BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic

04/16/2020
by   Qingyuan Zhao, et al.
0

The coronavirus disease 2019 (COVID-19) has quickly grown from a regional outbreak in Wuhan, China to a global pandemic. Early estimates of the epidemic growth and incubation period of COVID-19 may have been severely biased due to sample selection. Using detailed case reports from 14 locations in and outside mainland China, we obtained 378 Wuhan-exported cases who left Wuhan before an abrupt travel quarantine. We developed a generative model we call BETS for four key epidemiological events—Beginning of exposure, End of exposure, time of Transmission, and time of Symptom onset (BETS)—and derived explicit formulas to correct for the sample selection. We gave a detailed illustration of why some early and highly influential analyses of the COVID-19 pandemic were severely biased. All our analyses, regardless of which subsample and model were being used, point to an epidemic doubling time of 2 to 2.5 days during the early outbreak in Wuhan. A Bayesian nonparametric analysis further suggests that 5 infection.

READ FULL TEXT
05/01/2020

Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US

During a disease outbreak, timely non-medical interventions are critical...
08/22/2022

Statistics cannot prove that the Huanan Seafood Wholesale Market was the early epicenter of the COVID-19 pandemic

We criticize a statistical proof of the hypothesis that the Huanan seafo...
08/27/2020

What Can We Learn from the Travelers Data in Detecting Disease Outbreaks – A Case Study of the COVID-19 Epidemic

Background: Travel is a potent force in the emergence of disease. We dis...
05/08/2020

Pandemic influenza and the gender imbalance: Evidence from early twentieth century Japan

This study uses the 1918-1920 influenza pandemic in Japan with newly dig...
09/29/2021

Incorporating global dynamics to improve the accuracy of disease models: Example of a COVID-19 SIR model

Mathematical models of infectious diseases exhibit robust dynamics such ...
07/30/2020

Change Sign Detection with Differential MDL Change Statistics and its Applications to COVID-19 Pandemic Analysis

We are concerned with the issue of detecting changes and their signs fro...

Code Repositories

2019-nCov-Data

Data and analysis for the early COVID-19 outbreak


view repo