A Time-Dependent SEIRD Model for Forecasting the COVID-19 Transmission Dynamics

Abstract

The spread of a disease caused by a virus can happen through human to human contact or could be from the environment. A mathematical model could be used to capture the dynamics of the disease spread to estimate the infections, recoveries, and fatalities that may result from the disease. An estimation is crucial to make policy decisions and for the alerts for the medical emergencies that may arise. Many epidemiological models are being used to make such an estimation. One major factor that is important in the forecasts using the models is the dynamic nature of the disease spread. Unless we can come up with a way of estimating the parameters that guide this dynamic spread, the models may not give accurate forecasts. The main principle is to keep the model generic while making minimal assumptions. In this work, we have derived a data-driven model from SEIRD, where we attempt to forecast Infected, Recovered and Deceased rates of COVID-19 up to a week. A method of estimating the parameters of the model is also discussed thoroughly in this work. The model is tested for India at a district level along with the most affected foreign cities like Lombardia from Italy and Moscow from Russia.

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

“…This means that we divide the epidemic curve into multiple time-windows, which are considered separately by fitting a time-independent compartmental model. The epidemic model chosen is a SEIRD model including infection by pre-symptomatic individuals (for details see [31,32]) described by the ordinary differential equations system (3). β I and β E stand for the infection rate of infected and exposed individuals, respectively, c represents the inverse of the incubation period, γ and µ express the recovery and death rates, respectively.…”

A stochastic metapopulation state-space approach to modeling and estimating Covid-19 spread

“…Given the complexity and reality of epidemics, many different implementations of the classical SEIRD model have been proposed Loli Piccolomini and Zama [2020], Korolev [2021], Tiwari et al [2020], Rapolu et al [2020]. Two clear drawbacks of the classical SEIRD model is the inability to model variation over a geographic area, and the fact that that the SEIRD values are assumed to be directly observable.…”

“…The state model is a spatial-temporal stochastic dynamic model that allows hidden states in a given location to change over time and the disease dynamics in one location to affect neighbouring locations through human movements between locations. The proposed framework consists of a multinomial state model based on a variant of the SIR model -the SEIRD model Rapolu et al [2020], Piccolomiini and Zama [2020], Korolev [2021] -and an observation model to allow the assimilation of publicly available data, including daily testing rate, daily test positivity rate, specificity and sensitivity of the tests. In addition, the model considers the differential testing rate between symptomatic patients and asymptomatic and healthy individuals.…”

Mathematical Modeling and Forecasting of COVID-19 in Moscow and Novosibirsk Region

“…In [21], a SEIR-D model with time-dependent parameters and calculation results are presented for some Indian districts, as well as for Lombardy (Italy) and Moscow (Russia).…”