Is relative viral load an important metric for treatment and prognosis of influenza A?

Zane Billings, a student at Western Carolina University, worked with Dr. Andreas Handel and graduate student Brian McKay in the UGA College of Public Health.

Abstract
Influenza-like illnesses (ILIs) present with several of the same symptoms, including cough, fatigue, and weakness. However, ILIs can be caused by a range of different pathogens with vastly different treatments. Quantitative PCR is an incredibly specific and sensitive method to detect several ILIs, but until recently, qPCR methods were prohibitively expensive and required special training and equipment. Recent advances in qPCR technology have allowed for machines such as the Roche cobas Liat system to become available to point-of-care physicians. Using data collected from the University of Georgia Student Health Center, qPCR data was examined relative to patient and physician reported symptoms, as well as impacts and recovery from disease to determine if quantitative estimates of relative viral load are important for physicians to make informed decisions. While relative viral load estimates were found to be correlated to days since the onset of illness and patient temperature at diagnosis, no correlations were found between recovery or severity of illness and relative viral load. However, the study sample was very limited and more research should be performed on broader study populations.

Billings_revised

Understanding the dynamics of viral shedding within Norovirus infected subjects

Simran Budhwar from the University of Virginia, worked with Rachel Mercaldo, Brian McKay, and Dr. Andreas Handel to study shedding of Norovirus.

Abstract: Norovirus (NoV) is a common cause of acute gastroenteritis. Symptoms include vomiting and diarrhea, which can lead to complications such as dehydration and also serve to spread viral particles through bodily fluids. While some infections are asymptomatic, infected individuals shed the virus regardless of disease severity, primarily through stool. To understand the dynamics of viral shedding, previous studies measured viral load in healthy human subjects challenged with various doses of the virus. In the present analysis, data from these studies was combined to better describe NoV shedding over time. We calculated key variables such as peak viral titer, time to peak viral titer, and duration of shedding, in addition to estimating total shedding through the area under the curve (AUC) value of each participant’s total shedding time-series curve. On average, patients shed the virus for 22 days, with the peak viral titer appearing on day 5 following challenge. Peak viral titers were 10.551 (log10) genomic equivalence copies per gram of stool, while AUC averaged at 11.58 (log10) genomic equivalence copies per gram stool. Though these are key variables that are necessary to understand viral shedding, future work should focus on exploring the drivers of variation in viral load and shedding, such as symptoms or other patient-specific factors.

Budwhar

Development of infectious disease research and teaching software

Using computer models to study infectious diseases can be challenging for students and researchers who are not trained as modelers. To make this process easier, we have developed several software packages, implemented in the popular R language, to help individuals learn about and analyze infectious disease models both at the individual and population level. The goal of this project is to further advance this software by implementing new features, making tutorials, testing existing features, adding new features, and more. This will increase the usefulness and power of the software and will give future students and researchers better tools to learn about and analyze different infectious diseases. This project is quantitative. We will make use of the R language for all parts of this project. The project is offered by the Handel group.

Host Laboratory: Andreas Handel
Type of project: Quantitative/Computer-based

Data analysis to help inform norovirus vaccine design

Norovirus is a common cause of gastrointestinal disease. There is currently no vaccine, but several are under development. It is not clear how exactly a new norovirus vaccine should look like and to whom it should be mainly given (e.g. children or adults). Together with colleagues at Emory University, we are working on a project that analyzes different types of norovirus data to develop a comprehensive mathematical modeling framework to guide norovirus vaccine design and implementation. For this project, you will help with those analyses. The results of this analysis will allow us to better understand properties of norovirus infection and transmission and therefore will help inform the design and implementation of a future norovirus vaccine. This project is quantitative. We will make use of the R language for all analyses. The project is offered by the Handel group.

Host Laboratory: Andreas Handel
Type of project: Quantitative/Computer-based

Studying the relation between vaccine dose and outcomes

For every vaccine, the amount of the antigens of the pathogen one wants to vaccinate against is an important part. This amount that is currently not systematically determined but is instead based on sparse clinical data. We recently developed a framework that combines data with mathematical models to better determine the vaccine dose that would lead to optimal outcomes. The goal of this project is to further advance this framework by combining data from influenza infected individuals with computer models to determine what the impact of vaccine dose is on outcomes such as side effects and immune protection. This information will be useful for improved design of future vaccines. This project is quantitative. We will make use of the R language for all analyses. The project is offered by the Handel group.

Host Laboratory: Andreas Handel
Type of project: Quantitative/Computer-based

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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