Evaluating the impact of seasonal Influenza virus: A comprehensive epidemiological forecast and analysis in Ghana from 2021 to 2023 | ||||
| Microbes and Infectious Diseases | ||||
| Article 7, Volume 5, Issue 2, May 2024, Page 463-478 PDF (1.08 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mid.2024.258309.1734 | ||||
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| Authors | ||||
Md Nurul Raihen   1; Md Mostak Ahammed2; Sultana Akter3
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| 1Department of Mathematics and Computer Science, Assistant Professor, Fontbonne University, St. Louis, MO, 63105, USA | ||||
| 2Department of Statistics, Visiting Faculty, Grand Valley State University, Allendale, MI, 49401, USA | ||||
| 3Teaching Assistant, MS in Statistics, Western Michigan University, Kalamazoo, MI, 49006, USA | ||||
| Abstract | ||||
| Background: Infectious diseases are a leading cause of death and disability worldwide, so it is crucial to plan for their potential impact in order to implement an efficient response. Examining the seasonality and distribution of influenza viruses in Ghana, as well as susceptible demographic groups and circulating strains of the virus, were the objectives of this study. Methods: We worked with a modified version of the Susceptible-Exposed-Infectious-Recovered-Vaccinated (SEIR-V) transmission model to forecast the possible outcomes of the influenza pandemic in Ghana. Using the fourth-order Runge-Kutta method, we were able to get numerical simulations for changing the model parameters. We analyzed forecasts for the illness transmission rate , vaccination rate , and recovery rate on a daily and cumulative basis. The average fundamental reproduction number for the parameters and was also rendered graphically. Results: We effectively forecasted the trajectory of influenza-related morbidity using our model, which paves the way for future approaches of controlling and monitoring the flu in our study area. In order to restrict the seasonal influenza, we have provided visual evidence that vaccinated patients and a quarantine in Ghana for at least the next 10 days are needed. It has been noted that the recovery rates of non-vaccinated patients and the vaccination rate work together to reduce the contagious disease. Conclusion: Using precise parameter approximations, theoretical epidemic analysis has proven to be an effective method for predicting and managing the spread of pandemics such as seasonal influenza virus. This model has been transformed into an epidemic model by adding the hospitalized-vaccination compartment for patients with confirmed infections to the SEIR-V model. | ||||
| Keywords | ||||
| Seasonal Influenza virus; Vaccination; Numerical approximation; Sensitivity analysis; Epidemic dynamics | ||||
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