{"title":"Opportunistic approaches to threat reduction efforts in resource-limited countries","authors":"A. Sayitahunov, Y. Shlyonsky","doi":"10.3402/ehtj.v4i0.11045","DOIUrl":"https://doi.org/10.3402/ehtj.v4i0.11045","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85863779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michele D. Renshaw, S. Sapkota, Stephanie M. Dulin, G. Faler, Benjamin Erickson, Sarah Waite, L. Han, Caroline Westnedge, J. Tropper, Barb Nichols
{"title":"Distributing countermeasures for all hazards events and reporting their utilizations","authors":"Michele D. Renshaw, S. Sapkota, Stephanie M. Dulin, G. Faler, Benjamin Erickson, Sarah Waite, L. Han, Caroline Westnedge, J. Tropper, Barb Nichols","doi":"10.3402/EHTJ.V4I0.11422","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11422","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80876140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Geoffrey Fairchild, Alberto Maria Segre, G. Rushton, Eric D. Foster, P. Polgreen
Introduction ILI data are collected via an Influenza Sentinel Provider Surveillance Network at the state level. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. This study analyzes two different geographic placement schemes*a maximal coverage model (MCM) and a K-median model, two location-allocation models commonly used in geographic information systems (GIS) (1). The MCM chooses sites in areas with the densest population. The K-median model chooses sites, which minimize the average distance traveled by individuals to their nearest site. We have previously shown how a placement model can be used to improve population coverage for ILI surveillance in Iowa when considering the sites recruited by the Iowa Department of Public Health (IDPH) (2). We extend this work by evaluating different surveillance placement algorithms with respect to outbreak intensity and timing (i.e., being able to capture the start, peak and end of the influenza season).
{"title":"Comparing methods for sentinel surveillance site placement","authors":"Geoffrey Fairchild, Alberto Maria Segre, G. Rushton, Eric D. Foster, P. Polgreen","doi":"10.3402/EHTJ.V4I0.11145","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11145","url":null,"abstract":"Introduction ILI data are collected via an Influenza Sentinel Provider Surveillance Network at the state level. Because participation is voluntary, locations of the sentinel providers may not reflect optimal geographic placement. This study analyzes two different geographic placement schemes*a maximal coverage model (MCM) and a K-median model, two location-allocation models commonly used in geographic information systems (GIS) (1). The MCM chooses sites in areas with the densest population. The K-median model chooses sites, which minimize the average distance traveled by individuals to their nearest site. We have previously shown how a placement model can be used to improve population coverage for ILI surveillance in Iowa when considering the sites recruited by the Iowa Department of Public Health (IDPH) (2). We extend this work by evaluating different surveillance placement algorithms with respect to outbreak intensity and timing (i.e., being able to capture the start, peak and end of the influenza season).","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88413207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Hepatitis A surveillance evaluation in Mafraq Health Directorate, Jordan 2010","authors":"G. Sharkas, Sami Sheikh-ali, Sultan Abdulla","doi":"10.3402/ehtj.v4i0.11031","DOIUrl":"https://doi.org/10.3402/ehtj.v4i0.11031","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91332355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Speakman, E. McFowland, S. Somanchi, Daniel B. Neill
Introduction The spatial scan statistic (1) detects significant spatial clusters of disease by maximizing a likelihood ratio statistic F(S) over a large set of spatial regions, typically constrained by shape. The fast localized scan (2) enables scalable detection of irregular clusters by searching over proximity-constrained subsets of locations, using the linear-time subset scanning (LTSS) property to efficiently search over all subsets of each location and its k 1 nearest neighbors. However, for a fixed neighborhood size k, each of the 2 subsets are considered equally likely, and thus the fast localized scan does not take into account the spatial attributes of a subset. Hence, we wish to extend the fast localized scan by incorporating soft constraints, which give preference to spatially compact clusters while still considering all subsets within a given neighborhood.
