Online journal of public health informatics最新文献

{"title":"A Public Health Informatics Solution to Improving Food Safety in Restaurants: Putting the Missing Piece in the Puzzle.","authors":"Melanie J Firestone,&nbsp;Sripriya Rajamani,&nbsp;Craig W Hedberg","doi":"10.5210/ojphi.v13i1.11087","DOIUrl":"https://doi.org/10.5210/ojphi.v13i1.11087","url":null,"abstract":"<p><p>Foodborne illnesses remain an important public health challenge in the United States causing an estimated 48 million illnesses, 128,000 hospitalizations, and 3,000 deaths per year. Restaurants are frequent settings for foodborne illness transmission. Public health surveillance - the continual, systematic collection, analysis, and interpretation of reports of health data to prevent and control illness - is a prerequisite for an effective food control system. While restaurant inspection data are routinely collected, these data are not regularly aggregated like traditional surveillance data. However, there is evidence that these data are a valuable tool for understanding foodborne illness outbreaks and threats to food safety. This article discusses the challenges and opportunities for incorporating routine restaurant inspection data as a surveillance tool for monitoring and improving foodborne illness prevention activities. The three main challenges are: 1) lack of a national framework; 2) lack of data standards and interoperability; and 3) limited access to restaurant inspection data. Tapping into the power of public health informatics represents an opportunity to address these challenges. Advancing the food safety system by improving restaurant inspection information systems and making restaurant inspection data available to support decision-making represents an opportunity to practice smarter food safety.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 1","pages":"e5"},"PeriodicalIF":0.0,"publicationDate":"2021-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075413/pdf/ojphi-13-1-e6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38860336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"StayHome: A FHIR-Native Mobile COVID-19 Symptom Tracker and Public Health Reporting Tool.","authors":"Hannah A Burkhardt,&nbsp;Pascal S Brandt,&nbsp;Jenney R Lee,&nbsp;Sierramatice W Karras,&nbsp;Paul F Bugni,&nbsp;Ivan Cvitkovic,&nbsp;Amy Y Chen,&nbsp;William B Lober","doi":"10.5210/ojphi.v13i1.11462","DOIUrl":"https://doi.org/10.5210/ojphi.v13i1.11462","url":null,"abstract":"<p><p>As the COVID-19 pandemic continues to unfold and states experience the impacts of reopened economies, it is critical to efficiently manage new outbreaks through widespread testing and monitoring of both new and possible cases. Existing labor-intensive public health workflows may benefit from information collection directly from individuals through patient-reported outcomes (PROs) systems. Our objective was to develop a reusable, mobile-friendly application for collecting PROs and experiences to support COVID-19 symptom self-monitoring and data sharing with appropriate public health agencies, using Fast Healthcare Interoperability Resources (FHIR) for interoperability. We conducted a needs assessment and designed and developed StayHome, a mobile PRO administration tool. FHIR serves as the primary data model and driver of business logic. Keycloak, AWS, Docker, and other technologies were used for deployment. Several FHIR modules were used to create a novel \"FHIR-native\" application design. By leveraging FHIR to shape not only the interface strategy but also the information architecture of the application, StayHome enables the consistent standards-based representation of data and reduces the barrier to integration with public health information systems. FHIR supported rapid application development by providing a domain-appropriate data model and tooling. FHIR modules and implementation guides were referenced in design and implementation. However, there are gaps in the FHIR specification which must be recognized and addressed appropriately. StayHome is live and accessible to the public at https://stayhome.app. The code and resources required to build and deploy the application are available from https://github.com/uwcirg/stayhome-project.