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arXiv · 2004.11338

The TVBG-SEIR spline model for analysis of COVID-19 spread, and a Tool for prediction scenarios

Ognyan Kounchev·Georgi Simeonov·Zhana Kuncheva
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Abstract

Mathematical models are traditionally used to analyze the long-term global evolution of epidemics, to determine the potential and severity of an outbreak, and to provide critical information for identifying the type of disease interventions and intensity. One of the widely used mathematical models of long-term spreading of epidemics are the so-called deterministic compartmental models (SIR/SEIR type models). One of the main purposes of applying such models is to assess how the expensive restriction measures imposed by the authorities (home and social isolation/quarantine, travel restrictions, etc.) can effectively reduce the control reproduction number of the disease and its transmission risk. However the classical SIR/SEIR models have been primarily studied in what may be called stationary case, where the main parameters, the Transmission rate Beta (reflecting the virus spread by infected individuals) and the Removed rate Gamma (reflecting the hospitalization/isolation measures) remain constant during the whole period of interest. Hence, it is important to extend the classical SIR/SEIR models by creating new ansatzes for the dynamics of the transmission rates Beta(t) (which we will call further just Beta) and removed rates Gamma(t) (which we will call further just Gamma). The main purpose of the present research is to introduce a spline-based SEIR model with Time-varying Beta and Gamma parameters, or abbreviated TVBG-SEIR model, which is used to estimate the practical implications of the public health interventions and measures. We have designed a Tool based on the TVBG-SEIR model, which may be used as a Decision Support Tool to assist the health decision- and policy-makers in creating predictive scenarios.

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Ognyan Kounchev, Georgi Simeonov, Zhana Kuncheva. 2020-04-23. The TVBG-SEIR spline model for analysis of COVID-19 spread, and a Tool for prediction scenarios. https://arxiv.org/abs/2004.11338

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