Who infected whom? Creating a database of transmission trees for comparative outbreak analysis

Juliana Taube, a student at Bowdoin College, worked with Paige Miller and Dr. John Drake.

Abstract: Transmission trees contain valuable details about who infected whom in infectious disease outbreaks. We created a database with 81 published, standardized transmission trees consisting of 12 directly-transmitted pathogens (mostly viruses). We also demonstrated how the database could be used to help answer research questions in infectious disease epidemiology. First, we analyzed overall and pathogen-specific patterns between tree parameters (Rand variation in secondary infections). We found that outbreak size is nonlinearly associated with Rand the dispersion parameter, but emphasize that pathogen-specific patterns and intervention efforts may alter theoretical relationships between these variables. Second, we examined how superspreader contribution to onward transmission, either directly or through their tree descendants, varies across pathogens. Superspreaders were responsible for most cases via their descendants and the number of superspreaders varied across pathogens. Additional database exploration matched theoryabout how the proportion of superspreaders increases at intermediate levels of dispersion, an idea that should be further explored. We hope that our database will assist both theoretical and applied infectious disease epidemiology research in the future. 

1. Lloyd-Smith, JO, Schreiber, SJ, Kopp, PE, & Getz, WM (2005) “Superspreading and the effect of individual variation on disease emergence.”Nature438(7066): 355.


Extreme heat reduces fitness of monarchs and their parasites

Maya Sarkar, a student at the University of Minnesota, worked with Isabella Ragonese, Dr. Sonia Altizer and Dr. Richard Hall.

Abstract: It is important to understand the consequences of a warming climate, especially in organisms that are more sensitive to temperature changes and where the outcome of warming may not be intuitive. This project used the Monarch-OE system to study how temperature may affect host-parasite interactions. The monarch butterfly (Danaus plexippus) is an iconic North American migratory species and the specialist protozoan parasite OE (Ophryocystis elektroscirrha) is present in all monarch populations. It has been shown that monarch development proceeds faster with increasing temperatures and that increased temperature exposure lowers OE spore infectivity over time. However, the effect of temperature on the host and parasite during active infection is not known. This project examined how temperature affects the monarch-OE system, focusing on the interaction between monarch immune function and parasite replication. Monarchs were inoculated with strains of OE parasite and placed in different temperature treatments. Three lineages (B,F, and D) of migratory monarch were used to test genetic effects, while 2 spore lines (E3 and E10) were used to study virulence effects within 5 different temperature treatments (18, 22, 26, 30, and 34°C). The results of this study provide novel insight to how extreme temperatures affect the fitness of a host and its parasite.


Vive la resistance: the impact of antibiotic use in US livestock on emerging antibiotic resistance

Sydney Rentsch, a junior from Connecticut College, worked with Dr. JP Schmidt to examine the relationship between antibiotic use and resistance in US livestock.

Abstract:  The potential for livestock to spread antibiotic resistant pathogens to human populations is a cause for concern. This research focused on finding trends in data on US livestock antibiotic resistance, US livestock inventory and US livestock antibiotic consumption. Data was compiled from CDC, USDA and FDA reports and publicly available datasets. Data was analyzed in R and generalized additive models (GAMs) were used to test for increasing resistance as a function of time. We found that the tetracycline class of antibiotics had consistently high resistance over time. The antibiotic class lincosamides, had a sharp increase in resistance which was positively associated with the data from turkeys and chickens. Analyzes also found that poultry had the highest burden of antibiotic resistant pathogens. These results may lead future studies focused on antibiotic resistance in poultry and provide framework for future data analysis.


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Behavioral and environmental determinants of parasite transmission in a butterfly host

Chastity Ward, a senior from Fayetteville State University, worked on a project with Dr. Sonia Altizer, Dr. Richard Hall and Dr. Paola Barriga to examine how parasites of the Monarch butterfly are transmitted.