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 1","pages":"e2"},"PeriodicalIF":0.0,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075351/pdf/ojphi-13-1-e2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38860333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 Exposure Tracking Within Public Health & Safety Enterprises: Findings to Date & Opportunity for Further Research.","authors":"Jonathon S Feit,&nbsp;Christian C Witt","doi":"10.5210/ojphi.v13i1.11484","DOIUrl":"https://doi.org/10.5210/ojphi.v13i1.11484","url":null,"abstract":"Where there is limited access to COVID-19 tests, or where the results of such tests have been delayed or even invalidated (e.g., California and Utah), there is a need for scalable alternative approaches-such as a heuristic model or \"pregnancy test for COVID-19\" that can factor in the time denominator (i.e., duration of symptoms). This paper asks whether infection among these public health and safety agencies is a \"canary in the coal mine,\" litmus test, or microcosm (pick your analogy) for the communities in which they operate. Can COVID-19 infection counts and rates be seen \"moving around\" communities by examining the virus's effect on emergency responders themselves? The troubling question of emergency responders becoming \"human indicator values\" is relevant to maintaining the health of Mobile Medicine (EMS and Fire) personnel, as well as Police, who are an under-attended population, because these groups our collective resiliency would crash. It has further implications for policies regarding, and investments in, exposure tracking and contact tracing, PPE acquisition, and mental and physical wellness. Design We aggregated data from four (4) different EMS documentation systems across twelve (12) states using the MEDIVIEW BEACON Prehospital Health Information Exchange. We then outputted lists of charts containing critical ICD-10 values that had been identified by the WHO, the CDC, and the Los Angeles County Fire Deptartment's EMS Bureau as inclusion criteria for possible signs, symptoms, and clinical impressions of COVID-19 infection. Results Three important results emerged from this study: (1) a demonstration of frequent exposure to possible COVID-19 infection among Mobile Medical (EMS & Fire) care providers in the states whose data were included; (2) a demonstration of the nervousness of the general population, given that calls for help due to possible COVID-19 based on symptomology exceeded the number of responses with a correlating \"provider impression\" after an informed clinical assessment; and (3) the fact that this study was empowered by a public-private partnerships between a technology startup and numerous public health and public safety agencies, offers a template for success in rapidly implementing research and development collaborations. Limitations This study incorporates data from only (a) twelve (12) states, and (b) four (4) Mobile Medical documentation systems. We sought to combat these limitations by ensuring that our sample crosses agencies types, geographies, population demographics, and municipal environments (i.e., rural vs. urban). Conclusions Other studies have noted that EMS agencies are tasked with transporting the \"sickest of the sick.\" We found that PPE is particularly essential where the frequency of encounters between potentially-or actually-infected patients is high, because from Los Angeles County to rural Texas, without sufficient protection, public health and public safety agencies have beco","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 1","pages":"e3"},"PeriodicalIF":0.0,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075415/pdf/ojphi-13-1-e3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38860334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Informatics and Technology to Support Public Health Response: Framework and Illustrations using COVID-19.","authors":"Jane L Snowdon,&nbsp;William Kassler,&nbsp;Hema Karunakaram,&nbsp;Brian E Dixon,&nbsp;Kyu Rhee","doi":"10.5210/ojphi.v13i1.11072","DOIUrl":"https://doi.org/10.5210/ojphi.v13i1.11072","url":null,"abstract":"<p><strong>Objective: </strong>To develop a conceptual model and novel, comprehensive framework that encompass the myriad ways informatics and technology can support public health response to a pandemic.</p><p><strong>Method: </strong>The conceptual model and framework categorize informatics solutions that could be used by stakeholders (e.g., government, academic institutions, healthcare providers and payers, life science companies, employers, citizens) to address public health challenges across the prepare, respond, and recover phases of a pandemic, building on existing models for public health operations and response.