ABSTRACT
The COVID-19 global health crisis has had a significant impact on societies worldwide. To evaluate the effectiveness of transmission prevention strategies, infectivity modeling can be used to interpret and analyze various virus responses. In this study, compartmental modeling approaches are used to model and quantify the success of various control techniques, such as social behaviors. Newfoundland and Labrador (NL), a province in Canada, is a relatively isolated and easily-monitored region that can be used to study the characterization and effectiveness of infectivity model coefficients. The province has few points of entry and a lower contact frequency than densely populated urban districts. Based on the results, the implications of social distancing removed an estimated 85% of the susceptible population from potential exposure to a viral transmission. The modeling also determined a basic COVID-19 reproductive number of 3.7 in NL, higher than the globalized average of 2.3. Compartmental models are used to determine the validity of alternative pandemic responses, such as nonvaccine social practices to fight the wave of infection. The overall impact of COVID-19 in NL is concluded to have been effectively controlled by the implementation of social distancing behaviors, allowing for accurate quantification of modeling parameters.
ABSTRACT
Concerns regarding the impact of opening daycares and day camps are examined to explore the sensitivity on outbreaks of COVID-19. Overall, while controlling the spread of COVID-19 must be a major concern for all people, society needs to consider various options, including those of alternative reopening strategies to reflect both the impact on children when reopening of daycares/day camps but also the potential impact on viral growth transmittance. Infectivity modeling scenarios are described for the province of Ontario, indicating how the caseloads may reverberate through the population in response to the opening of daycare and summer day camps. An SEIR model, stratified by age, is used to model the primary compartments of the virus. The results show that the spread of COVID-19 reached a peak in April 2020 and steadily declined for Toronto and Peel Public Health Units (PHUs). Furthermore, the model indicates that reducing daycare and day camp capacities by 50%results in more than a 75%reduction in impacts on caseload and deaths, relative to not undertaking due care and diligence to control the virus growth. The findings indicate that combining reduced capacity with effective social distancing parameters is expected to be the most effective in reducing additional caseloads associated with reopening daycare and day camps within Ontario. By reducing capacity and contact rates by 50%through social distancing protocols, additional cases are expected to reduce by 88%for Toronto and 91%for Peel PHUs. These results highlight the importance of both reducing daycare/day camp capacity and managing social distancing protocols that are effective measures to help control the spread of COVID-19 within Ontario. IEEE
ABSTRACT
The numbers of novel coronavirus cases continue to grow at an unprecedented rate across the world. Attempts to control the growth of the virus using masks and social-distancing, and, recently, double-masking as well, continue to be difficult to maintain, in part due to the extent of asymptomatic cases. Analyses of large datasets consisting of 219,075 individual cases in Ontario, indicated that asymptomatic and pre-symptomatic cases are substantial in number. Large numbers of cases in children aged 0-9 were asymptomatic or had only one symptom (35.0% and 31.4% of total cases, respectively) and resulted in fever as the most common symptom (30.6% of total cases). COVID-19 cases in children were more likely to be milder symptomatic with cough not seen as frequently as in adults aged over 40, and past research has shown children to be index cases in familial clusters. These findings highlight the importance of targeting asymptomatic and mild infections in the continuing effort to control the spread of COVID-19. The Pearson correlation coefficient between test positivity rates and asymptomatic rates of -0.729 indicates that estimates of the asymptomatic rates should be obtained when the test positivity rates are lowest as the best approach.