State Wise Covid-19 Case Forecasting Based on Machine Learning by Using SEIR and Time Series Models

Suhas G. K; Charan K. V; Bhagappa .

International Journal of Broadband Cellular Communication

State Wise Covid-19 Case Forecasting Based on Machine Learning by Using SEIR and Time Series Models

Abstract

Forecasting mechanisms based on machine learning have proved to be significant to anticipate in peri-operative outcomes that improve decision-making in the future. Applications where it is necessary to identify risk factors with a negative impact have used machine learning models. To solve problems with forecasting, many prediction techniques are applied. The COVID-19 virus, which is active and is currently regarded as a serious threat to humanity, is shown in this research to have the potential to forecast the number of patients who would be affected by it. The three forecasts made by each model are the total number of newly identified cases, the number of accidental deaths, and the percentage of recoveries. One of the important factors is to look at the lifestyle of the spread of disease state-wise separately. To analyze data such as the number of infected people in each state and the number of infections for that state etc. We anticipate that such state-level forecasts will assist the central government in more effectively allocating its scarce healthcare funds. The analysis of the spreading of COVID-19 disease predicts the scale of the pandemic, along with the recovery rate and fatality rate. The SEIR model's projections are used to organize healthcare systems.

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How to cite

Suhas G. K, Charan K. V, Bhagappa .. State Wise Covid-19 Case Forecasting Based on Machine Learning by Using SEIR and Time Series Models. International Journal of Broadband Cellular Communication. 2024;9(1):1–9.

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State Wise Covid-19 Case Forecasting Based on Machine Learning by Using SEIR and Time Series Models

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