ABSTRACT
BackgroundAs COVID-19 vaccination coverage increases, public health and industries are contemplating re-opening measures of public spaces, including theme-parks. To re-open, theme-parks must provide public health mitigation plans. Questions on implementation of public health mitigation strategies such as park cleaning, COVID-19 testing, and enforcement of social distancing and the wearing of personal protective equipment (PPE) in the park remain. MethodsWe have developed a mathematical model of COVID-19 transmission in a theme-park that considers direct human-human and indirect environment-human transmission of the virus. The model thus tracks the changing infection/disease landscape of all visitors, workers, and environmental reservoirs in a theme park setting. FindingsModels results show that theme-park public health mitigation must include mechanisms that reduce virus contamination of the environment to ensure that workers and visitors are protected from COVID-19 transmission in the park. Thus, cleaning rates and mitigation of human-environment contact increases in importance. ConclusionOur findings have important practical implications in terms of public health as policy- and decision-makers are equipped with a mathematical tool that can guide theme-parks in developing public health mitigation strategies for a safe re-opening.
ABSTRACT
BackgroundInfections represent highly dynamic processes, characterized by evolutionary changes and events that involve both the pathogen and the host. Among infectious agents, viruses, such as the "Severe Acute Respiratory Syndrome-related Coronavirus type 2" (SARS-CoV-2), the infectious agent responsible for the currently ongoing "Coronavirus disease 2019" (COVID-2019) pandemic, have a particularly high mutation rate. Taking into account the mutational landscape of an infectious agent, it is important to shed light on its evolution capability over time. As new, more infectious strains of COVID-19 emerge around the world, it is imperative to estimate when these new strains may overtake the wild-type strain in different populations. Therefore, we developed a general-purpose framework to estimate the time at which a mutant variant is able to takeover a wild-type strain during an emerging infectious diseases outbreak. In this study, we used COVID-19 as a case-study, but the model is adaptable to any emerging pathogens. Methods and findingsWe devise a two-strain mathematical framework, to model a wild- and a mutant-type viral population and fit cumulative case data to parameterize the model, using Ontario as a case study. We found that, in the context of under-reporting and the current case levels, a variant strain is unlikely to dominate until March/April 2021. Current non-pharmaceutical interventions in Ontario need to be kept in place longer even with vaccination in order to prevent another outbreak. The spread of a variant strain in Ontario will mostly likely be observed by a widened peak of the daily reported cases. If vaccine efficacy is maintained across strains, then it is still possible to have an immune population by end of 2021. ConclusionsOur findings have important practical implications in terms of public health as policy-and decision-makers are equipped with a mathematical tool that can enable the estimation of the take-over of a mutant strain of an emerging infectious disease.
ABSTRACT
BackgroundRecently, 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. MethodsA modified epidemiological, compartmental SIR model was utilized and fit to the cumulative COVID-19 case data for the province of Ontario, Canada, from September 8, 2020 to December 8, 2020. Different vaccine roll-out strategies were simulated until 75 percent of the population is 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 either January 31, March 31, or May 1, 2021. ResultsBased 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 percent of the Ontarian population gets vaccinated by the end of the year, re-opening can occur with very little risk. InterpretationRelaxing 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.
