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A unified view of epidemiological modelling and artificial intelligence-based approach to understanding the COVID-19 disease spread for supporting public policy decisions
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- Author(s) / Creator(s)
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The Covid-19 epidemic has emerged as one of the most concerning global public health catastrophes of the twenty-first century, highlighting the critical need for robust forecasting approaches for disease identification, alleviation, and prevention, among other things. Forecasting is one of the most powerful statistical methods for detecting and evaluating trends and forecasting future consequences based on which timely and mitigating actions can be performed all over the world in numerous disciplines. Several statistical methodologies and machine learning techniques have been employed to this goal, depending on the study needed and the data available. Most of the predictions made in the past have been short-term and country-specific. In this paper, an assessment of the potential machine learning technique is suggested for forecasting Covid-19-related characteristics in the long run, both in Canada and globally. This recommended ML model seems to be well for forecasting data from the past and present. Three datasets were used in this analysis, from the Alberta Health Services, Statistics Canada, and Worldometers, respectively. Long-term data forecasts for both Alberta and Canada were detailed using these three datasets, and it was discovered that anticipated data was highly similar to real-time values. The experiment was also carried out for Canadian province predictions as well as country-level predictions around the world, and the results are presented in the Appendix [1].
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- Date created
- 2022
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- Subjects / Keywords
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- Type of Item
- Research Material