Predictive analysis of Covid 19 disease based on mathematical modelling and machine learning techniques

Rajarajeswari, P, Santhi, K, Saraswathi, R and Beg, OA ORCID: https://orcid.org/0000-0001-5925-6711 2022, 'Predictive analysis of Covid 19 disease based on mathematical modelling and machine learning techniques' , Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization .

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Abstract

During the emergence of a novel pandemic, predictive modelling process is more important in the phase of public health planning and response. Relating models to data provides a view into unseen variables, such as the occurrence of cryptic transmission and the prevalence of infection. These models allow exploration of counterfactuals and hypothetical interventions. However, although there have been tremendous advances in mathematical epidemiology, prognostications about epidemic outcomes are inherently prone to errors. Predictive modelling is a valuable model based on the clear definition and estimation of the variables. Researchers or policy makers who use the model outputs have a clear understanding of what can and cannot be achieved by this method. The results of this study are suggested that substantially more cases were present in many countries than were reported in the official statistics. In this paper we have identified the potential discrepancy between reported cases and true disease burden provided a crucial early warning to the international community. In this research paper we proposed statistical modelling and data-driven computer simulations provided accurate projections of global epidemic dispersal, quantifying the role of physical distancing in places and reductions in international travel on the spatiotemporal pattern of spread of COVID-19 based on Linear regression analysis.

Item Type:	Article
Additional Information:	“This is an Accepted Manuscript of an article published by Taylor & Francis in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization on September 20th 2022, available at: http://www.tandfonline.com/10.1080/21681163.2022.2120829
Schools:	Schools > School of Computing, Science and Engineering
Journal or Publication Title:	Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
Publisher:	Taylor & Francis
ISSN:	2168-1163
Depositing User:	OA Beg
Date Deposited:	14 Sep 2022 14:47
Last Modified:	27 Sep 2022 09:45
URI:	https://usir.salford.ac.uk/id/eprint/64703

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