Infectious diseases propagate along networks of contacts of infected hosts. Increasingly, epidemiological investigations have used molecular analysis and case investigation to reconstruct these infection paths, which are then quantified as “transmission trees”. Findings from such studies have shown that features like contact structure, heterogeneity, and the presence of “super spreaders” may be crucial to the propagation and containment of epidemics. Presently, such research is primarily case-based and there is no global understanding of the ubiquity of such features across epidemics more generally. The goal of this project is to develop the first comprehensive data base of transmission trees. The student will compile data from the published literature into a common format. These data will be analyzed to look for patterns in transmission that may be generalized to other epidemics. The work will be performed using the scientific programming language R.
Host Lab: John Drake
Project type: Quantitative/Computer-based
Kennedy Houck, a junior from Ursinus College, worked with Paige Miller in the lab of Dr. John Drake to study age-based interventions for Tuberculosis.
Abstract: Tuberculosis (TB) represents a widespread public health concern. The World Health Organization’s “End TB Strategy” has set the goal for global TB eradication by 2050. Previous studies have suggested that current public health intervention strategies may not achieve this goal in many parts of the world that experience high TB incidence rates. The goal of this project was to determine whether age-based interventions could enhance current interventions, which are currently implemented. A standard TB model, which includes five state variables (Susceptible, Latent, Infectious, Noninfectious, and Removed), was modified to include 16 different age classes, and parameterized with previously published information for India and South Africa. The model was run for 500 years until equilibrium was reached. Once equilibrium was reached, 18 different interventions, all simulating faster rates of testing and treating, or shorter infectious periods, among active TB cases, were tested by calculating the rate of decrease of TB cases in each population over time. A “baseline” scenario where the rate of treatment was held constant was compared to interventions where the infectious period was reduced by 10, 50, 70, and 90% independently for either a specific age class or overall (i.e. a “blanket strategy”). To test the validity of model predictions, we calculated the correlation between the stable age distribution of cases at equilibrium and WHO TB prevalence data. In general, age-targeted interventions were found to be more effective at reducing TB cases than the “blanket” strategy. In India, targeting 15-19 year olds was predicting to result in the greatest overall decline in incidence of both latent and active TB at all intervention levels. In South Africa, targeting 10-14 year olds was predicted to result in the greatest overall decline of latent TB at all intervention levels; however, targeting 10-14 year olds at lower intervention levels and a blanket strategy at higher intervention levels, were more effective at reducing infectious TB burden. These results suggest that age-based interventions may complement current public health interventions by further reducing TB burden to achieve WHO eradication goals. Future studies should utilize a more detailed model for TB dynamics to generate a more realistic prediction.
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Keri-Niyia Cooper, a student from Savannah State University, worked with Drs. John Drake and Andrew Park over the summer to investigate parasite sharing in mammals.
Abstract: After a literature review was performed to compile a list of parasites that infect marine mammals, we created a database of parasite-host pairings found in the articles. We then merged this database with information gathered from the GMPD and CLC Life Cycle, after subsetting certain traits. We used the resulting database to examine three questions: (1) Is parasite generalism greater in marine environments or terrestrial environments? (2) Is parasite sharing greater when hosts are grouped by taxonomy (cetacean/ungulate v carnivore) or habitat (marine v terrestrial)? (3) Are parasites that infect hosts of both environments drawn disproportionately from some parasite taxonomic groups? It was noted that parasite generalism is greater in terrestrial environments. Hosts have a higher chance of being infected by the same parasite if they are found in the same environment. There is a higher chance of helminth species being more commonly found in a single environment.
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Magdalene Walters, a student from the University of Notre Dame, worked with Dr. John Drake to study the age distribution of a measles outbreak.
