scholarly journals Resource fluctuations inhibit the reproduction and virulence of the human parasite Schistosoma mansoni in its snail intermediate host

2020 ◽  
Vol 287 (1919) ◽  
pp. 20192446
Author(s):  
David J. Civitello ◽  
Lucy H. Baker ◽  
Selvaganesh Maduraiveeran ◽  
Rachel B. Hartman

Resource availability can powerfully influence host–parasite interactions. However, we currently lack a mechanistic framework to predict how resource fluctuations alter individual infection dynamics. We address this gap with experiments manipulating resource supply and starvation for a human parasite, Schistosoma mansoni , and its snail intermediate host to test a hypothesis derived from mechanistic energy budget theory: resource fluctuations should reduce schistosome reproduction and virulence by inhibiting parasite ingestion of host biomass. Low resource supply caused hosts to remain small, reproduce less and produce fewer human-infectious cercariae. Periodic starvation also inhibited cercarial production and prevented infection-induced castration. The periodic starvation experiment also revealed substantial differences in fit between two bioenergetic model variants, which differ in their representation of host starvation. Simulations using the best-fit parameters of the winning model suggest that schistosome performance substantially declines with resource fluctuations with periods greater than 7 days. These experiments strengthen mechanistic theory, which can be readily scaled up to the population level to understand key feedbacks between resources, host population dynamics, parasitism and control interventions. Integrating resources with other environmental drivers of disease in an explicit bioenergetic framework could ultimately yield mechanistic predictions for many disease systems.

2013 ◽  
Vol 6 (1) ◽  
pp. 167 ◽  
Author(s):  
Sara Fneich ◽  
Nolwenn Dheilly ◽  
Coen Adema ◽  
Anne Rognon ◽  
Michael Reichelt ◽  
...  

2021 ◽  
Author(s):  
Catherine A. A. Beauchemin ◽  
Andreas Handel

Most mathematical models used to study the dynamics of influenza A have thus far focused on the between-host population level, with the aim to inform public health decisions regarding issues such as drug and social distancing intervention strategies, antiviral stockpiling or vaccine distribution. Here, we investigate mathematical modeling of influenza infection spread at a different scale; namely that occurring within an individual host or a cell culture. We review the models that have been developed in the last decades and discuss their contributions to our understanding of the dynamics of influenza infections. We review kinetic parameters (e.g., viral clearance rate, lifespan of infected cells) and values obtained through fitting mathematical models, and contrast them with values obtained directly from experiments. We explore the symbiotic role of mathematical models and experimental assays in improving our quantitative understanding of influenza infection dynamics. We also discuss the challenges in developing better, more comprehensive models for the course of influenza infections within a host or cell culture. Finally, we explain the contributions of such modeling efforts to important public health issues, and suggest future modeling studies that can help to address additional questions relevant to public health.


2021 ◽  
Author(s):  
Catherine A. A. Beauchemin ◽  
Andreas Handel

Most mathematical models used to study the dynamics of influenza A have thus far focused on the between-host population level, with the aim to inform public health decisions regarding issues such as drug and social distancing intervention strategies, antiviral stockpiling or vaccine distribution. Here, we investigate mathematical modeling of influenza infection spread at a different scale; namely that occurring within an individual host or a cell culture. We review the models that have been developed in the last decades and discuss their contributions to our understanding of the dynamics of influenza infections. We review kinetic parameters (e.g., viral clearance rate, lifespan of infected cells) and values obtained through fitting mathematical models, and contrast them with values obtained directly from experiments. We explore the symbiotic role of mathematical models and experimental assays in improving our quantitative understanding of influenza infection dynamics. We also discuss the challenges in developing better, more comprehensive models for the course of influenza infections within a host or cell culture. Finally, we explain the contributions of such modeling efforts to important public health issues, and suggest future modeling studies that can help to address additional questions relevant to public health.


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