Abstract
Computational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better understanding global epidemics, like influenza, measles, H1N1, SARS, and COVID-19, underestimate the role of heterogeneous mixing in populations, characterized by strong social structures and geography. Motivated by the reduced tractability of studies employing homogeneous mixing, which make conclusions hard to deduce, we propose a new, very fine-grained model incorporating the spatial distribution of population into geographical settlements, with a hierarchical organization down to the level of households (inside which we assume homogeneous mixing). In addition, population is organized heterogeneously outside households, and we model the movement of individuals using travel distance and frequency parameters for inter- and intra-settlement movement. Discrete event simulation, employing an adapted SIR model with relapse, reproduces important qualitative characteristics of real epidemics, like high variation in size and temporal heterogeneity (e.g., waves), that are challenging to reproduce and to quantify with existing measures. Our results pinpoint an important aspect, that epidemic size is more sensitive to the increase in distance of travel, rather that the frequency of travel. Finally, we discuss implications for the control of epidemics by integrating human mobility restrictions, as well as progressive vaccination of individuals.
Highlights
Computational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases
Even though COVID-19 is an ongoing pandemic, with final size and dynamics not yet known, the statistical analysis shows that the regional epidemic size distribution is consistent with historical data on other outbreaks
Most epidemic spreading models describe any outbreak though only two outcomes: (1) an epidemic trigger condition is not fulfilled and the disease subsides in a local sub-population, or (2) the condition is fulfilled, and the disease manages to spread globally to a large scale comparable to the entire population[47]
Summary
Computational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. The heavy socio-economical burden of epidemics has been demonstrated repeatedly during crises like SARS5, Ebola[6] or recent COVID-197 To this end, we need to be able to predict long-term epidemic evolution, and the impact of governmental interventions, like isolation, travel restrictions, and vaccination/immunization of the p opulation[8,9,10,11,12]. We need to be able to predict long-term epidemic evolution, and the impact of governmental interventions, like isolation, travel restrictions, and vaccination/immunization of the p opulation[8,9,10,11,12] In light of these challenges, we find recent studies that are predominantly augmenting mass-action models into tools suitable for analyzing large scale e pidemics[8,12,13,14,15,16]. Chen et al.[26] show that overlapping in communities leads to increased infection prevalence and a higher spread velocity in the early stages of emerging infections; Salathé et al.[4] show that the dynamics of epidemics is influenced by the structure of communities, which, in turn, has implications on immunization strategies for large epidemics; Shang et al.[27] show that overlapping communities and a higher network average degree accelerate spreading; Stegehuis et al.[28] show that the structure of communities has a significant influence on the behavior of percolation on networks, as community structure can stimulate or suppress spreading, based on the mesoscopic set of communities
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