Dataset Information

Prediction of the incubation period for COVID-19 and future virus disease outbreaks.

ABSTRACT:

Background

A crucial factor in mitigating respiratory viral outbreaks is early determination of the duration of the incubation period and, accordingly, the required quarantine time for potentially exposed individuals. At the time of the COVID-19 pandemic, optimization of quarantine regimes becomes paramount for public health, societal well-being, and global economy. However, biological factors that determine the duration of the virus incubation period remain poorly understood.

Results

We demonstrate a strong positive correlation between the length of the incubation period and disease severity for a wide range of human pathogenic viruses. Using a machine learning approach, we develop a predictive model that accurately estimates, solely from several virus genome features, in particular, the number of protein-coding genes and the GC content, the incubation time ranges for diverse human pathogenic RNA viruses including SARS-CoV-2. The predictive approach described here can directly help in establishing the appropriate quarantine durations and thus facilitate controlling future outbreaks.

Conclusions

The length of the incubation period in viral diseases strongly correlates with disease severity, emphasizing the biological and epidemiological importance of the incubation period. Perhaps, surprisingly, incubation times of pathogenic RNA viruses can be accurately predicted solely from generic features of virus genomes. Elucidation of the biological underpinnings of the connections between these features and disease progression can be expected to reveal key aspects of virus pathogenesis.

SUBMITTER: Gussow AB

PROVIDER: S-EPMC7703724 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:A novel human coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in China in December 2019. There is limited support for many of its key epidemiologic features, including the incubation period for clinical disease (coronavirus disease 2019 [COVID-19]), which has important implications for surveillance and control activities. Objective:To estimate the length of the incubation period of COVID-19 and describe its public health implications. Design:Pooled analysis of confirmed COVID-19 cases reported between 4 January 2020 and 24 February 2020. Setting:News reports and press releases from 50 provinces, regions, and countries outside Wuhan, Hubei province, China. Participants:Persons with confirmed SARS-CoV-2 infection outside Hubei province, China. Measurements:Patient demographic characteristics and dates and times of possible exposure, symptom onset, fever onset, and hospitalization. Results:There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10 000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine. Limitation:Publicly reported cases may overrepresent severe cases, the incubation period for which may differ from that of mild cases. Conclusion:This work provides additional evidence for a median incubation period for COVID-19 of approximately 5 days, similar to SARS. Our results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases. Primary Funding Source:U.S. Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases, National Institute of General Medical Sciences, and Alexander von Humboldt Foundation.