CASE-FATALITY RATE BY COVID-19: A HIERARCHICAL BAYESIAN ANALYSIS OF COUNTRIES IN DIFFERENT REGIONS OF THE WORLD
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Abstract
The main goal of this study is the statistical analysis of deaths/cases epidemiologically defined as Case-Fatality Rate (CFR) due to novel coronavirus (SARSCoV-2) for 113 countries in different regions of the world in presence of some economic, health and social factors. The considered dataset refers to the accumulated daily counts of reported cases and deaths for a period ranging from the beginning of the COVID-19 pandemics in each country until July 25, 2020 the final follow-up day period. A binomial logistic regression model in presence of a random effect is assumed in the data analysis. The statistical analysis is considered under a hierarchical Bayesian approach using MCMC (Markov Chain Monte Carlo) methods do get the posterior summaries of interest. The results we found are interesting considering the epidemiological interpretation, which could be of great interest to epidemiologists, health authorities, and the general public in the face of a complex pandemic in all its aspects, like the one we are experiencing.
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References
BATES, D. M.; WATTS, D. G. Relative curvature measures of nonlinearity. Journal of the Royal Statistical Society: Series B (Methodological), Wiley Online Library, v. 42, n. 1, p. 1–16, 1980.
BATES, D. M.; WATTS, D. G. Nonlinear regression analysis and its applications. New York: Wiley, 1988. v. 2.
BOX, G. E.; TIAO, G. C. Bayesian inference in statistical analysis. [S.l.]: JohnWiley & Sons, 1973.
BROOKS, S. P.; GELMAN, A. General methods for monitoring convergence of iterative simulations. Journal of computational and graphical statistics, Taylor & Francis Group, v. 7, n. 4, p. 434–455, 1998.
CHAN, J. F.-W. et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster.The Lancet, Elsevier, v. 395, n. 10223, p. 514–523, 2020.
CHAN, K.-H. et al. Factors affecting stability and infectivity of SARS-CoV-2. Journal of Hospital Infection, Elsevier, 2020.
CHEN, J. Pathogenicity and transmissibility of 2019-nCoV: A quick overview and comparison with other emerging viruses. Microbes and Infection, Elsevier, 2020.
CHEN, Y.; LIU, Q.; GUO, D. Emerging coronaviruses: genome structure, replication, and pathogenesis. Journal of Medical Virology, Wiley Online Library,v. 92, n. 4, p. 418–423, 2020.
CHIB, S.; GREENBERG, E. Understanding the Metropolis-Hastings algorithm. The american statistician, Taylor & Francis Group, v. 49, n. 4, p. 327–335, 1995.
CHODCIK, G.; WEIL, C. COVID-19 death toll: The role of the nation’s economic development. medRxiv, Cold Spring Harbor Laboratory Press, 2020.
CHU, D. K. et al. Physical distancing, face masks, and eye protection to prevent person-to-person transmission of SARS-CoV-2 and COVID-19: A systematic review and meta-analysis.The Lancet, Elsevier, 2020.
CLIFFORD, S. J. et al. Interventions targeting air travellers early in the pandemic may delay local outbreaks of SARS-CoV-2.medRxiv, Cold Spring HarborLaboratory Press, 2020.
CURTIS, C. R. et al. Ordinary versus random-effects logistic regression for analyzing herd-level calf morbidity and mortality data. Preventive Veterinary Medicine, Elsevier, v. 16, n. 3, p. 207–222, 1993.
DOWD, J. B. et al. Demographic science aids in understanding the spread and fatality rates of COVID-19. Proceedings of the National Academy of Sciences, National Acad Sciences, v. 117, n. 18, p. 9696–9698, 2020.
GELFAND, A. E.; SMITH, A. F. Sampling-based approaches to calculating marginal densities. Journal of the American statistical association, Taylor & Francis Group, v. 85, n. 410, p. 398–409, 1990.
GIANGRECO, G. Case fatality rate analysis of Italian COVID-19 outbreak. Journal of Medical Virology, Wiley Online Library, 2020.
GIBBONS, R. D.; HEDEKER, D. Random effects probit and logistic regression models for three-level data. Biometrics, JSTOR, p. 1527–1537, 1997.
HUANG, C. et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, Elsevier, v. 395, n. 10223, p. 497–506, 2020.
JOOB, B.; WIWANITKIT, V. COVID-19 in rural non-resort town: Observation from thailand.The Australian Journal of Rural Health, Wiley-Blackwell, v. 28,n. 2, p. 184, 2020.