Abstract: In 1997, São Paulo, Brazil experienced a measles outbreak with an unusually high average age of infection. It has since been hypothesized that this high age of infection was due to unvaccinated rural adults traveling to urban communities.1 This project tested this hypothesis through the use of descriptive statistics and nonparametric analyses of variance. Evidence was found for varying adult transmission patterns between urban and rural communities. Forty-nine counties display a multimodal distribution of age of infection, and the rest were categorized as moderately multimodal or non-multimodal. The average outbreak size was significantly different between the multimodal, moderately multimodal, and non-multimodal counties. Counties which were not multimodal, displayed a high modal age of infection. Small outbreak sizes consistently displayed patterns associated with spread of infection between adults and evidence suggests a correlation between outbreak size and proportion of children infected.
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Coronaviruses infect the respiratory and gastrointenstinal tract of birds and mammals. Six coronaviruses are known to infect humans, with severity ranging from cold-like symptoms (e.g., Human coronavirus 229E) to pneumonia, respiratory failure, and death (e.g., MERS-CoV, SARS-CoV). The phylogenetic relationships among known coronaviruses have recently been described (Nature Reviews Microbiology 11:836-848), but the functional ecological and evolutionary relationships among the coronaviruses have not been characterized. This project will seek to quantify candidate functional differences accounting for the variation in pathology ascribed to the cornoviruses, with a special emphasis on the genus Betacoronavirus (the genus containing MERS-CoV and SARS-CoV). This project is based in the Drake lab and is primarily quantitative. The student will be tasked with compiling a table of known traits of coronaviruses, devising analytic methods for filtering informative and uninformative features, and visualizing model output. The results are expected to help explain why coronaviruses differ so greatly in their propensity to cause disease and disease severity and to guide predictions about future emerging coronaviruses.
Sarah Rainey, a Biology major at Radford University, studied structural uncertainty in disease models with Dr. John Drake.
Abstract: Mathematical models are an idealization used for disease forecasting and decision making. There is a tradeoff between timeliness and detail for models produced in response to an epidemic. It is important to quantify the level of uncertainty in a model to mitigate inaccuracies that could arise from parameter uncertainty and structural uncertainty. The impact of parameter uncertainty has been heavily investigated; however structural uncertainty is scarcely addressed. The 2014 Ebola outbreak in West Africa was used as a case study to investigate structural uncertainty. Models produced early in the course of an epidemic tend to be simpler due to a lack of information with which to estimate parameters and an abbreviated development time. Complex models are more cumbersome to develop but take into account more complex transmission scenarios reported by in-country observers. The goal of this project is to determine when a simple model can capture the trajectory of an Ebola outbreak generated by a more complex transmission process. The statistical software R was used to solve three mathematical models: a branching-process model (Drake et al., 2015), a modified SEIR model (Legrand et al., 2007), and the classical SIR model (Kermack and McKendrick, 1927). During the 2014 Ebola outbreak in West Africa, the model produced by Legrand et al. was widely used because it included all of the key components to model an Ebola epidemic simplistically. The model published by Drake et al. (2015) included greater detail about contact patterns, interventions, behavior change, and other features that may be necessary to realistically model the Ebola outbreak. The complex branching-process model published by Drake et al. (2015) was used to generate an Ebola epidemic with different scenarios by varying parameter values. The two simpler models were fit to simulated data. The correlation coefficient was calculated to test the fit of the models to observe how well they were able to capture the trajectory of the outbreak. Our findings conclude that the modified SEIR model published by Legrand et al. (2007) was superior to the classical SIR model in representing the disease trajectory of multiple Ebola outbreaks simulated with unique circumstances.
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Nicole Solano, a student at Agnes Scott College, worked with Dr. John Drake to model factors that could influence the risk of Chikungunya virus in the United States.
