A novel compartmental model to capture the nonlinear trend of COVID-19

Ramezani, Somayeh Bakhtiari; Amirlatifi, Amin; Rahimi, Shahram

doi:10.1016/j.compbiomed.2021.104421

Cited by 24 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To account for inhomogeneous mixing in the population, reinfection due to poor immune response or immunity loss, specific social behaviors, or governmental policies that can change the infection dynamics, several groups work with modified SIRD/SEIRD models [16][17][18]. However, adding more compartments may drastically increase the number of parameters to be fitted in the model.…”

Section: Motivation and Objectivesmentioning

confidence: 99%

“…However, adding more compartments may drastically increase the number of parameters to be fitted in the model. For instance, Ramezani et al [17] implements a modified SEIRD model to account for asymptomatic patients and individuals who self isolate (SEARIDQ model), which uses a total of 14 parameters. Such an increase in the number of parameters also increases the chances of falling into a non-identifiable model, using the same dataset [19], given the same number of curves to be fitted.…”

Section: Motivation and Objectivesmentioning

confidence: 99%

See 1 more Smart Citation

Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting

Valeriano¹,

Cintra²,

Libotte³

et al. 2022

Preprint

View full text Add to dashboard Cite

The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an Approximate Bayesian Computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting.

show abstract

Section: Motivation and Objectivesmentioning

confidence: 99%

Section: Motivation and Objectivesmentioning

confidence: 99%

Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting

Valeriano¹,

Cintra²,

Libotte³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Researchers have used different epidemiological models to understand the dynamics of the Coronavirus disease 2019 (COVID-19) since it was first reported in Wuhan city of China [1]. The main focuses of those models are prediction [2], estimation of the basic reproduction number ( R 0 ) [3], trend detection [4], reinfection [5], the effect of preventive measures such as lockdown [6], social distancing [7], etc. All these approaches need high-quality training data to develop meaningful and effective models.…”

Section: Introductionmentioning

confidence: 99%

Jump-Drop Adjusted Prediction of Cumulative Infected Cases using the Modified SIS Model

Mohta

Prathapani

Ghosh

2022

Preprint

View full text Add to dashboard Cite

Accurate prediction of cumulative COVID-19 infected cases is essential for effectively managing the limited healthcare resources, specifically in India. Historically, epidemiological models have helped control such epidemics. Those models need accurate past data to predict for future. However, for various reasons, we observe sudden drops and jumps in the number of daily reported COVID-19 infected cases on some days, not aligned with the overall trend. If we incorporate those observations in the training data, the model's prediction accuracy may worsen, as they do not capture the correct trend in the training data. But it is not straightforward for the existing epidemiological models to decide a specific day as a sudden drop or jump. We propose an algorithm that automatically determines any drop or jump days in this work. Then, based on the overall trend in the data, we adjust the number of daily infected cases on those days and decide on the training data based on the adjusted observations. We have applied the proposed algorithm in a recently proposed modified Susceptible-Infected-Susceptible (SIS) to show that adjusted training data gives better prediction accuracy when jump and drop exist in the training data.

show abstract

“…The second wave of outbreaks in Iran and Japan was predicted through numerical simulations of different order derivatives. Ramezani et al [14] proposed a variant of the SEIRD (susceptibility, infection, recovery, and death) model, which captures the nonlinear behavior of the COVID-19 pandemic while accounting for asymptomatic infected individuals. Mathematical models of infectious diseases can be used to predict the spread of epidemics, but the introduction of model parameters is based on many assumptions.…”

Section: Introductionmentioning

confidence: 99%

Simulation and application of COVID-19 compartment model using physics-informed neural network

Ke¹,

Ma²,

Yin³

2022

Preprint

View full text Add to dashboard Cite

show abstract

A novel compartmental model to capture the nonlinear trend of COVID-19

Cited by 24 publications

References 27 publications

Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting

Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting

Jump-Drop Adjusted Prediction of Cumulative Infected Cases using the Modified SIS Model

Simulation and application of COVID-19 compartment model using physics-informed neural network

Contact Info

Product

Resources

About