Abstract:  Many pathogens can be transmitted when infectious stages shed into the environment are later encountered by susceptible hosts. Environmental transmission is common among insect parasites, and also occurs for human diseases such as cholera and polio. Understanding how host behavior and environmental variables affect the shedding of infectious stages is crucial for predicting patterns of infection risk. Monarch butterflies Danaus plexippus are commonly infected by the protozoan Ophryocystis elektroscirrha (OE); this parasite is transmitted environmentally when infected adults deposit spores onto host plants (milkweed) that are consumed by monarch larvae. To quantify host contact with milkweeds as an estimate of parasite transmission, we set up outdoor flight cages with adult monarchs and milkweed plants. Cages varied in the number of adult monarchs and milkweed plants, and were assigned to one of two milkweed species. We used captive-raised monarchs from several genetic lineages, and marked the monarchs with unique number and color codes to track activity. We observed cages for replicate intervals over a week-long period, during which we noted observed monarch contacts with plants, and recorded monarch and plant identity, activity type, temperature, weather, and time of day. Our results showed strong heterogeneity in plant visitation rates among monarchs that was best explained by monarch sex (females had 4.7 times higher visitation rates than males, owing to frequent oviposition on milkweeds).  We also found wide variation among individual plants in the number of visits by monarchs. Milkweed species, plant flowering status and plant leaf number did not affect visitation rates, but plants in cages with a higher number of monarchs were visited more frequently. In sum, our findings provided evidence for individual monarch’s serving as superspreaders of infection, and for some milkweed plants serving as hotspots of infection. This study provides a starting point for estimating environmental parasite transmission in wild milkweed patches, and suggests that individual-level heterogeneity might be more important than environmental variation in driving parasite transmission in this system.


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Age-structured model for Tuberculosis intervention planning

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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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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Using SpatialDE to characterize spatiotemporal changes in mitochondrial morphology

Brittany Dorsey, a sophomore from Mercer University, worked with Dr. Shannon Quinn and Dr. Fred Quinn to test the use of a new method to detect changes in organelle morphology.

Abstract:   Intracellular bacterial pathogens have the capacity to greatly alter target organelles’ morphology, which can easily be visualized through fluorescence microscopy, but is more difficult to quantify succinctly. Work is being done to consider Gaussian Mixture Models as a viable solution by viewing mitochondria as social networks, but there are difficulties with this method. Therefore, the goal of the current project is to explore the feasibility of SpatialDE as an alternative way to quantify the spatiotemporal changes in organelle morphology. Using time series footage of mitochondria, three morphological phenotypes were analyzed: control, fragmented, and fused. The raw video was converted to a three-dimensional matrix of pixel values, which was then raster scanned into a two-dimensional matrix. This matrix was normalized, then input into the SpatialDE framework using the programming language Python. The data frame output gave 18 different variable values for each pixel location throughout the footage, which was converted back into “image” format in order to be analyzed. The results showed little to no discernable patterns between treatments. In comparison, the Gaussian Mixture Model output shows clear similarities among phenotypes. Therefore, it was determined that Gaussian Mixture Models continue to be the best option to model spatiotemporal changes in diffuse organelles. Once this method is fully developed, the mechanisms by which bacterial virulence factors transform mitochondrial structure in host cells will be better understood, which will have crucial implications for structural biology and biomedicine.


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Associations between biotic and abiotic factors and Chagas disease vector abundance in palm trees across different habitat types

Jason Soriano, a freshman from University of California Berkeley, worked with Dr. Nicole Gottdenker and Christina Varian to investigate the factors influencing the abundance of an important disease vector.

Abstract:  In the Americas, an estimated 8 million people are infected with Chagas disease, a tropical, vector-borne infectious disease that can be life threatening if not properly treated. It is caused by the protozoan Trypanosoma cruzi which is transmitted by triatomine insect vectors called Rhodnius pallescens , reduviid bugs that are more commonly referred to as “kissing bugs” for their characteristic bites proximal to the lips and eyelids of humans. In Panama, sylvatic transmission of T. cruzi commonly occurs in the crown of the Attalea butyracea palm, the region of the palm tree where kissing bugs live. However, transmission can often spillover into human populations when infected vectors come into contact with humans. Previous studies have proven that land use change (e.g. deforestation) increase R. pallescens abundance, but the underlying mechanisms as to why this pattern occurs are largely unknown. Therefore, this research serves to shed some light on potential biotic and abiotic factors associated with vector abundance in A. butyracea palm trees across different habitat types. Through data visualization and statistical analysis of field data collected from four sites in central Panama, we show that microenvironment factors (primarily dead organic matter, relative humidity, and number of connected trees) are significantly correlated with R. pallescens abundance. Evaluating mechanisms as potential targets of palm management strategies aiming to control R. pallescens abundance will ultimately minimize disease risk and uphold the health, safety, and welfare of vulnerable communities.

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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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Research and Design of Biomedical Telemetry Device using Light Transmission

Mauricio Gallegos, a student from Clemson University, along with Austin Mesa from Florida International University, worked in the lab of Dr. Juan Guttierrez to design an implantable biomedical sensor.

