| Autor: |
Perepi, Rajarajeswari, Santhi, K., Saraswathi, R., Bég, O. Anwar |
| Předmět: |
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| Zdroj: |
Computer Methods in Biomechanics & Biomedical Engineering: Imaging & Visualisation; May2023, Vol. 11 Issue 3, p933-941, 9p |
| Abstrakt: |
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. 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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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