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Treating symptomatic infections and the co-evolution of virulence and drug resistanceuse asterix (*) to get italics
Samuel AlizonPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
2020
<p>Antimicrobial therapeutic treatments are by definition applied after the onset of symptoms, which tend to correlate with infection severity. Using mathematical epidemiology models, I explore how this link affects the coevolutionary dynamics between the virulence of an infection, measured via host mortality rate, and its susceptibility to chemotherapy. I show that unless resistance pre-exists in the population, drug-resistant infections are initially more virulent than drug-sensitive ones. As the epidemic unfolds, virulence is more counter-selected in drug-sensitive than in drug-resistant infections. This difference decreases over time and, eventually, the exact shape of genetic trade-offs govern long-term evolutionary dynamics. Using adaptive dynamics, I show that two types of evolutionary stable strategies (ESS) may be reached in the context of this simple model and that, depending on the parameter values, an ESS may only be locally stable. In general, the more the treatment rate increases with virulence, the lower the ESS value. Overall, both on the short-term and long-term, having treatment rate depend on infection virulence tend to favour less virulent strains in drug-sensitive infections. These results highlight the importance of the feedbacks between epidemiology, public health policies and parasite evolution, and have implications for the monitoring of virulence evolution.</p>
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https://www.biorxiv.org/content/10.1101/2020.02.29.970905v3.supplementary-materialYou should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://
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virulence, drug resistance, epidemiology, public health
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Evolutionary Applications, Evolutionary Dynamics, Evolutionary Epidemiology, Evolutionary Theory
e.g. John Doe john@doe.com
No need for them to be recommenders of PCIEvolBiol. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe john@doe.com
2020-03-04 10:18:39
Ludek Berec