Interpreting, analysing and modelling COVID-19 mortality data

We present results on the mortality statistics of the COVID-19 epidemic in a number of countries. Our data analysis suggests classifying countries in five groups, (1) Western countries, (2) East Block, (3) developed Southeast Asian countries, (4) Northern Hemisphere developing countries and (5) Southern Hemisphere countries. Comparing the number of deaths per million inhabitants, a pattern emerges in which the Western countries exhibit the largest mortality rate. Furthermore, comparing the running cumulative death tolls as the same level of outbreak progress in different countries reveals several subgroups within the Western countries and further emphasises the difference between the five groups. Analysing the relationship between deaths per million and life expectancy in different countries, taken as a proxy of the preponderance of elderly people in the population, a main reason behind the relatively more severe COVID-19 epidemic in the Western countries is found to be their larger population of elderly people, with exceptions such as Norway and Japan, for which other factors seem to dominate. Our comparison between countries at the same level of outbreak progress allows us to identify and quantify a measure of efficiency of the level of stringency of confinement measures. We find that increasing the stringency from 20 to 60 decreases the death count by about 50 lives per million in a time window of 20 days. Finally, we perform logistic equation analyses of deaths as a means of tracking the dynamics of outbreaks in the “first wave” and estimating the associated ultimate mortality, using four different models to identify model error and robustness of results. This quantitative analysis allows us to assess the outbreak progress in different countries, differentiating between those that are at a quite advanced stage and close to the end of the epidemic from those that are still in the middle of it. This raises many questions in terms of organisation, preparedness, governance structure and so on. Electronic supplementary material The online version of this article (10.1007/s11071-020-05966-z) contains supplementary material, which is available to authorized users.

[1]  B. Canard,et al.  The spike glycoprotein of the new coronavirus 2019-nCoV contains a furin-like cleavage site absent in CoV of the same clade , 2020, Antiviral Research.

[2]  Yan Zhao,et al.  Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. , 2020, JAMA.

[3]  Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China , 2020, Nature Medicine.

[4]  Didier Sornette,et al.  Predictability of catastrophic events: Material rupture, earthquakes, turbulence, financial crashes, and human birth , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Matthew S. Miller,et al.  Age-Specific Mortality During the 1918 Influenza Pandemic: Unravelling the Mystery of High Young Adult Mortality , 2013, PloS one.

[6]  Min Kang,et al.  SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients , 2020, The New England journal of medicine.

[7]  D. Mathieu,et al.  High Prevalence of Obesity in Severe Acute Respiratory Syndrome Coronavirus‐2 (SARS‐CoV‐2) Requiring Invasive Mechanical Ventilation , 2020, Obesity.

[8]  P. Vollmar,et al.  Transmission of 2019-nCoV Infection from an Asymptomatic Contact in Germany , 2020, The New England journal of medicine.

[9]  Gerardo Chowell,et al.  Global Mortality Impact of the 1957–1959 Influenza Pandemic , 2016, The Journal of infectious diseases.

[10]  Lei Liu,et al.  Obesity and COVID-19 Severity in a Designated Hospital in Shenzhen, China , 2020, Diabetes Care.

[11]  M. Nöthen,et al.  Infection fatality rate of SARS-CoV-2 infection in a German community with a super-spreading event , 2020 .

[12]  J. Xiang,et al.  Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study , 2020, The Lancet.

[13]  Minah Park,et al.  A Systematic Review of COVID-19 Epidemiology Based on Current Evidence , 2020, Journal of clinical medicine.

[14]  Chonggang Xu,et al.  High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2 , 2020, Emerging infectious diseases.

[15]  Carl A. B. Pearson,et al.  The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study , 2020, The Lancet Public Health.

[16]  Caetano Souto-Maior,et al.  Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold , 2020, medRxiv.

[17]  D. Sornette,et al.  Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world , 2020, medRxiv.

[18]  Eun Ji Kim,et al.  Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. , 2020, JAMA.

[19]  Big hopes for big data , 2020, Nature Medicine.

[20]  R. Busse,et al.  Regulating entrepreneurial behaviour in European health care systems , 2002 .

[21]  A. Walls,et al.  Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein , 2020, Cell.

[22]  P. Brennan,et al.  Susceptibility-adjusted herd immunity threshold model and potential R0 distribution fitting the observed Covid-19 data in Stockholm , 2020, medRxiv.

[23]  S. Bhatt,et al.  Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries , 2020 .

[24]  E. Lau,et al.  Effect of changing case definitions for COVID-19 on the epidemic curve and transmission parameters in mainland China: a modelling study , 2020, The Lancet Public Health.

[25]  W. Ko,et al.  Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges , 2020, International Journal of Antimicrobial Agents.

[26]  Navot Israeli,et al.  Computational irreducibility and the predictability of complex physical systems. , 2003, Physical review letters.

[27]  N. Banholzer,et al.  Impact of non-pharmaceutical interventions on documented cases of COVID-19 , 2020, medRxiv.

