About: 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.

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About: 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. Goto Sponge NotDistinct Permalink

An Entity of Type : fabio:Abstract, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	abstract
value	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.
Subject	COVID-19 Publishing Academic publishing Grey literature Publications by format Scientific documents Infectious disease blood tests Open-access archives Medical responses to the COVID-19 pandemic Eprint archives
part of	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.
is abstract of	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.
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