Belgian Covid-19 Mortality, Excess Deaths, Number of Deaths per Million, and Infection Fatality Rates (8 March - 9 May 2020)

Objective. Scrutiny of COVID-19 mortality in Belgium over the period 8 March-9 May 2020 (Weeks 11-19), using number of deaths per million, infection fatality rates, and the relation between COVID-19 mortality and excess death rates. Data. Publicly available COVID-19 mortality (2020); overall mortality (2009-2020) data in Belgium and demographic data on the Belgian population; data on the nursing home population; results of repeated sero-prevalence surveys in March-April 2020. Statistical methods. Reweighing, missing-data handling, rate estimation, visualization. Results. Belgium has virtually no discrepancy between COVID-19 reported mortality (confirmed and possible cases) and excess mortality. There is a sharp excess death peak over the study period; the total number of excess deaths makes April 2020 the deadliest month of April since WWII, with excess deaths far larger than in early 2017 or 2018, even though influenza-induced January 1951 and February 1960 number of excess deaths were similar in magnitude. Using various sero-prevalence estimates, infection fatality rates (IFRs; fraction of deaths among infected cases) are estimated at 0.38-0.73% for males and 0.20-0.39% for females in the non-nursing home population (non-NHP), and at 0.79-1.52% for males and 0.88-1.31% for females in the entire population. Estimates for the NHP range from 38 to 73% for males and over 22 to 37% for females. The IFRs rise from nearly 0% under 45 years, to 4.3% and 13.2% for males in the non-NHP and the general population, respectively, and to 1.5% and 11.1% for females in the non-NHP and general population, respectively. The IFR and number of deaths per million is strongly influenced by extensive reporting and the fact that 66.0% of the deaths concerned NH residents. At 764 (our re-estimation of the figure 735, presented by "Our World in Data"), the number of COVID-19 deaths per million led the international ranking on May 9, 2020, but drops to 262 in the non-NHP. The NHP is very specific: age-related increased risk; highly prevalent comorbidities that, while non-fatal in themselves, exacerbate COVID-19; larger collective households that share inadvertent vectors such as caregivers and favor clustered outbreaks; initial lack of protective equipment, etc. High-quality health care countries have a relatively older but also more frail population [1], which is likely to contribute to this result.

[1]  Y. Yazdanpanah,et al.  SARS-CoV-2 serological analysis of COVID-19 hospitalized patients, pauci-symptomatic individuals and blood donors. , 2020, medRxiv.

[2]  F. J. Richards A Flexible Growth Function for Empirical Use , 1959 .

[3]  Andrew T. Levin,et al.  ASSESSING THE AGE SPECIFICITY OF INFECTION FATALITY RATES FOR COVID-19: META-ANALYSIS & PUBLIC POLICY IMPLICATIONS , 2020, medRxiv.

[4]  Hiroshi Nishiura,et al.  Early Epidemiological Assessment of the Virulence of Emerging Infectious Diseases: A Case Study of an Influenza Pandemic , 2009, PloS one.

[5]  D. Cummings,et al.  A systematic review of antibody mediated immunity to coronaviruses: antibody kinetics, correlates of protection, and association of antibody responses with severity of disease , 2020, medRxiv.

[6]  C. Suetens,et al.  High impact of COVID-19 in long-term care facilities, suggestion for monitoring in the EU/EEA, May 2020 , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[7]  Sarah K. Helman,et al.  Quantifying antibody kinetics and RNA shedding during early-phase SARS-CoV-2 infection , 2020, medRxiv.

[8]  J. Sanderson,et al.  Mortality in Belgium from nineteenth century to today , 2020 .

[9]  R. Eggo,et al.  Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020 , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[10]  Salvatore Rubino,et al.  Similarity in Case Fatality Rates (CFR) of COVID-19/SARS-COV-2 in Italy and China. , 2020, Journal of infection in developing countries.

[11]  R. Eggo,et al.  Using a delay-adjusted case fatality ratio to estimate under-reporting | CMMID Repository , 2020 .

[12]  S. Abrams,et al.  Modelling the early phase of the Belgian COVID-19 epidemic using a stochastic compartmental model and studying its implied future trajectories , 2020, Epidemics.

[13]  Xiaolong Qi,et al.  Real estimates of mortality following COVID-19 infection , 2020, The Lancet Infectious Diseases.

[14]  H. Van Oyen,et al.  All-cause mortality supports the COVID-19 mortality in Belgium and comparison with major fatal events of the last century , 2020, Archives of Public Health.

[15]  D. Cummings,et al.  Age-specific mortality and immunity patterns of SARS-CoV-2 , 2020, Nature.

[16]  F. Murtin,et al.  Excess mortality , 2020, OECD Health Working Papers.

[17]  N. Hens,et al.  Seroprevalence of IgG antibodies against SARS coronavirus 2 in Belgium: a prospective cross-sectional study of residual samples , 2020, medRxiv.

[18]  Christel Faes,et al.  The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19 , 2020, Nature Communications.

[19]  Richard E. Grewelle,et al.  Estimating the Global Infection Fatality Rate of COVID-19 , 2020, medRxiv.

[20]  Geert Molenberghs,et al.  Missing Data in Clinical Studies , 2007 .

[21]  C. Faes,et al.  Time between Symptom Onset, Hospitalisation and Recovery or Death: a Statistical Analysis of Different Time-Delay Distributions in Belgian COVID-19 Patients , 2020, medRxiv.

[22]  Population vulnerability to COVID-19 in Europe: a burden of disease analysis , 2020, Archives of Public Health.

[23]  Morteza Abdullatif Khafaie,et al.  Cross-Country Comparison of Case Fatality Rates of COVID-19/SARS-COV-2 , 2020, Osong public health and research perspectives.