KANDEL, N. et al. Health security capacities in the context of COVID-19 outbreak: An analysis of International Health Regulations annual report data from 182 countries.The Lancet, Elsevier, 2020.
LAI, C.-C. et al. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): the epidemic and the challenges. International Journal of Antimicrobial Agents, Elsevier, 2020.
LAI, C.-C. et al. Global epidemiology of coronavirus disease 2019: disease incidence, daily cumulative index, mortality, and their association with country healthcare resources and economic status. International journal of antimicrobial agents, Elsevier, p. 105946, 2020.
LAM, T. T.-Y. et al. Identifying SARS-CoV-2 related coronaviruses in Malayan pangolins. Nature, Nature Publishing Group, p. 1–6, 2020.
LAN, J. et al. Structure of the SARS-CoV-2 spike receptor-binding domain boundto the ACE2 receptor. Nature, Nature Publishing Group, p. 1–9, 2020.
LARSEN, K. et al. Interpreting parameters in the logistic regression model with random effects. Biometrics, Wiley Online Library, v. 56, n. 3, p. 909–914, 2000.
LI, J.-Y. et al. The epidemic of 2019-novel-coronavirus (2019-nCoV) pneumonia and insights for emerging infectious diseases in the future. Microbes and Infection,Elsevier, v. 22, n. 2, p. 80–85, 2020.
LU, R. et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. The Lancet, Elsevier, v. 395, n.10224, p. 565–574, 2020.
LUMU, I. COVID-19 response in Sub-Saharan Africa: Lessons from Uganda. Disaster medicine and public health preparedness, Disaster Med Public HealthPrep, p. 1–5, 2020.
LUPIA, T. et al. 2019-novel coronavirus outbreak: A new challenge. Journal ofGlobal Antimicrobial Resistance, Elsevier, 2020.
NEPOMUCENO, M. R. et al. Besides population age structure, health and other demographic factors can contribute to understanding the COVID-19 burden. Proceedings of the National Academy of Sciences, National Acad Sciences, v. 117,n. 25, p. 13881–13883, 2020.
OHIA, C.; BAKAREY, A. S.; AHMAD, T. COVID-19 and Nigeria: Putting therealities in context. International Journal of Infectious Diseases, Elsevier, 2020.
ONGOLE, J. et al. Sustaining essential healthcare in Africa during the COVID-19 pandemic. 2020.
OSSENI, I. A. COVID-19 pandemic in Sub-Saharan Africa: Preparedness, response, and hidden potentials. Tropical Medicine and Health, BioMed Central,v. 48, n. 1, p. 1–3, 2020.
PUNG, R. et al. Investigation of three clusters of COVID-19 in Singapore: Implications for surveillance and response measures. The Lancet, Elsevier, 2020.
RACIBORSKI, F. et al. Dynamics of COVID-19 outbreak in Poland: An epidemiological analysis of the first two months of the epidemic. Polish Archives ofInternal Medicine, 2020.
RATKOWSKY, D. Nonlinear regression modelling. New York: Marcel Dekker,1983.
RITCHIE, H. et al. Cumulative confirmed covid-19 cases per million vs. gdp percapita. https://wjct.org/coronavirus-gdp-per-capita/, 2020.
ROLLSTON, R.; GALEA, S.COVID-19 and the Social Determinants of Health.[S.l.]: SAGE Publications Sage CA: Los Angeles, CA, 2020.
SEBER, G. A. F.; WILD, C. J. Nonlinear regression. New Jersey: John Wiley &Sons, 2003.
SHEREEN, M. A. et al. COVID-19 infection: Origin, transmission, and characteristics of human corona viruses. Journal of Advanced Research, Elsevier,2020.
SPIEGELHALTER, D. et al. WinBUGS user manual, version 1.4 MRCBiostatistics Unit. Cambridge, UK, 2003.
SRIWIJITALAI, W.; WIWANITKIT, V. COVID-19 in a remote village in atropical forest-a note.Rural and remote health, Rural Remote Health, v. 20, n. 3,p. 6014, 2020.
UNICEF. Child mortality and COVID-19.https://data.unicef.org/topic/child-survival/covid-19/, 2020.
World Health Organization. Coronavirus disease 2019: Pandemic. [S.l.]: WorldHealth Organization, 2020.
WU, A. et al. Genome composition and divergence of the novel coronavirus (2019-nCoV) originating in China. Cell Host & Microbe, Elsevier, 2020.
YUAN, J. et al. Monitoring transmissibility and mortality of COVID-19 in Europe. International Journal of Infectious Diseases, Elsevier, 2020