</p><p><strong>Results: </strong>Mapping existing solutions, technology assets, and ideas to the framework helped identify public health informatics solution requirements and gaps in responding to COVID-19 in areas such as applied science, epidemiology, communications, and business continuity. Two examples of technologies used in COVID-19 illustrate novel applications of informatics encompassed by the framework. First, we examine a hub from The Weather Channel, which provides COVID-19 data via interactive maps, trend graphs, and details on case data to individuals and businesses. Second, we examine IBM Watson Assistant for Citizens, an AI-powered virtual agent implemented by healthcare providers and payers, government agencies, and employers to provide information about COVID-19 via digital and telephone-based interaction.</p><p><strong>Discussion: </strong>Early results from these novel informatics solutions have been positive, showing high levels of engagement and added value across stakeholders.</p><p><strong>Conclusion: </strong>The framework supports development, application, and evaluation of informatics approaches and technologies in support of public health preparedness, response, and recovery during a pandemic. Effective solutions are critical to success in recovery from COVID-19 and future pandemics.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 1","pages":"e1"},"PeriodicalIF":0.0,"publicationDate":"2021-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075350/pdf/ojphi-13-1-e1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38940415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Data Driven Approach for Prioritizing COVID-19 Vaccinations in the Midwestern United States.","authors":"Greg Arling,&nbsp;Matthew Blaser,&nbsp;Michael D Cailas,&nbsp;John R Canar,&nbsp;Brian Cooper,&nbsp;Joel Flax-Hatch,&nbsp;Peter J Geraci,&nbsp;Kristin M Osiecki,&nbsp;Apostolis Sambanis","doi":"10.5210/ojphi.v13i1.11621","DOIUrl":"https://doi.org/10.5210/ojphi.v13i1.11621","url":null,"abstract":"<p><p>Considering the potential for widespread adoption of social vulnerability indices (SVI) to prioritize COVID-19 vaccinations, there is a need to carefully assess them, particularly for correspondence with outcomes (such as loss of life) in the context of the COVID-19 pandemic. The University of Illinois at Chicago School of Public Health Public Health GIS team developed a methodology for assessing and deriving vulnerability indices based on the premise that these indices are, in the final analysis, classifiers. Application of this methodology to several Midwestern states with a commonly used SVI indicates that by using only the SVI rankings there is a risk of assigning a high priority to locations with the lowest mortality rates and low priority to locations with the highest mortality rates. Based on the findings, we propose using a two-dimensional approach to rationalize the distribution of vaccinations. This approach has the potential to account for areas with high vulnerability characteristics as well as to incorporate the areas that were hard hit by the pandemic.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 1","pages":"e5"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075414/pdf/ojphi-13-1-e5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38860337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking COVID-19 burden in India: A review using SMAART RAPID tracker.","authors":"Ashish Joshi, Harpreet Kaur, L Nandini Krishna, Shruti Sharma, Gautam Sharda, Garima Lohra, Ashruti Bhatt, Ashoo Grover","doi":"10.5210/ojphi.v13i1.11456","DOIUrl":"10.5210/ojphi.v13i1.11456","url":null,"abstract":"<p><strong>Objective: </strong>India has seen a rapid rise in COVID-19 cases. Examine spatiotemporal variation of COVID-19 burden Tracker across Indian states and union territories using SMAART RAPID Tracker.</p><p><strong>Method: </strong>We used SMAART RAPID Tracker to visually display COVID-19 spread in space and time across various states and UTs of India. Data gathered from publicly available government information sources. Data analysis on COVID-19 conducted from March 1 2020 to October 1 2020. Variables recorded include COVID-19 cases and fatality, 7-day average change, recovery rate, labs and tests. Spatial and temporal trends of COVID-19 spread across Indian states and UTs is presented.</p><p><strong>Result: </strong>The total number of COVID-19 cases were 63, 12,584 and total fatality was 86,821 (October 1 2020). More than 85,000 new cases of COVID-19 were reported. There were 1,867 total COVID-19 labs throughout India. More than half of them were Government labs. The total number of COVID-19 tests was 76,717,728 and total recovered COVID-19 cases was 5,273,201. Results show an overall decline in the 7-day average change of new COVID-19 cases and new COVID-19 fatality. States such as Maharashtra, Chandigarh, Puducherry, Goa, Karnataka and Andhra Pradesh continue to have high COVID-19 infectivity rate.