8) Detecting influenza A virus antigenicity with density-based algorithms

Mentor(s): Dr. Pej Rohani group
Abstract: Influenza viruses, such as H3N3 and H1N1, are a major cause of illness and death worldwide, accounting for over 3 million severe cases and approximately 500,000 deaths each year. These viruses also have a significant economic impact. The use of predictive modelling has been a useful in detecting new strains of the virus and developing the vaccines against the virus.
In this project, we will compare the predictive performance of density-based outlier detection algorithms, specifically the Empirical-Cumulative-distribution-based Outlier Detection (ECOD) algorithm, with isolation forest and one-class support vector machines (SVMs) in detecting unusual physicochemical properties in influenza A viruses. By identifying viruses with significantly different physicochemical properties, we can identify those that may be antigenically distinct, which has important implications for vaccine development and public health efforts.
We will use both simulated and benchmark data from Smith et al. for the experiments and fit the models using Python libraries such as Sciklearn, Pandas, Numpy, and PyoD. We will then use R packages, including ggplot2, ggdensity, dplyr, and tidyverse, for data visualization and analysis.
The ECOD algorithm has three desirable properties for this task: it can be fitted with limited hyperparameter tuning, it is computationally effective, and it improves interpretability. We will compare the predictive and inferential performance of all three algorithms. The results for isolation forest and one-class SVMs will be provided from previous experiments. We will end the project with a final report.
Is the project computational, empirical, or both? Computational.

7) Environmental variability and mosquito-borne disease

Mentors: Dr. Kyle Dahlin and Dr. John Vinson
Abstract: Climate change is expected to lead to shifts in the risk of mosquito-borne disease outbreaks throughout the world. However, it is unclear how increases in the variability of temperature and rainfall might impact these outbreaks. The goal of this project is to investigate how environmental and demographic noise affect key measures of the transmission of mosquito-borne diseases. The student will be responsible for conducting simulation experiments in R to explore the effect of noise on the probability of an outbreak and the outbreak size distribution. The student will gain experience in the ecology of vector-borne diseases, mathematical modeling, and programming in R.
Is the project computational, empirical, or both? Computational.

6) Development of infectious disease research and teaching software

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.

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.

1) Quantifying and characterizing the Chagas disease parasite burden in kissing bug vectors across land use change gradients

Rhodnius pallescens, principal vector of Chagas disease in Panama

Mentors: Dr. Nicole Gottdenker and Juliana Hoyos
Abstract: Anthropogenic environmental change has been associated with the emergence of zoonotic vector borne pathogens. In this project, the REU scholar will investigate how parasite burden with the Chagas disease agent Trypanosoma cruzi varies in kissing bug vectors collected across different gradients of deforestation and reforestation in rural areas of central Panama. The student will learn laboratory methods in molecular detection of trypanosomes in kissing bugs and will also apply statistical methods to investigate how land cover, bug abundance, microclimate, stage, and bug population characteristics relate to parasite burden in the bugs. 
Is the project computational, empirical, or both? Both.

How Various Feeding Rates Affect Pupation Rates in Anopheles stephensi Larvae

Jacob Glover, a student at Franklin College, worked in the lab of Dr. Ash Pathak

Abstract Anopheles Stephensi are a dangerous vector for countless diseases without cures currently. If mosquitos could be contained or controlled, then this could eliminate these diseases they carry without having to find a vaccine or other types of cures for them. One of the best stages to control mosquitos is within their larval stage of life. In this stage, they are so fragile that minor changes in food or environment can kill them or stunt their growth. If mosquito larvae could be purposefully stunted or killed off, then there would be one less major vector for disease. Our experiment tests this hypothesis to see if manipulating the rate at which mosquito larvae are fed if that impacts their pupation rates.Within this experiment, we test to see if various feeding rates affect their pupation rates, ultimately testing if their growth stays the same under different feeding conditions. This was done by giving them different amounts of TetraFin pellets on Tuesdays and Wednesdays to see if there was a difference in growth rates. Overall, the results show that no significant changes occur in the pupation rates of mosquito larvae when feeding rates are altered.


