Date of Publication :17th January 2024

Abstract: On January 3, 2020, Chinese health officials identified an outbreak of pneumonia in the Chinese city of Wuhan. The virus swiftly spread to the majority of countries, infecting a substantial portion of the population. On September 28, 2020, reports of nearly a million deaths were made globally. Every day, huge amounts of data were gathered, and data analytics became crucial for identifying patterns and identifying how the infection spread. Numerous predictive models were employed to assess the impact of non-pharmaceutical interventions (NPIs) on the spread of SARS-CoV-2. Some of the models also predicted daily new cases and mortality patterns. The SEIR model is one of many that were employed. The SEIR approach calculates the end results using differential equations and requires other programming language skills to visualize the results, making it difficult for ordinary people to use predictive model. We created a basic mathematical model that is straightforward to apply, and the results show that it is successful in controlling pandemic spread. The proposed approach employs a predefined set of logics that are deployed in accordance with current developments. The present trend is established by comparing the volume of instances recorded in 7 days with the volume recorded in the previous 7 days. This model is used to forecast short- term trends, and the pre-defined set of logic advises appropriate actions to restrict illness spread during a pandemic.

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