Influenza inoculum dose and disease outcome

Wei-En Lu, a junior from Grove City College, worked with Dr. Brian McKay and Dr. Andreas Handel to examine the relationship between incolum dose and disease outcome in influenza.

Abstract:  The purpose of this study is to determine the relationship between influenza inoculum dose and disease outcomes.  A systematic review to identify and abstract data from all influenza challenge studies were conducted. Exponential and linear models were used to assess the impact of inoculum dose on disease outcomes. This study found that inoculum dose has a positive relationship on the proportion infected. However, there was a negative trend between inoculum dose and proportion of fever or systemic symptoms and between inoculum dose and the mean peak viral titers. There was also a rise of inoculum dose given to individuals and a decrease in the proportion of individuals with disease outcome over time. In conclusion, inoculum dose has a definite impact on disease outcomes.

 

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Inoculum Dose and Infection Outcome

Annaliese Wiens, a student from Tabor College, worked with Dr. Andreas Handel to examine the relationship between inoculum dose and infection outcome.

Abstract:  Dose-response models describe how different inoculum doses of a pathogen alter the probability of infection with a host. It is generally assumed that higher amounts of inoculum increase infection rates, but the exact relationship has yet to be determined. We performed a meta-analysis of systematically-reviewed influenza challenge studies in which the exact inoculum dose and proportion of people infected were given. This data was used to fit several models, including an exponential model and an approximate Beta-Poisson model. These models were also stratified by different covariates, such as the strain of influenza and preparation of the virus. We used the exponential model to show that viruses prepared by different methods (wild-type, cold-adapted, etc.) have differing levels of infectivity, implying some loss of fitness during passaging through human or non-human cells.

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Predicting the Effectiveness of Novel Tuberculosis Regimens

Taylor Joseph, a student from Michigan State University, worked with Dr. Andreas Handel to use computational methods to examine new tuberculosis treatment regimes.

Abstract: Tuberculosis (TB) remains one of the world’s most deadly diseases, as current treatment protocols are far outdated and often ineffective. Furthermore, current regimens are complicated and last many months, often leading to patient non-compliance and drug resistant bacteria. There is thus a need for more effective and efficient treatment strategies, yet conducting human trials on these new strategies is expensive and time consuming. As an alternative or supplement to human and animal trials, computational models may be used to predict the outcomes of new treatment strategies. In this study, we use a system of differential equations to describe within-host dynamics of TB and drug treatment, and we assess the model’s accuracy in comparison to data collected from previous clinical studies. We then use the model to predict and evaluate the outcome of new treatment regimens.

 

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Impact of patient non-compliance on tuberculosis treatment regimens

Kylie Balotin, a student at Rice University, and Dr. Andreas Handel, in the UGA Department of Epidemiology and Biostatistics, worked together to model the effect of patient compliance on the effectiveness of tuberculosis treatments.

Kylie Balotin1 and Andreas Handel2

1 Rice University, Houston, TX, 77005, USA1

2 Department of Epidemiology and Biostatistics, College of Public Health,

University of Georgia, Athens, GA 30602, USA

Tuberculosis is a leading cause of death in the world today and infects about one third of the world’s population. WHO currently recommends a standard treatment for TB consisting of multiple drugs. Alternative drug combinations are also being investigated as possible regimens. Although the current standard treatment is fairly effective, due to factors including the long treatment time of tuberculosis, many patients do not follow the entire treatment regimen. This noncompliance could lead to the relapse of the patient and the emergence of resistance to anti-TB drugs. The objective of this study is to use a mathematical model that simulates TB drug treatment and patient non-compliance in order to investigate the effect of patient compliance with three TB treatment regimens (the standard regimen, Remox 1, and Remox 2) a percentage of the time. We show that Remox 2 is generally more forgiving towards patient non-compliance than the other two regimens.

 

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