Digital Contact Tracing for Pandemic Response DOI Creative Commons
Jeffrey Kahn

Johns Hopkins University Press eBooks, Год журнала: 2020, Номер unknown

Опубликована: Янв. 1, 2020

Digital technologies are being developed and promoted to support the public health response COVID-19 pandemic, with discussion implementation planning in United States by localities, states, institutions, employers.Key decision makers stakeholders-including government officials, institutional leaders, employers, digital technology developers, public-require clear well-supported guidance inform deployment use of these as well data they collect, store, share.While technology-based approaches currently unable provide solutions on their own, experiences other countries indicate that could be used successfully conjunction traditional novel methods.This report reflects a rapid research expert consensus group effort led Berman Institute Bioethics Center for Health Security at Johns Hopkins University.It draws experts from both inside outside bioethics, security, health, development, engineering, policy, law.The highlights issues must addressed provides recommendations part contact tracing.The analysis offered here is focused answering following questions:• Can tracing (DCTT) effective responses if so, what degree, which specific types functions, confidence, requirements?• How can serve interests while respecting individual collective interests, such ensuring equitable distribution benefits burdens limiting infringement privacy civil liberties?x Preface• What ethical, legal, governance guardrails place around else needed?• additional required ensure goals using achievable ways ethically legally sound?To answer questions, examines some core aspects applied tracing, focusing on:• value basic methods surveillance tracing,• candidate technological products enhance how work, comparative health,• considerations, relate relevant features solutions, and• needed move forward responsibly surveillance, acknowledging gaps our current understanding.The project involved in-depth dedicated team faculty, postdoctoral fellows, staff working over course only few weeks but great intensity, drafting collaboration 26 total contributors writing, commenting, revising through multiple drafts, penultimate draft "pressure-tested" review virtual workshop invited stakeholders held May 13, 2020, final version completed 21, 2020.The builds excellent work others parts this territory, areas have not been sufficiently addressed.The goal offer comprehensive advance during pandemic.Given rapidly evolving territory into DCTT introduced, will, necessity, something living document, updated often information dictates order continue leading-edge guidance.Versions will noted print editions.xi Efforts like require teams even small armies carried out successfully, was no exception, except it many fewer people more hours than reasonably expected them.From initial kernel an idea publication book form, took just month total.That seems impossible, I know accurate, speaks incredible commitment, hard skills, analytic acumen colleagues Hopkins-the deservedly listed lead authors report.None would possible without support-moral financial-and encouragement University President Ronald J. Daniels, who first suggest me taking topic.He provided

Язык: Английский

A Descriptive Study of COVID-19–Related Experiences and Perspectives of a National Sample of College Students in Spring 2020 DOI Open Access
Alison K. Cohen, Lindsay T. Hoyt, Brandon D. Dull

и другие.

Journal of Adolescent Health, Год журнала: 2020, Номер 67(3), С. 369 - 375

Опубликована: Июнь 24, 2020

Язык: Английский

Процитировано

188
Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence DOI Creative Commons
Chad Melton, Olufunto A. Olusanya, Nariman Ammar

и другие.

Journal of Infection and Public Health, Год журнала: 2021, Номер 14(10), С. 1505 - 1512

Опубликована: Авг. 14, 2021

The COVID-19 pandemic fueled one of the most rapid vaccine developments in history. However, misinformation spread through online social media often leads to negative sentiment and hesitancy. To investigate vaccine-related discussion media, we conducted a analysis Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing Dec 1, 2020, May 15, 2021. Data were aggregated analyzed by month detect changes any latent topics. Polarity suggested these expressed more positive than regarding discussions has remained static over time. Topic revealed community members mainly focused side effects rather outlandish conspiracy theories. Covid-19 content subreddits show that sentiments are overall have not meaningfully changed since December 2020. Keywords indicating hesitancy detected throughout LDA modeling. Public vaccines could facilitate implementation appropriate messaging, digital interventions, new policies promote confidence.

Язык: Английский

Процитировано

172
The Geographic Spread of COVID-19 Correlates with the Structure of Social Networks as Measured by Facebook DOI Open Access
Theresa Kuchler, Dominic Russel, Johannes Stroebel

и другие.

Опубликована: Апрель 1, 2020

Язык: Английский

Процитировано

162
JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook DOI Open Access
Theresa Kuchler, Dominic Russel, Johannes Stroebel

и другие.

Journal of Urban Economics, Год журнала: 2021, Номер 127, С. 103314 - 103314

Опубликована: Янв. 11, 2021

Язык: Английский

Процитировано

162
Using Reports of Symptoms and Diagnoses on Social Media to Predict COVID-19 Case Counts in Mainland China: Observational Infoveillance Study DOI Creative Commons
Cuihua Shen, Anfan Chen, Chen Luo

и другие.

