In the past Alison P. Galvani has collaborated on articles with Travis C. Porco and Eunha Shim. One of their most recent publications is The effect of treatment on pathogen virulence. Which was published in journal Journal of Theoretical Biology.

More information about Alison P. Galvani research including statistics on their citations can be found on their Copernicus Academic profile page.

Alison P. Galvani's Articles: (8)

The effect of treatment on pathogen virulence

AbstractThe optimal virulence of a pathogen is determined by a trade-off between maximizing the rate of transmission and maximizing the duration of infectivity. Treatment measures such as curative therapy and case isolation exert selective pressure by reducing the duration of infectivity, reducing the value of duration-increasing strategies to the pathogen and favoring pathogen strategies that maximize the rate of transmission. We extend the trade-off models of previous authors, and represents the reproduction number of the pathogen as a function of the transmissibility, host contact rate, disease-induced mortality, recovery rate, and treatment rate, each of which may be influenced by the virulence. We find that when virulence is subject to a transmissibility-mortality trade-off, treatment can lead to an increase in optimal virulence, but that in other scenarios (such as the activity-recovery trade-off) treatment decreases the optimal virulence. Paradoxically, when levels of treatment rise with pathogen virulence, increasing control efforts may raise predicted levels of optimal virulence. Thus we show that conflict can arise between the epidemiological benefits of treatment and the evolutionary risks of heightened virulence.

Epidemiology meets evolutionary ecology

AbstractThe rapid expansion and increasing mobility of human populations make understanding the evolution of parasite virulence a public health priority. The potential for the swift evolution of virulence in response to changes in host ecology has motivated the integration of evolutionary ecology with epidemiological theory, as part of the emerging field of evolutionary epidemiology. Virulence is the product of complex interactions among evolutionary, ecological and epidemiological processes. Recent models that incorporate ideas from both evolutionary ecology and epidemiology generate predictions that could not be made by either discipline alone. These models predict that the ecological or evolutionary changes affecting population dynamics of disease, such as spatial structuring, within-host dynamics, polymorphism in host resistance, host longevity and population size, impose selection on virulence. As disease incidence increases, it becomes particularly important to take into account the implications of infection by multiple parasite strains. Evolutionary epidemic models also identify the potential importance of immune evasion and optimal virulence for the selection of sex in parasites. Thus, merging epidemiology with evolutionary ecology has widespread potential to help us answer evolutionary questions and to guide public health policy.

Impact of transmission dynamics on the cost-effectiveness of rotavirus vaccination

AbstractThe objective of this study is to estimate the cost-effectiveness of mass vaccination of US infants with the recently available rotavirus vaccine, RotaTeq. We developed a dynamic transmission model of rotavirus to incorporate herd immunity into cost-effectiveness analysis. Our study indicates that a rotavirus vaccination program would prevent about 90% of rotavirus incidence, mortality, hospitalization and emergency department visits annually. We conclude that a universal rotavirus vaccine program in the US would cost $77.30 per case averted from the health care and give a net saving of $80.75 per case averted from the societal perspectives, respectively. The cost per QALY gained was found to be $104,610 when we considered child with one caregiver, making the rotavirus vaccination program a cost-effective intervention.

Distinguishing vaccine efficacy and effectiveness

AbstractBackgroundMathematical models of disease transmission and vaccination typically assume that protective vaccine efficacy (i.e. the relative reduction in the transmission rate among vaccinated individuals) is equivalent to direct effectiveness of vaccine. This assumption has not been evaluated.MethodsWe used dynamic epidemiological models of influenza and measles vaccines to evaluate the common measures of vaccine effectiveness in terms of both the protection of individuals and disease control within populations. We determined how vaccine-mediated reductions in attack rates translate into vaccine efficacy as well as into the common population measures of ‘direct’, ‘indirect’, ‘total’, and ‘overall’ effects of vaccination with examples of compartmental models of influenza and measles vaccination.ResultsWe found that the typical parameterization of vaccine efficacy using direct effectiveness of vaccine can lead to the underestimation of the impact of vaccine. Such underestimation occurs when the vaccine is assumed to offer partial protection to every vaccinated person, and becomes worse when the level of vaccine coverage is low. Nevertheless, estimates of ‘total’, ‘indirect’ and ‘overall’ effectiveness increase with vaccination coverage in the population. Furthermore, we show how the measures of vaccine efficacy and vaccine effectiveness can be correctly calculated.ConclusionsTypical parameterization of vaccine efficacy in mathematical models may underestimate the actual protective effect of the vaccine, resulting in discordance between the actual effects of vaccination at the population level and predictions made by models. This work shows how models can be correctly parameterized from clinical trial data.

Optimal targeting of seasonal influenza vaccination toward younger ages is robust to parameter uncertainty

Highlights•We develop a mathematical model of seasonal influenza in the United States.•We identify optimal vaccination allocation strategies under parameter uncertainty.•Strategies targeting schoolchildren and young adults are robust to uncertainty.•Parameter uncertainty is vital to the evaluation of public health interventions.

Cost-effectiveness of next-generation vaccines: The case of pertussis

Highlights•We develop the MCPI metric to quantify the value of improving an existing vaccine.•The MCPI is the maximum cost-effective price increase for incremental improvements.•We apply the MCPI to evaluate potential improvements in pertussis vaccines.•It would be most valuable to extend the duration of the childhood series.•Improvements to childhood series are most valuable, despite waning of adult boosters.

Probabilistic uncertainty analysis of epidemiological modeling to guide public health intervention policy☆

Highlights•Dynamic disease modeling of public health interventions rarely accounts for known uncertainties probabilistically.•Uncertainty distributions for model parameters can be derived by analysis of data.•Probabilistic parameterization of analytical solutions yields outcome uncertainty.•Best point estimate predictions would achieve disease mitigation ∼50% of the time.•Our uncertainty analysis of influenza conveys outcome risk for antiviral and vaccination policy.

Evaluating the potential impact of mass praziquantel administration for HIV prevention in Schistosoma haematobium high-risk communities

Highlights•We model the joint dynamics of schistosomiasis and HIV transmission in three African countries.•We evaluate the potential effect of schistosomiasis treatment for reducing HIV transmission.•Outcome highly dependent on relationship between schistosomiais as a child and FGS as an adult.•Impact of control intervention depends on phase of the HIV epidemic.

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