Abstract
We have developed a mathematical model and stochastic numerical simulation for the transmission of COVID-19 and other similar infectious diseases that accounts for the geographic distribution of population density, detailed down to the level of location of individuals, and age-structured contact rates. Our analytical framework includes a surrogate model optimization process to rapidly fit the parameters of the model to the observed epidemic curves for cases, hospitalizations, and deaths. This toolkit (the model, the simulation code, and the optimizer) is a useful tool for policy makers and epidemic response teams, who can use it to forecast epidemic development scenarios in local settings (at the scale of cities to large countries) and design optimal response strategies. The simulation code also enables spatial visualization, where detailed views of epidemic scenarios are displayed directly on maps of population density. The model and simulation also include the vaccination process, which can be tailored to different levels of efficiency and efficacy of different vaccines. We used the developed framework to generate predictions for the spread of COVID-19 in the canton of Geneva, Switzerland, and validated them by comparing the calculated number of cases and recoveries with data from local seroprevalence studies.
Highlights
Numerous epidemic modeling and simulation toolkits have been developed or adapted for COVID-19, ranging from educational models to global-scale comprehensive frameworks [1]
To improve the accuracy of epidemic models, it is necessary to account for population heterogeneity in terms of, for example, age, social groups and mobility patterns, as well as geographical clustering of infection spreading that can arise from higher contact rates in places with higher population density
The simulation reproduced the initial epidemic wave of COVID-19 that happened in March-April 2020, as well as subsequent periods of rising numbers of cases and hospitalizations (Fig. 1, 2, 3)
Summary
Numerous epidemic modeling and simulation toolkits have been developed or adapted for COVID-19, ranging from educational models to global-scale comprehensive frameworks [1]. Examples of models with a detailed representation of these factors include the age structured household model of Pellis et al [6] and the two-level (global and local) mixing model proposed by Ball et al [7] which has further been expanded to account for network structure [8]. These models do not include distance metrics or account for differences in population density. We aimed to develop a geospatial network model, and calibrate it to the COVID-19 data from the canton of Geneva, Switzerland, to test its applicability and validity
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