Abstract
Mathematical modeling is a powerful tool to study the process of the spread of infectious diseases. Among various mathematical methods for describing the spread of infectious diseases, the cellular automaton makes it possible to explicitly simulate both the spatial and temporal evolution of epidemics with intuitive local rules. In this paper, a model is proposed and realized on a cellular automata platform, which is applied to simulate the spread of coronavirus disease 2019 (COVID-19) for different administrative districts. A simplified social community is considered with varying parameters, e.g., sex ratio, age structure, population movement, incubation and treatment period, immunity, etc. COVID-19 confirmation data from New York City and Iowa are adopted for model validation purpose. It can be observed that the disease exhibits different spread patterns in different cities, which could be well accommodated by this model. Then, scenarios under different control strategies in the next 100 days in Iowa are simulated, which could provide a valuable reference for decision makers in identifying the critical factors for future infection control in Iowa.
Highlights
Rapid globalization, frequent travel, and contacts between people across states can make infectious diseases spread at an incredible rate
Infectious diseases have repeatedly caused an increase in human mortality and social panic throughout history [1], and countries all over the world are struggling with coronavirus disease 2019 (COVID-19) as this paper is being put together
COVID-19 confirmation data from New York City (NYC) and Iowa are adopted for model validation purpose
Summary
Frequent travel, and contacts between people across states can make infectious diseases spread at an incredible rate. A theoretical model is helpful to intuitively impress upon people how serious an infectious disease could be, which may convince people to take proper precautions and voluntarily follow the protocols (such as social distancing and quarantine) during the outbreak of an epidemic. It can provide decision makers and clinical professionals helpful information from theoretic simulations [5]. Studies on the spread process of infectious disease can be traced back to 1927, when Kermack and McKendrick constructed the susceptible–infective–recovered (SIR) model to study the spread of the black death in London [6]. E.g., transmission rates, incubation period, people movements, hospital capacity [7], quarantine, the availability of vaccination, etc., are needed to be taken into account for a certain disease; the SIR model was modified
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