| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435219 |
| 005 | | 20200227163427 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781687985231 |
| 035 | |
▼a (MiAaPQ)AAI22622371 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 614.4 |
| 100 | 1 |
▼a Gorris, Morgan Elizabeth. |
| 245 | 10 |
▼a Environmental Infectious Disease Dynamics in Relation to Climate and Climate Change. |
| 260 | |
▼a [S.l.]:
▼b University of California, Irvine.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 170 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Randerson, James T |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Irvine, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Climate change poses multiple threats to human health, including changes in the burden of infectious diseases. Rising temperatures and shifts in precipitation patterns may reshape the geographical distributions of pathogenic organisms and disease vectors, potentially placing new communities at risk. Projections of environmental infectious diseases in response to climate change will help public health officials create disease surveillance programs and mitigation strategies. A precursor to modeling these projections is a basic understanding of the relationships between each infectious disease and the environment.My dissertation examined how climate conditions influence two different environmental infectious diseases in the United States: coccidioidomycosis (Valley fever) and West Nile virus. In my first study, I examined the climate and environmental conditions that structure the spatiotemporal dynamics of Valley fever incidence. To do so, I compiled a Valley fever case dataset for the southwestern U.S. From this study, I found areas endemic to Valley fever are described by hot and dry climate thresholds. In my second study, I used these climate thresholds to create a predictive model of the area currently endemic to Valley fever. Then, I used climate projections to create the first maps of future Valley fever endemicity. In my third chapter, I used machine learning to explore which climate conditions structure West Nile virus incidence throughout the U.S. I found the highest disease incidence in the northern Great Plains, which is categorized by dry and cold winters. This predictive model of disease incidence may be used for future projections of West Nile virus risk in response to climate change.The collective results of my dissertation help us understand how climate conditions influence two of the most important environmental infectious diseases in the U.S. and how climate change may affect the future burden of each disease. I am now sharing the results from my dissertation with the U.S. Environmental Protection Agency, state health agencies and epidemiologists, and physicians in hopes to alleviate the future burden of disease. |
| 590 | |
▼a School code: 0030. |
| 650 | 4 |
▼a Climate change. |
| 650 | 4 |
▼a Environmental studies. |
| 650 | 4 |
▼a Epidemiology. |
| 690 | |
▼a 0404 |
| 690 | |
▼a 0477 |
| 690 | |
▼a 0766 |
| 710 | 20 |
▼a University of California, Irvine.
▼b Earth System Science - Ph.D.. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0030 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493896
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |