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dc.contributor.authorTelionis, Pyrros A.en_US
dc.date.accessioned2019-05-09T08:00:32Z
dc.date.available2019-05-09T08:00:32Z
dc.date.issued2019-05-08
dc.identifier.othervt_gsexam:19801en_US
dc.identifier.urihttp://hdl.handle.net/10919/89432
dc.description.abstractAt the intersection of geography and public health, the field of spatial epidemiology seeks to use the tools of geospatial analysis to answer questions about disease. In this work we explore two areas: the use of geostatistical modeling as an extension of niche modeling, and the use of mobility metrics to augment modeling for epidemic responses. Niche modeling refers to the practice of using statistical methods to relate the underlying spatially distributed environmental variables to an outcome, typically presence or absence of a species. Such work is common in disease ecology, and often focuses on exploring the range of a disease vector or pathogen. The technique also allows one to explore the importance of each underlying regressor, and the effect it has on the outcome. We demonstrate that this concept can be extended, through geostatistical modeling, to explore non-logistic phenomena such as incidence. When combined with weather forecasts, such efforts can even predict incidence of an upcoming season, allowing us to estimate the total number of expected cases, and where we would expect to find them. We demonstrate this in Chapter 2, by forecasting the incidence of melioidosis in Australia given weather forecasts a year prior. We also evaluate the efficacy of this technique and explore the impact of environmental variables such as elevation on melioidosis. But these techniques are not limited to free-living and vector-borne pathogens. We theorize that they can also be applied to diseases that spread exclusively by person-to-person contact. Exploring this allows us to find areas of underreporting, as well as areas with unusual local forcing which might merit further investigation by the health department. We also explore this in Chapter 4, by relating the incidence of hepatitis C in rural Virginia to demographic data. The West African Ebola Outbreak of 2014 demonstrated the need to include mobility in predictive disease modeling. One can no longer assume that neglected tropical diseases will remain contained and immobile, and the assumption of random mixing across large areas is unwise. Our efforts with modeling mobility are twofold. In Chapter 3, we demonstrate the creation of mobility metrics from open source road and river network data. We then demonstrate the usefulness of such data in a meta-population patch model meant to forecast the spread of Ebola in the Democratic Republic of Congo. In Chapter 4, we also demonstrate that mobility data can be used to strengthen outbreak detection via hotspot analysis, and to augment incidence models by factoring in the incidence rates of neighboring areas. These efforts will allow health departments to more accurately forecast incidence, and more readily identify disease hotspots of atypical size and shape.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectAutocovariateen_US
dc.subjectEbolaen_US
dc.subjectForecasten_US
dc.subjectGISen_US
dc.subjectGravity Modelen_US
dc.subjectHepatitis Cen_US
dc.subjectIncidenceen_US
dc.subjectInfectious Diseaseen_US
dc.subjectMelioidosisen_US
dc.subjectMetapopulation-Patchen_US
dc.subjectMobilityen_US
dc.subjectSocial Transmission Nicheen_US
dc.subjectSpatial Autocorrelationen_US
dc.subjectSpatial Epidemiologyen_US
dc.subjectTravel Network.en_US
dc.titleNovel Applications of Geospatial Analysis  in the Modeling of Infectious Diseasesen_US
dc.typeDissertationen_US
dc.contributor.departmentGenetics, Bioinformatics, and Computational Biologyen_US
dc.description.degreeDoctor of Philosophyen_US
thesis.degree.nameDoctor of Philosophyen_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineGenetics, Bioinformatics, and Computational Biologyen_US
dc.contributor.committeechairLewis, Bryan Leroyen_US
dc.contributor.committeechairEubank, Stephen G.en_US
dc.contributor.committeememberAbbas, Kaja Moinudeenen_US
dc.contributor.committeememberKolivras, Korine N.en_US
dc.description.abstractgeneralThe focus of this work is called “spatial epidemiology”, which combines geography with public health, to answer the where, and why, of disease. This is a growing field, and you’ve likely seen it in the news and media. Have you ever seen a map of the United States turning red in some virus disaster movie? The real thing looks a lot like that. After the Ebola outbreak of 2014, public health agencies wanted to know where the next one might hit. Now that there is another outbreak, we need to ask where and how will it spread? What areas are hardest hit, and how bad is it going to get? We can answer all these questions with spatial epidemiology. Our work adds to two aspects of spatial epidemiology: niche modeling, and mobility. We use niche modeling to determine where we could find certain diseases, usually those that are spread by insects or animals. Consider Lyme disease, you get it from the bite of a tick, and the tick gets it from a white-footed mouse. But both the mice and ticks only live in certain parts of the country. With niche modeling we can determine where those are, and we can also guess at what makes those areas attractive to the mice and ticks. Is it winter harshness, summer temperatures, rainfall, and/or elevation? Is it something else? In Chapter 2, we show that you can extend this idea. Instead of just looking at where the disease is, what if we could guess how many people will get infected? What if we could do so, a year in advance? We show that this can be done, but we need a good idea of what the weather will be like next year. In Chapter 4, we show that you can do the same thing with hepatitis C. Instead of Lyme’s ticks and mice, hepatitis C depends on drug-use, unregulated tattooing, and unsafe sex. And like with Lyme, these things are only found in certain places. Instead of temperature or rainfall, we now need to find areas with drug-problems and poverty. But we can get an idea of this from the Census Bureau, and we can make a map of hepatitis C as easily as we did for Lyme. But hepatitis C spreads person-to-person. So, we need some idea of how people move around the area. This is where mobility comes in. Mobility is important for most public health work, from detecting outbreaks to estimating where the disease will spread next. In Chapter 3, we show how one could create a mobility model for a rural area where few maps exist. We also show how to use that model to guess where the next cases of Ebola will show up. In Chapter 4, we show how you could use mobility to improve outbreak and hotspot detection. We also show how it’s used to help estimate the number of cases in an area. Because that number depends on how many cases are imported from the surrounding areas. And the only way to estimate that is with mobility.en


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