Modeling Fitness of Immune Evading Pertussis Mutants

Gowri Vadmal, a student at Stanford University, worked in the lab of Dr. Pej Rohani

Abstract Pertussis was considered one of the great diseases of childhood with most people experiencing a bout of the infection by the age of 15.  The initial roll out of vaccines in the 1950s led to a marked decline in pertussis incidence, with optimism over its potential elimination.  Over the past 20-30 years, however, a clear increasing trend in pertussis cases has emerged.  A number of putative Pertussis (whooping cough) is caused mainly by the bacterium Bordetella pertussis. Many countries use acellular pertussis vaccines containing the antigen pertactin (PRN), which plays an important role in pathogenesis. In recent years, we’ve observed an increasing number of B. pertussis isolates that are PRN-deficient and able to infect people even in highly vaccinated countries. We used SIRV models to look at the fitness cost of the bacterium losing PRN and the advantage of being able to infect already vaccinated people. Strains can invade the population only if their leakiness (ability to infect vaccinated people) and transmission are above the threshold for invasion, which depends on vaccination coverage and the cost of immune evasion for the strain. At low vaccination coverage, strains with high leakiness dominate the system when the fitness cost to evade immunity is low, but as cost increases for the strains to infect people, there is bistability between the wildtype and mutant strains. However, at higher vaccination coverage, the wild type completely fades out and the strains with the highest leakiness dominate the system. Thus, the conditions for B. pertussis mutant invasion can change depending on a population’s vaccine coverage, the cost of losing the pertactin gene, and the advantage of being able to evade vaccine immunity.


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.


Predictors for SARS-CoV-2 Seropositivity in Owned and Feral Cats in North Georgia

Sarah Blankespoor, a student at California Polytechnic University, worked in the lab of Dr. Mark Tompkins

Abstract Little is known about the epidemiology of SARS-CoV-2 in animal populations. Cats are a host for the virus, with cat-to-cat transmission demonstrated in lab settings. Both feral and owned cats interact with many species and could drive interspecies transmission. This project investigates dynamics of SARS-CoV-2 by evaluating seropositivity predictors in cats. Serum samples were taken from owned cats brought to the University of Georgia Veterinary Teaching Hospital from 08/2021-06/2022 and feral cats captured locally from 01/2022-06/2022. Samples were tested for anti-SARS-CoV-2 antibodies using indirect ELISAs. For feral cats with n=33, none of the samples were positive. For owned cats with n=193, 10 of the samples were positive (5.2%). There is preliminary evidence for lasting antibodies with two repeat positive cats, with samples taken up to 3 months apart. Binary logistic regression models for the owned cats were determined in R through multi-model inference. Two terms were present in the 3 equivalent best models: cumulative human COVID-19 cases by county, with a positive coefficient; and days since the pandemic started, with a negative coefficient. These results suggest that cats acquire SARS-CoV-2 infections from humans rather than other cats or wildlife. The negative coefficient for time in the models can be explained by the delta and omicron surges at the beginning of the study period. SARS-CoV-2 surges in humans have a ripple effect into the larger ecosystem, particularly for cats owned by humans with COVID-19. Future research should continue to investigate this impact over a larger time scale and expand feral cat sample size to confirm observed trends.


The Tradeoff of Nutrition in Malaria Transmission

Nathan Garcia-Diaz, a student from Willamette University, worked in the lab of Dr. Ash Pathak.

Abstract The effects of nutrition on malaria transmission was studied by collecting the most influential components of Vectorial Capacity. Vectorial Capacity (C) measures the Anopheline mosquito’s efficacy at transmitting the Plasmodium berghei parasites, and the largest factors impacting C are Extrinsic Incubation Period (EIP) and Vectoral Survival Probability (VSP). In order to force the mosquito to decide where to allocate nutrients, four conditions were created by combining two different treatments: Low Nutrient Treatment (1% Dextrose) and High Nutrient Treatment (10% Dextrose); Gravid (No Oviposition Site) and Not Gravid (Oviposition Site). Data was collected for EIP by examining sporozoite prevalence, and VSP was measured by mosquito mortality at fixed intervals after the infectious blood meal. To gain a more comprehensive notion on mosquitoes’ infectiousness, sporozoite density was measured alongside the other variables. The results from VSP data indicate that mosquitoes were most likely to survive if given the high nutrient treatment, and less likely to survive if the mosquitoes had the not gravid status. Additionally, when comparing EIP data between Gravid and Not Gravid statuses in the low nutrient treatment, gravid mosquitoes were infected sooner and at a higher rate than the not gravid counterparts. This pattern was seen again in the parasite density, gravid mosquitoes being more infectious than not gravid mosquitoes. It can be concluded that when infected mosquitoes are in a nutrient deficient state, gravid mosquitoes prioritize caring for its progeny rather than assembling an immunological response.


Investigating 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.