COVID-19 pandemics modeling with modified determinist SEIR, social distancing, and age stratification. The effect of vertical confinement and release in Brazil

Abstract

The ongoing COVID-19 epidemics poses a particular challenge to low and middle income countries, making some of them consider the strategy of "vertical confinement". In this strategy, contact is reduced only to specific groups (e.g. age groups) that are at increased risk of severe disease following SARS-CoV-2 infection. We aim to assess the feasibility of this scenario as an exit strategy for the current lockdown in terms of its ability to keep the number of cases under the health care system capacity. We developed a modified SEIR model, including confinement, asymptomatic transmission, quarantine and hospitalization. The population is subdivided into 9 age groups, resulting in a system of 72 coupled nonlinear differential equations. The rate of transmission is dynamic and derived from the observed delayed fatality rate; the parameters of the epidemics are derived with a Markov chain Monte Carlo algorithm. We used Brazil as an example of middle income country, but the results are easily generalizable to other countries considering a similar strategy. We find that starting from 60% horizontal confinement, an exit strategy on May 1st of confinement of individuals older than 60 years old and full release of the younger population results in 400 000 hospitalizations, 50 000 ICU cases, and 120 000 deaths in the 50-60 years old age group alone. Sensitivity analysis shows the 95% confidence interval brackets a order of magnitude in cases or three weeks in time. The health care system avoids collapse if the 50-60 years old are also confined, but our model assumes an idealized lockdown where the confined are perfectly insulated from contamination, so our numbers are a conservative lower bound. Our results discourage confinement by age as an exit strategy.

The Bayesian Susceptible-Exposed-Infected-Recovered model for the outbreak of COVID-19 on the Diamond Princess Cruise Ship

“…Although the conventional deterministic approach by fitting the compartment model with the ordinal differential equation (ODE) system to the observed number of reported cases can shed light on the dynamic of COVID-19 transmission and R 0 on population level (Lyra et al 2020 ), such an approach may not be appropriate for assessing the outbreak occurring on the cruise ship with modest size of susceptibles particularly when the uncertainty of parameters needs to be considered. As far as the conventional maximum likelihood estimate (MLE) method is concerned, it may involve the identifiability problem while estimating multiple parameters such as transmission coefficient, recovery rate, and incubation period in this study.…”

A model based on cellular automata to estimate the social isolation impact on COVID-19 spreading in Brazil

“…Regarding what we know so far about COVID-19, Susceptible-Exposed-Infected-Removed (SEIR) models have been used for predictions and analysis [19] , [20] , [21] , [28] , [53] , [61] . In this model, susceptible individuals become exposed when contacting infected, and after an incubation period, exposed individuals become infected.…”

“…Also, COVID-19 requires different approaches for the infected state due to different outcomes for the disease case-by-case. Other works also consider parameters for externalities of the epidemiological model (such as the parameters to be presented here which are related to the healthcare system dynamic) [3] , [28] , [39] , [61] .…”

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