Abstract: The goal of this project was to design and miniaturize an implantable biomedical telemetry sensor that provides continuous, long-term telemetry data that could, in turn, be used with predictive algorithms to pre-diagnose disease or predict the severity of an oncoming disease. Current sensors that accomplish the same goal are power-hungry due to radio transmission and bulky due to a large battery, making them poor implants. Our team designed a proof-of- concept device for telemetry data transmission that uses visible light instead of radio waves, which should theoretically reduce overall battery consumption and minimize size. The system features two components, one for data collection and transmission, and the other for data reception. These components work simultaneously to provide real-time telemetry data that can be analyzed further. For our particular transmission device, the sensors used were a 3-axis accelerometer, which detects the overall activity of the subject being studied, and a temperature sensor which provides a general measurement of biological health. This hardware along with code loaded to the microcontroller allows the system to transmit live data through an LED, which is instantaneously received by the photoresistor on the receiving end. By parsing and interpreting the binary data, it can be organized and plotted for further analysis.

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Differences in age distribution patterns in urban and rural counties of São Paulo state, Brazil

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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Testing the Enemy Release Hypothesis in Ungulates (Artiodactyl and Perissodactyl) and Carnivores

Lauren Kleine, a student from Colorado State University, examined the enemy release hypothesis in a project directed by Dr. Patrick Stephens and Dr. J.P. Schmidt.

Abstract:  The Enemy Release Hypothesis (ERH) predicts that invasive species will achieve greater success in non-native ranges due in part to escape from parasites found in their native ranges. The purpose of our study was to determine whether members of mammal populations occurring outside their native ranges are generally infected by fewer parasites than those from populations of the same species within their native ranges. We used the Global Mammal Parasite Database version 2.0 to investigate 39 species with entries from both inside and outside of their native ranges. For each species, Parasite Species Richness (PSR) was calculated for each species in native and non-native ranges, as well as measures of sampling effort. We used a Generalized Additive Model (GAM) of PSR as a function of sampling effort to generate residual values of PSR. Residual values were then used to test for differences in PSR in native vs. invasive ranges. In final analyses restricted to well-studied hosts, we found a significant reduction in PSR in invasive ranges. This study highlights the importance of considering sampling effort measures when comparing species richness values, and lends support to the enemy release hypothesis.

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Modelling the incidence and transmission dynamics of the Hepatitis A virus

Jesus Cantu, a Sociology major from Princeton University, worked with Drs. Tobias Brett and Pejman Rohani to model Hepatitis A infection.

Abstract: Hepatitis A is an acute infectious disease caused by the hepatitis A virus (HAV). In the US, an incremental approach to vaccination was initiated after the vaccine became available in 1995. In effect, a continuous decline has been experienced in the overall HAV incidence from 6.0 cases per 100,0000 individuals in 1999 to 0.4 cases per 100,000 individuals in 2011. Recently, an increasing trend in the proportion of HAV cases who were hospitalized was observed, in the US, from 7.3% in 1999 to 24.5% in 2011. Asymptomatic and non-jaundiced HAV-infected persons, especially children, have previously been identified as an important source of HAV transmission. However, the number of asymptomatic HAV-infections, through time, and their role in sustaining transmission have not clearly identified. To answer these questions, we constructed a mechanistic SIR-model with high and low risk classes implemented as a system of ordinary differential equations which were numerically integrated in R. Particular attention was placed on the effect of the implementation of different vaccination strategies on disease burden and transmission. Preliminary results show that infections from low risk individuals contribute negligibly to the number of symptomatic cases.

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Kissing Bug (R. pallescens) Population Structure in Panamanian Rural Landscapes

Anaija Hardmon, a Biology major from Spelman College, worked in the lab of Dr. Nicole Gottdenker to describe the population structure of an important disease vector.

Abstract: Describing the population structure of zoonotic disease vectors includes understanding their life history strategies and population dynamics, as well as the development of vector-borne diseases, and control strategies for the Chagas disease. This disease is caused by the protozoan parasite Trypanosoma cruzi and transmitted by hematophagous members of the familyTriatominae to humans and mammals alike.  The objective of this study was to describe and compare the population structure of the principal vector of Chagas disease in Panama, R. pallescens, across different types of anthropogenic land use. We evaluated the population structure of N= 1123 bugs in total, collected from 5 different habitat types in Panama during the wet seasons of 2008 with N= 759 collected and between 2013-2015 in forest patch, pasture, and peridomestic habitats. There was a significant association between bug stage and habitat type (Chi-squared = 37.3, df = 20, p =0.02). The N1 and N2 nymphs were under-represented in the sample, and the estimated numbers of N3, N4, and N5 stage nymphs were significantly greater in disturbed habitats, with N5 stages being particularly scarce in contiguous forest and cattle pasture where the lowest number of bugs were collected. Nymph: Adult ratios did not significantly differ between habitat types, but tended to be higher in pasture sites. In bugs captured between 2013-2015, more bugs were captured in the Trinidad de las Minas site compared to Las Pavas and there were no apparent inter-annual trends in age structure in these sites. Once errors in detectability are accounted for, this data can be used for the analysis of bug population dynamics within and between habitats.

