{"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}
Kenneth Dufault, E. Daly, S. Bascom, Christopher Taylor, Paul Lakevicius, S. alroy-Preis
{"title":"School absenteeism surveillance data during the 2009 influenza A/H1N1 pandemic","authors":"Kenneth Dufault, E. Daly, S. Bascom, Christopher Taylor, Paul Lakevicius, S. alroy-Preis","doi":"10.3402/EHTJ.V4I0.11112","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11112","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73943206","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. Tolentino, S. Pemmaraju, P. Polgreen, Anson Tai YatHo, M. Monsalve, Alberto Maria Segre
Introduction Public health officials and epidemiologists have been attempting to eradicate syphilis for decades, but national incidence rates are again on the rise. It has been suggested that the syphilis epidemic in the United States is a ‘rare example of unforced, endogenous oscillations in disease incidence, with an 8 11-year period that is predicted by the natural dynamics of syphilis infection, to which there is partially protective immunity’ (1). While the time series of aggregate case counts seems to support this claim, between 1990 and 2010, there seems to have been a significant change in the spatial distribution of the syphilis epidemic. It is unclear if this change can also be attributed to ‘endogenous’ factors or whether it is due to exogenous factors such as behavioral changes (e.g., the widespread use of the internet for anonymous sexual encounters). For example, it is pointed out that levels of syphilis in 1989 were abnormally high in counties in North Carolina (NC) immediately adjacent to highways (2). The hypothesis was that this may be due to truck drivers and prostitution and/or the emerging cocaine market (1). Our results indicate that syphilis distribution in NC has changed since 1989, diffusing away from highway counties (see Fig. 1).
{"title":"Changes in the spatial distribution of syphilis","authors":"S. Tolentino, S. Pemmaraju, P. Polgreen, Anson Tai YatHo, M. Monsalve, Alberto Maria Segre","doi":"10.3402/EHTJ.V4I0.11093","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11093","url":null,"abstract":"Introduction Public health officials and epidemiologists have been attempting to eradicate syphilis for decades, but national incidence rates are again on the rise. It has been suggested that the syphilis epidemic in the United States is a ‘rare example of unforced, endogenous oscillations in disease incidence, with an 8 11-year period that is predicted by the natural dynamics of syphilis infection, to which there is partially protective immunity’ (1). While the time series of aggregate case counts seems to support this claim, between 1990 and 2010, there seems to have been a significant change in the spatial distribution of the syphilis epidemic. It is unclear if this change can also be attributed to ‘endogenous’ factors or whether it is due to exogenous factors such as behavioral changes (e.g., the widespread use of the internet for anonymous sexual encounters). For example, it is pointed out that levels of syphilis in 1989 were abnormally high in counties in North Carolina (NC) immediately adjacent to highways (2). The hypothesis was that this may be due to truck drivers and prostitution and/or the emerging cocaine market (1). Our results indicate that syphilis distribution in NC has changed since 1989, diffusing away from highway counties (see Fig. 1).","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75026438","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":"An evaluation of electronic laboratory data quality and a health information exchange","authors":"B. Dixon, S. Grannis","doi":"10.3402/EHTJ.V4I0.11104","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11104","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74905280","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":"Surveillance of poison center data using the National Poison Data System web service","authors":"Melissa Powell, K. Ryff, S. Giffin","doi":"10.3402/EHTJ.V4I0.11036","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11036","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76606471","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}
Julio C. Silva, D. Rumoro, Marilyn M. Hallock, S. Shah, G. Gibbs, M. Waddell
{"title":"Disease profile development methodology for syndromic surveillance of biological threat agents","authors":"Julio C. Silva, D. Rumoro, Marilyn M. Hallock, S. Shah, G. Gibbs, M. Waddell","doi":"10.3402/EHTJ.V4I0.11129","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11129","url":null,"abstract":"","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74326045","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}
Emerson C. Bodevan, L. Duczmal, Gladston J. P. Moreira, A. Duarte, F. C. O. Magalhães
Introduction The Voronoi Based Scan (VBScan) (1) is a fast method for the detection and inference of point data set space-time disease clusters. A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases’ points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance Minimum Spanning Tree (MST) linking the cases. The successive removal of its edges generates subtrees, which are the potential space-time clusters, which are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate cluster significance. In the present work, we modify VBScan to find the best partition dividing the map into multiple lowand high-risk regions.
{"title":"Significant multiple high-and low-risk regions in event data maps","authors":"Emerson C. Bodevan, L. Duczmal, Gladston J. P. Moreira, A. Duarte, F. C. O. Magalhães","doi":"10.3402/EHTJ.V4I0.11131","DOIUrl":"https://doi.org/10.3402/EHTJ.V4I0.11131","url":null,"abstract":"Introduction The Voronoi Based Scan (VBScan) (1) is a fast method for the detection and inference of point data set space-time disease clusters. A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases’ points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance Minimum Spanning Tree (MST) linking the cases. The successive removal of its edges generates subtrees, which are the potential space-time clusters, which are evaluated through the scan statistic. Monte Carlo replications of the original data are used to evaluate cluster significance. In the present work, we modify VBScan to find the best partition dividing the map into multiple lowand high-risk regions.","PeriodicalId":72898,"journal":{"name":"Emerging health threats journal","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2011-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80140795","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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