Rapidly evolving technology, data and analytic landscapes are permeating many fields and professions. In public health, the need for data science skills including data literacy is particularly prominent given both the potential of novel data types and analysis methods to fill gaps in existing public health research and intervention practices, as well as the potential of such data or methods to perpetuate or augment health disparities. Through a review of public health courses and programs at the top 10 U.S. and globally ranked schools of public health, this article summarizes existing educational efforts in public health data science. These existing practices serve to inform efforts for broadening such curricula to further schools and populations. Data science ethics course offerings are also examined in context of assessing how population health principles can be blended into training across levels of data involvement to augment the traditional core of public health curricula. Parallel findings from domestic and international 'outside the classroom' training programs are also synthesized to advance approaches for increasing diversity in public health data science. Based on these program reviews and their synthesis, a four-point formula is distilled for furthering public health data science education efforts, toward development of a critical and inclusive mass of practitioners with fluency to leverage data to advance goals of public health and improve quality of life in the digital age.
Health research capacity strengthening is of importance to reach health goals. The ARCADE projects' aim was to strengthen health research across Africa and Asia using innovative educational technologies. In the four years of the EU funded projects, challenges also of technical nature were identified. This article reports on a study conducted within the ARCADE projects. The study focused on addressing challenges of video conferencing in resource constrained settings and was conducted using action research. As a result, a plugin for the open source video conferencing system minisip was implement…
For a long time, public health events, such as disease incidence or vaccination activity, have been monitored to keep track of the health status of the population, allowing to evaluate the effect of public health initiatives and to decide where resources for improving public health are best spent. This thesis investigates the use of web data mining for public health monitoring, and makes contributions in the following two areas: New approaches for predicting public health events from web mined data, and novel applications of web mined data for public health monitoring.
Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individuals, but communities as a whole. We present here a very brief introduction into research in these fields, as well as connections to existing machine learning work to help activate the machine learning community on such topics and highlight specific opportunities where machine learning, public and population health may synergize to better achieve health equity.
For what purposes are crowds being implemented in health care? Which crowdsourcing methods are being used? This work begins to answer these questions by reporting the early results of a systematic literature review of 110 pieces of relevant research. The results of this exploratory research in progress reveals that collective intelligence outcomes are being generated in three broad categories of public health care; health promotion, health research, and health maintenance, using all three known forms of crowdsourcing. Stemming from this fundamental analysis, some potential implications of the …
Public health interventions are a fundamental tool for mitigating the spread of an infectious disease. However, it is not always possible to obtain a conclusive estimate for the impact of an intervention, especially in situations where the effects are fragmented in population parts that are under-represented within traditional public health surveillance schemes. To this end, online user activity can be used as a complementary sensor to establish alternative measures. Here, we provide a summary of our research on formulating statistical frameworks for assessing public health interventions based…
The link between data and governance are key to making public health supply chains more integrated and responsive in order to get life-saving commodities to those in need. In particular, considering its significant health challenges, poor maternal and child health indicators, and major recent devolution in political authority, Kenya represents a country in need of an innovative revamp of their data management and governance. John Snow, Inc. (JSI) adapted various elements of proven interventions to build a customized structure to support routine data collection in order to drive decision making…
Urban agglomerations are constantly and rapidly evolving ecosystems, with globalization and increasing urbanization posing new challenges in sustainable urban development well summarized in the United Nations' Sustainable Development Goals (SDGs). The advent of the digital age generated by modern alternative data sources provides new tools to tackle these challenges with spatio-temporal scales that were previously unavailable with census statistics. In this review, we present how new digital data sources are employed to provide data-driven insights to study and track (i) urban crime and public…
The way people respond to messaging from public health organizations on social media can provide insight into public perceptions on critical health issues, especially during a global crisis such as COVID-19. It could be valuable for high-impact organizations such as the US Centers for Disease Control and Prevention (CDC) or the World Health Organization (WHO) to understand how these perceptions impact reception of messaging on health policy recommendations. We collect two datasets of public health messages and their responses from Twitter relating to COVID-19 and Vaccines, and introduce a pred…
University of British Columbia（不列颠哥伦比亚大学）；British Columbia Centre for Disease Control（不列颠哥伦比亚省疾病控制中心）
Social media is a rich source where we can learn about people's reactions to social issues. As COVID-19 has significantly impacted on people's lives, it is essential to capture how people react to public health interventions and understand their concerns. In this paper, we aim to investigate people's reactions and concerns about COVID-19 in North America, especially focusing on Canada. We analyze COVID-19 related tweets using topic modeling and aspect-based sentiment analysis, and interpret the results with public health experts. We compare timeline of topics discussed with timing of implement…
Digital contact tracing is a public health intervention. It should be integrated with local health policy, provide rapid and accurate notifications to exposed individuals, and encourage high app uptake and adherence to quarantine. Real-time monitoring and evaluation of effectiveness of app-based contact tracing is key for improvement and public trust.