[28]  Shaojun Dai,et al.  A high-quality genome sequence of alkaligrass provides insights into halophyte stress tolerance , 2020, Science China Life Sciences.

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

[30]  W. Wei,et al.  Presymptomatic Transmission of SARS-CoV-2 — Singapore, January 23–March 16, 2020 , 2020, MMWR. Morbidity and mortality weekly report.

[31]  Juan B. Gutierrez,et al.  A Model Describing COVID-19 Community Transmission Taking into Account Asymptomatic Carriers and Risk Mitigation , 2020 .

[32]  A nicotinic hypothesis for Covid-19 withpreventive and therapeutic implications , 2020 .

[33]  S. Kitayama,et al.  Mandated Bacillus Calmette-Guérin (BCG) vaccination predicts flattened curves for the spread of COVID-19 , 2020, Science Advances.

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

[35]  W. Gardner,et al.  The Coronavirus and the Risks to the Elderly in Long-Term Care , 2020, Journal of aging & social policy.

[36]  Anna Stachel,et al.  Obesity in patients younger than 60 years is a risk factor for Covid-19 hospital admission , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[37]  Hannah R. Meredith,et al.  The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application , 2020, Annals of Internal Medicine.

[38]  K. Bhaskaran,et al.  OpenSAFELY: factors associated with COVID-19 death in 17 million patients , 2020, Nature.

[39]  G. Chowell,et al.  The COVID-19 pandemic in the USA: what might we expect? , 2020, The Lancet.

[40]  T. Meunier Full lockdown policies in Western Europe countries have no evident impacts on the COVID-19 epidemic. , 2020, medRxiv.

[41]  Holger Moch,et al.  Endothelial cell infection and endotheliitis in COVID-19 , 2020, The Lancet.

[42]  K. Yuen,et al.  Clinical Characteristics of Coronavirus Disease 2019 in China , 2020, The New England journal of medicine.

[43]  Amber C. Donahue,et al.  Avelumab plus axitinib versus sunitinib in advanced renal cell carcinoma: biomarker analysis of the phase 3 JAVELIN Renal 101 trial , 2020, Nature Medicine.

[44]  L. D. Prins,et al.  Original Paper: Home visits in Belgium: a multivariate analysis , 1999 .

[45]  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.

[46]  A nicotinic hypothesis for Covid-19 with preventive and therapeutic implications. , 2020, Comptes rendus biologies.

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

[48]  S. Kirov Association Between BCG Policy is Significantly Confounded by Age and is Unlikely to Alter Infection or Mortality Rates , 2020, medRxiv.

[49]  D. Sornette Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder: Concepts and Tools , 2000 .

[50]  Jian-ming Wang,et al.  Clinical characteristics of 24 asymptomatic infections with COVID-19 screened among close contacts in Nanjing, China , 2020, Science China Life Sciences.

[51]  R. Szigeti,et al.  BCG protects against COVID-19? A word of caution , 2020, medRxiv.

[52]  A. Heald,et al.  LIVING WITH COVID-19: BALANCING COSTS AGAINST BENEFITS IN THE FACE OF THE VIRUS , 2020, National Institute Economic Review.

[53]  J. Low,et al.  Epidemiologic Features and Clinical Course of Patients Infected With SARS-CoV-2 in Singapore. , 2020, JAMA.

[54]  Yhu-Chering Huang,et al.  Are children less susceptible to COVID-19? , 2020, Journal of Microbiology, Immunology and Infection.

[55]  D. Wang,et al.  The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status , 2020, Military Medical Research.

[56]  R. Neher,et al.  Potential impact of seasonal forcing on a SARS-CoV-2 pandemic , 2020, medRxiv.

[57]  Debashree Ray,et al.  Differential COVID-19-attributable mortality and BCG vaccine use in countries , 2020, medRxiv.

[58]  W. Dietz,et al.  Obesity and its Implications for COVID‐19 Mortality , 2020, Obesity.

[59]  K. Leder,et al.  Zika among international travelers presenting to GeoSentinel sites, 2012-2019: implications for clinical practice. , 2020, Journal of travel medicine.

[60]  Johannes Zierenberg,et al.  Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions , 2020, Science.

[61]  E. Holmes,et al.  The proximal origin of SARS-CoV-2 , 2020, Nature Medicine.

[62]  S. Cauchemez,et al.  Estimates of the reproduction number for seasonal, pandemic, and zoonotic influenza: a systematic review of the literature , 2014, BMC Infectious Diseases.

[63]  S. Blower,et al.  Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1) , 2009, BMC medicine.

[64]  Jacob B. Aguilar,et al.  Investigating the Impact of Asymptomatic Carriers on COVID-19 Transmission , 2020, medRxiv.

[65]  C. Vardavas,et al.  COVID-19 and smoking: A systematic review of the evidence , 2020, Tobacco induced diseases.