2019
DOI: 10.1016/j.idm.2019.04.003
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A mathematical modelling study of HIV infection in two heterosexual age groups in Kenya

Abstract: The control of HIV demands different interventions for different age groups. In the present manuscript, we formulate and analyze a mathematical compartmental models of HIV transmission within and between two age groups in Kenya. We fitted the model to data using MCMC technique and inferred the parameters. We also estimate the reproduction numbers, namely within age group transmission and between age groups transmission basic reproduction numbers. The analysis of the data revealed that there is significant diff… Show more

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Cited by 23 publications
(13 citation statements)
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References 16 publications
(15 reference statements)
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“…In this year, we used microdata on the Mathematical modelling of HIV/AIDS and TB. The mathematical models dealing with HIV/AIDS and TB transmission dynamics are described in many papers [16,17,18,19,20]. However, the inclusion of social determinants of health, and in particular poverty, in epidemiological models is still barely addressed.…”
Section: Resultsmentioning
confidence: 99%
“…In this year, we used microdata on the Mathematical modelling of HIV/AIDS and TB. The mathematical models dealing with HIV/AIDS and TB transmission dynamics are described in many papers [16,17,18,19,20]. However, the inclusion of social determinants of health, and in particular poverty, in epidemiological models is still barely addressed.…”
Section: Resultsmentioning
confidence: 99%
“…e number of research studies that employ mathematical modeling to study the dynamics of infectious disease has rapidly increased over the last two decades. e research foci in this area are ranging from the study of respiratory diseases such as measles, influenza, and tuberculosis; vector-borne diseases such as malaria, Ebola, zikav, and dengue; to sexually transmitted diseases such as HIV/AIDS (see for instance the works of Beay [23], Reynolds et al [24], Mitchell and Ross [25], Egonmwan and Okuonghae [26], Nkamba et al [27], Bakary et al [28], Irwan et al [29], Akgül et al [30], Ainisa et al [31], Carvalho et al [32], Omondi et al [33], and Chong et al [34]). Mathematical modeling is usually used to characterize the epidemiological parameters of disease during outbreaks and to evaluate the effectiveness and schedule of various prevention and control strategies, considering limited resource availability [35].…”
Section: Related Workmentioning
confidence: 99%
“…In our model, the population is grouped into six sections: active-TB infected individuals, who are infectious ( ) . For this modelling purposes, we assume individuals between ages 15 and 54 are sexually active and highly susceptible to HIV infection [11]. Table 1 shows model parameters and represents the variables in the equations.…”
Section: Model Descriptionmentioning
confidence: 99%
“…In Ghana, although the HIV prevalence in the general population is low, TB prevalence is high at 152 per 100,000 persons [11]. The average TB/HIV co-infection rate is 21% as at 2017, [12], which varies from 9.4% in the Upper East Region to 33.4% in the Eastern Region, as shown in Figure 2.…”
Section: Introductionmentioning
confidence: 99%