Modeling containment and mitigation of COVID-19: experiences from different countries worldwide

Wednesday, June 16 at 04:15am (PDT)
Wednesday, June 16 at 12:15pm (BST)
Wednesday, June 16 08:15pm (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "MS12" time block.
Note: this minisymposia has multiple sessions. The second session is MS15-MEPI (click here).

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Andrei Akhmetzhanov (National Taiwan University College of Public Health, Taiwan), Natalie Linton (Hokkaido University, Japan)


This mini-symposium aims to share valuable experiences related to the containment and mitigation of COVID-19 in contexts, as well as draw connections between mathematical models of disease spread. Presentations will encompass the views of experts who have worked for national COVID-19 task force teams and mathematical modelers offering a wider perspective for transmission dynamics. The first session includes talks on the epidemiological characteristics of SARS-CoV-2 while also sharing the experience of COVID-19 control in Taiwan, which had extremely low incidence throughout the pandemic. The second session will begin with an emphasis on gender inequality observed during the COVID-19 pandemic using examples from Japan. The rest of the session will be devoted to an overview of mathematical modeling techniques that are used to quantify the spread of COVID-19.

Michael Hochberg

(Institute for Evolutionary Sciences, University of Montpellier, France)
"Modeling COVID-19: Seeing the forest for the trees"
Given the pace of SARS-CoV-2 outbreaks, epidemiologists and mathematical biologists have had to apply their expertise in real time to understand COVID-19 epidemiology, sometimes modify traditional SEIR models, and evaluate how mitigation and suppression measures might impact outbreaks. This has produced a wide variety of mathematical and statistical models, from the overly simple to the extremely complex. I discuss the pros and cons of employing simple SEIR models for COVID-19 dynamics, and how simple but important constructs are often missed by more complex models. I then turn to how an SEIR model of intermediate complexity produces a rich range of outcomes when coupled with the optimization of stay-in-place decisions. These studies show how intuition and models combine to increase simulation and forecasting accuracy, and are integral in developing more effective control strategies.

Natalie Linton

(Hokkaido University, Kyoto University, Japan)
"Variation in serial interval distribution among reported cases in Japan"
This study looks how the serial interval of coronavirus disease 2019 (COVID-19) cases in Japan changed over time during 2020 and assesses whether differences in the length of the serial interval exists based on age, sex, and transmission context. We collected publicly reported data on cases in Japan and determined 758 likely transmission pairs based on the types and timings of exposures. The serial interval for pairs detected during the second wave and third waves of COVID-19 transmission in Japan was shorter than the serial interval of cases detected in the first wave. Serial interval length did not vary greatly by sex or transmission context, however serial intervals tended to be a bit shorter when infectors were under 30 years of age and when secondary transmission occurred in a household setting. Accounting for shortening of the serial interval compared to estimates from early in the pandemic may improve inference of transmission dynamics.

Robin Thompson

(University of Warwick, U.K.)
"Inferring the effectiveness of interventions during infectious disease outbreaks"
The effectiveness of interventions is assessed in real-time during outbreaks to guide public health policy. Estimates of quantities such as the time-dependent reproduction number and the epidemic growth rate help to provide a picture of an ongoing outbreak, alongside data describing incidence of cases, hospitalisations and deaths. In this talk, I will present a simple method for estimating the time-dependent reproduction number using disease incidence time series and an estimate of the serial interval distribution (the times between successive cases in chains of transmission). I will demonstrate some extensions of the simple method (including accounting for differences between infected individuals who acquired the pathogen locally and imported cases),and describe some challenges for improving estimates of the time-dependent reproduction number going forwards. Since, as described in another talk in this session, serial intervals can change during an outbreak, a key challenge is including up-to-date estimates of the serial interval (or generation time) when estimating the time-dependent reproduction number.

Sumire Sorano

(London School of Hygiene and Tropical Medicine, U.K.)
"The impact of COVID-19 from social and gender perspectives in Japan"
COVID-19 pandemic disproportionately affected vulnerable populations, revealing the weakness of society in the world. According to the Ministry of Health, Labor and Welfare in Japan, the number of suicides nationwide in 2020 exceeded 21,000, marking the first increase since 2009. While the number of male suicides decreased, female suicides showed a marked increase (6091 in 2019 to 7026 in 2020; an increase by 15%), especially among young age (44% increase among girls below 20 years and 32% increase among women in their 20s). Unemployment and economic hardship during COVID-19 pandemic affected women harder. and there was an increased concerns over unintended pregnancy as domestic violence and sexual violence increased. This presentation overviews the impact of COVID-19 from social and gender perspectives in Japan.

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Virtual conference of the Society for Mathematical Biology, 2021.