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Containing 2019-nCoV (Wuhan) coronavirus.

Edward H Kaplan¹

¹William N. and Marie A. Beach Professor of Operations Research, Professor of Public Health, Professor of Engineering, Yale School of Management, 165 Whitney Avenue, New Haven, 06511, CT, USA. edward.kaplan@yale.edu.

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|March 9, 2020

Summary

This study models the effectiveness of isolation and quarantine strategies for controlling the spread of novel coronavirus 2019-nCoV (COVID-19) during the early stages of an outbreak. Probability models assess containment effectiveness when vaccines are unavailable.

Keywords:

Containment Coronavirus Isolation Probability modeling Quarantine

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Area of Science:

Epidemiology
Public Health
Mathematical Modeling

Background:

The novel coronavirus 2019-nCoV (COVID-19) emerged in Wuhan, China, in December 2019.
Initial cases were linked to the Huanan Seafood Wholesale Market, but person-to-person transmission is confirmed.
A vaccine is not expected for at least a year, necessitating containment strategies.

Purpose of the Study:

To present probability models for assessing containment strategies.
To evaluate the effectiveness of case isolation and quarantine in the initial phase of an outbreak.
To use early Wuhan data for model illustration.

Main Methods:

Development of probability models.
Assessment of isolation and quarantine effectiveness.
Application of models to early Wuhan outbreak data.

Main Results:

The study provides a framework for quantifying the impact of isolation and quarantine on disease transmission.
Model illustrations demonstrate the potential effectiveness of these containment measures.
Early Wuhan data provides context for the model's applicability.

Conclusions:

Isolation of known cases and quarantine of high-risk individuals are crucial for controlling early-stage outbreaks.
Mathematical modeling can inform public health interventions when facing novel infectious diseases.
Containment strategies are vital in the absence of vaccines.