|
|
 |
| |
|
|
|
Analysis of Fractional-Order Model for COVID-19: Implications for Transmission, Hospitalisation, and Recovery Trends |
|
|
|
PP: 467-489 |
|
|
|
doi:10.18576/pfda/110304
|
|
|
|
Author(s) |
|
|
|
John Olajide Akanni,
Saheed Ajao,
Abayomi Ayotunde Ayoade,
Chinwendu Emilian Madubueze,
Fatmawati Fatmawati,
|
|
|
|
Abstract |
|
|
| This research uses fractional-order differential equations to explore the dynamics of COVID-19 spread, providing insights into how different parameters influence the epidemic. The study examines how varying fractional order, transmission rates, and recovery rates affect vital metrics such as the number of acute infections, hospitalisations, and other epidemiological factors. Our findings indicate that fractional-order models are adept at capturing complex behaviours, including crossover effects, which are crucial for understanding disease progression. The analysis shows that increasing the transmission rate leads to an increase in acute cases and hospitalisations, highlighting the importance of controlling transmission to reduce the strain on healthcare systems. In addition, higher recovery rates are associated with fewer acute infections, underlining the effectiveness of efficient recovery strategies. The impact of changes in hospitalisation rates and movement dynamics between different infection states on the overall trajectory of the epidemic is also discussed. This research emphasises the utility of fractional-order models in making accurate predictions and guiding public health interventions, ultimately aiding in more informed decision-making and response strategies for managing disease outbreaks.
|
|
|
|
|
|
|
|