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Epidemiological data, parameter estimation and pitfalls

Jonathan Waring, a Computer Science major at the University of Georgia, worked with Ana Bento in the lab of Pejman Rohani to examine how the the choice of data used in a disease model affects the results.

Abstract: During an emerging infectious disease outbreak, epidemiological parameters, such as transmission potential and mean infectious period, are estimated for a timely and effective response. The standard procedure for attaining quick estimates of these quantities is fitting transmission models to incidence data. Cumulative incidence (total number of infections to date) is often used rather than raw incidence (number of new cases in a defined reporting period), but there is evidence to suggest that this choice of data can affect our perceptions of the variability in the parameters and hence the uncertainty in our predictions. To further elaborate on this problem, we fit deterministic and stochastic models with both raw and cumulative simulated epidemic data in order to assess the biases and errors associated with data choice. Fitted simulations to the data using deterministic and stochastic methods result in comparable variances, with cumulative models under predicting the true incidence. However, in stochastic parameter estimation and posterior sampling using particle Markov chain Monte Carlo (pMCMC), cumulative data produces much wider confidence intervals, and thus better quantifies uncertainty than models using raw data. When we consider the entire time-series of an epidemic, cumulative and raw data will both be useful in parameter estimation depending on the level of uncertainty we are willing to accept.


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Diet, Dispersal, and Disease: How Food Supplemented Habitat Alters Metapopulation Disease Spread

Celine Snedden, a Mathematics major at the University of California Berkeley, worked with Drs. Richard Hall and Sonia Altizer to look at how supplemental feeding of wildlife can affect disease spread.

Abstract: Recreational and unintentional feeding of wildlife occurs frequently but can have negative consequences, such as increasing pathogen transmission within provisioned sites. However, it is unclear how resource supplementation influences the spatial spread of pathogens. Provisioning could increase pathogen spread if the corresponding sites produce more offspring with higher dispersal success; alternatively, supplementation might reduce pathogen spread if provisioned sites promote site fidelity. Infection may also affect spatial dynamics by reducing wildlife mobility. In this project, we extend the Levins metapopulation model to account for heterogeneity in colonization rates caused by provisioning-induced changes to patch attractiveness, animal site fidelity, and infection-induced costs to movement. We derive two key parameters, the net effect of provisioning on movement (ρ) and the pathogen basic reproductive number (R0) that are crucial determinants of host occupancy and pathogen prevalence. We also explore how increasing the number of provisioned patches across the landscape influences host occupancy and pathogen prevalence under different supplementation scenarios. We find that provisioning should be avoided when infection has only small effects on animal mobility and when supplementation increases net movement of hosts between patches. However, provisioning can be beneficial to hosts when (i) infected patches produce fewer dispersers or when (ii) highly transmissible pathogens are present and supplemental feeding promotes site fidelity. To improve the effects of supplemental feeding on wildlife and decrease the risk of pathogen spillover, future work should aim to obtain empirical estimates of the effects of infection and resource provisioning on animal movement.


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Simple models for complicated situations: Ebola in West Africa

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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Using a large spatial database to explore relationships between fungal pathogens and their insect hosts

Chevana Dorris, a Biology major from Jackson State University, and Dr. J.P. Schmidt looked at relationships between fungal pathogens and their insect hosts.

Abstract: The USDA-ARS Collection of Entomopathogenic Fungal Cultures (ARSEF) database features nearly 8,000 fungal pathogen-insect host entries. For each fungal entomopathogen and its insect host, the database lists taxonomy and geographic location. Relying on the data from ARSEF, our project explores biases and patterns in relationships between fungal pathogens and their insect hosts. After removing entries in which hosts or pathogens were not resolved to species, we summarized the data on unique host-pathogen pairs by fungal class and insect host order. We ran analyses to create visualizations of the cleaned data. We summarized the number of fungal pathogens per host, the number of hosts per fungal pathogen, and the latitudinal range of pathogens. From these visualizations, we identified a set of biases and patterns in the data. Fungal species that have been investigated for use as biocontrol agents dominated the database and infected many hosts, especially species within the insect classes Coleoptera, Hemiptera, and Lepidopotera which include many economically damaging pests. We also found that the database has more fungal pathogens from the more-studied temperate zones as opposed to the tropics where fungi are actually more abundant and fungal richness is greater. Despite clear taxonomic and geographic biases, this large database has potential for exploring patterns in generalism and specialism among well-studied pathogen species.

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Mapping autochthonous transmission potential of Chikungunya virus in the United States

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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Visualizing the Effect of Interventions during the 2014-2015 West Africa Ebola Outbreak

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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