This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
The coronavirus disease 2019 (COVID-19) pandemic and its impact on global healthcare systems have caused a pressing need for rapid development of preventive and therapeutic approaches to fight the virus. As of today, there have been over 43.9 million cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection worldwide, over 225,000 cases in Canada, and over 74,000 cases in the province of Ontario.
During the early stages of the COVID-19 outbreak, many studies focused on mathematical modeling of disease dynamics, including the basic reproduction number and transmission of disease. It was clear that it is important to consider transmission through asymptomatic people with undetected infections in these models. This led to a global policy shift focusing on travel restrictions, social distancing protocols, and community lockdowns.
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Mathematical model to aid policy decisions on increasing or decreasing social distancing
Recently, researchers from York University, Toronto, Canada, presented a mathematical model for COVID-19 comprising groups of people with various levels of exposure to the disease based on data from Ontario, Canada. Their work is published on the preprint server medRxiv*. The researchers presented a model where the decision to increase or decrease social distancing is mathematically modeled as a function of the active and total COVID-19 cases and perceived cost of isolating people. They also defined the healthcare cost as well as a total cost.
The team explored these costs by adjusting parameters that could influence policy decisions. They found that minimum costs were not always associated with increased spending and vigilance because of a lack of proper social distancing and the fatigue caused by social distancing.
"Understanding how people will react to a change in policy surrounding lockdowns or bans on social gatherings is essential in gauging the impact that COVID-19 and mitigation strategies will have on infections and mortality."
Minimizing healthcare costs may only be possible at relatively huge costs to keep people at home
The authors presented a mathematical model for COVID-19 that guides policy decisions and allows dynamic implementation and relaxation of social distancing based on the active number of cases and individual isolation costs. This approach's main advantage is that it precisely showcases human psychology and behavior, unlike other approaches where behaviors are turned on and off at pre-determined times.
Using this approach to model human behavior dynamically, the authors produced unintuitive results concerning relative total cost. They concluded that increased vigilance and relaxation costs and decreased reactivity to increasing caseloads do not necessarily decrease the total cost. This is because people want to socialize, and social distancing measures cause fatigue in people. The study also demonstrated that additional healthcare costs can be prevented in some circumstances, but only at relatively huge costs to keep people at home.
Multiple lockdowns of shorter durations can lead to minimal costs
The study found that a number of lockdowns of shorter durations can help minimize costs. Their results are based on case data from Ontario, Canada, between March to August 2020. According to the authors, this model's dynamic framework is not restricted to one particular city or disease; it can be adapted to other scenarios by changing the behavior parameters and the disease.
"An advantage of the dynamic framework used in this model is that it is not restricted to Ontario, nor is it even restricted to COVID-19. Changing the disease and behavior parameters will allow this model to adapt to other scenarios."
The authors also want to acknowledge that their model does not take into account pharmaceutical interventions such as vaccination, which play a crucial role in mitigating the impact on the healthcare system and the peak time and duration of the outbreak. According to the team, the fact that social distancing is not truly discrete, and people do not decrease their socializing right away is another important consideration. People belong to a spectrum with fluid contact rates, and this aspect needs to be explored in further detail.
"Another important consideration is that social distancing is not truly discrete in that people do not suddenly reduce their contacts. In reality, it is a spectrum with fluid contact rates, and this needs to be further explored."
This news article was a review of a preliminary scientific report that had not undergone peer-review at the time of publication. Since its initial publication, the scientific report has now been peer reviewed and accepted for publication in a Scientific Journal. Links to the preliminary and peer-reviewed reports are available in the Sources section at the bottom of this article. View Sources
Article Revisions
- Mar 29 2023 - The preprint preliminary research paper that this article was based upon was accepted for publication in a peer-reviewed Scientific Journal. This article was edited accordingly to include a link to the final peer-reviewed paper, now shown in the sources section.
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