Alec Thompson, a Microbiology major from the University of Oklahoma, worked in the lab of Dr. Michael Yablsey to increase understanding of the genetic diversity of a parasite.
Abstract: Dracunculus spp. are spiruroid nematode parasites that live in the subcutaneous tissues or abdominal cavity of mammals. The most important species is D. medinensis, the human Guinea Worm. In 1985, over 3.5 million people were infected, but due to control efforts by the Carter Center and public health agencies, there were only 22 cases in 2015. Control was primarily through the use of water filters. A related species, D. insignis, is found in various wildlife species, and rarely dogs and cats, in North America. We have been conducting studies on D. insignis as a model parasite to better understand the ecology of D. medinensis. In this study, various potential vertebrate hosts were examined to determine the host-parasite interactions and genetic diversity of Dracunculus parasites within the United States. Analysis of the preliminary results suggests that the raccoon (Procyon lotor) is the preferential host for the parasite but opossums (Didelphis virginiana) are often frequently infected. Molecular characterization was attempted to investigate the intraspecific variation between hosts and regions and also to definitively identify adult female worms which cannot be identified using morphologic characteristics. However, the PCR was problematic as low specificity was observed with the PCR primers we used. Most sequencing attempts were either host DNA or mixed products. We did get amplicons from four worms that were parasite DNA and they were all D. insignis. Methods for better specificity and amplification and other gene targets of are currently being researched and results are pending. Finally, we conducted experimental infection trials to investigate the potential role of amphibians and fish as hosts. Several species of amphibians were exposed to copepods infected with D. insignis. Infections of 2 species of tadpoles (gray tree frog and northern cricket frog) were confirmed by necropsy. These parasites were fed to ferrets and results will take 6-8 months. Bufo tadpoles did not consume copepods. Previous studies suggested that fish did not become infected with larvae, but we investigated the possible role as a transport host. Three species of fish (tilapia, fathead minnows, and gambusia) were exposed to infected copepods and then immediately fed to ferrets. Ferrets will be tested after 6-8 months. Although results of several studies are pending, this study has provided new data on the natural history of D. insignis and intiated additional studies that may help understand the continued transmission of D. medinensis by the use of alternative hosts.
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Temitayo Adanlawo, a Biology major from Howard University, worked with Kerri Miazgowicz in the lab of Dr. Courtney Murdock to study an important disease vector.
Abstract: Malaria is a disease endemic to sub-Saharan Africa, India, southeast Asia and parts of Central and South America, and affects 300-600 million people every year. Malaria is a temperature-sensitive disease that varies between species. Currently, there is a disconnect between malaria transmission risk models and actual malaria incidence. This is due to species temperature-specific data substitution which increases uncertainty in results for the transmission risk equation (R0). In order to increase the accuracy of the temperature-dependent malaria transmission risk equation, a life table study was performed on Anopheles stephensi mosquitoes Using thirty mosquitoes at each of six different temperatures (16 °C, 20 °C, 24 °C, 28 °C, 32 °C, 36 °C), mortality, fecundity, and bite rate were recorded daily. Mosquitoes were given the opportunity to feed for fifteen minutes daily. We used the results of this study to create a thermal performance curve to determine a minimum, optimal, and maximum point for thermally-dependent malaria transmission risk and decrease overall malaria transmission risk uncertainty. Bite rate increased with temperature, as did fecundity. We concluded that the three variables study are, in fact, extremely temperature dependent and that mortality plays a huge role in the development of bite rate and fecundity.
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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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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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Sydney Keane, a Biology and Chemistry major from East Texas Baptist University, worked with Dr. Kelly Dyer examining the effects of infection on reproduction in Drosophila.
