Abstract
With the recent outbreak of COVID-19, the reach and scale of COVID-19 cases is top of mind for everyone and many research groups are actively monitoring and exploring the potential spread. A positive consequence of past epidemics and pandemics is that there are sound epidemiological compartmental modelling approaches that can effectively model disease spread. With minor changes to the underlying dynamical system of equations, many different strategies and situations can be explored. In particular, one such strategy of social distancing is top of mind for many Canadians as our political leaders, local businesses, and fellow Canadians promote and adopt this approach with the hopes that it will effectively `flatten the curve' and reduce or prevent further spread. In this paper, the baseline SIR model is introduced with its close counterpart, the SEIR model. Social distancing is modelled through the isolation of a subset of the susceptible population and comparative studies are performed considering a range in the proportion of individuals isolated. Robust and accurate numerical approximation techniques are used to simulate the pessimistic base case for which no preventative measures are taken and for various social distancing regimes. The results of social distancing are consolidated into two groups - those that flatten the curve and those that completely halt the disease spread. Mathematical formulations show that the turning point between these two regimes is when the effective reproductive rate, denoted $R_{e}$ , is equal to 1. Conclusions are made regarding the impacts and extent of the spread in relation to the severity of social distancing measures.
Highlights
As the COVID-19 outbreak continues and expands rapidly into numerous countries, governmental bodies are starting to move toward conventional containment and response tactics, such as isolation and social distancing
Recent papers have provided evidence of pre-symptomatic transmission [30], [31], the aim of this paper is to provide a comparative study of the impacts of varying the seriousness of social distancing and the relationship that has on the spread of COVID-19 cases
The authors recognize that social distancing measures are stochastic in nature, this study aims at the implications that different degrees of social distancing could have on the caseload evolution
Summary
As the COVID-19 outbreak continues and expands rapidly into numerous countries, governmental bodies are starting to move toward conventional containment and response tactics, such as isolation and social distancing. It has been observed that these have a dramatic impact on the flattening of the curve and reduction of the virus spread [1]. There have been observational studies that have demonstrated how these strategies reduce the virus caseloads [2]–[5] as well as simulations performed by COVID-19 response teams showing the progression under different paradigms. Authors have studied the effects of social media and its use to acquire and exchange situational information so that relevant and accurate information is provided to the population [6]. Prediction of the COVID-19 outbreak in mainland China has been considered using simple mathematical models and limited
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