</p><p><strong>Discussion: </strong>Findings highlight need for both national guidelines combined with state specific recommendations to help manage the spread of COVD-19.</p><p><strong>Conclusion: </strong>The heterogeneity represented in India in terms of its geography and various population groups highlight the need of state specific approach to monitor and combat the ongoing pandemic. This would further facilitate the tailored approach for each state to mitigate and contain the spread of the disease.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"13 1","pages":"e4"},"PeriodicalIF":0.0,"publicationDate":"2021-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075416/pdf/ojphi-13-1-e4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38860335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A second wave of COVID-19 in Cook County: What lessons can be applied?","authors":"Gregory W Arling,&nbsp;Matthew Blaser,&nbsp;Michael D Cailas,&nbsp;John R Canar,&nbsp;Brian Cooper,&nbsp;Peter J Geraci,&nbsp;Kristin M Osiecki,&nbsp;Apostolis Sambanis","doi":"10.5210/ojphi.v12i2.11506","DOIUrl":"https://doi.org/10.5210/ojphi.v12i2.11506","url":null,"abstract":"<p><p>During the ongoing public health crisis, many agencies are reporting COVID-19 health outcome information based on the overall population. This practice can lead to misleading results and underestimation of high risk areas. To gain a better understanding of spatial and temporal distribution of COVID-19 deaths; the long term care facility (LTCF) and household population (HP) deaths must be used. This approach allows us to better discern high risk areas and provides policy makers with reliable information for community engagement and mitigation strategies. By focusing on high-risk LTCFs and residential areas, protective measures can be implemented to minimize COVID-19 spread and subsequent mortality.  These areas should be a high priority target when COVID-19 vaccines become available.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 2","pages":"e15"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758067/pdf/ojphi-12-2-e15.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39114620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Mobile Phone Data Collection Tool, Surveda, for Noncommunicable Disease Surveillance in Five Low- and Middle-income Countries.","authors":"Yang Song,&nbsp;Rachael Phadnis,&nbsp;Jennifer Favaloro,&nbsp;Juliette Lee,&nbsp;Charles Q Lau,&nbsp;Manuel Moreira,&nbsp;Leenisha Marks,&nbsp;Matías García Isaía,&nbsp;Jason Kim,&nbsp;Veronica Lea","doi":"10.5210/ojphi.v12i2.10574","DOIUrl":"https://doi.org/10.5210/ojphi.v12i2.10574","url":null,"abstract":"<p><strong>Objectives: </strong>The Noncommunicable Disease (NCD) Mobile Phone Survey, a component of the Bloomberg Philanthropies Data for Health Initiative, determines the prevalence of NCDs and their associated risk factors and demonstrates the use of mobile phone administered surveys to supplement periodic national household surveys. The NCD Mobile Phone Survey uses Surveda to administer the survey; Surveda is an open-source, multi-modal software specifically developed for the project. The objective of the paper is to describe Surveda, review data collection methods used in participating countries and discuss how Surveda and similar approaches can improve public health surveillance.</p><p><strong>Methods: </strong>Surveda features full-service survey design and implementation through a web application and collects data via Short Messaging Service (SMS), Interactive Voice Response (IVR) and/or mobile web. Surveda's survey design process employs five steps: creating a project, creating questionnaires, designing and starting a survey, monitoring survey progress, and exporting survey results.</p><p><strong>Results: </strong>The NCD Mobile Phone Survey has been successfully conducted in five countries, Zambia (2017), Philippines (2018), Morocco (2019), Malawi (2019), and Sri Lanka (2019), with a total of 23,682 interviews completed.</p><p><strong>Discussion: </strong>This approach to data collection demonstrates that mobile phone surveys can supplement face-to-face data collection methods. Furthermore, Surveda offers major advantages including automated mode-switch, question randomization and comparison features.</p><p><strong>Conclusion: </strong>Accurate and timely survey data informs a country's abilities to make targeted policy decisions while prioritizing limited resources. The high acceptance of Surveda demonstrates that the use of mobile phones for surveillance can deliver accurate and timely data collection.