Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation
The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pi...
Gespeichert in:
| Veröffentlicht in: | PloS one 2011-06, Vol.6 (6), p.e21009 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 6 |
| container_start_page | e21009 |
| container_title | PloS one |
| container_volume | 6 |
| creator | Tamerius, James D Comrie, Andrew C |
| description | The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a "primary" exposure season that spans August-March and a "secondary" season that spans April-June which are then used in subsequent analyses. We show that October-December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = -0.79, p = 0.004) and Pima (R = -0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the "grow and blow" hypothesis. |
| doi_str_mv | 10.1371/journal.pone.0021009 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_plos_</sourceid><recordid>TN_cdi_plos_journals_1304908319</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A476887755</galeid><doaj_id>oai_doaj_org_article_222e74ca661e46f9bdb3581f9f432f94</doaj_id><sourcerecordid>A476887755</sourcerecordid><originalsourceid>FETCH-LOGICAL-c691t-8060be15cd4c301f871f6029a5eccdb9c108788dc4eb000309917e2b4df14ef93</originalsourceid><addsrcrecordid>eNqNkl2L1DAUhoso7rr6D0QHBMWLGU-atEluhGHwY3Bhwa_bkCYnM1k6zdi04vjrTZ3uMpW9kF708PY570lO3yx7SmBBKCdvrkPfNrpe7EODC4CcAMh72TmRNJ-XOdD7J_VZ9ijGa4CCirJ8mJ3lhAMpJJxnn1bBGG998DbsDiZEH2e-SQo2BlM1W7b-d2j0bN-i9aZDO6sOs4g6JrEeVOP3vtOdD83j7IHTdcQn4_si-_b-3dfVx_nl1Yf1ank5N6Uk3VxACRWSwlhmKBAnOHEl5FIXaIytpCEguBDWMKwAgIKUhGNeMesIQyfpRfb86LuvQ1TjHqIiFJgEQclArI-EDfpa7Vu_0-1BBe3VXyG0G6XbzpsaVZ7nyJnRZUmQlU5WtqKFIE46RnMnWfJ6O07rqx1ag03X6npiOv3S-K3ahJ-KEiIEgWTwajRow48eY6d2Phqsa91g6KMSnHJgnA6jXvxD3n25kdrodH7fuJDGmsFTLRkvheC8KBK1uINKj8WdNykzzid90vB60pCYDn91G93HqNZfPv8_e_V9yr48Ybeo624bQ90PiYlTkB1B04YYW3S3OyaghsjfbEMNkVdj5FPbs9P_c9t0k3H6B312-5k</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1304908319</pqid></control><display><type>article</type><title>Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>Public Library of Science (PLoS)</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Tamerius, James D ; Comrie, Andrew C</creator><contributor>Deepe, George</contributor><creatorcontrib>Tamerius, James D ; Comrie, Andrew C ; Deepe, George</creatorcontrib><description>The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a "primary" exposure season that spans August-March and a "secondary" season that spans April-June which are then used in subsequent analyses. We show that October-December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = -0.79, p = 0.004) and Pima (R = -0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the "grow and blow" hypothesis.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0021009</identifier><identifier>PMID: 21701590</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Arizona - epidemiology ; Autocorrelation ; Climate ; Coccidioides ; Coccidioidomycosis ; Coccidioidomycosis - epidemiology ; Data processing ; Earth Sciences ; Exposure ; Fever ; Fungi ; Genomes ; Health services ; Health surveillance ; Humans ; Hypotheses ; Incidence ; Infections ; Landscape ecology ; Medicine ; Pathogens ; Pneumonia ; Population ; Precipitation ; Precipitation (Meteorology) ; Public health ; Seasonal precipitation ; Seasonal variability ; Seasonal variations ; Seasons ; Summer precipitation ; Surveillance ; Time series ; Weather forecasting</subject><ispartof>PloS one, 2011-06, Vol.6 (6), p.e21009</ispartof><rights>COPYRIGHT 2011 Public Library of Science</rights><rights>2011 Tamerius, Comrie. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Tamerius, Comrie. 