A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: Simulating control scenarios and multi-scale epidemics

Abstract

After the spread of the SARS-CoV-2 epidemic out of China, evolution in the pandemic worldwide shows dramatic differences among countries. In Europe, the situation of Italy first and later Spain has generated great concen, and despite other countries show better prospects, large uncertainties yet remain on the future evolution and the e, or attack strategies. Here we applied a modified SEIR compartmental model accounting for the spread of infection during the latent period, in which we also incorporate effects of varying proportions of containment. We fit data to reported infected populations at the beginning of the first peak of the pandemic to account for the uncertainties in case reporting and study the scenario projections for the individual regions (CCAA). The aim of this model it’s to evaluate the confinement rate at the first stages of the epidemic outbreak in order to evaluate the scenarios that minimize the incidence but also the mortality. Results indicate that with data for March 23, the epidemics follow an evolution similar to the isolation of percent of the population, and if there were no effects of intervention actions it might reach a maximum of over infected around April 27. The effect on the epidemics of the ongoing partial confinement measures is yet unknown (an update of results with data until March 31st is included), but increasing the isolation around ten times more could drastically reduce the peak to over cases by early April, while each day of delay in taking this hard containment scenario represents a 90 percent increase of the infected population at the peak. Dynamics at the sub aggregated levels of CCAA show epidemics at the different levels of progression with the most worrying situation in Madrid and Catalonia. Increasing alpha values up to 10 times, in addition to a drastic reduction in clinical cases, would also more than half the number of deaths. Updates for March 31st simulations indicate a substantial reduction in burden is underway. A similar approach conducted for Italy pre-and post-intervention also begins to suggest a substantial reduction in both infected and deaths has been achieved, showing the efficacy of drastic social distancing interventions. At last we show the real evolution of the pandemic up to the end of May and the beginning of July in order to calculate the real confinement rate from data to compare with the scenarios formulated at March.

Control strategies against COVID-19 in China: Significance of effective testing in the long run

“…). The initial ranges for the infection periods D in /ref is set as [2,5], since detection and treatment of infected patients may be reduce their infectious period, so we extend the range of the average duration of infection D to 1-5 days, where The average duration of infection for tested infected patients D T t is 1-3 days and the average duration of infection for untested infected patients D NT t equal to 3-5 days. Cities in Hubei published confirm case including clinical confirm cases from 2.13, which counted patients who met clinical criteria through chest imaging and may not have had epidemiological links or a positive PCR test Confirmed cases in Wuhan was more than 12 times higher than that in the previous day, so the reported rate in Wuhan may be different from other cities and we create a multiplication factor μ Wuhan for Wuhan and testing rate in Wuhan equal to μ Wuhan ×α.…”

“…Drawing lessons from China’s COVID-19 experiences can be essential for policy-makers to take effective measures to stop the epidemic from continuing indefinitely. Previous research highlight the effectiveness of social distancing interventions and discuss about the the need for lockdowns [ 1 , 2 ]. Our study aimed to quantitatively evaluate the contribution of three types of policies in the successful containment of COVID-19: city lockdown that aims at reducing within-city contact, cross-city travel restrictions, and an effective way of testing and isolating infected persons.…”

Who Should Get Vaccinated First? An Effective Network Information-Driven Priority Vaccination Strategy

“…We adopt a network-based SEIR model [8] to describe the evolution and behavior of COVID-19 and contagion processes in a network. The SEIR model has been widely used in modeling dynamical behavior of pandemic and epidemic spreading [19,20,21]. Here, the network-based SEIR model differs from other models that describe the epidemic spreading dynamics by a set of differential equations [9] and generate the numbers of individuals in various states as functions of time.…”

A Review of Spatiotemporal Models for Count Data in R Packages. A Case Study of COVID-19 Data

“…The susceptible-infected-recovered (SIR) models developed by Kermack and McKendrick [14] were the first mathematical models developed to study the transmission dynamics of infectious diseases. The SIR and SEIR (Susceptible, Exposed, Infectious, and Removed) models have been improved and used for analyzing and characterizing the COVID-19 epidemic [15][16][17], as well, in Spain [18][19][20]. In addition to the crucial role played by the above described epidemic models, other models are also being adapted to examine different aspects of the COVID-19 pandemic [21,22].…”