Abstract: are bacterial parasites that commonly infect arthropods and nematodes. These parasites have damaging effects on the progeny of those they infect, including cytoplasmic incompatibility (CI). CI occurs when an infected male and an uninfected female mate, resulting in fewer eggs that successfully hatch into larvae than normal. In this study, infected virgin males from Drosophila recens were collected from multiple strains across three locations, and coupled with uninfected virgin females from the same species. After allowing the females to lay eggs for 72 hours, I recorded the numbers of eggs that hatched and that did not hatch. Males were tested for Wolbachia infection using PCR. After analyzing the data, I found that the overall hatch rate in each location was low, the amount of CI in each location did not vary significantly, the amount of CI in the experimental group compared to the control was significantly high, and that the number of total eggs produced varied significantly between the locations. The overall percentage of CI found within all of the locations examined was approximately 72%. These results show that the presence of Wolbachia is similarly effecting various populations of the fly throughout North America and that the level of CI occurring within this species may cause a drastic decrease in the population size over time.
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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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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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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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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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Kennesaw State University Biology Major Victoria Mendiola, worked in the lab of Dr. Nicole Gottdenker to study hookworm infectioun in South American fur seals.
Abstract: Hookworm infection is endemic in many otaiird species and can cause up to 70 % pup mortality in some populations (Lyons 2001, Seguel in press). South American fur seal (SAFS) neonates are able to clear hookworm infection in a matter of months, which is vital for pup survival (Seguel in press). Regardless of the significance of parasite clearance, little is known about the mechanisms involved in this process in wild populations. The aim of this research was to identify the mechanisms the fur seal pups use to expel the hookworms. In order to do so, the immune response was split into cell mediated and humoral responses. Blood and hookworm samples were collected from Guafo Island, Chile during breeding seasons 2013 to 2015, with the inclusion of a control group (treatment of SAFS with Invermectin) starting in 2014. Blood smear slides were used for differential counting of leukocytes (cellular immune response), which was performed at the University of Georgia by standard methods. To detect antibodies in SAFS pup against hookworm parasites (humoral immune response), sections of Uncinaria sp. nematodes were incubated with sera from a pup that had successfully expelled the hookworms. The SAFS antibodies were labeled and visualized by standard immunohistochemistry techniques. Our examination of blood smears found there is a proportional increase in the number eosinophils with the severity of hookworm infection. The number of basophils and lymphocytes were highest in the group with mild hookworm infection, which suggests these leukocytes could play a role on regulating the severity of the hookworm infection. We also found evidence that the pups are able to produce an antibody that binds to in intestinal tract of the hookworm. The identification of fur seal antibodies reacting to the brush borders of the hookworm’s gastrointestinal tract suggests this is a necessary immune response for the fur seals to successfully expel the hookworms. In conclusion, we believe that a combination of these immune responses contribute to the successful expulsion of the parasite. The Chilean population of South American fur seals has declined more than 50% over the last 20 years. The most important breeding colony in Chile is located on Guafo Island, a remote island in the Northern Patagonia. Although this isolated population of fur seals lives in a very diverse and rich marine ecosystem, it has been in unrecoverable decline. The loss of genetic diversity is one of the consequences that face isolated mammal populations and low genetic variability has been associated with detrimental effects on marine mammals’ health (Acevedo et al. 2006). On Guafo Island, our prior research shows that hookworm infection is the major cause of pup mortality (Seguel et al. 2013) and that survival of hookworm infection is strongly mediated by the immune response to the parasite (Seguel et al. in preparation). Since the immune response is strongly linked to genetic variability, isolated populations are exposed to the expression of deleterious gene copies, which can limit the capacity to respond against parasites. Our hypothesis is that limited variability of the fur seal immune system genes is associated with susceptibility to hookworm disease. To test this we will extract DNA from fur seal skin samples and the genetic variability of each individual will be assessed by genotyping microsatellites loci cloned based on sequences published for other pinniped species and the MHC class II DQB locus. We expect to find lower genetic variability in animals with higher parasitic burden and clinical disease due to hookworms. Our student work will be centered on DNA extraction and PCR for the amplification of MHC II genes and later analyses of the sequence data.
