our stochastic approach to generating frequency-distance distributions useful and informative in understanding the underlying probability distributions for these works, especially in relation to demonstrating the regimes in radial spatial scales which are relevant to location-specific geometric effects and human movement distance predispositions.…”

“…The findings in all cases above, and other examples of broken power-laws for human mobility in the NTD disease modelling literature (see, e.g., Refs. [27, 28]), will motivate us to use a similar description to develop a simple model of human movement from households to focal points of infection in this work.…”

Estimating SARS-CoV-2 variant fitness and the impact of interventions in England using statistical and geo-spatial agent-based models

“…When modelling SARS-CoV-2, we are primarily interested in the effects of commuters, shoppers, family visits and day trips on spreading the infection, as opposed to long-term relocations. Therefore, we used a Lagrangian model where individuals travel from their home domain to others for a time before returning home [23,36]. We modelled each inter-domain movement as occurring only during a single day, and these interactions are in addition to the interactions of the individual in their home domain.…”

Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina

“…In particular, the well-known susceptible-exposed-infectious-recovered (SEIR) model, builds on this homogeneous-mixing assumption. Computational tools that include factors such as long range human mobility have been developed to account for pandemic spread on a global scale [1][2][3][4]. Some extensions of SEIR-like [5][6][7][8][9] and other models [10][11][12][13][14][15][16][17][18] that account for spatial variability divided the population to different sub-populations (where the term "sub-population" refers to people under a certain stage of the disease, otherwise termed "compartment") [19].…”