Abstract: Chikungunya virus (CHIKV) is an arbovirus endemic to Africa and South and East Asia, that is transmitted to humans by the bite of an infected mosquito, primarily Aedes aegypti or Aedes albopictus. Since its identification in Tanzania in 1952, CHIKV has spread around the globe, making itself a very prevalent infectious disease. To date (20 June 2015) there have been eleven reported cases of autochthonous transmission in the U.S. (in Florida). Since its introduction into the Americas, concerns have been raised about which areas in the United States are most vulnerable to importation of CHIKV. We examined the correlation between human West Nile Virus (WNV) cases and human Meningitis and Encephalitis cases. A strong correlation was observed (p-value < 2.2-16) informing us that Meningitis and Encephalitis is a good predictor of WNV infection. Given this, we wanted to know which socio-economic covariates were important to consider when thinking about exposure to a disease. A regression analysis helped us identify age and poverty level as the most important covariates. Presence of Aedes albopictus and relative exposure per county was mapped to depict which counties are most vulnerable for onward Chikungunya virus transmission.
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Timothy Wildauer, a student from Bethany Lutheran College, worked with Dr. John Drake to test new methods of determining time of infection for Ebola patients.
Abstract: Data recorded during the 2014-2015 West Africa Ebola Outbreak indicates when patients presented themselves at a hospital for treatment. However, to know if interventions were successful, we need to know when patients became infected. The number of people who became infected on a given date is corrupted by the incubation period for us to see when patients begin showing symptoms. The current method for finding when people became infected is to shift the dates on the observed data set backwards by the average infection period. We developed two non-parametric filters to find when patients became infected with Ebola: multiple trials of randomly selecting incubation periods for patients, and a Ridge Regression. These methods were tested on simulated outbreaks of different difficulties to see which method worked best. We selected four interventions during the outbreak to see if there was an effect on the transmissibility of the disease. In Sierra Leone, the transmission rate dropped significantly after the country was shut down for three days in September 2014. After other events, the transmission changed significantly, but the change may be the natural course of the outbreak. Through testing we found that Random deconvolution achieves a high correlation with the infection curve.
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Evans Lodge, a student from Calvin College, worked with Dr. John Drake to measure how human behaviors change during disease outbreaks.
Abstract: In outbreaks of emerging infectious disease, public health interventions aim at increasing the speed with which infected individuals are removed from the susceptible population, limiting opportunities for secondary infection. Isolation, hospitalization, and barrier-nursing practices are crucial for controlling disease spread in these contexts. Ebola virus disease (EVD), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are all zoonotic infections that have caused significant international outbreaks in the past. Here, we use patient-level data from the 2014-2015 Liberian Ebola epidemic, 2003 Hong Kong SARS epidemic, 2014 Saudi Arabia MERS outbreaks, and 2015 South Korea MERS outbreak to quantify changing removal rates, burial practices, contact tracing, and other measures of protective behavior change. Using the removal rate, Î³, as a measure of protective behavior change allows direct comparison of health behavior development in different outbreaks and locations. Robust regression analysis and analyses of covariance are used to estimate the rate at which Î³ increases in each outbreak by epidemic week and serial interval. Measured interactions between models show that mean removal rates varied within a factor of three, falling between the 2003 Hong Kong SARS outbreak and the 2014-2015 Ebola epidemic in Liberia.
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Richard Williams, a student from Morehouse College, worked with Dr. John Drake to develop a model examining the effectiveness of low-sensitivity interventions in disease outbreaks.
Abstract: We conducted a theoretical study to investigate the effect of low sensitivity interventions on the containment of an emerging pathogen. Low sensitivity interventions such as thermal scans for febrile patients in airports are outbreak interventions that may yield many false negatives, and are implemented because of their political or logistical feasibility. The first step of our study was to derive a discrete-time SEIR model for transmission at a given site and develop computer code to implement the associated stochastic simulation algorithm. We next developed a simulation program to link multiple sites updated as in the first step, incorporating pairwise movement probabilities between sites (which may be randomly constructed or data-driven) and an intervention that prohibits movement based on low sensitivity diagnostic (thermal scans) with tunable sensitivity and specificity. For reporting, this code also tracks the number of uninfected persons incorrectly denied permission to travel. In future work, this software will be used to compare the effectiveness of low-sensitivity interventions applied to pathogens with a range of incubation periods.
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