Computational simulations of the effects of social distancing interventions on the COVID-19 pandemic

Abstract

<abstract> <p>The spread of the COVID-19 pandemic has been considered as a global issue. Based on the reported cases and clinical data, there are still required international efforts and more preventative measures to control the pandemic more effectively. Physical contact between individuals plays an essential role in spreading the coronavirus more widely. Mathematical models with computational simulations are effective tools to study and discuss this virus and minimize its impact on society. These tools help to determine more relevant factors that influence the spread of the virus. In this work, we developed two computational tools by using the R package and Python to simulate the COVID-19 transmissions. Additionally, some computational simulations were investigated that provide critical questions about global control strategies and further interventions. Accordingly, there are some computational model results and control strategies. First, we identify the model critical factors that helps us to understand the key transmission elements. Model transmissions can significantly be changed for primary tracing with delay to isolation. Second, some types of interventions, including case isolation, no intervention, quarantine contacts and quarantine contacts together with contacts of contacts are analyzed and discussed. The results show that quarantining contacts is the best way of intervening to minimize the spread of the virus. Finally, the basic reproduction number <italic>R</italic><sub>0</sub> is another important factor which provides a great role in understanding the transmission of the pandemic. Interestingly, the current computational simulations help us to pay more attention to critical model transmissions and minimize their impact on spreading this disease. They also help for further interventions and control strategies.</p> </abstract>

COVID-19 multiwaves as multiphase percolation: a general N-sigmoidal equation to model the spread

“…In response, various techniques and disinfectant agents have been employed to combat the pandemic [ 7 , 8 ]. To understand the mechanism of the pandemic's spread, several models have been used, including statistical [ 9 ], mathematical [ 10 – 15 ], computational simulation [ 16 – 18 ], and numerical simulation [ 19 – 21 ]. The techniques of machine learning [ 22 – 25 ] and Big Data are also used [ 26 – 28 ].…”

Probabilistic Analysis Depending on the Distance from A COVID-19 Outbreak

“…SEIRD transmission models have been developed and used as a predictive model for the spread and control of COVID-19 [25] and to analyze healthcare demand and capacity for predicting and forecasting the impact of local COVID-19 outbreaks [26]. A SIR transmission model has been used to compute the effects of social distancing interventions during the COVID-19 pandemic [27]. Moreover, a SIRD compartmental model and variational autoencoder neural network have been used to forecast a COVID-19 pandemic in the future from the available historical big data on the subject [28].…”