Browsing by Author "Jin, Zhen"

Now showing 1 - 7 of 7
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    A hierarchical intervention scheme based on epidemic severity in a community network
    (Springer, 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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    A mathematical model of corruption dynamics endowed with fractal–fractional derivative
    (Elsevier, 2023-08) Nwajeri, Ugochukwu Kizito; Asamoah, Joshua Kiddy K.; Ugochukwu, Ndubuisi Rich; Omamea, Andrew; Jin, Zhen; 0000-0002-7066-246X
    Numerous organisations across the globe have significant challenges about corruption, characterised by a systematic, endemic, and pervasive nature that permeates various societal establishments. Hence, we propose the fractional order model of corruption, which encompasses the involvement of corrupt individuals across various stages of education and employment. Specifically, we examine the presence of corruption among children in elementary schools, youths in tertiary institutions, adults in civil services, adults in government and public offices, and individuals who have renounced their involvement in corrupt practices. The basic reproduction number of the system was determined by utilising the next-generation matrix. The strength number was obtained by calculating the second derivative of the corruption-related compartments. The examined model solution’s existence, uniqueness, and stability were established using the Krasnoselski fixed point theorem, the Banach contraction principle, and the Ulam–Hyers theorem, respectively. Based on the numerous figures presented, our simulations indicate a positive correlation between the decline in fractal– fractional order and the increase in the number of individuals susceptible to corruption. This phenomenon results in an increase in the prevalence of corruption among designated sectors of the general population. The persistence of corruption in society is a significant challenge to its eradication, as individuals who see personal gains from engaging in corrupt practices tend to exhibit a recurring inclination towards such behaviour. Nevertheless, it is recommended that to mitigate corruption within various corruption-prone subcategories, there is a need to enhance the level of consciousness and promotion of anti-corruption measures throughout all societal establishments.
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    Cost–benefit analysis of the COVID-19 vaccination model incorporating different infectivity reductions
    (2024-05) Asamoah, Joshua Kiddy K.; Appiah,, Raymond Fosu; Jin, Zhen; Yang, Junyuan; 0000-0002-7066-246X
    The spread and control of coronavirus disease 2019 (COVID-19) present a worldwide economic and medical burden to public health. It is imperative to probe the effect of vaccination and infectivity reductions in minimizing the impact of COVID-19. Therefore, we analyze a mathematical model incorporating different infectivity reductions. This work provides the most economical and effective control methods for reducing the impact of COVID-19. Using data fromGhana as a sample size, we study the sensitivity of the parameters to estimate the contributions of the transmission routes to the effective reproduction number Re. We also devise optimal interventions with cost–benefit analysis that aim to maximize outcomes while minimizing COVID-19 incidences by deploying cost-effectiveness and optimization techniques. The outcomes of this work contribute to a better understanding of COVID-19 epidemiology and provide insights into implementing interventions needed to minimize the COVID-19 burden in similar settings worldwide.
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    Global stability dynamics and sensitivity assessment of COVID-19 with timely-delayed diagnosis in Ghana
    (De Gruyter, 2022-05) Moore, Stephen E.; Nyandjo-Bamen, Hetsron L.; Menoukeu-Pamen, Olivier; Asamoah, Joshua Kiddy K.; Jin, Zhen; 0000-0002-7066-246X
    In this paper, we study the dynamical e ects of timely and delayed diagnosis on the spread of COVID-19 in Ghana during its initial phase by using reported data from March 12 to June 19, 2020. The estimated basic reproduction number, R0, for the proposed model is 1.04. One of the main focus of this study is global stability results. Theoretically and numerically, we show that the disease persistence depends on R0. We carry out a local and global sensitivity analysis. The local sensitivity analysis shows that the most positive sensitive parameter is the recruitment rate, followed by the relative transmissibility rate from the infectious with delayed diagnosis to the susceptible individuals. And that the most negative sensitive parameters are: self-quarantined, waiting time of the infectious for delayed diagnosis and the proportion of the infectious with timely diagnosis. The global sensitivity analysis using the partial rank correlation coe cient con rms the directional ow of the local sensitivity analysis. For public health bene t, our analysis suggests that, a reduction in the in ow of new individuals into the country or a reduction in the inter community in ow of individuals will reduce the basic reproduction number and thereby reduce the number of secondary infections (multiple peaks of the infection). Other recommendations for controlling the disease from the proposed model are provided in Section 7.
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    Mathematical modeling of two strains tuberculosis and COVID-19 vaccination model: a co-infection study with cost-e􀀀ectiveness analysis
    (Frontier, 2024-05) Appiah, Raymond Fosu; Jin, Zhen; Yang, Junyuan; Asamoah, Joshua Kiddy K.; Wen, Yuqi; 0000-0002-7066-246X
    Tuberculosis and COVID-19 co-infection is currently the major issue of public health in many nations, including Ghana. Therefore, to explore the e􀀀ects of the two Tuberculosis strains on COVID-19, we suggest a Tuberculosis and COVID-19 co-infection model. The study also provides the most economical and e􀀀ective control methods to reduce the co-infection of tuberculosis and COVID-19. Based on the behavioral patterns of the two Tuberculosis strains and COVID- 19 reproduction numbers, the stability of the co-infection model is examined. We explore the sensitivity of the parameters to examine the e􀀀ect of the drug- resistant and drug-sensitive strain of Tuberculosis on the co-infection of COVID- 19. We determine the most cost-e􀀀ective and optimal treatment strategies that aim to maximize outcomes while minimizing tuberculosis and/or COVID-19 incidences, cost-e􀀀ectiveness, and optimization approaches. The outcomes of this work contribute to a better understanding of Tuberculosis and COVID-19 epidemiology and provide insights into implementing interventions needed to minimize Tuberculosis and COVID-19 burden in similar settings worldwide.
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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.
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    Optimal control and cost‑effectiveness analysis for a tuberculosis vaccination model with two latent classes
    (Springer, 2024-08) Appiah, Raymond Fosu; Jin, Zhen; Yang, Junyuan; Asamoah, Joshua Kiddy K.; 0000-0002-7066-246X
    Tuberculosis (TB) persists as a significant public health challenge in many regions, including Ghana. Effective treatment strategies are crucial in controlling the spread of TB, particularly in populations with diverse characteristics. This study delves into designing an optimal treatment strategy for the TB vaccination model, considering two latent classes based on Ghanaian data. We estimate and analyze the parameters to study TB dynamics based on the reproduction numbers of the two latent classes. We look into how sensitive the parameters are to see the transmission dynamics of the slow latent class of TB infectious with drug-sensitive strains and the rapid latent class of TB infectious with drug-resistant strains. Using optimal control theory techniques, we devise an optimal control strategy with a cost–benefit analysis to minimize TB incidence and maximize vaccination coverage within budget constraints. This strategy aims to guide policymakers in allocating resources efficiently to control the spread of TB. The results of this work give in-depth knowledge about the dominance of the two strains of TB transmission and provide insights into the design of effective vaccination and treatment programs. By tailoring interventions to the population's specific needs, we can work towards reducing the burden of TB worldwide.