To mitigate inefficiencies in manual contact tracing processes, Digital Contact Tracing and Exposure Notifications Systems were developed for use as public-interest technologies during the SARS-CoV-2 global pandemic. Effective implementation of these tools requires alignment across several factors, including local regulations and policies and trust in government and public health officials. Careful consideration should also be made to minimize any potential conflicts with existing processes in public health which has demonstrated effectiveness. Four unique cases-of Ireland, Guayaquil, Haiti, a…
In this paper, we have proposed a new smartphone based system for health care, monitoring and diagnosis, which is specially designed to efficiently increase the public health care system in the distant, rural, unreached areas of the underdeveloped and developing countries. In this all-in-one system, we have digitized the health monitoring and diagnostic devices in a way so that each device works as a minimum `plug and play' sensor module of the total system, reducing the cost radically. Besides, the easy-to-use smartphone application for operating the whole system reduces the necessity of skil…
Social networking sites such as Twitter and Facebook have been shown to function as effective social sensors that can "feel the pulse" of a community. The aim of the current study is to test the feasibility of designing, implementing and evaluating a bespoke social media-enabled intervention that can be effective for sharing and changing knowledge, attitudes and behaviours in meaningful ways to promote public health, specifically with regards to prevention of skin cancer. We present the design and implementation details of the campaign followed by summary findings and analysis.
Several projects have shown the feasibility to use textual social media data to track public health concerns, such as temporal influenza patterns or geographical obesity patterns. In this paper, we look at whether geo-tagged images from Instagram also provide a viable data source. Especially for "lifestyle" diseases, such as obesity, drinking or smoking, images of social gatherings could provide information that is not necessarily shared in, say, tweets. In this study, we explore whether (i) tags provided by the users and (ii) annotations obtained via automatic image tagging are indeed valuabl…
Without proper medication and vaccination for the COVID-19, many governments are using automated digital healthcare surveillance system to prevent and control the spread. There is not enough literature explaining the concerns and privacy issues; hence, we have briefly explained the topics in this paper. We focused on digital healthcare surveillance system's privacy concerns and different segments. Further research studies should be conducted in different sectors. This paper provides an overview based on the published articles, which are not focusing on the privacy issues that much. Artificial …
Bill and Melinda Gates Foundation（比尔及梅琳达·盖茨基金会）；Universidad de Barcelona（巴塞罗那大学）
Malaria can be prevented, diagnosed, and treated; however, every year, there are more than 200 million cases and 200.000 preventable deaths. Malaria remains a pressing public health concern in low- and middle-income countries, especially in sub-Saharan Africa. We describe how by means of mobile health applications, machine-learning-based adaptive interventions can strengthen malaria surveillance and treatment adherence, increase testing, measure provider skills and quality of care, improve public health by supporting front-line workers and patients (e.g., by capacity building and encouraging b…
This paper aims to study the fight against COVID-19 in Bangladesh and digital intervention initiatives. To achieve the purpose of our research, we conducted a methodical review of online content. We have reviewed the first digital intervention that COVID-19 has been used to fight against worldwide. Then we reviewed the initiatives that have been taken in Bangladesh. Our paper has shown that while Bangladesh can take advantage of the digital intervention approach, it will require rigorous collaboration between government organizations and universities to get the most out of it. Public health ca…
In the current pandemic, testing continues to be the most important tool for monitoring and curbing the disease spread and early identification of the disease to perform health-related interventions like quarantine, contact tracing and etc. Therefore, the ability to verify the testing status is pertinent as public places prepare to safely open. Recent advances in cryptographic tools have made it possible to build a secure and resilient digital-id system. In this work, we propose to build an end to end COVID-19 results verification protocol that takes privacy, computation, and other practical c…
We surveyed every state and the District of Columbia to see what patient specific information states release on hospital visits and how much potentially identifiable information is released in those records. Thirty-three states release hospital discharge data in some form, with varying levels of demographic information and hospital stay details such as hospital name, admission and discharge dates, diagnoses, doctors who attended to the patient, payer, and cost of the stay. We compared the level of demographic and other data to federal standards set by the Health Information Portability and Acc…
Global Health Institute, School of Life Sciences, EPFL（洛桑联邦理工学院生命科学学院全球健康研究所）；EPFL（洛桑联邦理工学院）
In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much o…
