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

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 $$ \lambda $$ 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.

The interplay between subcritical fluctuations and import: understanding COVID-19 epidemiology dynamics

“…These classes are recorded as incidences of cumulative cases C , giving a SHARUCD modelling framework to be compared with the empirical data at hand. For complete descriptions of this modelling framework and its application to COVID-19 epidemic in the Basque Country, see [1, 2, 4, 5].…”

“…The SHARUCD modeling framework developed within the Basque Modeling Task Force (BMTF) to assist the Basque Health managers and the Basque Government during the COVID-19 responses is an extension of the basic epidemiological Susceptible-Infected-Recovered (SIR-type) models, and was able to describe the COVID-19 epidemic in terms of disease spreading and control, providing projections on the national health systems necessities during the first wave of the pandemic. The model was then refined to describe the disease transmission during the country lockdown [1, 2, 4] and is used, up to date, to monitor disease dynamics after social distancing measures started to be lifted (from May 4 - phase 0 and from May 11 -phase 1 towards the “new normality”) [5].…”

Epidemic Progression and Vaccination in a Heterogeneous Population. Application to the Covid-19 epidemic

“…In the beginning of epidemic, observed exponential growth of the number of infected individuals can be described for any population structure [5, 10, 24, 25]. The heterogeneity of the population becomes important at the later stages of the epidemic development when it deviates from the exponential growth and approaches the stage of collective immunity and when it decays approaching the final time, defined as time when the number of infected individuals becomes less than 1.…”

“…Covid-19 epidemic has stimulated an unprecedented interest to the epidemiological models, mostly, compartmental ODE models. There are numerous recent works devoted to fitting the available data, calculating the basic reproduction number, and making predictions about the further epidemic progression (see [1, 2, 3, 4, 5, 6, 7] and the references therein). These models give a good description of the evolution of the number of infected individuals and the sizes of other classes involved with the epidemiological models in the beginning of epidemic, and they take into account the influence of the measures of social distancing and some other meausres to prevent the rapid epidemic spread.…”

An Iterative Model of The COVID-19 Virus Spread Based on Population Density - A Case Study of Poland

“…Considering potential threats, recent research projects demonstrate that the health care system may collapse four weeks after the outbreak of coronavirus epidemic, and this involves an uncontrolled increase in infections and a dramatic growth in mortality rate. An example is provided by the growing problems of the health care system in the United States 38 and the collapse of the health care system in Italian Lombardy and in Spain 39 . At present, there is no reason to argue that without the measures aimed to maintain social distance (masks, physical distance, limited mobility) and without improved immunity acquired by vaccination the spread of disease will be contained and will not cause a dramatic growth in incidence and in fatal cases.…”