Monday, June 14 at 03:15pm (PDT)Monday, June 14 at 11:15pm (BST)Tuesday, June 15 07:15am (KST)
SMB2021 FollowMonday (Tuesday) during the "CT01" time block.
Simon Fraser University
"Quantifying transmissibility of COVID-19 and the impact of intervention in long-term care facilities"
Estimates of the basic reproduction number (R0) for Coronavirus disease 2019 (COVID-19) are particularly variable in the context of transmission within locations such as long-term health care (LTHC) facilities. We sought to characterise the heterogeneity of R0 across known outbreaks within these facilities. We used a unique comprehensive dataset of all outbreaks that have occurred within LTHC facilities in British Columbia, Canada. We estimated R0 with a Bayesian hierarchical dynamic model of susceptible, exposed, infected, and recovered individuals, that incorporates heterogeneity of R0 between facilities. We further compared these estimates to those obtained with standard methods that utilize the exponential growth rate and maximum likelihood. The total size of an outbreak varied dramatically, with a range of attack rates of 2%-86%. The Bayesian analysis provides more constrained overall estimates of R0 = 2.83 (90% CrI 0.25--7.19) than standard methods, with a range within facilities of 0.66 - 10.06. We further estimated that intervention led to 67% (56%-73%) of all cases being averted within the LTHC facilities. Understanding the risks and impact of intervention are essential in planning during the ongoing global pandemic, particularly in high-risk environments such as LTHC facilities.
Centers for Disease Control and Prevention
"Analysis of the yearly transition function in measles disease modeling"
Globally, there were an estimated 9.8 million measles cases and 207,500 measles deaths in 2019. As the worldwide effort to eliminate measles continues, modeling remains a valuable tool for public health decision makers and program implementers. This study presents a novel approach to the use of a yearly transition function to account for the effects of the timing of vaccination (based on vaccination schedules for different age groups) and disease seasonality on the yearly number of measles cases in a given country.Our methodology adds to and expands on the existing modeling framework of Eilertson et al. (Stat. Med. 2019; 38: 4146-4158) by developing explicit functional expressions for each underlying component of the transition function in order to adjust for the temporal interaction between vaccination and exposure to disease. Assumption of specific distributional forms provides multipliers that can be applied to estimated yearly counts of cases and vaccine doses to estimate impacts more precisely on population immunity. These new model features provide the ability to forecast and compare the effects of different vaccination timing scenarios and seasonality of transmission on the expected disease incidence. Although this application is to measles, the method has potential relevance to modeling other vaccine-preventable diseases.
Luis Manuel Munoz-Nava
Center for Research and Advanced Studies
"‘Learning Bubbles’ are an effective and safe alternative to schools reopening during the COVID-19 pandemic"
According to estimates of the UNESCO, the COVID-19 pandemic has affected more than 1.4 billion (aprox. 84 %) students worldwide. In many countries, schools have remained closed for more than a year and this situation is likely to persist for several additional months before local vaccination programs start to slow down virus propagation. While policymakers debate on how and when children should go back to school buildings, closures are expecting to have a profound and long-term impact in children education, nutrition, social skills, and mental health, as well as in the economy and psychosocial behavior of students and their families. As an alternative to reopening of schools, 'Learning Bubbles' are groups of a few children that their parents voluntarily set-up for in-person instruction either from one of the parents or an external tutor. 'Learning bubbles' were very popular in the United States started remotely the academic year in the Fall of 2020, but to the best of our knowledge, a report on the effectiveness of 'learning bubbles' in mitigating the propagation of the COVID-19 disease has not been analyzed. We developed a mathematical model of 'learning bubbles' and discuss its effectiveness in mitigating the disease compared with schools reopening.
University of Nebraska-Lincoln
"A Model for COVID-19 with Limited Vaccination"
Now that vaccines for COVID-19 are available and distribution has begun, a critical question arises: To what extent do protective measures need to be maintained as more people are vaccinated? Addressing this question requires careful attention to the way vaccination is incorporated into the model. We augment our SEAIHRD (Susceptible, Exposed, Asymptomatic, (symptomatic) Infectious, Hospitalized, Recovered, Deceased) model by breaking up the susceptible class into a standard (S)usceptible class and a (P)re-vaccinated class, with proportions determined by a vaccine acceptance parameter. Susceptible and pre-vaccinated individuals move to the Exposed through infection in the standard way. In addition, a vaccination process moves individuals directly out of the pre-vaccinated class at a rate that follows a Michaelis-Menten mechanism; that is, the rate is linear when the pre-vaccinated class is small but quickly saturates due to limitations in the distribution speed. The most recent update accounts for prioritization of high-risk people. Most individuals who leave the pre-vaccinated class move into the recovered class, but a small fraction move back to the standard susceptible class, representing the probability of failing to mount a proper immune response. We use the model to investigate the impact of reduced compliance with protective measures.