Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy

Antonelli, Erminia; Piccolomini, Elena Loli; Zama, Fabiana

doi:10.1101/2021.06.21.21259230

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Two broad types of modeling approaches have been used for COVID-19 modeling so far. Traditional compartmental models with different variants have been used for forecasting and to evaluate the role of various interventions (or lack thereof) in multiple countries including Bangladesh, Italy, Egypt, Japan, Belgium, Nigeria, Germany and among specific populations (e.g., [2], [3], [4]). These models, however, are limited in their ability to capture the stochastic effects and complex interactions among and behaviour of the underlying entities and environment of the model.…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Model for COVID-19 in Bangladesh

Farheen

Shamil

Jony

et al. 2022

Preprint

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Background: The COVID-19 pandemic, that has resulted in millions of deaths and hundreds of millions of cases worldwide, continues to affect the lives, health and economy of various countries including Bangladesh. Despite the high proportion of asymptomatic cases and relatively low mortality, the virus's spread had been a significant public health problem for densely populated Bangladesh. With the healthcare system at stress, understanding the disease dynamics in the unique Bangladesh context became essential to guide policy decisions. Methods: With a goal to capture the COVID-19 disease dynamics, we developed two stochastic Agent-Based Models (ABMs) considering the key characteristics of COVID-19 in Bangladesh, which vastly differ from the developed countries. We have implemented our ABMs extending the popular (but often inadequate) SIR model, where the infected population is sub-divided into Asymptomatic, Mild Symptomatic and Severe Symptomatic populations. One crucial issue in Bangladesh is the lack of enough COVID-19 tests as well as unwillingness of people to do the tests resulting in much less number of official positive cases than the actual reality. Although not directly relevant to the epidemiological process, our model attempts to capture this crucial aspect while calibrating against official daily test-positive cases. Our first model, ABM-BD, divides the population into age-groups that interact among themselves based on an aggregated Contact Matrix. Thus ABM-BD considers aggregate agents and avoids direct agent level interactions as the number of agents are prohibitively large in our context. We also implement a scaled down model, ABM-SD, that is capable of simulating agent level interactions. Results: ABM-BD was quite well-calibrated for Dhaka: the Mean Absolute Percentage Error (MAPE) between official and forecasted cases was 1.845 approximately during the period between April 4, 2020 and March 31, 2021. After an initial model validation, we conducted a number of experiments - including retrospective scenario analysis, and hypothetical future scenario analysis. For example, ABM-BD has demonstrated the trade off between a strict lockdown with low infections and a relaxed lockdown with reduced burden on the economy. Leveraging the true agent level interaction capability of ABD-SD, we have also successfully analyzed the relative severity of different strains thereby (confidently) capturing the effect of different virus mutations. Conclusions: Our models have adequately captured the COVID-19 disease transmission dynamics in Bangladesh. This is a useful tool to forecast the impact of interventions to assist policymakers in planning appropriate COVID response. Our models will be particularly useful in a resource constrained setting in countries like Bangladesh where the population size is huge.

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Section: Introductionmentioning

confidence: 99%

An Agent-Based Model for COVID-19 in Bangladesh

Farheen

Shamil

Jony

et al. 2022

Preprint

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“…To model the effect of vaccination on this cyclical behavior of COVID-19 we are using SEIRD (Susceptible-Exposed-Infectious-Recovered-Dead) model. [20][21][22][23] Our main motivation to study is to look at the possibility of the resurgence of the third wave by studying the interplay between the infection rate and the vaccination rate. In this model, one of the important assumptions is that the infection rate (β(t)) is oscillatory.…”

Section: Introductionmentioning

confidence: 99%

A robust prediction from a minimal model of COVID-19 -- Can we avoid the third wave?

Chowdhury,

Roychowdhury,

Chaudhuri

2021

Preprint

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COVID-19 pandemic is one of the major disasters that humanity has ever faced. In this paper, we try to model the effect of vaccination in controlling the pandemic, particularly in context to the third wave which is predicted to hit globally. Here we have modified the SEIRD model by introducing a vaccination term. One of our main assumptions is that the infection rate (β(t)) is oscillatory. This oscillatory nature has been discussed earlier in literature with reference to the seasonality of epidemics. However, in our case we invoke this nature of the infection rate (β(t)) to model the cyclical behavior of the COVID-19 pandemic within a short period. This study focuses on a minimalistic approach where we have logically deduced that the infection rate (β(t)) and the vaccination rate (λ) are the most important parameters while the other parameters can be assumed to be constants throughout the simulation. Finally, we have studied the rich interplay between the infection rate (β(t)) and the vaccination rate (λ) on the infectious cases of COVID-19 and made some robust conclusions regarding the global behavior of this pandemic in near future.

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Switched forced SEIRDV compartmental models to monitor COVID-19 spread and immunization in Italy

Cited by 2 publications

References 25 publications

An Agent-Based Model for COVID-19 in Bangladesh

An Agent-Based Model for COVID-19 in Bangladesh

A robust prediction from a minimal model of COVID-19 -- Can we avoid the third wave?

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