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

Rajarajeswari, P; Santhi, K; Saraswathi, R; Beg, OA

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.

Citation

Rajarajeswari, P., Santhi, K., Saraswathi, R., & Beg, O. (2022). Predictive analysis of Covid 19 disease based on mathematical modelling and machine learning techniques. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, https://doi.org/10.1080/21681163.2022.2120829

• Journal Article Type: Article
• Acceptance Date: Aug 31, 2022
• Online Publication Date: Sep 20, 2022
• Publication Date: Sep 20, 2022
• Deposit Date: Sep 14, 2022
• Publicly Available Date: Sep 21, 2023
• Journal: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
• Print ISSN: 2168-1163
• Electronic ISSN: 2168-1171
• Publisher: Taylor and Francis
• DOI: https://doi.org/10.1080/21681163.2022.2120829
• Publisher URL: https://doi.org/10.1080/21681163.2022.2120829
• Additional Information: 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