On Identifying and Mitigating Bias in the Estimation of the COVID-19 Case Fatality Rate

The relative case fatality rates (CFRs) between groups and countries are key measures of relative risk that guide policy decisions regarding scarce medical resource allocation during the ongoing COVID-19 pandemic. In the middle of an active outbreak when surveillance data is the primary source of information, estimating these quantities involves compensating for competing biases in time series of deaths, cases, and recoveries. These include time- and severity- dependent reporting of cases as well as time lags in observed patient outcomes. In the context of COVID-19 CFR estimation, we survey such biases and their potential significance. Further, we analyze theoretically the effect of certain biases, like preferential reporting of fatal cases, on naive estimators of CFR. We provide a partially corrected estimator of these naive estimates that accounts for time lag and imperfect reporting of deaths and recoveries. We show that collection of randomized data by testing the contacts of infectious individuals regardless of the presence of symptoms would mitigate bias by limiting the covariance between diagnosis and death. Our analysis is supplemented by theoretical and numerical results and a simple and fast open-source codebase at https://github.com/aangelopoulos/cfr-covid-19 .

[1]  J. Pedrosa,et al.  Influenza Infectious Dose May Explain the High Mortality of the Second and Third Wave of 1918–1919 Influenza Pandemic , 2010, PloS one.

[2]  Derek A T Cummings,et al.  Estimating Absolute and Relative Case Fatality Ratios from Infectious Disease Surveillance Data , 2012, Biometrics.

[3]  J. Ioannidis,et al.  COVID-19 antibody seroprevalence in Santa Clara County, California , 2020, medRxiv.

[4]  Roberto Maroldi,et al.  Cardiac Involvement in a Patient With Coronavirus Disease 2019 (COVID-19). , 2020, JAMA cardiology.

[5]  Zunyou Wu,et al.  Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention. , 2020, JAMA.

[6]  F. Abad‐Franch,et al.  Sex Bias in Infectious Disease Epidemiology: Patterns and Processes , 2013, PloS one.

[7]  Jon Brassey,et al.  SARS-CoV-2 viral load and the severity of COVID-19 , 2020 .

[8]  Gerardo Chowell,et al.  Mathematical and statistical estimation approaches in epidemiology , 2009 .

[9]  R. Redfield,et al.  Covid-19 — Navigating the Uncharted , 2020, The New England journal of medicine.

[10]  M. Hernán,et al.  Potential Biases in Estimating Absolute and Relative Case-Fatality Risks during Outbreaks , 2015, PLoS neglected tropical diseases.

[11]  D. Cummings,et al.  Novel coronavirus 2019-nCoV: early estimation of epidemiological parameters and epidemic predictions , 2020, medRxiv.

[12]  S. Holmberg,et al.  Estimating acute viral hepatitis infections from nationally reported cases. , 2014, American journal of public health.

[13]  W. Gong,et al.  Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. , 2020, JAMA cardiology.

[14]  Novel Coronavirus Pneumonia Emergency Response Epidemiol Team [The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) in China]. , 2020, Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi.

[15]  Samuel L Groseclose,et al.  Evaluation of reporting timeliness of public health surveillance systems for infectious diseases , 2004, BMC public health.

[16]  T L Chorba,et al.  Mandatory reporting of infectious diseases by clinicians. , 1989, JAMA.

[17]  Bradley Efron,et al.  Censored Data and the Bootstrap , 1981 .

[18]  Kazuyuki Aihara,et al.  The Time Required to Estimate the Case Fatality Ratio of Influenza Using Only the Tip of an Iceberg: Joint Estimation of the Virulence and the Transmission Potential , 2012, Comput. Math. Methods Medicine.

[19]  M. Battegay,et al.  2019-novel Coronavirus (2019-nCoV): estimating the case fatality rate - a word of caution. , 2020, Swiss medical weekly.

[20]  J. Ioannidis,et al.  Population-level COVID-19 mortality risk for non-elderly individuals overall and for non-elderly individuals without underlying diseases in pandemic epicenters , 2020, Environmental Research.

