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{"title"=>"Evaluation of local media surveillance for improved disease recognition and monitoring in global hotspot regions", "type"=>"journal", "authors"=>[{"first_name"=>"Jessica S.", "last_name"=>"Schwind", "scopus_author_id"=>"56387305900"}, {"first_name"=>"David J.", "last_name"=>"Wolking", "scopus_author_id"=>"56387532000"}, {"first_name"=>"John S.", "last_name"=>"Brownstein", "scopus_author_id"=>"8872411400"}, {"first_name"=>"Consortium", "last_name"=>"Predict", "scopus_author_id"=>"56387519700"}, {"first_name"=>"Jonna A.K.", "last_name"=>"Mazet", "scopus_author_id"=>"7005723108"}, {"first_name"=>"Woutrina A.", "last_name"=>"Smith", "scopus_author_id"=>"8552521600"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"25333618", "doi"=>"10.1371/journal.pone.0110236", "issn"=>"19326203", "scopus"=>"2-s2.0-84908052576", "pui"=>"600170311", "sgr"=>"84908052576"}, "id"=>"6f959aec-3a04-3873-ab46-15935ccd69b7", "abstract"=>"Digital disease detection tools are technologically sophisticated, but dependent on digital information, which for many areas suffering from high disease burdens is simply not an option. In areas where news is often reported in local media with no digital counterpart, integration of local news information with digital surveillance systems, such as HealthMap (Boston Children's Hospital), is critical. Little research has been published in regards to the specific contribution of local health-related articles to digital surveillance systems. In response, the USAID PREDICT project implemented a local media surveillance (LMS) pilot study in partner countries to monitor disease events reported in print media. This research assessed the potential of LMS to enhance digital surveillance reach in five low- and middle-income countries. Over 16 weeks, select surveillance system attributes of LMS, such as simplicity, flexibility, acceptability, timeliness, and stability were evaluated to identify strengths and weaknesses in the surveillance method. Findings revealed that LMS filled gaps in digital surveillance network coverage by contributing valuable localized information on disease events to the global HealthMap database. A total of 87 health events were reported through the LMS pilot in the 16-week monitoring period, including 71 unique reports not found by the HealthMap digital detection tool. Furthermore, HealthMap identified an additional 236 health events outside of LMS. It was also observed that belief in the importance of the project and proper source selection from the participants was crucial to the success of this method. The timely identification of disease outbreaks near points of emergence and the recognition of risk factors associated with disease occurrence continue to be important components of any comprehensive surveillance system for monitoring disease activity across populations. The LMS method, with its minimal resource commitment, could be one tool used to address the information gaps seen in global 'hot spot' regions.", "link"=>"http://www.mendeley.com/research/evaluation-local-media-surveillance-improved-disease-recognition-monitoring-global-hotspot-regions", "reader_count"=>34, "reader_count_by_academic_status"=>{"Researcher"=>10, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>10, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>10, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Environmental Science"=>5, "Nursing and Health Professions"=>3, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>11, "Veterinary Science and Veterinary Medicine"=>2, "Social Sciences"=>3, "Computer Science"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>11}, "Social Sciences"=>{"Social Sciences"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>3}, "Unspecified"=>{"Unspecified"=>1}, "Environmental Science"=>{"Environmental Science"=>5}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"Canada"=>1, "Brazil"=>1, "Malaysia"=>1, "Switzerland"=>1}, "group_count"=>1}

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• http://doi.org/10.1186/s12914-017-0134-2
• http://doi.org/10.1038/nrg.2017.88
• http://doi.org/10.3201/eid2108.141156
• http://doi.org/10.1093/jamia/ocz112
• http://doi.org/10.1007/s40264-015-0385-6
• http://doi.org/10.1016/j.ijid.2017.07.020
• http://doi.org/10.1007/s12553-020-00456-z
• http://doi.org/10.1177/2053951716652914
• http://doi.org/10.1371/journal.pone.0165688
• http://doi.org/10.1016/j.prevetmed.2016.11.023
• http://doi.org/10.1186/s42522-020-00017-4
• http://doi.org/10.1016/j.compag.2019.105163
• http://doi.org/10.3389/fpubh.2016.00036

