key: cord-0732699-nhm24vmy
authors: Manathunga, S.S.; Abeyagunawardena, I.A.; Dharmaratne, S.D.
title: A stochastic process based modular tool-box for simulating COVID-19 infection spread
date: 2022-03-05
journal: Inform Med Unlocked
DOI: 10.1016/j.imu.2022.100899
sha: ada1bdc625893108341062ccb68a6df1128e01fa
doc_id: 732699
cord_uid: nhm24vmy

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.

The toolbox consists of three main components; a stochastic process model for simulation, a assuming an inverse proportionality and use it instead of the default negative linear function. The quarantine module also has three attributes; the proportion of infected individuals that are and contact tracing. However, the parameter that the model takes from the user is the infectivity 177 (Inf) of an infector to trigger the quarantine function, which is a value ranging from zero to one.

Assuming the infectivity of an infector decreases linearly with time during the infectious period, 

The function then creates an array of infected individuals whose infectivity parameter is equal to 184 Ti, and randomly samples the fraction defined by the quarantine efficacy parameter by calling the 185 function 186 sample(which(tab$infectivity == Ti), (efficacy * length(which(tab$infectivity == Ti)))).

The position coordinates of the sampled array are immediately changed into the predefined The model run with the default parameters is illustrated in Animation 05 and variation of the total 336 number of cases with time and the epidemic curve is represented in Figures 11 and 12 respectively. 

Advice for the public: Coronavirus disease (COVID-19)

A SIR model assumption for the spread of COVID-19 422 in different communities

Mitigation strategies and compliance in the COVID-19 fight; 424 how much compliance is enough?

A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time

Not applicable 415