2011</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c691t-8060be15cd4c301f871f6029a5eccdb9c108788dc4eb000309917e2b4df14ef93</citedby><cites>FETCH-LOGICAL-c691t-8060be15cd4c301f871f6029a5eccdb9c108788dc4eb000309917e2b4df14ef93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3118810/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3118810/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21701590$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Deepe, George</contributor><creatorcontrib>Tamerius, James D</creatorcontrib><creatorcontrib>Comrie, Andrew C</creatorcontrib><title>Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a "primary" exposure season that spans August-March and a "secondary" season that spans April-June which are then used in subsequent analyses. We show that October-December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = -0.79, p = 0.004) and Pima (R = -0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the "grow and blow" hypothesis.</description><subject>Analysis</subject><subject>Arizona - epidemiology</subject><subject>Autocorrelation</subject><subject>Climate</subject><subject>Coccidioides</subject><subject>Coccidioidomycosis</subject><subject>Coccidioidomycosis - epidemiology</subject><subject>Data processing</subject><subject>Earth Sciences</subject><subject>Exposure</subject><subject>Fever</subject><subject>Fungi</subject><subject>Genomes</subject><subject>Health services</subject><subject>Health surveillance</subject><subject>Humans</subject><subject>Hypotheses</subject><subject>Incidence</subject><subject>Infections</subject><subject>Landscape ecology</subject><subject>Medicine</subject><subject>Pathogens</subject><subject>Pneumonia</subject><subject>Population</subject><subject>Precipitation</subject><subject>Precipitation (Meteorology)</subject><subject>Public health</subject><subject>Seasonal precipitation</subject><subject>Seasonal variability</subject><subject>Seasonal variations</subject><subject>Seasons</subject><subject>Summer precipitation</subject><subject>Surveillance</subject><subject>Time series</subject><subject>Weather forecasting</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl2L1DAUhoso7rr6D0QHBMWLGU-atEluhGHwY3Bhwa_bkCYnM1k6zdi04vjrTZ3uMpW9kF708PY570lO3yx7SmBBKCdvrkPfNrpe7EODC4CcAMh72TmRNJ-XOdD7J_VZ9ijGa4CCirJ8mJ3lhAMpJJxnn1bBGG998DbsDiZEH2e-SQo2BlM1W7b-d2j0bN-i9aZDO6sOs4g6JrEeVOP3vtOdD83j7IHTdcQn4_si-_b-3dfVx_nl1Yf1ank5N6Uk3VxACRWSwlhmKBAnOHEl5FIXaIytpCEguBDWMKwAgIKUhGNeMesIQyfpRfb86LuvQ1TjHqIiFJgEQclArI-EDfpa7Vu_0-1BBe3VXyG0G6XbzpsaVZ7nyJnRZUmQlU5WtqKFIE46RnMnWfJ6O07rqx1ag03X6npiOv3S-K3ahJ-KEiIEgWTwajRow48eY6d2Phqsa91g6KMSnHJgnA6jXvxD3n25kdrodH7fuJDGmsFTLRkvheC8KBK1uINKj8WdNykzzid90vB60pCYDn91G93HqNZfPv8_e_V9yr48Ybeo624bQ90PiYlTkB1B04YYW3S3OyaghsjfbEMNkVdj5FPbs9P_c9t0k3H6B312-5k</recordid><startdate>20110620</startdate><enddate>20110620</enddate><creator>Tamerius, James D</creator><creator>Comrie, Andrew C</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20110620</creationdate><title>Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation</title><author>Tamerius, James D ; Comrie, Andrew C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c691t-8060be15cd4c301f871f6029a5eccdb9c108788dc4eb000309917e2b4df14ef93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Analysis</topic><topic>Arizona - epidemiology</topic><topic>Autocorrelation</topic><topic>Climate</topic><topic>Coccidioides</topic><topic>Coccidioidomycosis</topic><topic>Coccidioidomycosis - epidemiology</topic><topic>Data processing</topic><topic>Earth Sciences</topic><topic>Exposure</topic><topic>Fever</topic><topic>Fungi</topic><topic>Genomes</topic><topic>Health services</topic><topic>Health surveillance</topic><topic>Humans</topic><topic>Hypotheses</topic><topic>Incidence</topic><topic>Infections</topic><topic>Landscape ecology</topic><topic>Medicine</topic><topic>Pathogens</topic><topic>Pneumonia</topic><topic>Population</topic><topic>Precipitation</topic><topic>Precipitation (Meteorology)</topic><topic>Public health</topic><topic>Seasonal precipitation</topic><topic>Seasonal variability</topic><topic>Seasonal variations</topic><topic>Seasons</topic><topic>Summer precipitation</topic><topic>Surveillance</topic><topic>Time series</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tamerius, James