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/1719068"], "description"=>"<p>These general guidelines were provided at the onset of pilot implementation in each country to help the team members select the best weekly media sources for surveillance.</p>", "links"=>[], "tags"=>["USAID PREDICT project", "healthmap", "monitoring disease activity", "Local Media Surveillance", "Global Hotspot Regions Digital disease detection tools", "surveillance system attributes", "disease events", "method", "lms", "surveillance systems", "information", "media", "surveillance network coverage", "236 health events", "87 health events"], "article_id"=>1205888, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jessica S. Schwind", "David J. Wolking", "John S. Brownstein", "Jonna A. K. Mazet", "Woutrina A. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110236.g001", "stats"=>{"downloads"=>0, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Guidelines_provided_for_the_media_source_selection_process_/1205888", "title"=>"Guidelines provided for the media source selection process.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-15 03:52:05"}
• {"files"=>["https://ndownloader.figshare.com/files/1719069"], "description"=>"<p>From source selection at LMS implementation to the weekly reporting of local health events, the addition of offline and local information, especially from languages unsupported by digital surveillance networks, fill critical data gaps in global disease recognition and monitoring. <b>*Striking image</b>.</p>", "links"=>[], "tags"=>["USAID PREDICT project", "healthmap", "monitoring disease activity", "Local Media Surveillance", "Global Hotspot Regions Digital disease detection tools", "surveillance system attributes", "disease events", "method", "lms", "surveillance systems", "information", "media", "surveillance network coverage", "236 health events", "87 health events"], "article_id"=>1205889, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jessica S. Schwind", "David J. Wolking", "John S. Brownstein", "Jonna A. K. Mazet", "Woutrina A. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110236.g002", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_and_information_flow_through_the_local_media_surveillance_pilot_/1205889", "title"=>"*Data and information flow through the local media surveillance pilot.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-15 03:52:05"}
• {"files"=>["https://ndownloader.figshare.com/files/1719070"], "description"=>"<p>Local media surveillance contributed to overall reporting of health events, often resulting in greater geographic surveillance coverage (Map source: ArcGIS, ESRI).</p>", "links"=>[], "tags"=>["USAID PREDICT project", "healthmap", "monitoring disease activity", "Local Media Surveillance", "Global Hotspot Regions Digital disease detection tools", "surveillance system attributes", "disease events", "method", "lms", "surveillance systems", "information", "media", "surveillance network coverage", "236 health events", "87 health events"], "article_id"=>1205890, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jessica S. Schwind", "David J. Wolking", "John S. Brownstein", "Jonna A. K. Mazet", "Woutrina A. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110236.g003", "stats"=>{"downloads"=>2, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_geographic_comparison_of_local_media_surveillance_and_HealthMap_reports_in_Bangladesh_during_the_evaluation_period_/1205890", "title"=>"A geographic comparison of local media surveillance and HealthMap reports in Bangladesh during the evaluation period.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-10-15 03:52:05"}
• {"files"=>["https://ndownloader.figshare.com/files/1719071"], "description"=>"<p>*Risk event: any report containing information on events, circumstances, or contexts that could increase potential transmission of a disease.</p><p>**Disease event: any report containing news of an actual disease (infectious or non-infectious).</p><p>Performance characteristics of the local media surveillance (LMS) pilot by country.</p>", "links"=>[], "tags"=>["USAID PREDICT project", "healthmap", "monitoring disease activity", "Local Media Surveillance", "Global Hotspot Regions Digital disease detection tools", "surveillance system attributes", "disease events", "method", "lms", "surveillance systems", "information", "media", "surveillance network coverage", "236 health events", "87 health events"], "article_id"=>1205891, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jessica S. Schwind", "David J. Wolking", "John S. Brownstein", "Jonna A. K. Mazet", "Woutrina A. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110236.t001", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_characteristics_of_the_local_media_surveillance_LMS_pilot_by_country_/1205891", "title"=>"Performance characteristics of the local media surveillance (LMS) pilot by country.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-15 03:52:05"}
