State-by-state influenza outbreaks and oversee: A Markov chain study of California and North Carolina, USA
Influenza, a significant public health concern, spreads rapidly and causes seasonal epi- demics and pandemics. Mathematical models are essential tools for devising effec- tive strategies to combat this pandemic. Various models have been utilized to study influenza’s transmission dynamics and contro...
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| פורמט: | Article |
| שפה: | אנגלית |
| יצא לאור: |
PLOS Global Public Health
2025
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| גישה מקוונת: | https://repository.auw.edu.bd/handle/123456789/1266 |
| _version_ | 1857448906866032640 |
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| author | Kamrujjaman, Md |
| author_facet | Kamrujjaman, Md |
| author_sort | Kamrujjaman, Md |
| collection | institutional Repository |
| description | Influenza, a significant public health concern, spreads rapidly and causes seasonal epi-
demics and pandemics. Mathematical models are essential tools for devising effec-
tive strategies to combat this pandemic. Various models have been utilized to study
influenza’s transmission dynamics and control measures. This paper presents the SEIRS
(Susceptible-Exposed-Infectious-Recovered-Susceptible) model to analyze the dis-
ease’s transmission dynamics. The model analyzes real data from California and North
Carolina to assess trends, identify key factors, and project the nationwide spread of the
disease. Subsequently, we calculate the basic reproduction number (R0) using the next-
generation matrix method. Sensitivity analysis using Latin Hypercube Sampling (LHS)
has been conducted to identify the model’s most influential parameters. We graphically
demonstrate how different parameters affect the exposed and infected populations, as
well as the variation in the basic reproduction number with changes in parameters. We
illustrate the interconnected behavior of the effective reproduction number alongside the
different compartments and the basic reproduction number. We use phase plane anal-
ysis to examine the relationship between two compartments under varying parameters.
Visual tools like boxplots, contour plots, and heat maps provide insights into how dif-
ferent factors influence the basic reproduction number and disease transmission. We
investigate the stochastic behavior of the model by transforming it into a Continuous-
Time Markov Chain (CTMC) model and visualizing the results graphically. We apply the
SEIRS model to real influenza data, showcasing its effectiveness in analyzing transmis-
sion dynamics, predicting outbreaks, and evaluating public health strategies for better
epidemic management. |
| format | Article |
| id | 123456789-1266 |
| institution | Asian University for Women |
| language | English |
| publishDate | 2025 |
| publisher | PLOS Global Public Health |
| record_format | dspace |
| spelling | 123456789-12662026-02-18T06:15:05Z State-by-state influenza outbreaks and oversee: A Markov chain study of California and North Carolina, USA Kamrujjaman, Md Influenza, a significant public health concern, spreads rapidly and causes seasonal epi- demics and pandemics. Mathematical models are essential tools for devising effec- tive strategies to combat this pandemic. Various models have been utilized to study influenza’s transmission dynamics and control measures. This paper presents the SEIRS (Susceptible-Exposed-Infectious-Recovered-Susceptible) model to analyze the dis- ease’s transmission dynamics. The model analyzes real data from California and North Carolina to assess trends, identify key factors, and project the nationwide spread of the disease. Subsequently, we calculate the basic reproduction number (R0) using the next- generation matrix method. Sensitivity analysis using Latin Hypercube Sampling (LHS) has been conducted to identify the model’s most influential parameters. We graphically demonstrate how different parameters affect the exposed and infected populations, as well as the variation in the basic reproduction number with changes in parameters. We illustrate the interconnected behavior of the effective reproduction number alongside the different compartments and the basic reproduction number. We use phase plane anal- ysis to examine the relationship between two compartments under varying parameters. Visual tools like boxplots, contour plots, and heat maps provide insights into how dif- ferent factors influence the basic reproduction number and disease transmission. We investigate the stochastic behavior of the model by transforming it into a Continuous- Time Markov Chain (CTMC) model and visualizing the results graphically. We apply the SEIRS model to real influenza data, showcasing its effectiveness in analyzing transmis- sion dynamics, predicting outbreaks, and evaluating public health strategies for better epidemic management. 2025-09-22T10:12:04Z 2025-09-22T10:12:04Z 9/18/2025 Article https://repository.auw.edu.bd/handle/123456789/1266 en application/pdf PLOS Global Public Health |
| spellingShingle | Kamrujjaman, Md State-by-state influenza outbreaks and oversee: A Markov chain study of California and North Carolina, USA |
| title | State-by-state influenza outbreaks and oversee: A Markov chain study of California and North Carolina, USA |
| title_full | State-by-state influenza outbreaks and oversee: A Markov chain study of California and North Carolina, USA |
| title_fullStr | State-by-state influenza outbreaks and oversee: A Markov chain study of California and North Carolina, USA |
| title_full_unstemmed | State-by-state influenza outbreaks and oversee: A Markov chain study of California and North Carolina, USA |
| title_short | State-by-state influenza outbreaks and oversee: A Markov chain study of California and North Carolina, USA |
| title_sort | state by state influenza outbreaks and oversee a markov chain study of california and north carolina usa |
| url | https://repository.auw.edu.bd/handle/123456789/1266 |
| work_keys_str_mv | AT kamrujjamanmd statebystateinfluenzaoutbreaksandoverseeamarkovchainstudyofcaliforniaandnorthcarolinausa |