Modeling COVID-19 to inform control efforts, Part I

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Jane Heffernan and John Glasser


Modelers have responded to the outbreak of respiratory illness caused by a novel (new human pathogen) coronavirus that emerged in China, but has since spread to other countries, by evaluating potential mitigation measures (e.g., contact-tracing, quarantine, isolation, social distancing). Currently, most infections elsewhere are among travelers from China, with some person-to-person spread among their close contacts. Should transmission be sustained in those communities, however, a global pandemic likely will result. Symptoms range from mild to severe, including death. Infectiousness seems to be re- lated to symptom severity, but infected people may be infectious before developing symptoms and those whose infections are asymptomatic may be infectious. While this complicates control, it is possible to detect infections via laboratory testing, facilitating contact-tracing and quarantine.

Jonathan Read

Lancatser University
"Modelling early transmission of Covid-19 within China"
In this talk, I will describe rapid modelling work conducted between 20-27 January 2020 to estimate key epidemiological parameters during the early stages of the Covid-19 outbreak in China. Key uncertain- ties at the time were the transmission potential of the new virus as well as the case ascertainment ratio and likely full size of the epidemic. We fitted a deterministic metapopulation SEIR model of transmission to reported case information across Chinese cities as well as in other locations around the world, up until large-scale movement restrictions were imposed on 23 January. We estimated that the R0 in China was 3.11 (95%CI, 2.39-4.13) and the case ascertainment ratio in Wuhan, the center of the outbreak at the time, was only 5.0% (3.6-7.4), demonstrating the potential for sizable epidemics that may be difficult to control.

Yanni Xiao

Xi'an Jiaotang Univeresity
"Modeling COVID-19 epidemic in mainland China based on multi-source data"
Since December 2019, the outbreak of new coronavirus in Wuhan has continued to spread, which has attracted worldwide attention. It is essential to effectively predict the development trend of the epidemic, including when the 2019-nCoV infection will peak, what are the specific peak value and the final size, etc. How do the large-scale directional movement of the national population and random movement of individuals influence on the national epidemic during the Spring Festival, and what is the role of the control strategies on the epidemic? Address these questions fall within the the scope of this talk. We develop a novel modeling approach with multiple control measures and parameterize the model based on a small amount of constantly updated data to quick predict the development trend and transmission risk of the disease, to reveal the development trend of the new coronavirus. We develop spatial network model to study the influences of population movement, information transmission, enhanced prevention and control measures on epidemic transmission, to identify the key factors that significantly affect the spread of disease, and to analyze the effectiveness of prevention and control measures of 2019-nCoV in- fection. The findings provide quantitative decision basis for national epidemic prevention and control.

Julien Riou

University of Bern
"Early transmission pattern and severity of COVID-19 in China"
The coronavirus disease 2019 (COVID-19) epidemic that originated in Wuhan, China, has spread globally. Early in the epidemic, we estimated the basic reproduction number R0 of 2019-nCoV to be around 2.2 (1.43.8), indicating the potential for sustained human-to-human transmission. As more data was becoming available, we estimated the age-specific case fatality ratio (CFR) by fitting a transmis- sion model to data from China, accounting for underreporting and the time delay to death. Overall CFR among all infections was 1.6% (1.4-1.8) and increased considerably for the elderly, highlighting the expected burden for healthcare systems with further expansion of the COVID-19 epidemic around the globe. This presentation aims to highlight methods that can be used to inform public health authorities in real time in situations of disease emergence, with a particular focus on how to handle uncertainty.

Zhilan Feng

Purdue University
"Staggered Release Policies for COVID-19 Control: Costs and Benefits of Relaxing Restrictions by Age and Risk"
Lockdown and social distancing restrictions have been widely used as part of policy efforts aimed at controlling the ongoing COVID-19 pandemic. Since these restrictions have a negative impact on the economy, there exists a strong incentive to relax these policies while protecting public health. Using a multigroup SEIR epidemiological model, we explore the costs and benefits associated with the sequential release of specific groups based on age and risk from isolation. The results suggest that properly designed staggered-release policies can do better than simultaneous-release policies in terms of protecting the most vulnerable members of a population, reducing health risks overall, and increasing economic activity.

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