[21]  B. Gersh Spectrum of heart disease and risk factors in a black urban population in South Africa (the Heart of Soweto Study): a cohort study , 2009 .

[22]  Benjamin J Cowling,et al.  Non‐parametric estimation of the case fatality ratio with competing risks data: an application to Severe Acute Respiratory Syndrome (SARS) , 2006, Statistics in medicine.

[23]  Jarek Kobiela,et al.  Estimating case fatality rates of COVID-19 , 2020, The Lancet Infectious Diseases.

[24]  Matt J Keeling,et al.  Contact tracing and disease control , 2003, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[25]  Luna Yue Huang,et al.  The Effect of Large-Scale Anti-Contagion Policies on the Coronavirus (COVID-19) Pandemic , 2020, medRxiv.

[26]  Alison P Galvani,et al.  Under-reporting and case fatality estimates for emerging epidemics , 2015, BMJ : British Medical Journal.

[27]  Weier Wang,et al.  Updated understanding of the outbreak of 2019 novel coronavirus (2019‐nCoV) in Wuhan, China , 2020, Journal of medical virology.

[28]  David J. Warne,et al.  Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic , 2020, BMC Public Health.

[29]  C. Fraser,et al.  Epidemiological determinants of spread of causal agent of severe acute respiratory syndrome in Hong Kong , 2003, The Lancet.

[30]  Michael I. Jordan Graphical Models , 2003 .

[31]  Michael M. Wagner,et al.  Automatic Electronic Laboratory-Based Reporting of Notifiable Infectious Diseases , 2002, Emerging infectious diseases.

[32]  N. Linton,et al.  Incubation Period and Other Epidemiological Characteristics of 2019 Novel Coronavirus Infections with Right Truncation: A Statistical Analysis of Publicly Available Case Data , 2020, medRxiv.

[33]  A. Siddique,et al.  Cholera epidemic among Rwandan refugees: experience of ICDDR,B in Goma, Zaire. , 1994, Glimpse.

[34]  J. Rocklöv,et al.  The reproductive number of COVID-19 is higher compared to SARS coronavirus , 2020, Journal of travel medicine.

[35]  Joshua M Sharfstein,et al.  Diagnostic Testing for the Novel Coronavirus. , 2020, JAMA.

[36]  Andrew Gelman,et al.  Struggles with survey weighting and regression modeling , 2007, 0710.5005.

[37]  K. Sliwa,et al.  Spectrum of heart disease and risk factors in a black urban population in South Africa (the Heart of Soweto Study): a cohort study , 2008, The Lancet.

[38]  Megan Andrew,et al.  Longitudinal associations between poverty and obesity from birth through adolescence. , 2014, American journal of public health.

[39]  C. Whittaker,et al.  Estimates of the severity of coronavirus disease 2019: a model-based analysis , 2020, The Lancet Infectious Diseases.

[40]  E. Dong,et al.  An interactive web-based dashboard to track COVID-19 in real time , 2020, The Lancet Infectious Diseases.

[41]  H. Checkoway,et al.  Epidemiologic programs for computers and calculators. Use of Poisson regression models in estimating incidence rates and ratios. , 1985, American journal of epidemiology.

[42]  C. Ronco,et al.  Kidney involvement in COVID-19 and rationale for extracorporeal therapies , 2020, Nature Reviews Nephrology.

[43]  Per Block,et al.  Demographic science aids in understanding the spread and fatality rates of COVID-19 , 2020, Proceedings of the National Academy of Sciences.

[44]  N. Wilson,et al.  Case-Fatality Risk Estimates for COVID-19 Calculated by Using a Lag Time for Fatality , 2020, Emerging infectious diseases.

[45]  Hua Cai,et al.  Sex difference and smoking predisposition in patients with COVID-19 , 2020, The Lancet Respiratory Medicine.

[46]  Jarvis T. Chen,et al.  U.S. county-level characteristics to inform equitable COVID-19 response , 2020, medRxiv.