From a single host to global spread. The global mobility based modelling of the COVID-19 pandemic implies higher infection and lower detection rates than current estimates.

Background: Since the outbreak of the COVID-19 pandemic, multiple efforts of modelling of the geo-temporal transmissibility of the virus have been undertaken, but none succeeded in describing the pandemic at the global level. We propose a set of parameters for the first COVID-19 Global Epidemic and Mobility Model (GLEaM). The simulation starting with just a single pre-symptomatic, yet infectious, case in Wuhan, China, results in an accurate prediction of the number of diagnosed cases after 125 days in multiple countries across three continents. Methods: We have built a modified SIR model and parameterized it analytically, according to the literature and by fitting the missing parameters to the observed dynamics of the virus spread. We compared our results with the number of diagnosed cases in sixeight selected countries which provide reliable statistics but differ substantially in terms of strength and speed of undertaken precautions. The obtained 95% confidence intervals for the predictions fit well to the empirical data. Findings: The parameters that successfully model the pandemic are: the basic reproduction number R0, ~4.4; a latent non-infectious period of 1.1. days followed by 4.6 days of the presymptomatic infectious period; the probability of developing severe symptoms, 0.01; the probability of being diagnosed when presenting severe symptoms of 0.6; the probability of diagnosis for cases with mild symptoms or asymptomatic, 0.001. Also, the higher the testing rate per country, the lower the discrepancy between data (diagnosed cases) and model. Interpretation: Parameters that successfully reproduce the observed number of cases indicate that both R0 and the prevalence of the virus might be underestimated. This is in concordance with the newest research on undocumented COVID-19 cases. Consequently, the actual mortality rate is putatively lower than estimated. Confirmation of the pandemic characteristic by further refinement of the model and screening tests is crucial for developing an effective strategy for the global epidemiological crisis.

[1]  Yongli Cai,et al.  A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action , 2020, International Journal of Infectious Diseases.

[2]  Z. Tong,et al.  Potential Presymptomatic Transmission of SARS-CoV-2, Zhejiang Province, China, 2020 , 2020, Emerging infectious diseases.

[4]  S. Cui,et al.  Potential false-negative nucleic acid testing results for Severe Acute Respiratory Syndrome Coronavirus 2 from thermal inactivation of samples with low viral loads , 2020, Clinical chemistry.

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

[6]  Miyo Ota Will we see protection or reinfection in COVID-19? , 2020, Nature Reviews Immunology.

[7]  Ying Xuan Chen,et al.  2019 Novel coronavirus infection and gastrointestinal tract , 2020, Journal of digestive diseases.

[8]  J. Wallinga,et al.  Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures , 2004, American journal of epidemiology.

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

[10]  Anne Kimball,et al.  Asymptomatic and Presymptomatic SARS-CoV-2 Infections in Residents of a Long-Term Care Skilled Nursing Facility — King County, Washington, March 2020 , 2020, MMWR. Morbidity and mortality weekly report.

[11]  G. Remuzzi,et al.  COVID-19 and Italy: what next? , 2020, The Lancet.

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

[13]  Ruiyun Li,et al.  Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2) , 2020, Science.

[14]  G. Leung,et al.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study , 2020, The Lancet.

[15]  M. Baguelin,et al.  Report 3: Transmissibility of 2019-nCoV , 2020 .

[16]  M. Day Covid-19: identifying and isolating asymptomatic people helped eliminate virus in Italian village , 2020, BMJ.

[17]  E. Lavezzo,et al.  Suppression of COVID-19 outbreak in the municipality of Vo, Italy , 2020, medRxiv.

[18]  S. Zhang,et al.  Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis , 2020, International Journal of Infectious Diseases.

[19]  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.

[20]  Tobias Bleicker,et al.  Clinical presentation and virological assessment of hospitalized cases of coronavirus disease 2019 in a travel-associated transmission cluster , 2020, medRxiv.

[21]  B. Foley,et al.  Evolutionary history, potential intermediate animal host, and cross‐species analyses of SARS‐CoV‐2 , 2020, Journal of medical virology.

[22]  P. Vollmar,et al.  Virological assessment of hospitalized cases of coronavirus disease 2019 , 2020 .

[23]  Benjamin J. Cowling,et al.  Impact assessment of non-pharmaceutical interventions against COVID-19 and influenza in Hong Kong: an observational study , 2020, medRxiv.

[24]  Alessandro Vespignani,et al.  The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale , 2011, BMC infectious diseases.

[25]  Zhongyi Jiang,et al.  Epidemiology of COVID-19 Among Children in China , 2020, Pediatrics.

[26]  Christel Faes,et al.  Estimating the generation interval for COVID-19 based on symptom onset data , 2020, medRxiv.

[27]  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.

[28]  Kenneth D Mandl,et al.  Early Transmissibility Assessment of a Novel Coronavirus in Wuhan, China. , 2020, SSRN.

[29]  Tian-mu Chen,et al.  A mathematical model for simulating the phase-based transmissibility of a novel coronavirus , 2020, Infectious Diseases of Poverty.

[30]  Alessandro Vespignani,et al.  Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm , 2012, BMC Medicine.

[31]  Alessandro Vespignani,et al.  Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model , 2010, J. Comput. Sci..

[32]  Dena Goffman,et al.  Universal Screening for SARS-CoV-2 in Women Admitted for Delivery , 2020, The New England journal of medicine.

[33]  Christl A. Donnelly,et al.  Estimates of the severity of coronavirus disease 2019: a model-based analysis , 2020, The Lancet Infectious Diseases.

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

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

[36]  Jing-Yuan Fang,et al.  Novel coronavirus infection and gastrointestinal tract (vol 21, pg 125, 2020) , 2020 .

[37]  L. Yang,et al.  Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak , 2020, bioRxiv.

[38]  N. Linton,et al.  Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19) , 2020, International Journal of Infectious Diseases.

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

[40]  P. Klepac,et al.  Early dynamics of transmission and control of COVID-19: a mathematical modelling study , 2020, The Lancet Infectious Diseases.