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Nicole Solano, a dance and biology major from Agnes Scott College, worked with Michelle Evans in the lab of Dr. Courtney Murdock to examine the effects of temperature on mosquito life history traits.
Abstract: The Asian Tiger mosquito, Aedes albopictus, is an invasive mosquito vector that can transmit up to 27 different arboviruses. Since mosquitoes are small ectotherms, variations in temperature largely impact their physiology, development, and potential to transmit human pathogens. Small changes due to microclimate significantly impact mosquito life history traits relevant for transmission (i.e. body size). Body size is an indicator of fecundity, population growth, and mosquito immunity; therefore understanding the effect of microclimate can inform small-scale variation in disease transmission. Last summer, a study was conducted to test the relationship between microclimate and body size in a semi-field system. They found that mosquitoes in urban sites were significantly smaller than those in rural sites; most likely due to warmer temperatures in urban sites. To validate these findings in the field, we conducted field mosquito surveys and quantified Ae. albopictus wing length across land use.
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Yaw Kumi-Ansu, a Biology major from Emory University, worked with Dr. Andrew Kramer on an ongoing project to model White-Nose syndrome in bats.
Abstract: White-Nose Syndrome is an epizootic fungal disease caused by Pseudogymnoascus destructans which has caused a significant decline in Vespertilionid bat populations in the United States. It is a psychrophilic fungus and produces infectious conidia during the hibernation season of most cave-dwelling bat species. Studies have found strong ties between the rate and pattern of spread and factors such as the density of caves within an area and temperature. In our foundational paper (Maher et al. 2012), studies based on models designed to predict the spatial spread of WNS showed that the model based on average length of winter (number of days under 10°C) and density of caves within and between counties (Gravity(caves)+Winter) provided the best fit for projections and observed spread of the disease. In this project, we wanted to know whether yearly variations in temperature (weather) was a better environmental factor than climate (average length of winter) in predicting the spread of White-Nose Syndrome in the contiguous United States. We modified code for the Gravity(caves)+Winter model to run yearly maximum and minimum temperature in place of average length of winter and we also calculated average temperature from 2006 to 2014. Our results showed that Average length of winter remained the best environmental factor in predicting the spatial spread of WNS based on NLL and AIC values which were obtained from the MLE parameter sets. Spatial spread in both the climate and yearly weather models were similar but climate models projected faster spread to counties with caves. In the future, we hope to improve upon our study of spatial spread by using yearly variations in length of winter as well as data on co-occurring species to get a better understanding of how inter-specific differences in hibernation patterns and length of hibernation could contribute to the spread of the disease.
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Lexi Lerner, a biology major from Brown University, worked with Dr. John Drake to look for critical slowing down in consumer fad systems.
Abstract: Consumer fads are often characterized by unpredictable explosive outbreaks. Early warning signals have been retroactively successful at anticipating critical phenomena of complex systems such as infectious disease epidemics, but they have yet to be extended to consumer fads. Here we study both theoretical and real-world consumer fad systems to see whether their approach to an epidemic is characterized by critical slowing down (CSD). We propose a new model of social contagion transmission that includes the accumulation of buzz, or aggregate ubiquity, around an idea. We derived deterministic and stochastic solutions for this model and showed that it can push a subcritical system to criticality. We evaluated four candidate early warning signals by their sensitivity and specificity using various rolling window bandwidths to understand CSD detection performance. We also analyzed data for a faddish product line over a two-year timespan to determine if CSD was detectable in real-world systems. Our results show that variance was the best-scoring and most consistent predictor (AUC > 0.8) of CSD in the theoretical stochastic SIBR model. It was also determined that CSD is present in the product line data and, at small bandwidths, is characterized by “breaking points” that can be traced back to events in the original time series. We hope these findings will help guide statistical analysis of consumer behavior and further early warning signal analysis of economic interest.
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