Accurately defining disease distributions and calculating disease risk is an important step in the control and prevention of diseases. This study used geographical information systems and remote sensing technologies within the MaxEnt ecological niche modeling program to create predictive risk maps for leprosy and Schistosomiasis in Brazil and Chagas disease in both Brazil and Bolivia.
New disease cases were compiled for leprosy, Schistosomiasis, and Chagas disease from the Brazilian ministry of Health for 2001 to 2009 and the data was stratified to a 10,000 population for each municipality. Bolivian Chagas prevalence rates were calculated from 2007 to 2009 survey data. Environmental data was compiled from MODIS satellite imagery, and WorldClim data for both countries. Socioeconomic data was compiled from the Brazilian IBGE and the Bolivian INE.
Leprosy results showed that areas of lower moisture and specific temperature ranges were related to areas of high leprosy case detection especially in the central western, north eastern and northern regions of the country. The states of Bahia and Minas Gerais continue to show the highest levels of new Schistosomiasis cases and also were predicted to have some of the highest risks for the disease in our study. This study confirmed the importance of sanitation and educational level in relation to Schistosomiasis, which has been previously established in other studies.
Chagas disease models identified altitude as being important, as well as lower levels of precipitation, and higher ranges of temperature which correspond to the biological requirements of the insect vectors. Information for housing materials was only found for Bolivia, but demonstrated the importance of improved housing materials. Adobe wall materials were found to be highly related to the disease while areas with hardwood floors demonstrated a direct negative correlation.
These studies demonstrated that MaxEnt can be successfully adapted to disease prevalence and incidence studies and provides governmental agencies with an easily understandable method to define disease risk area for use in resource planning, targeting, and implementation. This study emphasizes the need for more refined socioeconomic data to create better socioeconomic and smaller regional study areas to better elucidate region specific disease characteristics.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-08302011-155548 |
| Date | 31 August 2011 |
| Creators | Mischler, Paula |
| Contributors | Malone, John, Dorn, Patricia, Diaz, James, Mores, Christopher, Truman, Richard, Constant, David |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-08302011-155548/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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