RiskEstim: A Software Package to Quantify COVID-19 Importation Risk

Mingda Xu,Yuan Bai,Peng Wu,Songwei Shan,Eric H Y Lau,Benjamin J Cowling,Zhanwei Du,Xiaoke Xu

doi:10.3389/fphy.2022.835992

Abstract

We present an R package developed to quantify coronavirus disease 2019 (COVID-19) importation risk. Quantifying and visualizing the importation risk of COVID-19 from inbound travelers is urgent and imperative to trigger public health responses, especially in the early stages of the COVID-19 pandemic and emergence of new SARS-CoV-2 variants. We provide a general modeling framework to estimate COVID-19 importation risk using estimated pre-symptomatic prevalence of infection and air traffic data from the multi-origin places. We use Hong Kong as a case study to illustrate how our modeling framework can estimate the COVID-19 importation risk into Hong Kong from cities in Mainland China in real time. This R package can be used as a complementary component of the pandemic surveillance system to monitor spread in the next pandemic.

Highlights

The ongoing global pandemic of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused incredible global disruption and challenges, in addition to the substantial health impact [1]
The estimated number of imported cases from our model was 7.6 from 15 higher-risk Mainland China cities into Hong Kong which was consistent with the reported 7 cases originating from Mainland China in Hong Kong before the Wuhan travel ban (January 23, 2020) [14, 25]
The estimated probability of importation of at least one case indicated that Wuhan exported the highest number of cases (5.8, 95% confidence interval (CI): 4.6–7.1) into Hong Kong, followed by Shanghai (0.5, 95% CI: 0.2–0.9) and Beijing (0.5, 95% CI: 0.2–0.9), during the study period

Summary

Introduction

The ongoing global pandemic of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused incredible global disruption and challenges, in addition to the substantial health impact [1]. Quantifying and visualizing the importation risk of COVID-19 from inbound travelers is important for public health responses, especially in the early stage of an epidemic wave [3, 4]. Some studies showed that border control measures, such as flight restrictions and quarantine for inbound travelers from high-risk places (e.g., based on the number of new daily cases [5]), might have delayed epidemics in the destination countries [6,7,8]. Assessment of the COVID-19 importation risk is needed for places where a high level of population immunity to COVID-19 has not been achieved in the target populations [9] or the government is considering relaxing border control measures [10,11,12]. We outline the general modeling framework of the R package to estimate COVID-19 importation risk using daily pre-symptomatic prevalence data from multi-origin locations and air traffic data

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Methods

Results

Conclusion