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

Wednesday, June 16 at 05:45pm (PDT)
Thursday, June 17 at 01:45am (BST)
Thursday, June 17 09:45am (KST)

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

Jonathan Dushoff

(McMaster University, Canada)
"Defining, estimating and applying transmission-interval distributions"
The spread of epidemics is structured by delay distributions, including the now-famous “serial interval” between the symptom-onset times of an infector and an infectee (often conflated with the “generation interval” between infection times). Defining these time distributions clearly, and describing how they relate to each other, and to key parameters of disease spread, poses interesting theoretical and practical questions, some of which are still open. I will discuss how transmission intervals link the “speed” and “strength” of epidemics, issues in their estimation, and their role in helping monitor changes in the parameters underlying the spread of COVID-19 disease.

Sarah Kada

(Centers for Disease Control and Prevention (CDC), U.S.A.)
"Early assessment of SARS-CoV-2 controllability with contribution of asymptomatic and pre-symptomatic individuals"
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the infectious agent responsible for coronavirus disease 2019 (COVID-19), is readily transmitted person to person. Optimal control of COVID-19 depends on directing resources and health messaging to mitigation efforts that are most likely to prevent transmission. We used an analytical model to assess the proportion of SARS-CoV-2 transmissions in the community that likely occur from persons without symptoms. This model assessed the relative amount of transmission from pre-symptomatic, asymptomatic, and symptomatic individuals across a range of scenarios in which the proportion of transmission from people who never develop symptoms (i.e., remain asymptomatic) and the infectious period were varied according to published best estimates. We used multiple scenarios of proportions of asymptomatic individuals with COVID-19 and infectious periods to estimate that transmission from asymptomatic individuals most likely accounted for more than half of all transmissions. In addition to identification and isolation of persons with symptomatic COVID-19, effective control of spread therefore require reducing the risk of transmission from people with infection who do not have symptoms. These findings suggested that measures such as wearing masks, hand hygiene, social distancing, and strategic testing of people who are not ill would be foundational to slowing the spread of COVID-19 until safe and effective vaccines are available and widely used.

Hao-Yuan Cheng

(Epidemic Intelligence Center, Taiwan CDC, Taiwan)
"Experience of COVID-19 elimination in Taiwan"
In my presentation I will review prevention and control measures against COVID-19 spread in Taiwan that have been centered on stringent border control, obligatory quarantine of all incoming travelers, and intensified contact tracing.

Ta-Chou Ng

(National Taiwan University, Taiwan)
"Comparison of Estimated Effectiveness of Case-Based and Population-Based Interventions on COVID-19 Containment in Taiwan"
Taiwan is one of the few countries with initial success in COVID-19 control without strict lockdown or school closure, yet reasons remain to be fully elucidated. This comparative effectiveness study evaluated the effectiveness of case-based (including contact tracing and quarantine) and population-based (including social distancing and facial masking) interventions for COVID-19 in Taiwan. We used a stochastic branching process model using COVID-19 epidemic data from Taiwan for model development and calibration. Effective reproduction number of COVID-19 cases and the probability of outbreak extinction were used to evaluate the effectiveness of each combination of interventions. Case detection, contact tracing, and 14-day quarantine of close contacts (regardless of symptoms) was estimated to decrease the reproduction number from the counterfactual value of 2.50 to 1.53 (95% CrI 1.50-1.57), which would not be sufficient for epidemic control, which requires a value of less than 1. In our estimated analysis, voluntary population-based interventions, if used alone, were estimated to have reduced the reproduction number to 1.30 (95% CrI 1.03-1.58). Combined case-based and population-based interventions were estimated to reduce the reproduction number to below unity (0.85; 95% CrI 0.78-0.89). Results were similar for additional analyses with influenza data and sensitivity analyses. We showed that only the combination of case-based and population-based interventions (with wide adherence) may explain the success of COVID-19 control in Taiwan in 2020. Either category of interventions alone would have been insufficient, even in a country with an effective public health system and comprehensive contact tracing program. Mitigating the COVID-19 pandemic requires the collaborative effort of public health professionals and the general public. Full article is available at:

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Virtual conference of the Society for Mathematical Biology, 2021.