Generic approach for mathematical model of multi-strain pandemics

Abstract

Multi-strain pandemics have emerged as a major concern. We introduce a new model for assessing the connection between multi-strain pandemics and mortality rate, basic reproduction number, and maximum infected individuals. The proposed model provides a general mathematical approach for representing multi-strain pandemics, generalizing for an arbitrary number of strains. We show that the proposed model fits well with epidemiological historical world health data over a long time period. From a theoretical point of view, we show that the increasing number of strains increases logarithmically the maximum number of infected individuals and the mean mortality rate. Moreover, the mean basic reproduction number is statistically identical to the single, most aggressive pandemic strain for multi-strain pandemics.

Time delay of the appearance of a new strain can affect vaccination behavior and disease dynamics: An evolutionary explanation

“…The decision to be vaccinated is influenced by the timing of the emergence of the new strain, its severity, transmission rate, and the cost and effectiveness of the vaccine. Most previous studies concerning two-strain or multi-strain epidemic models focused on stability analysis with non-monotone incidence rates, complex network with latency, general incidence rate, age structure and mutation ( Allali & Amine, 2022 ; Meskaf et al., 2020 ; Yang et al., 2016 ; Yang & Zhang, 2012 ), competitive coexistence with periodic infection rate ( Li et al., 2020 ), optimal control with general incidence function and time delay, imperfect vaccination, covid-19 application ( Arruda et al., 2021 ; Barro et al., 2018 ; Li & Guo, 2022 ), vaccination behavior with imitation dynamic approach, social distance effect, covid-19 modeling, awareness decay ( Dashtbali & Mirzaie, 2021 ; De León et al., 2022 ; Deka & Bhattacharyya, 2022 ; Zuo, Zhu, & Ling, 2022 ), and disease dynamics with cross-immunity, in patchy environments, generic approach ( Amador et al., 2019 ; Lazebnik & Bunimovich-Mendrazitsky, 2022 ; Li et al., 2022 ; Theprungsimankul et al., 2011 ), etc.…”

“…Simply put, this show portrays how ailment voyages in individuals from the susceptible compartment (S) to the infected compartment (I) and after that to the recovered compartment (R), where individuals construct insusceptibility to reinfection. Exposed (E), quarantine (Q), hospitalized (H), and asymptomatic (A) compartments can be used in some epidemics to adequately examine disease dynamics ( Ma et al., 2002 ; Barro et al., 2018 ; Xia et al., 2018 ; Epstein et al., 2021 ; Amador, Armesto, & Gómez-Corral, 2019 ; De León, Avila-Vales, & lin Huang, 2022 ; Amaral, Oliveira, & Javarone, 2021 ; Kabir, Risa, & Tanimoto, 2021 ; M. et al., 2005 ; Meskaf, Khyar, Danane, & Allali, 2020 ; Farah, Amine, & Allali, 2021 ; Dong, Li, Wan, & Liu, 2017 ; Li, Wang, Xu, & Rong, 2022 ; Khyar & Allali, 2020 ; Li, Liu, & Martcheva, 2010 ; Lazebnik & Bunimovich-Mendrazitsky, 2022 ; Beretta & Breda, 2011 ; Rihan, Alsakaji, & Rajivganthi, 2020 ; Khan, Ikram, Din, Humphries, & Akgul, 2021 ; Niño-Torres, Ríos-Gutiérrez, Arunachalam, Ohajunwa, & Seshaiyer, 2022 ; Zuo, Zhu, & Ling, 2022 ; Li & Liu, 2014 ). Examination of supervision and moderation measures, like immunization, establishing of vector-borne maladies, and the impact of birthing and passing elements is an extra application of compartmental models in the study of disease transmission ( Dashtbali & Mirzaie, 2021 ; De León et al., 2022 ; Deka & Bhattacharyya, 2022 ; Epstein et al., 2021 ; Han & Li, 2022 ; Helbing et al., 2015 ; Jusup et al., 2022 ; Kabir et al., 2021 ; Lobinska, Pauzner, Traulsen, Pilpel, & Nowak, 2022 ; Nakata & Omori, 2015 ; Rajib Arefin et al., 2019 ; Tchoumi & Tchuenche, 2021 ; Tori & Tanimoto, 2022 ; Yin, Wang, Xia, & Bauch, 2022 ; Zuo, Zhu, & Ling, 2022 ).…”

“…However, compared to investigations of the dynamical behaviors of multistrain epidemic models with vaccination and time delays, early studies have received little attention. The dynamics of a two-strain epidemic model were studied in ( Rajib Arefin et al., 2019 ; De León et al., 2022 ; M. et al., 2005 ; Meskaf et al., 2020 ; Farah et al., 2021 ; Li et al., 2022 ; Khyar & Allali, 2020 ; Li et al., 2010 ; Lazebnik & Bunimovich-Mendrazitsky, 2022 ; Tchoumi & Tchuenche, 2021 ; Yang, Chen, & Liu, 2016 ; Theprungsimankul, Junsank, Abdulloh, & Chinviriyasit, 2011 ; Khatua, Pal, & Kar, 2014 ; Allali & Amine, 2022 ; Li, Zhang, & Zhou, 2020 ). Epidemic models with time delay were studied in ( Barro et al., 2018 ; Beretta & Breda, 2011 ; Dong et al., 2017 ; Farah et al., 2021 ; Ma et al., 2002 ; Nakata & Omori, 2015 ; Nastasi, Perrone, Taffara, & Vitanza, 2022 ; Yang et al., 2016 ; Yang & Zhang, 2012 ).…”

Identification of Multiple Variant Extensively Drug-Resistant Typhoid Infections across Pakistan

“…24 Understanding the genomic architecture and prevalence of variants will play a critical role in building predictive models of the XDR typhoid outbreak. 25…”

How containment policy and medical service impact COVID-19 transmission: A cross-national comparison among China, the USA, and Sweden

“…Nowadays, a rich amount of studies analyze the influencing factors, policy interpretation, and evaluation based on the transmission model of the COVID-19 pandemic. As a widely used infectious disease model, the Susceptible-Infected-Recovered (SIR) model and its extended models validated that they could simulate COVID-19 transmission [ [13] , [14] , [15] ]. For instance, Kudryashov et al prove that the SIR model could describe the transmission rules of COVID-19 based on data from China, Italy, Austria, South Korea, and Russia [ 16 ].…”