Journal of Medical Internet Research, Год журнала: 2020, Номер 22(5), С. e19421 - e19421

Опубликована: Май 25, 2020

Can public social media data be harnessed to predict COVID-19 case counts? We analyzed approximately 15 million related posts on Weibo, a popular Twitter-like platform in China, from November 1, 2019 March 31, 2020. developed machine learning classifier identify "sick posts," which are reports of one's own and other people's symptoms diagnosis COVID-19. then modeled the predictive power sick daily counts. found that significantly predicted counts, up 14 days ahead official statistics. But did not have similar power. For subset geotagged (3.10% all retrieved posts), we pattern held true for both Hubei province rest mainland regardless unequal distribution healthcare resources outbreak timeline. Researchers disease control agencies should pay close attention infosphere regarding On top monitoring overall search posting activities, it is crucial sift through contents efficiently signals noise.

Язык: Английский

Процитировано

156
The dawn of digital public health in Europe: Implications for public health policy and practice DOI Creative Commons
Brian Li Han Wong, Laura Maaß,

Alice Vodden

и другие.

The Lancet Regional Health - Europe, Год журнала: 2022, Номер 14, С. 100316 - 100316

Опубликована: Фев. 3, 2022

The COVID-19 pandemic has highlighted the importance of digital health technologies and role effective surveillance systems. While recent events have accelerated progress towards expansion public (DPH), there remains significant untapped potential in harnessing, leveraging, repurposing for health. There is a particularly growing need comprehensive action to prepare citizens DPH, regulate effectively evaluate adopt DPH strategies as part policy services optimise systems improvement. As representatives European Public Health Association's (EUPHA) Digital Section, we reflect on current state share our understanding at level, determine how application developed during pandemic. We also discuss opportunities, challenges, implications increasing digitalisation Europe.

Язык: Английский

Процитировано

109
Social Media Role and Its Impact on Public Health: A Narrative Review DOI Open Access
Sushim Kanchan, Abhay Gaidhane

Cureus, Год журнала: 2023, Номер unknown

Опубликована: Янв. 13, 2023

Social media refers to online social networking sites and is a broad example of Web 2.0, such as Twitter, YouTube, TikTok, Facebook, Snapchat, Reddit, Instagram, WhatsApp, blogs. It new ever-changing field. Access the internet, platforms mobile communications are all tools that can be leveraged make health information available accessible. This research aimed conduct an introductory study existing published literature on why choose how use obtain population gain knowledge about various sectors like disease surveillance, education, research, behavioral modification, influence policy, enhance professional development doctor-patient relation development. We searched for publications using databases PubMed, NCBI, Google Scholar, combined 2022 usage statistics from PWC, Infographics Archive, Statista websites. The American Medical Association (AMA) policy Professionalism in Media Use, College Physicians-Federations State Boards (ACP-FSMB) guidelines Online Professionalism, Health Insurance Portability Accountability Act (HIPAA) violations were also briefly reviewed. Our findings reflect benefits drawbacks web they impact public ethically, professionally, socially. During our we discovered media's concerns both positive negative, attempted explain networks assisting people achieving health, which still source much debate.

Язык: Английский

Процитировано

103
Using artificial intelligence to improve public health: a narrative review DOI Creative Commons
David B. Olawade,

Ojima J. Wada,

Aanuoluwapo Clement David-Olawade

и другие.

Frontiers in Public Health, Год журнала: 2023, Номер 11

Опубликована: Окт. 26, 2023

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, public health, the widespread employment only began recently, with advent COVID-19. This review examines advances health potential challenges that lie ahead. Some ways aided delivery are via spatial modeling, risk prediction, misinformation control, surveillance, disease forecasting, pandemic/epidemic diagnosis. implementation not universal due to factors including limited infrastructure, lack technical understanding, data paucity, ethical/privacy issues.

Язык: Английский

Процитировано

101
The application of large language models in medicine: A scoping review DOI Creative Commons
Xiangbin Meng,

Xiangyu Yan,

Kuo Zhang

и другие.

iScience, Год журнала: 2024, Номер 27(5), С. 109713 - 109713

Опубликована: Апрель 23, 2024

This study systematically reviewed the application of large language models (LLMs) in medicine, analyzing 550 selected studies from a vast literature search. LLMs like ChatGPT transformed healthcare by enhancing diagnostics, medical writing, education, and project management. They assisted drafting documents, creating training simulations, streamlining research processes. Despite their growing utility diagnosis improving doctor-patient communication, challenges persisted, including limitations contextual understanding risk over-reliance. The surge LLM-related indicated focus on patient but highlighted need for careful integration, considering validation, ethical concerns, balance with traditional practice. Future directions suggested multimodal LLMs, deeper algorithmic understanding, ensuring responsible, effective use healthcare.

Язык: Английский

Процитировано

60
The Future of Social Determinants of Health: Looking Upstream to Structural Drivers DOI
Tyson H. Brown, Patricia Homan

Milbank Quarterly, Год журнала: 2023, Номер 101(S1), С. 36 - 60

Опубликована: Апрель 1, 2023

Policy Points Policies that redress oppressive social, economic, and political conditions are essential for improving population health achieving equity. Efforts to remedy structural oppression its deleterious effects should account multilevel, multifaceted, interconnected, systemic, intersectional nature. The U.S. Department of Health Human Services facilitate the creation maintenance a national publicly available, user-friendly data infrastructure on contextual measures oppression. Publicly funded research social determinants be mandated (a) analyze inequities in relation relevant (b) deposit available repository.

Язык: Английский

Процитировано

46