{"title":"Scalable detection of irregular disease clusters using soft compactness constraints","authors":"S. Speakman, E. McFowland, S. Somanchi, Daniel B. Neill","doi":"10.3402/EHTJ.V4I0.11121","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11121","url":null,"abstract":"Introduction The spatial scan statistic (1) detects significant spatial clusters of disease by maximizing a likelihood ratio statistic F(S) over a large set of spatial regions, typically constrained by shape. The fast localized scan (2) enables scalable detection of irregular clusters by searching over proximity-constrained subsets of locations, using the linear-time subset scanning (LTSS) property to efficiently search over all subsets of each location and its k 1 nearest neighbors. However, for a fixed neighborhood size k, each of the 2 subsets are considered equally likely, and thus the fast localized scan does not take into account the spatial attributes of a subset. Hence, we wish to extend the fast localized scan by incorporating soft constraints, which give preference to spatially compact clusters while still considering all subsets within a given neighborhood.","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88528962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deborah Lee, R. Chang, K. Liske, S. Shetty, H. Burke, R. Philen, J. Painter
{"title":"U.S. destinations of newly arrived immigrants and refugees with suspect TB, 2009–2010","authors":"Deborah Lee, R. Chang, K. Liske, S. Shetty, H. Burke, R. Philen, J. Painter","doi":"10.3402/EHTJ.V4I0.11072","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11072","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88172153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Johnson, Sarah L. Goff, D. Hanchett, H. Plavin, G. Birkhead
{"title":"Linking informatics and cross-programmatic public health strategic objectives","authors":"G. Johnson, Sarah L. Goff, D. Hanchett, H. Plavin, G. Birkhead","doi":"10.3402/ehtj.v4i0.11167","DOIUrl":"https://doi.org/10.3402/ehtj.v4i0.11167","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"456 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82953534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Patient management system programmed alert to notify providers of suspected TB cases","authors":"R. Gamache, Shandy Dearth, S. Grannis, P. Dexter","doi":"10.3402/EHTJ.V4I0.11192","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11192","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81036547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wai-Ling Mui, B. White, Emily A Iarocci, Aimee R Reilly, Noele P. Nelson, David M. Hartley
Introduction Argus is an event-based surveillance system, which captures information from publicly available Internet media in multiple languages. The information is contextualized, and indications and warning (I&W) of disease are identified. Reports are generated by regional experts and are made available to the system’s users (1). In this study a small-scale disease event, plague emergence, was tracked in a rural setting, despite media suppression and a low availability of epidemiological information.
{"title":"Application of event-based biosurveillance to disease emergence in isolated regions","authors":"Wai-Ling Mui, B. White, Emily A Iarocci, Aimee R Reilly, Noele P. Nelson, David M. Hartley","doi":"10.3402/EHTJ.V4I0.11171","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11171","url":null,"abstract":"Introduction Argus is an event-based surveillance system, which captures information from publicly available Internet media in multiple languages. The information is contextualized, and indications and warning (I&W) of disease are identified. Reports are generated by regional experts and are made available to the system’s users (1). In this study a small-scale disease event, plague emergence, was tracked in a rural setting, despite media suppression and a low availability of epidemiological information.","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84799487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Mamiya, K. Schwartzman, Aman Verma, Christian Jauvin, M. Behr, D. Buckeridge
Introduction A new TB case can be classified as: (1) a source case for transmission leading to other, secondary active TB cases; (2) a secondary case, resulting from recent transmission; or (3) an isolated case, uninvolved in recent transmission (i.e., neither source nor recipient). Source and secondary cases require more intense intervention due to their involvement in a chain of transmission; thus, accurate and rapid classification of new patients should help public health personnel to effectively prioritize control activities. However, the currently accepted method for classification, DNA fingerprint analysis, takes many weeks to produce the results (1); therefore, public health personnel often solely rely on their intuition to identify the case who is most likely to be involved in transmission. Various clinical and sociodemographic features are known to be associated with TB transmission (2). By using these readily available data at the time of diagnosis, it is possible to rapidly estimate the probabilities of the case being source, secondary and isolated.
{"title":"Aiding the practice of tuberculosis control: a decision support model to predict transmission","authors":"H. Mamiya, K. Schwartzman, Aman Verma, Christian Jauvin, M. Behr, D. Buckeridge","doi":"10.3402/EHTJ.V4I0.11066","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11066","url":null,"abstract":"Introduction A new TB case can be classified as: (1) a source case for transmission leading to other, secondary active TB cases; (2) a secondary case, resulting from recent transmission; or (3) an isolated case, uninvolved in recent transmission (i.e., neither source nor recipient). Source and secondary cases require more intense intervention due to their involvement in a chain of transmission; thus, accurate and rapid classification of new patients should help public health personnel to effectively prioritize control activities. However, the currently accepted method for classification, DNA fingerprint analysis, takes many weeks to produce the results (1); therefore, public health personnel often solely rely on their intuition to identify the case who is most likely to be involved in transmission. Various clinical and sociodemographic features are known to be associated with TB transmission (2). By using these readily available data at the time of diagnosis, it is possible to rapidly estimate the probabilities of the case being source, secondary and isolated.","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82525193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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