Modeling the effect of age on quantiles of the incubation period distribution of COVID-19

Xiaohui Liu,Xiansi Ma,Jiewen Wang,Lei Wang,Liwen Wu

doi:10.1186/s12889-021-11761-1

Abstract

BackgroundThe novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. Note that the incubation period distribution is key to the prevention and control efforts of COVID-19. This study aimed to investigate the conditional distribution of the incubation period of COVID-19 given the age of infected cases and estimate its corresponding quantiles from the information of 2172 confirmed cases from 29 provinces outside Hubei in China.MethodsWe collected data on the infection dates, onset dates, and ages of the confirmed cases through February 16th, 2020. All the data were downloaded from the official websites of the health commission. As the epidemic was still ongoing at the time we collected data, the observations subject to biased sampling. To address this issue, we developed a new maximum likelihood method, which enables us to comprehensively study the effect of age on the incubation period.ResultsBased on the collected data, we found that the conditional quantiles of the incubation period distribution of COVID-19 vary by age. In detail, the high conditional quantiles of people in the middle age group are shorter than those of others while the low quantiles did not show the same differences. We estimated that the 0.95-th quantile related to people in the age group 23 ∼55 is less than 15 days.ConclusionsObserving that the conditional quantiles vary across age, we may take more precise measures for people of different ages. For example, we may consider carrying out an age-dependent quarantine duration in practice, rather than a uniform 14-days quarantine period. Remarkably, we may need to extend the current quarantine duration for people aged 0 ∼22 and over 55 because the related 0.95-th quantiles are much greater than 14 days.

Highlights

The novel coronavirus Severe acute respiratory syndrome (SARS)-CoV-2 has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019
In December 2019, some cases of pneumonia with unknown etiology were identified in Wuhan, Hubei province in China
We propose to model the relationship between the true incubation period, say T, and the age, i.e., X, through the conditional distribution G(t|λ(X), η) on X

Summary

Introduction

The novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. It turns out that the novel coronavirus, like SARSCOV, is the seventh member of the Nidovirales family of coronaviruses [2], but COVID-19 has a shorter serial interval than that of SARS [3] and higher transmissibility than MERS in the Middle East countries [4] It is highly infectious [5] and even contagious during the incubation period [6]. It is important to take necessary measures to prevent and control it as quickly as possible

Objectives

Methods

Results

Discussion

Conclusion