JMIR Public Health Surveill 2021 7(1):e24132 The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence.Ĭonclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies.
We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This practice may lead to ineffective mitigation. JMIR Perioperative Medicine 38 articlesĮmail: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently.
JMIR Biomedical Engineering 42 articles.Journal of Participatory Medicine 57 articles.JMIR Rehabilitation and Assistive Technologies 114 articles.JMIR Pediatrics and Parenting 117 articles.Interactive Journal of Medical Research 207 articles.JMIR Public Health and Surveillance 669 articles.Journal of Medical Internet Research 5463 articles.
0 kommentar(er)
