An analytical approach to evaluate the impact of age demographics in a pandemic

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dc.authoridGhazzai, Hakim/0000-0002-8636-4264
dc.authoridDelen, Dursun/0000-0001-8857-5148
dc.authoridTopuz, Kazim/0000-0001-7990-5475
dc.authorwosidGhazzai, Hakim/K-2518-2019
dc.authorwosidDelen, Dursun/AGA-9892-2022
dc.authorwosidTopuz, Kazim/K-8287-2014
dc.contributor.authorAbdulrashid, Ismail
dc.contributor.authorFriji, Hamdi
dc.contributor.authorTopuz, Kazim
dc.contributor.authorGhazzai, Hakim
dc.contributor.authorDelen, Dursun
dc.contributor.authorMassoud, Yehia
dc.date.accessioned2024-05-19T14:41:00Z
dc.date.available2024-05-19T14:41:00Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a six-state compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model's efficiency is proved by testing the age-stratified model's performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population.en_US
dc.identifier.doi10.1007/s00477-023-02477-2
dc.identifier.endpage3705en_US
dc.identifier.issn1436-3240
dc.identifier.issn1436-3259
dc.identifier.issue10en_US
dc.identifier.pmid37362847en_US
dc.identifier.scopus2-s2.0-85161423908en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3691en_US
dc.identifier.urihttps://doi.org10.1007/s00477-023-02477-2
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5049
dc.identifier.volume37en_US
dc.identifier.wosWOS:001002408100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofStochastic Environmental Research and Risk Assessmenten_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDynamical Modelingen_US
dc.subjectRisk Factorsen_US
dc.subjectStatistical Methodsen_US
dc.subjectAge Demographicsen_US
dc.subjectSars-Cov-2en_US
dc.titleAn analytical approach to evaluate the impact of age demographics in a pandemicen_US
dc.typeArticleen_US

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