In March 2020, a multidisciplinary task force (so-called Basque Modelling Task Force, BMTF) was created to assist the Basque health managers and Government during the COVID-19 responses. BMTF is a modelling team, working on different approaches, including stochastic processes, statistical methods and artificial intelligence. Here we describe the efforts and challenges to develop a flexible modeling framework able to describe the dynamics observed for the tested positive cases, including the modelling development steps. The results obtained by a new stochastic SHARUCD model framework are presented. Our models differentiate mild and asymptomatic from severe infections prone to be hospitalized and were able to predict the course of the epidemic, providing important projections on the national health system’s necessities during the increased population demand on hospital admissions. Short and longer-term predictions were tested with good results adjusted to the available epidemiological data. We have shown that the partial lockdown measures were effective and enough to slow down disease transmission in the Basque Country. The growth rate λ was calculated from the model and from the data and the implications for the reproduction ratio r are shown. The analysis of the growth rates from the data led to improved model versions describing after the exponential phase also the new information obtained during the phase of response to the control measures. This framework is now being used to monitor disease transmission while the country lockdown was gradually lifted, with insights to specific programs for a general policy of “social distancing” and home quarantining.

Modelling COVID 19 in the Basque Country from introduction to control measure response / Aguiar, Maíra; Millán Ortuondo, Eduardo; Bidaurrazaga Van-Dierdonck, Joseba; Mar, Javier; Stollenwerk, Nico. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - ELETTRONICO. - 10:1(2020), pp. 1730601-1730616. [10.1038/s41598-020-74386-1]

Modelling COVID 19 in the Basque Country from introduction to control measure response

Aguiar, Maíra;
2020

Abstract

In March 2020, a multidisciplinary task force (so-called Basque Modelling Task Force, BMTF) was created to assist the Basque health managers and Government during the COVID-19 responses. BMTF is a modelling team, working on different approaches, including stochastic processes, statistical methods and artificial intelligence. Here we describe the efforts and challenges to develop a flexible modeling framework able to describe the dynamics observed for the tested positive cases, including the modelling development steps. The results obtained by a new stochastic SHARUCD model framework are presented. Our models differentiate mild and asymptomatic from severe infections prone to be hospitalized and were able to predict the course of the epidemic, providing important projections on the national health system’s necessities during the increased population demand on hospital admissions. Short and longer-term predictions were tested with good results adjusted to the available epidemiological data. We have shown that the partial lockdown measures were effective and enough to slow down disease transmission in the Basque Country. The growth rate λ was calculated from the model and from the data and the implications for the reproduction ratio r are shown. The analysis of the growth rates from the data led to improved model versions describing after the exponential phase also the new information obtained during the phase of response to the control measures. This framework is now being used to monitor disease transmission while the country lockdown was gradually lifted, with insights to specific programs for a general policy of “social distancing” and home quarantining.
1
Aguiar, Maíra; Millán Ortuondo, Eduardo; Bidaurrazaga Van-Dierdonck, Joseba; Mar, Javier; Stollenwerk, Nico
Modelling COVID 19 in the Basque Country from introduction to control measure response / Aguiar, Maíra; Millán Ortuondo, Eduardo; Bidaurrazaga Van-Dierdonck, Joseba; Mar, Javier; Stollenwerk, Nico. - In: SCIENTIFIC REPORTS. - ISSN 2045-2322. - ELETTRONICO. - 10:1(2020), pp. 1730601-1730616. [10.1038/s41598-020-74386-1]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/292075
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