Responses to COVID-19 have largely been led by local, national, and international public health agencies, who have activated their pandemic plans and opened the epidemiological toolkit of modelling, testing, isolation and movement restrictions, surveillance, and contact tracing. In the contemporary tech-heavy world, many assumed that the common manual process of human investigators and phone calls could or should be replaced by digital solutions. But it's not as simple as "add more technology" - the complex way in which users and societies interact with the technology has significant impacts o…
The Covid-19 pandemic has intersected with the opioid epidemic to create a unique public health crisis, with the health and economic consequences of the virus and associated lockdowns compounding pre-existing social and economic stressors associated with rising opioid and heroin use and abuse. In order to better understand these interlocking crises, we use social media data to extract qualitative and quantitative insights on the experiences of opioid users during the Covid-19 pandemic. In particular, we use an unsupervised learning approach to create a rich geolocated data source for public he…
We present three case studies of organizations using a data science competition to answer a pressing question. The first is in education where a nonprofit that creates smart school budgets wanted to automatically tag budget line items. The second is in public health, where a low-cost, nonprofit women's health care provider wanted to understand the effect of demographic and behavioral questions on predicting which services a woman would need. The third and final example is in government innovation: using online restaurant reviews from Yelp, competitors built models to forecast which restaurants…
#EpiTwitter: Public Health Messaging During the COVID-19 Pandemic
University of Southern California（南加州大学）；Information Sciences Institute（信息科学研究所）；University of California - San Diego（加州大学圣迭戈分校）；University of Maryland（马里兰大学）；Institute for Advanced Computer Studies（高级计算机研究所）
University of Florida（佛罗里达大学）；TAO Connect, Inc.（TAO Connect公司）
As the popularity of social media platforms continues to rise, an ever-increasing amount of human communication and self- expression takes place online. Most recent research has focused on mining social media for public user opinion about external entities such as product reviews or sentiment towards political news. However, less attention has been paid to analyzing users' internalized thoughts and emotions from a mental health perspective. In this paper, we quantify the semantic difference between public Tweets and private mental health journals used in online cognitive behavioral therapy. We…
Many public health threats exist, motivating the need to find optimal intervention strategies. Given the stochastic nature of the threats (e.g., the spread of pandemic influenza, the occurrence of drug overdoses, and the prevalence of alcohol-related threats), deterministic optimization approaches may be inappropriate. In this paper, we implement a stochastic optimization method to address aspects of the 2009 H1N1 and the COVID-19 pandemics, with the spread of disease modeled by the open source Monte Carlo simulations, FluTE and Covasim, respectively. Without testing every possible option, the…
Can LLMs Help Allocate Public Health Resources? A Case Study on Childhood Lead Testing
Department of Computer and Information Sciences, Fordham University（福特汉姆大学计算机与信息科学系）；Department of Systems Engineering, Stevens Institute of Technology（史蒂文斯理工学院系统工程系）
University of California, Los Angeles (UCLA)（加利福尼亚大学洛杉矶分校（UCLA））
Traditionally, machine learning algorithms rely on the assumption that all features of a given dataset are available for free. However, there are many concerns such as monetary data collection costs, patient discomfort in medical procedures, and privacy impacts of data collection that require careful consideration in any real-world health analytics system. An efficient solution would only acquire a subset of features based on the value it provides while considering acquisition costs. Moreover, datasets that provide feature costs are very limited, especially in healthcare. In this paper, we pro…
An AI-Enabled Framework Within Reach for Enhancing Healthcare Sustainability and Fairness
AI Research Center, Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院AI研究中心）；School of Computer Science, Hangzhou Dianzi University（杭州电子科技大学计算机学院）；School of Automation Science and Electrical Engineering, Beihang University（北京航空航天大学自动化科学与电气工程学院）；Institute of Medical Technology, Health Science Center of Peking University（北京大学健康科学中心医学技术研究所）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Information Hub, Hong Kong University of Science and Technology（香港科技大学信息中心）；Department of Biomedical Engineering, City University of Hong Kong（香港城市大学生物医学工程系）；Department of Biomedical Engineering, Southern University of Science and Technology（南方科技大学生物医学工程系）；Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College（中国医学科学院北京协和医学院医学信息研究所）
We propose and develop a Lexicocalorimeter: an online, interactive instrument for measuring the "caloric content" of social media and other large-scale texts. We do so by constructing extensive yet improvable tables of food and activity related phrases, and respectively assigning them with sourced estimates of caloric intake and expenditure. We show that for Twitter, our naive measures of "caloric input", "caloric output", and the ratio of these measures are all strong correlates with health and well-being measures for the contiguous United States. Our caloric balance measure in many cases out…
