| LDR | | 03436nmm uu200505 4500 |
| 001 | | 000000333897 |
| 005 | | 20240805174738 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438169449 |
| 035 | |
▼a (MiAaPQ)AAI10826463 |
| 035 | |
▼a (MiAaPQ)umn:19295 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 613 |
| 100 | 1 |
▼a Liu, Yang. |
| 245 | 10 |
▼a Environmental Health Nexus: Designing Predictive Models for Improving Public Health Interventions. |
| 260 | |
▼a [S.l.] :
▼b University of Minnesota.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 220 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Matteo Convertino. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Minnesota, 2018. |
| 520 | |
▼a The environment embodies all surroundings of humans, including natural and built components. The environment is closely related to population health both directly and indirectly. Ambient temperature exposure and air pollution, for example, can d |
| 520 | |
▼a Second, along with the wide range of indicators comes a large volume of environmental data that is now available. Some ambient environmental indicators, such as air temperature, are available on a three-hour basis globally with high resolution s |
| 520 | |
▼a Last but not least, from a computational standpoint, it is challenging to work with high-dimensional data, especially given different research objectives. Environmental health issues do not usually deal with only a pair of exposure and response |
| 520 | |
▼a Despite the technical challenges, environmental information has tremendous potential in terms of ecosystem service for population health research. Existing research has already generated many valuable outcomes with great real-life implications. |
| 520 | |
▼a Targeting the challenges discussed above, this dissertation focuses on designing quantitative predictive models for improving environmental health policy and decisions. More specifically, it generates evidence-based science to improve policies a |
| 520 | |
▼a Within the overarching theme, two projects were completed over the course of this dissertation. The first project used environmental information to forecast infectious disease outbreaks. Infectious diseases that rely on vector-borne, water-borne |
| 520 | |
▼a The forecasting methods used in these disease forecast models were uniquely designed for environmentally sensitive infectious diseases. Based on the nature of the transmission mechanisms involved, the models considered substantial temporal delay |
| 520 | |
▼a The second component was to design evidence-based and policy-oriented models for managing population health risks associated with ambient temperature exposure. This component was a collaborative effort with the Minnesota Department of Health and |
| 590 | |
▼a School code: 0130. |
| 650 | 4 |
▼a Environmental health. |
| 650 | 4 |
▼a Public health. |
| 650 | 4 |
▼a Epidemiology. |
| 690 | |
▼a 0470 |
| 690 | |
▼a 0573 |
| 690 | |
▼a 0766 |
| 710 | 20 |
▼a University of Minnesota.
▼b Environmental Health. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0130 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998891
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |