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Title: Modelling and analysis of tropical diseases
Authors: Musa, Salihu Sabiu
Degree: Ph.D.
Issue Date: 2021
Abstract: Tropical diseases (TD) are mainly prevalent in tropical and subtropical regions. They are also regarded as emerging and re-emerging infectious disease (EIDs) pathogens which remains an enormous challenge for public health and socioeconomic evolution globally. In this thesis, we introduce several mathematical and statistical modeling approaches and paradigms to investigate and qualitatively analyze the transmission dynamics of EIDs. The COVID-19 is caused by the SARS-CoV-2, which emerged in China and resulted in the first pandemic of coronaviruses in human history. We study and analyze the initial phase of the outbreaks to estimate the exponential growth rate and basic reproduction number (R0) of COVID-19 in Africa. We quantify the instantaneous transmissibility of the outbreak by the time-varying effective reproductive number (Reff ) to show the potential of the disease to spread across Africa. We estimate the under-ascertainment of COVID-19 cases in Kano, Nigeria during the early epidemics. Further, a compartmental model is developed, which incorporates different cases of quarantine and isolation strategies for mild and severe cases to study the dynamics of COVID-19 in Wuhan, China. Our results suggest that timely hospitalization/isolation are vital to mitigate the spread of COVID-19.
Dengue virus (DENV) epidemics pose a serious public health threat globally. We develop and analyze a model to study the transmission dynamics of the DENV epidemics in Kaohsiung and Tainan cities of Taiwan. Further analyzes reveal that the model exhibit the phenomena of backward bifurcation (BB), which increases the difficulty of control since disease control is not merely relying on R0. We show the similarity of the DENV outbreaks in the two cities. We find that despite the proximity, the estimated transmission rates are neither completely synchronized nor periodically in-phase perfectly in the two cities. Further, we propose an epidemic model to study the dynamics of Meningococcal meningitis (MCM). The model considers two different groups of susceptible individuals depending on the availability of medical resources (MR, such as hospitals, health workers), which varies the infection risk. We find that the model exhibits the phenomenon of BB. Our findings suggest that providing adequate MR in a community is crucial in mitigating MCM incidences and deaths. Furthermore, Lassa hemorrhagic fever (LHF), is a virus that has generated recurrent outbreaks in West Africa. We use a mechanistic model to study the dynamics of the LHF epidemics in Nigeria. Our model describes the interaction between human and rodent populations with the consideration of quarantine, isolation, and hospitalization processes. We find that the model captures well the incidence curves from the surveillance data, and can reconstruct the periodic rodent and human forces of infection. Our results suggest that the initial susceptibility likely increased across the three outbreaks from 2016-19. Finally, we adopt a wavelet analysis to investigate the long-term recurrence of global rabies outbreaks. We find that a 3-to 4-year periodicity has existed from 2005 to 2018. Our findings indicate the existence of the oscillation patterns (recurrence), and the epidemic is at its peak since 2018.
Subjects: Tropical medicine -- Mathematical models
Hong Kong Polytechnic University -- Dissertations
Pages: xxii, 144 pages : color illustrations
Appears in Collections:Thesis

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