A stochastic process based modular tool-box for simulating COVID-19 infection spread.
| Autor: | Manathunga SS; National Hospital of Sri Lanka, Sri Lanka., Abeyagunawardena IA; National Hospital of Sri Lanka, Home Address- 155, George E De Silva Mawatha, Kandy, Sri Lanka., Dharmaratne SD; Department of Community Medicine, Faculty of Medicine, University of Peradeniya, Sri Lanka, Institute for Health Metrics and Evaluation, Department of Health Metrics Sciences, School of Medicine, University of Washington, United States. |
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| Jazyk: | angličtina |
| Zdroj: | Informatics in medicine unlocked [Inform Med Unlocked] 2022; Vol. 29, pp. 100899. Date of Electronic Publication: 2022 Mar 05. |
| DOI: | 10.1016/j.imu.2022.100899 |
| Abstrakt: | Background: The novel coronavirus disease (COVID-19) culminated in a pandemic with many countries affected in varying stages. We aimed to develop a simulation environment for COVID-19 spread, taking environmental and social factors into account. Methods: The program was written in R language. A stochastic point process simulation model for simulating epidemics, a maximum-likelihood estimation model, an exponential growth rate model for calculating the basic reproduction number (R0), and functions for generating graphical representations of the simulations were utilized.Geographical area definition, population size, the number of initial infected individuals, period of simulation, parameters accounting for the radius of spread like masks usage, mobility level, intrinsic viral virulence, average infectious period, fraction of population vaccinated, time of vaccination, the efficacy of the vaccine, presence or absence of quarantine centers, time of establishment of quarantine centers, the efficacy of case detection and average time to quarantine from the detection of the infection were considered. Results: When the defined parameters were input, the model performed successfully producing the epidemic curve, R0 and an animation of infection spread. It was found that when parameters of known epidemics such as COVID-19 in California, Texas and, Florida were input, the epidemic curve generated was comparable to the epidemic curve in reality. Conclusion: This model can be utilized by many countries to visualize the effects of various mitigation strategies applied in their stage of disease and for policy makers to make informed decisions. It is applicable to many infectious diseases and hence can be used for research and educational purposes. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (© 2022 The Author(s).) |
| Databáze: | MEDLINE |
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