Optimal control analysis of malaria&amp;#8211;schistosomiasis co-infection dynamics

Okosun, Kazeem Oare; Smith, Robert J.

doi:10.3934/mbe.2017024

Cited by 4 publications

(2 citation statements)

References 38 publications

(44 reference statements)

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“…Recent work demonstrates, for example, that controlling schistosomiasis infections can contribute to population-level control of malaria independent of any direct increase in malaria susceptibility from schistosomiasis infection within individual hosts. [ 35 ] Using the method described here for translating the results of such research into practical applications provides a powerful tool to apply the most up-to-date research methods are applied directly to the challenges faced by those tasked with implementing disease control measures on-the ground.…”

Section: Discussionmentioning

confidence: 99%

Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis

et al. 2018

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Managing infectious disease requires rapid and effective response to support decision making. The decisions are complex and require understanding of the diseases, disease intervention and control measures, and the disease-relevant characteristics of the local community. Though disease modeling frameworks have been developed to address these questions, the complexity of current models presents a significant barrier to community-level decision makers in using the outputs of the most scientifically robust methods to support pragmatic decisions about implementing a public health response effort, even for endemic diseases with which they are already familiar. Here, we describe the development of an application available on the internet, including from mobile devices, with a simple user interface, to support on-the-ground decision-making for integrating disease control programs, given local conditions and practical constraints. The model upon which the tool is built provides predictive analysis for the effectiveness of integration of schistosomiasis and malaria control, two diseases with extensive geographical and epidemiological overlap, and which result in significant morbidity and mortality in affected regions. Working with data from countries across sub-Saharan Africa and the Middle East, we present a proof-of-principle method and corresponding prototype tool to provide guidance on how to optimize integration of vertical disease control programs. This method and tool demonstrate significant progress in effectively translating the best available scientific models to support practical decision making on the ground with the potential to significantly increase the efficacy and cost-effectiveness of disease control.Author summaryDesigning and implementing effective programs for infectious disease control requires complex decision-making, informed by an understanding of the diseases, the types of disease interventions and control measures available, and the disease-relevant characteristics of the local community. Though disease modeling frameworks have been developed to address these questions and support decision-making, the complexity of current models presents a significant barrier to on-the-ground end users. The picture is further complicated when considering approaches for integration of different disease control programs, where co-infection dynamics, treatment interactions, and other variables must also be taken into account. Here, we describe the development of an application available on the internet with a simple user interface, to support on-the-ground decision-making for integrating disease control, given local conditions and practical constraints. The model upon which the tool is built provides predictive analysis for the effectiveness of integration of schistosomiasis and malaria control, two diseases with extensive geographical and epidemiological overlap. This proof-of-concept method and tool demonstrate significant progress in effectively translating the best available scientific models to support pragmat...

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Section: Discussionmentioning

confidence: 99%

Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis

et al. 2018

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“…[31] formulated a model for the co-interaction of schistosomiasis and HIV/AIDS for the purpose of assessing their symbiotic connection in the company of therapeutic measures. [33] investigated, mathematically, malaria and schistosomiasis co-infection for the purpose of scrutinizing the symbiotic connection that exists between them in the presence of treatment.…”

Section: Introductionmentioning

confidence: 99%

Causes of Backward Bifurcation in a Tuberculosis-Schistosomiasis Co-infection Dynamics

Ako,

Olowu

2024

EJMS

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To obtain a thorough understanding of the influence of schistosomiasis infections on the transmission dynamics of tuberculosis, a deterministic mathematical model for the transmission dynamics of tuberculosis (TB) co-infection with schistosomiasis is created and examined. The aim of the research is to examine the reasons behind the backward bifurcation in the co-infection dynamics of tuberculosis and schistosomiasis. The backward bifurcation phenomena can be caused by the following parameters, according to the model's analysis (when the associated reproduction number is less than one), other than the well established route of exogeneous re-infection of latently infected TB individuals, the relative rates at which humans with latent schistosomiasis ($\eta_1$) and active schistosomiasis ($\eta_2$) are infected with TB, respectively, the lowered rate of reinfection with schistosomiasis ($\psi$), the fraction of individuals who experience fast progression to active TB ($p$), the adjustment parameter which accounts for the increased probability of infectiousness of humans with active TB and latent schistosomiasis ($\Pi_1$), the treatment rate of people infected with active TB exposed to schistosomiasis ($\zeta_{T1}$) and the rate of progression to active TB and exposed to schistosomiasis to active TB and active schistosomiasis ($\sigma$).

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Chemical and biological control of parasite-borne disease Schistosomiasis: An impulsive optimal control approach

Das

2021

Nonlinear Dyn

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Optimal control analysis of malaria–schistosomiasis co-infection dynamics

Cited by 4 publications

References 38 publications

Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis

Decision support for evidence-based integration of disease control: A proof of concept for malaria and schistosomiasis

Causes of Backward Bifurcation in a Tuberculosis-Schistosomiasis Co-infection Dynamics

Chemical and biological control of parasite-borne disease Schistosomiasis: An impulsive optimal control approach

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