MEPI Subgroup Contributed Talks

Marcelo Eduardo Borges

Observatório COVID-19 BR

"Impact of school reopening and contact tracing strategies in covid-19 epidemiological dynamics in Brazilian capitals"

In Brazil, school closure was one of the first interventions adopted to contain the spread of Sars-Cov-2. While maintaining this intervention for an extended period presents increasing social costs, there is a reasonable concern regarding the epidemiological consequences of reopening schools during the pandemic. Here, we model the epidemiological dynamics in scenarios of school reopening for 3 capitals of Brazil, and how mitigation measures such as contact tracing can contribute to reduce the risk of transmissions. We implement an extended SEIR model stratified by age and contact networks at different environments (school, home, work, and others), and the transmission rate is affected by distinct intervention measures. After fitting epidemiological and demographic data, we simulate scenarios of increasing school transmission due to school reopening. The effects of contact tracing strategies in reducing transmission in school contacts are explored. Our results indicate that a flexibilization in the school closure intervention results in a non-linear increase of reported cases and deaths, that is dependent on the previous prevalence of cases in the population. Also, when contact tracing is restricted to school settings, a large number of daily tests are required to produce significant effects in reducing the total number of infections and deaths.

Cole Butler

North Carolina State University

"Density-dependence and the effectiveness of gene drives in controlling prevalence of mosquito-borne infections"

A gene drive is a genetic mechanism that can spread a gene through a population. Gene drives have the potential to significantly change the way we control vector-borne diseases, most notably dengue and malaria, with mathematical models having demonstrated their potential effectiveness. A key consideration absent from the mathematical modeling literature is the influence of density-dependence on target populations. Density-dependence is a natural ecological process that can counteract population suppression by a gene drive. The purpose here is then to study gene drive performance in a population of mosquitoes with density-dependent characteristics and determine how this affects infectious disease control. We use a mathematical framework in which a model for mosquito population genetics and dynamics is coupled to an epidemiological model. We explore a variety of different scenarios relating to the type of transgene released (e.g. varying sex-specific lethality). We undertake a sensitivity analysis to explore performance over a wide range of scenarios and to identify key parameters that influence the effectiveness of the approach. In each situation, results of system analysis with and without density-dependence is compared, and to what extent such factors influence certain quantities of interest such as disease prevalence in a human population.

Jonathan Forde

Hobart and William Smith Colleges

"The tradeoff between sensitivity and frequency in COVID-19 testing"

Control strategies that employ real-time polymerase chain reaction (RT-PCR) tests for the diagnosis and surveillance of COVID-19 epidemic are inefficient in fighting the epidemic due to high cost, delays in obtaining results, and the need of specialized personnel and equipment for laboratory processing. Cheaper and faster alternatives have been proposed, which return results rapidly, but are less sensitive in detecting virus. To quantify the effects of the tradeoffs between sensitivity, cost, testing frequency, and delay in test return on the overall course of an outbreak, we built a multi-scale immuno-epidemiological model that connects the virus profile of infected individuals with transmission and testing at the population level. For fixed testing capacity, lower sensitivity tests with shorter return delays slightly flatten the daily incidence curve and delay the time to the peak daily incidence. However, compared with RT-PCR testing, they do not always reduce the cumulative case count at half a year into the outbreak. When testing frequency is increased to account for the lower cost of less sensitive tests, we observe a large reduction in cumulative case counts. We predict that surveillance testing that employs low-sensitivity tests at high frequency can be an effective tool for epidemic control.

Iraj Yadegari

Postdoctoral fellow

"Updating the herd-immunity threshold under multiple-vaccine strategies"

Several vaccines with different efficacies are currently being distributed across the world to control COVID-19. Having enough doses from the most efficient vaccines in a short time is not possible for all countries. Hence, policy-makers may propose using various combinations of available vaccines to eliminate the disease quickly by achieving herd immunity. The classic herd-immunity threshold suggests that we can eliminate outbreaks from a population by vaccinating a fraction of the population. However, that classic threshold is for a single vaccine and is invalid and biased when we have multiple-vaccine strategies for a disease. Therefore, making decisions for vaccine-allocation policies based on this threshold may be costly. Here, we formulate the problem and find the exact threshold for the case with multiple-vaccine strategies for a single disease and show that there is more than one strategy to achieve herd immunity. Unlike the single-vaccine case, herd immunity can be achieved with an unlimited number of vaccine-allocation policies when multiple vaccines are available. Moreover, we propose methods to find the optimal strategy in a set of multiple-vaccination strategies.