tetano
Editor, Senior Moderator
BMC Public Health
. 2021 Sep 27;21(1):1762.
doi: 10.1186/s12889-021-11761-1.
Modeling the effect of age on quantiles of the incubation period distribution of COVID-19
Xiaohui Liu[SUP] #[/SUP][SUP] 1 2 [/SUP], Lei Wang[SUP] 3 [/SUP], Xiansi Ma[SUP] 1 2 [/SUP], Jiewen Wang[SUP] 1 2 [/SUP], Liwen Wu[SUP] #[/SUP][SUP] 4 [/SUP]
Affiliations
Abstract
Background: 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. 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.
Methods: We 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.
Results: Based 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.
Conclusions: Observing 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.
Keywords: Biased sampling; COVID-19; Conditional quantiles; Incubation period.
. 2021 Sep 27;21(1):1762.
doi: 10.1186/s12889-021-11761-1.
Modeling the effect of age on quantiles of the incubation period distribution of COVID-19
Xiaohui Liu[SUP] #[/SUP][SUP] 1 2 [/SUP], Lei Wang[SUP] 3 [/SUP], Xiansi Ma[SUP] 1 2 [/SUP], Jiewen Wang[SUP] 1 2 [/SUP], Liwen Wu[SUP] #[/SUP][SUP] 4 [/SUP]
Affiliations
- PMID: 34579681
- DOI: 10.1186/s12889-021-11761-1
Abstract
Background: 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. 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.
Methods: We 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.
Results: Based 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.
Conclusions: Observing 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.
Keywords: Biased sampling; COVID-19; Conditional quantiles; Incubation period.