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A multidisciplinary journal that focuses on the intersection of public health and technology, public health informatics, mass media campaigns, surveillance, participatory epidemiology, and innovation in public health practice and research.
JMIR Public Health & Surveillance (JPHS, Editor-in-chief: Travis Sanchez, Emory University/Rollins School of Public Health) is a PubMed-indexed, peer-reviewed sister journal of the Journal of Medical Internet Research (JMIR), the top cited journal in health informatics, ranked #1 by Clarivate's Journal Impact Factor. JPH is a multidisciplinary journal with a unique focus on the intersection of innovation and technology in public health, and includes topics like health communication, public health informatics, surveillance, participatory epidemiology, infodemiology and infoveillance, digital disease detection, digital public health interventions, mass media/social media campaigns, and emerging population health analysis systems and tools.
We publish regular articles, reviews, protocols/system descriptions and viewpoint papers on all aspects of public health, with a focus on innovation and technology in public health.
Apart from publishing traditional public health research and viewpoint papers as well as reports from traditional surveillance systems, JPH was one of the first (if not the only) peer-reviewed journal which publishes papers with surveillance or pharmacovigilance data from non-traditional, unstructured big data and text sources such as social media and the Internet (infoveillance, digital disease detection), or reports on novel participatory epidemiology projects, where observations are solicited from the public.
Among other innovations, JPH is also dedicated to support rapid open data sharing and rapid open access to surveillance and outbreak data. As one of the novel features we plan to publish rapid or even real-time surveillance reports and open data. The methods and description of the surveillance system may be peer-reviewed and published only once in detail, in a "baseline report" (in a JMIR Res Protoc or a JMIR Public Health & Surveill paper), and authors then have the possibility to publish data and reports in frequent intervals rapidly and with only minimal additional peer-review (we call this article type "Rapid Surveillance Reports"). JMIR Publications may even work with authors/researchers and developers of selected surveillance systems on APIs for semi-automated reports (e.g. weekly reports to be automatically published in JPHS and indexed in PubMed, based on data-feeds from surveillance systems and minmal narratives and abstracts).
Furthermore, duing epidemics and public health emergencies, submissions with critical data will be processed with expedited peer-review to enable publication within days or even in real-time.
We also publish descriptions of open data resources and open source software. Where possible, we can and want to publish or even host the actual software or dataset on the journal website.
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Background: Wet markets are critical for food security and sustainable development in their respective regions but are also associated with health risks. Due to their cultural significance, they attra...
Background: Wet markets are critical for food security and sustainable development in their respective regions but are also associated with health risks. Due to their cultural significance, they attract numerous visitors and generate tourist-geared information on the Web (i.e. on social networks as TripAdvisor). These unexploited data can be used to create an internationally-comprehensive wet market inventory to support epidemiological surveillance and control in these settings, which to our knowledge, does not yet exist. Objective: Using social network data, we aim to: assess the level of wet markets’ touristic importance online; produce the first distribution map of wet markets of touristic interest; and identify common diseases facing visitors in these settings. Methods: TripAdvisor was selected as the data source of this study following an analysis of food markets’ touristic relevance on the web. A web scraping tool (ParseHub) was used to extract wet market names, locations, and reviews from TripAdvisor. The latter were analyzed and when possible, assigned GeoSentinel diagnosis codes. This syndromic information was overlaid onto a mapping of wet market locations. Results: 89 of the first 150 Google Search results (59.3%) for “wet market” (July 2017) were tourism-related. Of the 1,090 hits on TripAdvisor for this keyword, 393 (36%) were confirmed wet markets; syndromic information was available for 57 of these (14.5%). The confirmed wet markets were heterogeneously distributed: Asia concentrated 246 (62.6%) of them, Europe 76 (19.3%), North America 31 (7.9%), Oceania 20 (5.1%), Africa 12 (3.1%), and South America 8 (2.0%). Analysis of reviews corresponding to these wet markets revealed the most frequently occurring disease among visitors was food poisoning, accounting for 51 of 95 diagnoses (54%). This proved most prevalent among those visiting South American markets (18 of 51 food poisoning incidents [35%]) but less for Asian markets (6 of 51 food poisoning incidents [12%]) when normalizing for wet market number. Conclusions: To our knowledge, this study is first to map the global distribution of wet markets of touristic importance and adverse health events experienced by their visitors, highlighting the potential of social network data in global epidemiological surveillance.
Background: Time lag in detecting disease outbreaks remains a threat to global health security. Currently, our research team is working towards a system called EDMON, which uses blood glucose level an...
Background: Time lag in detecting disease outbreaks remains a threat to global health security. Currently, our research team is working towards a system called EDMON, which uses blood glucose level and other supporting parameters from people with type 1 diabetes, as indicator variables for outbreak detection. In the right mix of cluster detection, big data from self-management of diabetes, internet availability and the prevailing pervasiveness of devices, it is feasible and efficient to detect infectious disease outbreak as early as the incubation stage by using the vulnerability of diabetes patients as a sensor. Therefore, this paper aims to pinpoint the state of the art cluster detection mechanism towards developing an efficient framework and prototype to be used in EDMON and other similar syndromic surveillance systems. Various challenges such as user mobility, privacy and confidentiality, geographical location estimation and other factors have been considered. Objective: The general objective of this studies is to pinpoint the state of the art cluster detection mechanism towards developing an efficient framework and prototype to be used in EDMON and other similar syndromic surveillance systems. Various challenges such as user mobility, privacy and confidentiality, geographical location estimation and other factors have been considered. Methods: To this end, we conducted a systematic review exploring different online scholarly databases. Considering peer reviewed journals and articles, literatures search was conducted between January and March 2018. Results: Relevant literature were identified using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria and a full text review were done for literature that were found to be relevant. A total of 28 articles were included in the study. The result indicates that various clustering and aberration detection algorithms have been developed and tested up to the task. A framework for cluster detection mechanism in EDMON and other similar syndromic surveillance systems have been developed. Conclusions: The study revealed Space-Time Permutation Scan Statistics as the most implemented algorithm. The uniqueness and efficiency of STPSS is that its baseline or expected count is based on its detected cases within a defined geographical distance (cylinder radius) and temporal window (cylinder height). This approach provides significant trend of baseline data while avoiding inclusion of historical data that is irrelevant to the current period. Guided with results from the review, a framework for syndromic surveillance has been developed. Privacy preserving policies and high computational power requirement were found challenging since it restrict usage of specific locations for syndromic surveillance.
The use of social networking sites has exponentially increased in the recent years and the increment was most noticeable among the youth. However, suicide is the second leading cause of death among th...
The use of social networking sites has exponentially increased in the recent years and the increment was most noticeable among the youth. However, suicide is the second leading cause of death among this group and they are the most active group in the different social networking sites. They share their thoughts, photos, opinions and news including the news of suicide. Whenever, they came to know the death of a friend or follower they share the news express their grief. In addition they also show their condolence to the death of the relative or friends of Facebook friends Moreover, all the television channels and newspapers share their information and news in Facebook, Tweeter and other social media from where we can collect information. Bangladesh is a Muslim developing country in South East Asia that lacks any national suicide database due a number of sociocultural, religious and political factors. As a result, the existing the data on suicide in the country shows about 20 fold variation in different reports. However, the country can develop national suicide database by extracting the information shared in the Facebook and verifying each incidents from different Facebook users. This idea can solve a long lasting problem in many developing low and middle income countries.