Abstract
Recently, two coronavirus disease 2019 (COVID-19) vaccine products have been authorized in Canada. It is of crucial importance to model an integrated/combined package of non-pharmaceutical (physical/social distancing) and pharmaceutical (immunization) public health control measures. A modified epidemiological, compartmental SIR model was used and fit to the cumulative COVID-19 case data for the province of Ontario, Canada, from 8 September 2020 to 8 December 2020. Different vaccine roll-out strategies were simulated until 75% of the population was vaccinated, including a no-vaccination scenario. We compete these vaccination strategies with relaxation of non-pharmaceutical interventions. Non-pharmaceutical interventions were supposed to remain enforced and began to be relaxed on 31 January, 31 March or 1 May 2021. Based on projections from the data and long-term extrapolation of scenarios, relaxing the public health measures implemented by re-opening too early would cause any benefits of vaccination to be lost by increasing case numbers, increasing the effective reproduction number above 1 and thus increasing the risk of localized outbreaks. If relaxation is, instead, delayed and 75% of the Ontarian population gets vaccinated by the end of the year, re-opening can occur with very little risk. Relaxing non-pharmaceutical interventions by re-opening and vaccine deployment is a careful balancing act. Our combination of model projections from data and simulation of different strategies and scenarios, can equip local public health decision- and policy-makers with projections concerning the COVID-19 epidemiological trend, helping them in the decision-making process.
Highlights
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel, emerging coronavirus, belonging to the class of enveloped, single-stranded, positive-sense RNA viruses [1]
We use Ontario, Canada as a case study as it is the largest province in Canada and has consistently been an area of concern within the country
We fit our model to the cumulative case data for the province of Ontario from 8 September 2020 to 8 December 2020 for the 2020 COVID-19 pandemic; 8 September 2020 was chosen because of the observed significant increase in the number of reported infected cases
Summary
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel, emerging coronavirus, belonging to the class of enveloped, single-stranded, positive-sense RNA viruses [1]. SARS-CoV-2 is the infectious agent responsible for the still ongoing coronavirus disease 2019 (COVID-19) pandemic This respiratory disease is generally mild and even asymptomatic, but in a proportion of cases can worsen, becoming severe and even life-threatening [2]. We will take Ontario, Canada, as a case study, but our model is a general-purpose one and, after being properly parametrized and informed with locally available data, can be adapted to any country/scenario. The mechanistic nature of the model allows us to modify the model a posteriori to account for dynamics that are notably missing in the data. In this case, we focus on modifications involving vaccination strategies competing with reopening strategies. We use Ontario, Canada as a case study as it is the largest province in Canada and has consistently been an area of concern within the country
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