Yuejiao Wang1, Zhidong Cao2, Daniel Dajun Zeng3, Qingpeng Zhang4, Tianyi Luo1. 1. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China. 2. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: zhidong.cao@ia.ac.cn. 3. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. 4. City University of Hong Kong, Hong Kong 999077, China.
Abstract
OBJECTIVES: We aimed to explore the collective wisdom of preprints related to COVID-19 by comparing and synthesizing them with results of peer-reviewed publications. METHODS: PubMed, Google Scholar, medRxiv, bioRxiv, arXiv, and SSRN were searched for papers regarding the estimation of four epidemiological parameters of COVID-19: the basic reproduction number, incubation period, infectious period, and case-fatality-rate. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared. Four parameters in two groups were synthesized by bootstrapping, and their validities were evaluated by simulated cumulative cases of the susceptible-exposed-infectious-recovered-dead-cumulative (SEIRDC) model. RESULTS: A total of 106 papers were included for analysis. The distributions of four parameters in two literature groups were close, and the timeliness of preprints was better. Synthesized estimates of the basic reproduction number (3.18, 95% CI 2.85-3.53), incubation period (5.44 days, 95% CI 4.98-5.99), infectious period (6.25 days, 95% CI 5.09-7.51), and case-fatality-rate (4.51%, 95% CI 3.41%-6.29%) were obtained. Simulated cumulative cases of the SEIRDC model matched well with the onset cases in China. CONCLUSIONS: The validity of the COVID-19 parameter estimations of the preprints was on par with that of peer-reviewed publications, and synthesized results of literatures could reduce the uncertainty and be used for epidemic decision-making.
OBJECTIVES: We aimed to explore the collective wisdom of preprints related to COVID-19 by comparing and synthesizing them with results of peer-reviewed publications. METHODS: PubMed, Google Scholar, medRxiv, bioRxiv, arXiv, and SSRN were searched for papers regarding the estimation of four epidemiological parameters of COVID-19: the basic reproduction number, incubation period, infectious period, and case-fatality-rate. Distributions of parameters and timeliness of preprints and peer-reviewed papers were compared. Four parameters in two groups were synthesized by bootstrapping, and their validities were evaluated by simulated cumulative cases of the susceptible-exposed-infectious-recovered-dead-cumulative (SEIRDC) model. RESULTS: A total of 106 papers were included for analysis. The distributions of four parameters in two literature groups were close, and the timeliness of preprints was better. Synthesized estimates of the basic reproduction number (3.18, 95% CI 2.85-3.53), incubation period (5.44 days, 95% CI 4.98-5.99), infectious period (6.25 days, 95% CI 5.09-7.51), and case-fatality-rate (4.51%, 95% CI 3.41%-6.29%) were obtained. Simulated cumulative cases of the SEIRDC model matched well with the onset cases in China. CONCLUSIONS: The validity of the COVID-19 parameter estimations of the preprints was on par with that of peer-reviewed publications, and synthesized results of literatures could reduce the uncertainty and be used for epidemic decision-making.