Browsing by Author "Sun, Gui-Quan"

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    A hierarchical intervention scheme based on epidemic severity in a community network
    (Journal of Mathematical Biology, 2023-07) He, Runzi; Luo, Xiaofeng; Asamoah, Joshua Kiddy K.; Zhang, Yongxin; Li, Yihong; Jin, Zhen; Sun, Gui-Quan; 0000-0002-7066-246X
    As there are no targeted medicines or vaccines for newly emerging infectious diseases,isolation among communities (villages, cities, or countries) is one of themost effectiveintervention measures. As such, the number of intercommunity edges (NIE) becomesone of themost important factor in isolating a place since it is closely related to normallife. Unfortunately, how NIE affects epidemic spread is still poorly understood. In this paper, we quantitatively analyzed the impact of NIE on infectious disease transmissionby establishing a four-dimensional SIR edge-based compartmental model with two communities. The basic reproduction number R0( l ) is explicitly obtained subjectto NIE l . Furthermore, according to R0(0) with zero NIE, epidemics spreadcould be classified into two cases. When R0(0) > 1 for the case 2, epidemics occurwith at least one of the reproduction numbers within communities greater than one,and otherwise when R0(0) < 1 for case 1, both reproduction numbers within communitiesare less than one. Remarkably, in case 1, whether epidemics break out stronglydepends on intercommunity edges. Then, the outbreak threshold in regard to NIEis also explicitly obtained, below which epidemics vanish, and otherwise break out.The above two cases form a severity-based hierarchical intervention scheme for epidemics.It is then applied to the SARS outbreak in Singapore, verifying the validityof our scheme. In addition, the final size of the system is gained by demonstrating theexistence of positive equilibrium in a four-dimensional coupled system. Thoretical results are also validated through numerical simulation in networks with the Poisson and Power law distributions, respectively. Our results provide a new insight into controlling epidemics.
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    Fractional Caputo and sensitivity heat map for a gonorrhea transmission model in a sex structured population
    (Elsevier, 2023-09) Asamoah, Joshua Kiddy K.; Sun, Gui-Quan; 0000-0002-7066-246X
    Gonorrhea is a disease that is spread by sexual contact, and it can potentially cause infections in the genital region, the rectum, and even the throat. Due to the shared history between infected individuals and their sexual partners, infected individuals will likely continue to have sexual relations with those same partners. As a result, this article aims to investigate how memory affects the transmission of gonorrhea in a structured population using the Caputo fractional derivative and sensitivity analysis. The model is shown to be positively invariant with a unique bound. The existence and uniqueness criteria of the fractional model are established using fixed-point theory. The stable nature of the model is obtained using the Ulam Hyers and Ulam Hyers Rassias ideas. To highlight the stability of the fractional model, the stability of solution trajectories to the disease-free and endemic steady states is graphically illustrated for the gonorrhea basic reproduction number, 𝜚∗0 < 1 and 𝜚∗0 > 1, respectively. We showed the sensitivities linked to the proposed model using the Latin hypercube sampling, singular value analysis, box plots, scatter plots, contour plots, three-dimensional plots, and sensitivity heat maps. We noticed that the transmission rate from females to males, 𝛽𝑓𝑚, is the most influential parameter in the spread of the disease. From the sensitivity heat maps, it is noticed that using the first four principal components analysis, the most sensitive state variables to the parameters in the model are symptomatic females, recovered males, susceptible females, and recovered females. In conjunction with the modified Adams–Bashforth method, the numerical trajectories of the fractional Caputo model are investigated. Finally, we noticed that memory changes impact the number of incubative females and incubative males.
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    Optimal control and comprehensive cost-effectiveness analysis for COVID-19
    (Elsevier, 2022-01) Asamoah, Joshua Kiddy K.; Okyere, Eric; Abidemi, Afeez; Moore, Stephen E.; Sun, Gui-Quan; Jin, Zhen; Acheampong, Edward; Gordon, Joseph Frank; 0000-0002-7066-246X
    Cost-effectiveness analysis is a mode of determining both the cost and economic health outcomes of one or more control interventions. In this work, we have formulated a non-autonomous nonlinear deterministic model to study the control of COVID-19 to unravel the cost and economic health outcomes for the autonomous nonlinear model proposed for the Kingdom of Saudi Arabia. We calculated the strength number and noticed the strength number is less than zero, meaning the proposed model does not capture multiple waves, hence to capture multiple wave new compartmental model may require for the Kingdom of Saudi Arabia. We proposed an optimal control problem based on a previously studied model and proved the existence of the proposed optimal control model. The optimality system associated with the non-autonomous epidemic model is derived using Pontryagin’s maximum principle. The optimal control model captures four time-dependent control functions, thus, 𝑢1-practising physical or social distancing protocols; 𝑢2-practising personal hygiene by cleaning contaminated surfaces with alcohol-based detergents; 𝑢3-practising proper and safety measures by exposed, asymptomatic and symptomatic infected individuals; 𝑢4-fumigating schools in all levels of education, sports facilities, commercial areas and religious worship centres. We have performed numerical simulations to investigate extensive cost-effectiveness analysis for fourteen optimal control strategies. Comparing the control strategies, we noticed that; Strategy 1 (practising physical or social distancing protocols) is the most costsaving and most effective control intervention in Saudi Arabia in the absence of vaccination. But, in terms of the infection averted, we saw that strategy 6, strategy 11, strategy 12, and strategy 14 are just as good in controlling COVID-19.