Examining the effects of voluntary avoidance behaviour and policy-mediated behaviour change on the dynamics of SARS-CoV-2:A mathematical model

Background Throughout the SARS-CoV-2 pandemic,policymakers have had to navigate between recommending voluntary behaviour change and policy-driven behaviour change to mitigate the impact of the virus.While individuals will voluntarily engage in self-protective behaviour when there is an increasing infectious disease risk,the extent to which this occurs and its impact on an epidemic is not known.Methods This paper describes a deterministic disease transmission model exploring the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave of SARS-CoV-2 infections in Ontario,Canada(September 1,2020 to February 28,2021).The model incorporates an information feedback function based on empirically derived behaviour data describing the degree to which avoidance behaviour changed in response to the number of new daily cases COVID-19.Results Voluntary avoidance behaviour alone was estimated to reduce the final attack rate by 23.1%,the total number of hospitalizations by 26.2%,and cumulative deaths by 27.5%over 6 months compared to a counterfactual scenario in which there were no interventions or avoidance behaviour.A provincial shutdown order issued on December 26,2020 was estimated to reduce the final attack rate by 66.7%,the total number of hospitalizations by 66.8%,and the total number of deaths by 67.2%compared to the counterfactual scenario.Conclusion Given the dynamics of SARS-CoV-2 in a pre-vaccine era,individual avoidance behaviour in the absence of government action would have resulted in a moderate reduction in disease however,it would not have been sufficient to entirely mitigate transmission and the associated risk to the population in Ontario.Government action during the second wave of the COVID-19 pandemic in Ontario reduced infections,protected hospital capacity,and saved lives.

Bidirectional impact of imperfect mask use on reproduction number of COVID-19:A next generation matrix approach

The use of masks as a means of reducing transmission of COVID-19 outside healthcare settings has proved controversial.Masks are thought to have two modes of effect:they prevent infection with COVID-19 in wearers;and prevent transmission by individuals with subclinical infection.We used a simple next-generation matrix approach to estimate the conditions under which masks would reduce the reproduction number of COVID-19 under a threshold of 1.Our model takes into account the possibility of assortative mixing,where mask users interact preferentially with other mask users.We make 3 key observations:1.Masks,even with suboptimal efficacy in both prevention of acquisition and transmission of infection,could substantially decrease the reproduction number for COVID-19 if widely used.2.Widespread masking may be sufficient to suppress epidemics where R has been brought close to 1 via other measures(e.g.,distancing).3.“Assortment”within populations(the tendency for interactions between masked individuals to be more likely than interactions between masked and unmasked individuals)would rapidly erode the impact of masks.As such,mask uptake needs to be fairly universal to have an effect.This simple model suggests that widespread uptake of masking could be determinative in suppressing COVID-19 epidemics in regions with R(t)at or near 1.