D</creatorcontrib><creatorcontrib>Comrie, Andrew C</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Proquest Nursing & Allied Health Source</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>ProQuest Engineering Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Agricultural Science Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tamerius, James D</au><au>Comrie, Andrew C</au><au>Deepe, George</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2011-06-20</date><risdate>2011</risdate><volume>6</volume><issue>6</issue><spage>e21009</spage><pages>e21009-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The environmental mechanisms that determine the inter-annual and seasonal variability in incidence of coccidioidomycosis are unclear. In this study, we use Arizona coccidioidomycosis case data for 1995-2006 to generate a timeseries of monthly estimates of exposure rates in Maricopa County, AZ and Pima County, AZ. We reveal a seasonal autocorrelation structure for exposure rates in both Maricopa County and Pima County which indicates that exposure rates are strongly related from the fall to the spring. An abrupt end to this autocorrelation relationship occurs near the the onset of the summer precipitation season and increasing exposure rates related to the subsequent season. The identification of the autocorrelation structure enabled us to construct a "primary" exposure season that spans August-March and a "secondary" season that spans April-June which are then used in subsequent analyses. We show that October-December precipitation is positively associated with rates of exposure for the primary exposure season in both Maricopa County (R = 0.72, p = 0.012) and Pima County (R = 0.69, p = 0.019). In addition, exposure rates during the primary exposure seasons are negatively associated with concurrent precipitation in Maricopa (R = -0.79, p = 0.004) and Pima (R = -0.64, p = 0.019), possibly due to reduced spore dispersion. These associations enabled the generation of models to estimate exposure rates for the primary exposure season. The models explain 69% (p = 0.009) and 54% (p = 0.045) of the variance in the study period for Maricopa and Pima counties, respectively. We did not find any significant predictors for exposure rates during the secondary season. This study builds on previous studies examining the causes of temporal fluctuations in coccidioidomycosis, and corroborates the "grow and blow" hypothesis.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>21701590</pmid><doi>10.1371/journal.pone.0021009</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2011-06, Vol.6 (6), p.e21009 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1304908319 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Analysis Arizona - epidemiology Autocorrelation Climate Coccidioides Coccidioidomycosis Coccidioidomycosis - epidemiology Data processing Earth Sciences Exposure Fever Fungi Genomes Health services Health surveillance Humans Hypotheses Incidence Infections Landscape ecology Medicine Pathogens Pneumonia Population Precipitation Precipitation (Meteorology) Public health Seasonal precipitation Seasonal variability Seasonal variations Seasons Summer precipitation Surveillance Time series Weather forecasting |
| title | Coccidioidomycosis incidence in Arizona predicted by seasonal precipitation |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T23%3A45%3A22IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Coccidioidomycosis%20incidence%20in%20Arizona%20predicted%20by%20seasonal%20precipitation&rft.jtitle=PloS%20one&rft.au=Tamerius,%20James%20D&rft.date=2011-06-20&rft.volume=6&rft.issue=6&rft.spage=e21009&rft.pages=e21009-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0021009&rft_dat=%3Cgale_plos_%3EA476887755%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1304908319&rft_id=info:pmid/21701590&rft_galeid=A476887755&rft_doaj_id=oai_doaj_org_article_222e74ca661e46f9bdb3581f9f432f94&rfr_iscdi=true |