Modeling the Transmission Dynamics of HIV/AIDS epidemics: an introduction and a review.

Akpa, Onoja Matthew; Oyejola, B. A.

doi:10.3855/jidc.542

Cited by 22 publications

(17 citation statements)

References 16 publications

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“…To an extent our ignorance of HIV transmission patterns in MSM is attributable to a dearth of well-structured theory about how different hypothetical transmission system conformations lead to specific transmission patterns and what mechanisms underlie these effects. A body of HIV transmission system theory is growing and thousands of modeling studies of HIV transmission have been published (Akpa and Oyejola 2010). Still, theory and data are barely beginning to come together in a way that allows us to see clearly how and why acute HIV transmissions cluster, and how this could explain the failure to control the spread of HIV infection in the MSM population.…”

Section: Discussionmentioning

confidence: 99%

Episodic HIV Risk Behavior Can Greatly Amplify HIV Prevalence and the Fraction of Transmissions from Acute HIV Infection

Zhang¹,

Zhong²,

Romero-Severson³

et al. 2012

Statistical Communications in Infectious Diseases

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A deterministic compartmental model was explored that relaxed the unrealistic assumption in most HIV transmission models that behaviors of individuals are constant over time. A simple model was formulated to better explain the effects observed. Individuals had a high and a low contact rate and went back and forth between them. This episodic risk behavior interacted with the short period of high transmissibility during acute HIV infection to cause dramatic increases in prevalence as the differences between high and low contact rates increased and as the duration of high risk better matched the duration of acute HIV infection. These same changes caused a considerable increase in the fraction of all transmissions that occurred during acute infection. These strong changes occurred despite a constant total number of contacts and a constant total transmission potential from acute infection. Two phenomena played a strong role in generating these effects. First, people were infected more often during their high contact rate phase and they remained with high contact rates during the highly contagious acute infection stage. Second, when individuals with previously low contact rates moved into an episodic high-risk period, they were more likely to be susceptible and thus provided more high contact rate susceptible individuals who could get infected. These phenomena make test and treat control strategies less effective and could cause some behavioral interventions to increase transmission. Signature effects on genetic patterns between HIV strains could make it possible to determine whether these episodic risk effects are acting in a population.

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Section: Discussionmentioning

confidence: 99%

Episodic HIV Risk Behavior Can Greatly Amplify HIV Prevalence and the Fraction of Transmissions from Acute HIV Infection

Zhang¹,

Zhong²,

Romero-Severson³

et al. 2012

Statistical Communications in Infectious Diseases

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“…Second, it is not necessarily the case that these patterns can be easily expressed in tractable mathematics. Refined models based on typical social patterns such as partnership formation and dissolution [22,23], sexual network formations [24], or concurrent partnership [25] have been analyzed by various authors (see also [26,27,28]), and more recently in the context of HIV/AIDS transmission [29,30]. However such models are susceptible to unreliable data since sexual intercourses are difficult to track through statistical surveys or marital status.…”

Section: Discussionmentioning

confidence: 99%

Renaissance model of an epidemic with quarantine

Dobay

Gall

Rankin

et al. 2013

Journal of Theoretical Biology

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Quarantine is one possible solution to limit the propagation of an emerging infectious disease. Typically, infected individuals are removed from the population by avoiding physical contact with healthy individuals. A key factor for the success of a quarantine strategy is the carrying capacity of the facility. This is often a known parameter, while other parameters such as those defining the population structure are more difficult to assess. Here we develop a model where we explicitly introduce the carrying capacity of the quarantine facility into a susceptible-infected-recovered (SIR) framework. We show how the model can address the propagation and control of contact and sexually transmitted infections. We illustrate this by a case study of the city of Zurich during the 16th century, when it had to face an epidemic of syphilis. After Swiss mercenaries came back from a war in Naples in 1495, the authorities of the city addressed subsequent epidemics by, among others, placing infected members of the population in quarantine. Our results suggest that a modestly sized quarantine facility can successfully prevent or reduce an epidemic. However, false detection can present a real impediment for this solution. Indiscriminate quarantine of individuals can lead to the overfilling of the facility, and prevent the intake of infected individuals. This results in the failure of the quarantine policy. Hence, improving the rate of true over false detection becomes the key factor for quarantine strategies. Moreover, in the case of sexually transmitted infections, asymmetries in the male to female ratio, and the force of infection pertaining to each sex and class of sexual encounter can alter the effectiveness of quarantine measures. For example, a heterosexually transmitted disease that mainly affects one sex is harder to control in a population with more individuals of the opposite sex. Hence an imbalance in the sex ratios as seen in situations such as mining colonies, or populations at war, can present impediments for the success of quarantine policies. AbstractQuarantine is one possible solution to limit the propagation of an emerging infectious disease. Typically, infected individuals are removed from the population by avoiding physical contact with healthy individuals. A key factor for the success of a quarantine strategy is the carrying capacity of the facility. This is often a known parameter, while other parameters such as those defining the population structure are more difficult to assess. Here we develop a model where we explicitly introduce the carrying capacity of the quarantine facility into a susceptible-infected-recovered (SIR) framework. We show how the model can address the propagation and control of contact and sexually transmitted infections. We illustrate this by a case study of the city of Zurich during the 16 th century, when it had to face an epidemic of syphilis. After Swiss mercenaries came back from a war in Naples in 1495, the authorities of the city addressed subsequent epidemics by, amon...

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“…Without any treatment, this infection leads to the failure of the host immune system and ultimately to death [1]. The spread of infection is considered to be a vital part of any infectious disease where the dynamics of the disease primarily depends on the rate of transmission from an infectious individual to a susceptible individual [2]. The deadly and epidemic nature of HIV has several biological characteristics and has attracted epidemiologists, mathematicians and biologists resulting in various mathematical models that have proven to be valuable for understanding the evolution of the disease, the epidemiological patterns of HIV, especially the mechanism associated with the spread of the disease [3].…”

Section: Introductionmentioning

confidence: 99%

Between-host HIV model: stability analysis and solution using memetic computing

Ahmed¹,

Qureshi²,

Saleem³

et al. 2019

Turk J Elec Eng & Comp Sci

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HIV poses a great threat to humanity for two major reasons. First it attacks the immunity system of the body and second, it is epidemic in nature. Mathematical models of HIV have been instrumental in understanding and controlling the infection. In this paper, we solve the between host epidemic model of HIV, described by nonlinear coupled differential equations, by using memetic computing. Under this model, the sexually active population is divided into four classes and we investigate the transfer of individuals from one class to another. The solution consists of Bernstein polynomials whose parameters have been optimized by using differential evolution as global and sequential quadratic programming as local optimizer. Our second contribution is the stability analysis of this model. The disease-free equilibrium is stable while endemic equilibrium is unstable within the practical range of the values of parameters.

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Modeling the Transmission Dynamics of HIV/AIDS epidemics: an introduction and a review.

Cited by 22 publications

References 16 publications

Episodic HIV Risk Behavior Can Greatly Amplify HIV Prevalence and the Fraction of Transmissions from Acute HIV Infection

Episodic HIV Risk Behavior Can Greatly Amplify HIV Prevalence and the Fraction of Transmissions from Acute HIV Infection

Renaissance model of an epidemic with quarantine

Between-host HIV model: stability analysis and solution using memetic computing

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