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 2","pages":"e13"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758069/pdf/ojphi-12-2-e13.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39114618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Unified Data Exchange Formats for Reporting Molecular Drug Susceptibility Testing.","authors":"Wilfred Bonney,&nbsp;Sandy F Price,&nbsp;Swapna Abhyankar,&nbsp;Riki Merrick,&nbsp;Varsha Hampole,&nbsp;Tanya A Halse,&nbsp;Charles DiDonato,&nbsp;Tracy Dalton,&nbsp;Beverly Metchock,&nbsp;Angela M Starks,&nbsp;Roque Miramontes","doi":"10.5210/ojphi.v12i2.10644","DOIUrl":"https://doi.org/10.5210/ojphi.v12i2.10644","url":null,"abstract":"<p><strong>Background: </strong>With the rapid development of new advanced molecular detection methods, identification of new genetic mutations conferring pathogen resistance to an ever-growing variety of antimicrobial substances will generate massive genomic datasets for public health and clinical laboratories. Keeping up with specialized standard coding for these immense datasets will be extremely challenging. This challenge prompted our effort to create a common molecular resistance Logical Observation Identifiers Names and Codes (LOINC) panel that can be used to report any identified antimicrobial resistance pattern.</p><p><strong>Objective: </strong>To develop and utilize a common molecular resistance LOINC panel for molecular drug susceptibility testing (DST) data exchange in the U.S. National Tuberculosis Surveillance System using California Department of Public Health (CDPH) and New York State Department of Health as pilot sites.</p><p><strong>Methods: </strong>We developed an interface and mapped incoming molecular DST data to the common molecular resistance LOINC panel using Health Level Seven (HL7) v2.5.1 Electronic Laboratory Reporting (ELR) message specifications through the Orion Health™ Rhapsody Integration Engine v6.3.1.</p><p><strong>Results: </strong>Both pilot sites were able to process and upload/import the standardized HL7 v2.5.1 ELR messages into their respective systems; albeit CDPH identified areas for system improvements and has focused efforts to streamline the message importation process. Specifically, CDPH is enhancing their system to better capture parent-child elements and ensure that the data collected can be accessed seamlessly by the U.S. Centers for Disease Control and Prevention.</p><p><strong>Discussion: </strong>The common molecular resistance LOINC panel is designed to be generalizable across other resistance genes and ideally also applicable to other disease domains.</p><p><strong>Conclusion: </strong>The study demonstrates that it is possible to exchange molecular DST data across the continuum of disparate healthcare information systems in integrated public health environments using the common molecular resistance LOINC panel.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 2","pages":"e14"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7758061/pdf/ojphi-12-2-e14.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39114619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generation and Classification of Activity Sequences for Spatiotemporal Modeling of Human Populations.","authors":"Albert M Lund,&nbsp;Ramkiran Gouripeddi,&nbsp;Julio C Facelli","doi":"10.5210/ojphi.v12i1.10588","DOIUrl":"https://doi.org/10.5210/ojphi.v12i1.10588","url":null,"abstract":"<p><p>Human activity encompasses a series of complex spatiotemporal processes that are difficult to model but represent an essential component of human exposure assessment. A significant empirical data source, like the American Time Use Survey (ATUS), can be leveraged to model human activity. However, tractable models require a better stratification of activity data to inform about different, but classifiable groups of individuals, that exhibit similar activity sequences and mobility patterns. Using machine learning algorithms, we developed an unsupervised classification and sequence generation method that is capable of generating coherent and stochastic sequences of activity from the ATUS data. This classification, when combined with any spatiotemporal exposure profile, allows the development of stochastic models of exposure patterns and records for groups of individuals exhibiting similar activity behaviors.</p>","PeriodicalId":74345,"journal":{"name":"Online journal of public health informatics","volume":"12 1","pages":"e9"},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462521/pdf/ojphi-12-1-e9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38460674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}