• {"files"=>["https://ndownloader.figshare.com/files/1719072"], "description"=>"<p>Health events identified through local media surveillance (LMS) and HealthMap’s digital disease surveillance over the 16-week evaluation period.</p>", "links"=>[], "tags"=>["USAID PREDICT project", "healthmap", "monitoring disease activity", "Local Media Surveillance", "Global Hotspot Regions Digital disease detection tools", "surveillance system attributes", "disease events", "method", "lms", "surveillance systems", "information", "media", "surveillance network coverage", "236 health events", "87 health events"], "article_id"=>1205892, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jessica S. Schwind", "David J. Wolking", "John S. Brownstein", "Jonna A. K. Mazet", "Woutrina A. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110236.t002", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Health_events_identified_through_local_media_surveillance_LMS_and_HealthMap_8217_s_digital_disease_surveillance_over_the_16_week_evaluation_period_/1205892", "title"=>"Health events identified through local media surveillance (LMS) and HealthMap’s digital disease surveillance over the 16-week evaluation period.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-15 03:52:05"}
• {"files"=>["https://ndownloader.figshare.com/files/1719073"], "description"=>"<div><p>Digital disease detection tools are technologically sophisticated, but dependent on <i>digital</i> information, which for many areas suffering from high disease burdens is simply not an option. In areas where news is often reported in local media with no digital counterpart, integration of local news information with digital surveillance systems, such as HealthMap (Boston Children’s Hospital), is critical. Little research has been published in regards to the specific contribution of local health-related articles to digital surveillance systems. In response, the USAID PREDICT project implemented a local media surveillance (LMS) pilot study in partner countries to monitor disease events reported in print media. This research assessed the potential of LMS to enhance digital surveillance reach in five low- and middle-income countries. Over 16 weeks, select surveillance system attributes of LMS, such as simplicity, flexibility, acceptability, timeliness, and stability were evaluated to identify strengths and weaknesses in the surveillance method. Findings revealed that LMS filled gaps in digital surveillance network coverage by contributing valuable localized information on disease events to the global HealthMap database. A total of 87 health events were reported through the LMS pilot in the 16-week monitoring period, including 71 unique reports not found by the HealthMap digital detection tool. Furthermore, HealthMap identified an additional 236 health events outside of LMS. It was also observed that belief in the importance of the project and proper source selection from the participants was crucial to the success of this method. The timely identification of disease outbreaks near points of emergence and the recognition of risk factors associated with disease occurrence continue to be important components of any comprehensive surveillance system for monitoring disease activity across populations. The LMS method, with its minimal resource commitment, could be one tool used to address the information gaps seen in global ‘hot spot’ regions.</p></div>", "links"=>[], "tags"=>["USAID PREDICT project", "healthmap", "monitoring disease activity", "Local Media Surveillance", "Global Hotspot Regions Digital disease detection tools", "surveillance system attributes", "disease events", "method", "lms", "surveillance systems", "information", "media", "surveillance network coverage", "236 health events", "87 health events"], "article_id"=>1205893, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Jessica S. Schwind", "David J. Wolking", "John S. Brownstein", "Jonna A. K. Mazet", "Woutrina A. Smith"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0110236", "stats"=>{"downloads"=>0, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Evaluation_of_Local_Media_Surveillance_for_Improved_Disease_Recognition_and_Monitoring_in_Global_Hotspot_Regions_/1205893", "title"=>"Evaluation of Local Media Surveillance for Improved Disease Recognition and Monitoring in Global Hotspot Regions", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-10-15 03:52:05"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime