Mentor(s): Dr. Andreas Handel group (http://handelgroup.uga.edu/)
Abstract: 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.
Is the project computational, empirical, or both? This project is computational. We will make use of the R language for all parts of this project.
Tag Archives: Handel
5) Cataloging norovirus challenge study data
Mentor(s): Dr. Andreas Handel group (http://handelgroup.uga.edu/)
Abstract: A challenge study is a type of trial where patients are intentionally infected with a pathogen. Norovirus, everyone’s favorite cruise ship disease, has been studied in several challenge trials in the past, since its symptoms are relatively mild, and it is difficult to grow in the lab outside of live animals. While certain norovirus challenge studies are widely cited, there is no consensus among experts of exactly how many norovirus challenge studies have been conducted. We will review the published literature to find all previously published challenge studies, and where possible, extract the data from these studies in order to gain a more comprehensive understanding of how many norovirus challenge trials have been conducted and what information they have provided.
Is the project computational, empirical, or both? This project is computational and will use the R programming language where necessary. We do not expect this project to be programming intensive.
4) Modelling outcomes of influenza infection and vaccination
Mentor(s): Dr. Andreas Handel group (http://handelgroup.uga.edu/)
Abstract: Everyone responds differently to the flu shot – the vaccine does not provide 100% protection like some other diseases, and the vaccine needs to be updated every year because flu is able to rapidly evolve and quickly evade immune defenses. The immunological mechanisms for these differences are still not well understood, despite years of previous scientific work. As part of a multi-center collaboration, we will combine data from several studies of seasonal influenza vaccines, and hunt for clues. By leveraging the power of data visualization, machine learning, and statistical modeling techniques, we can identify which aspects of a person, vaccine, or flu strain might affect an individual’s response to the flu vaccine.
Is the project computational, empirical, or both? This project will be entirely computational, and will consist of data exploration and statistical analyses. We will use the R programming language, and this project will involve a substantial amount of programming.
Designing and Evaluating the Need for Patient Based Clinical Prediction Rules for Influenza Triage Telemedicine
Annika Cleven, a student at St. Olaf College, worked in the lab of Dr. Andreas Handel
Abstract Using data that was collected from a university health center where patients and clinicians were asked to report the presence of a list of respiratory-related symptoms, we analyzed the need for patient based Clinical Prediction Rules (CPRs). We investigated inter-rater agreement between clinicians and patients on symptom reporting and found they often disagreed. A Bland-Altman analysis indicated that the disagreement on symptom reporting was enough to invalidate using patient reported symptom data on a CPR that was designed using clinician reported data. Further analysis determined that using patient versus clinician reported symptom data could lead to different risk category designations and advised care. In response, we built risk prediction models that were fit to patient reported data and they provided minimal improvement from the clinician based CPRs. Our research indicates that clinician based CPRs cannot be effectively used with patient reported symptoms data, like in telemedicine. Our results also show that we can create a patient-based CPR, but because of the minimal improvement, a combination of at-home testing and a patient-based CPR would be the best approach to influenza triage telemedicine.
Cleven_posterInvestigating the Clinical Relevance of Patient-Reported Symptoms for Influenza Triage
Jacqueline Dworaczyk, a student at Arizona State University, worked in the lab of Dr. Andreas Handel.
Telemedicine has become increasingly popular during the age of Covid-19. During a public health crisis, telemedicine could be used as a tool to triage patients and prevent burden on the health care system. In an exploratory data analysis, we investigated whether a symptom questionnaire could be used to predict influenza diagnosis. A symptom questionnaire containing 19 upper respiratory symptoms was administered to patients and clinicians (n = 2475 patients) at University of Georgia’s Health Center during the 2016-2017 flu season. Five clinical decision rules were applied to the symptoms reported by the patients and clinicians. The clinical decision rules’ performance when predicting influenza diagnosis was assessed using AUC, F1, MCC, sensitivity and specificity. A 7-11% drop in AUC was observed across all clinical decision rules when using the patient-reported symptoms as opposed to the clinician-reported symptoms.
In a sensitivity analysis, we evaluated the clinical decision rules’ ability to predict true, lab-confirmed influenza in a subset of our population who received PCR testing. While clinician-reported symptoms still performed better than patient-reported symptoms, the difference in AUCs when predicting PCR was significantly smaller. These differences in performance may be partially explained by a lack of agreement between patients and clinicians on the presence of signs and symptoms. Agreement between the patients and clinician’s questionnaire responses (n = 2475) was quantified using Cohen’s Kappa statistic and found that at best, patient’s and clinician’s had moderate agreement on three of the nineteen symptoms assessed. Overall, it was found that using a patient symptom questionnaire to predict physician diagnosis led to a reduction in accuracy. Further studies need to be done to assess the clinical relevance of this reduction.
DworaczykInfectious disease professionals need better training in modeling
Salil Goyal, a student at the University of California Berkeley, worked with Dr. Andreas Handel.
Models have become increasingly important in the field of infectious disease epidemiology, and broadly in the field of public health, in recent years because the ability of scientists and officials to make educated decisions based on data is important. However, many authors have stated recently (and especially during COVID) that programs that train people working with infectious diseases generally do not impart adequate training in understanding models. While many resources aimed at teaching infectious disease modeling do exist, they are aimed at different audiences and there currently exists no synthesis of these resources. As a potential solution to the problem, we offer here a comprehensive review of resources relevant to the pedagogy of infectious disease epidemiology, with a focus on modeling.
GoyalIs 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.
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.
BudwharInfluenza 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.
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.
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.
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.