A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Epidemic transmission modeling with fractional derivatives and environmental pathogens
Tekijät: Khalighi, Moein; Ndaïrou, Faïçal; Lahti, Leo
Kustantaja: WORLD SCIENTIFIC PUBL CO PTE LTD
Kustannuspaikka: SINGAPORE
Julkaisuvuosi: 2024
Journal: International Journal of Biomathematics
Tietokannassa oleva lehden nimi: INTERNATIONAL JOURNAL OF BIOMATHEMATICS
Lehden akronyymi: INT J BIOMATH
Sivujen määrä: 28
ISSN: 1793-5245
eISSN: 1793-7159
DOI: https://doi.org/10.1142/S1793524524500852
Verkko-osoite: https://www.worldscientific.com/doi/10.1142/S1793524524500852
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/457935376
This research presents an advanced fractional-order compartmental model designed to delve into the complexities of COVID-19 transmission dynamics, specifically accounting for the influence of environmental pathogens on disease spread. By enhancing the classical compartmental framework, our model distinctively incorporates the effects of order derivatives and environmental shedding mechanisms on the basic reproduction numbers, thus offering a holistic perspective on transmission dynamics. Leveraging fractional calculus, the model adeptly captures the memory effect associated with disease spread, providing an authentic depiction of the virus's real-world propagation patterns. A thorough mathematical analysis confirming the existence, uniqueness and stability of the model's solutions emphasizes its robustness. Furthermore, the numerical simulations, meticulously calibrated with real COVID-19 case data, affirm the model's capacity to emulate observed transmission trends, demonstrating the pivotal role of environmental transmission vectors in shaping public health strategies. The study highlights the critical role of environmental sanitation and targeted interventions in controlling the pandemic's spread, suggesting new insights for research and policy-making in infectious disease management.
Julkaisussa olevat rahoitustiedot:
This study has been supported by the Academy of Finland (330887 to MK, LL), the European Unions Horizon 2020 research and innovation program (952914 to LL), and the UTUGS graduate school of the University of Turku (to MK).
