Epidemic Modelling of COVID-19

COVID-19 has had a disastrous impact on millions of lives all over the world.199,466,211 confirmed cases of COVID19 and 4,244,541 deaths have been reported to WHO till 4th august. Analyzing the available data and predicting the pandemic trend is important since the situation can be controlled only when there is adequate preparation. Research using epidemiological models helps in analyzing different facets of COVID including infection, recovery and death rate. Predicting the daily increase of cases can help reduce the burden on health care workers and government by aiding them in planning the required resources in advance. Thus, in this project data driven epidemic modelling approach is used. COVID cases of 10 forthcoming days using three modelling techniques namely Polynomial Regression, Bayesian Ridge Regression and Support Vector Machine are predicted. The performance metric used to identify the best model are MSE and MAE. Polynomial Regression is found to have best performance followed by Bayesian ridge regression. Support Vector Machine has a poor performance. Keywords: Epidemic Modelling, COVID-19, Machine Learning, Polynomial Regression, Bayesian Ridge Regression, Support Vector Machine

A physically plausible incidence rate for compartmental epidemiological models

Motivated by the recent trajectory of SARS-Cov-2 new infection incidences in Germany and other European countries, this note reconsiders the need to use a non-linear incidence rate function in deterministic compartmental models for current SARS-Cov-2 epidemic modelling. Employing a homogenous contact model, it derives such function systematically using stochastic arguments. The presented result, which is relevant to modelling of proliferation of arbitrary infectious diseases, integrates well with previous analyses, in particular closes an analytical "gap" mentioned in London and Yorke (1973) and complements the stability related work on incidence rate functions of the form βIpSq seen for example in Liu, Hethcote and Levin (1987).

Data Analytics Epidemic Modelling and Human Dynamics Approaches for Pandemic Outbreak

A pandemic is a disease that spreads across countries or continents. It affects more people and takes more lives than an epidemic. Examples are Influenza A, HIV-1, Ebola, SARS, pneumonic plague. Currently, the ongoing COVID-19 pandemic is one of the major health emergencies in decades that has affected almost every country in the world. As of 23 October 2020, it has caused an outbreak with more than 40 million confirmed cases, and more than 1 million reported deaths globally. Also, as of 23 October 2020, the reproduction number (R) and growth rate of coronavirus (COVID-19) in the UK range is 1.2-1.4. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. This chapter explores data analytics epidemic modelling and human dynamics approaches for pandemic outbreaks.