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Hot Topics of the Day are picked by experts to capture the latest information and publications on public health genomics and precision health for various diseases and health topics. Sources include published scientific literature, reviews, blogs and popular press articles.

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44 hot topic(s) found with the query "Data science"

Advancing Data Science Among the Federal Public Health Workforce: The Data Science Upskilling Program, Centers for Disease Control and Prevention.
Mary Catherine P Bertulfo et al. J Public Health Manag Pract 2024 1 (2) E41-E46 (Posted: Jan 29, 2024 8AM)

From the abstract: " The Data Science Upskilling (DSU) program increases data science literacy among staff and fellows working and training at CDC. The DSU program was established in 2019 as a team-based, project-driven, on-the-job applied upskilling program. Learners, within interdisciplinary teams, use curated learning resources to advance their CDC projects. The program has rapidly expanded from upskilling 13 teams of 31 learners during 2019-2020 to upskilling 36 teams of 143 learners during 2022-2023."

Get up to Speed on the Latest Developments in the Field! Register for the ORISE Current Issues in Genomics and Precision Public Health Online Training Event, September 7–8, 2023.
W White et al, CDC Blog Post, August 9, 2023 (Posted: Aug 09, 2023 11AM)

Advances in genomics, data science, machine learning, and artificial intelligence are transforming practice. Next generation public health and medical workforces need to understand these developments and how they can be used to benefit population health. Recognizing this challenge, Oak Ridge Institute for Science and Education (ORISE) is partnering with the Office of Genomics and Precision Public Health at the Centers for Disease Control and Prevention (CDC) to offer a free 2-day in-person training event covering the latest developments in these fields: Current Issues in Genomics and Precision Public Health – Using Genomics and Big Data to Improve Population Health and Reduce Health Inequities.

Genomics and Precision Public Health Issues Enrichment Event
Oak Ridge Institute for Science Education Enrichment Event, Atlanta, Georgia, September 7-8, 2023 Brand (Posted: Jul 17, 2023 8AM)

In the past decade, genomics, and precision health approaches such as big data science and machine learning have emerged as important tools for public health. Those entering the public health and medical workforces must keep pace with these evolving fields to maximize the benefit to public health. Recognizing this need, ORISE is partnering with the Office of Genomics and Precision Public Health at the Centers for Disease Control and Prevention to offer a two-day in-person enrichment event covering the latest developments in these fields.

Data science and health economics in precision public health
MA Talias et al, Frontiers in Public Health, December, 2022 (Posted: Dec 07, 2022 8AM)

Theory, methods, and models from AI and data science are already changing the public health landscape in community settings and have shown promising results in multiple applications in public health, including geocoding health data, digital public health, predictive modeling and decision support, and mobile health. Overall, Precision Public Health utilizes tools and methods to extract health and non-health data at different levels of granularity, harmonize and integrate information about populations and communities to tailor cost-effective interventions for specific population groups, improving people's health.

Can cancer blood tests live up to promise of saving lives?
CK Johnson, AP News, April 11, 2022 (Posted: Apr 11, 2022 2PM)

With advances in DNA sequencing and data science making the blood tests possible, California-based Grail and other companies are racing to commercialize them. And U.S. government researchers are planning a large experiment — possibly lasting seven years and with 200,000 participants — to see if the blood tests can live up to the promise of catching more cancers earlier and saving lives. “They sound wonderful, but we don’t have enough information,” said Dr. Lori Minasian of the National Cancer Institute, who is involved in planning the research. “We don’t have definitive data that shows that they will reduce the risk of dying from cancer.”

Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework
RJ Desai et al, NPJ Digital Medicine, December 20, 2021 (Posted: Dec 20, 2021 10AM)

We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center’s initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance.

Omicron blindspots: why it’s hard to track coronavirus variants
A Maxmen, Nature, December 16, 2021 (Posted: Dec 17, 2021 6AM)

Researchers are racing to detect Omicron, the latest SARS-CoV-2 variant of concern, by sequencing the genomes of coronaviruses infecting people. But surveillance through genomic sequencing can be slow and patchy, complicating the picture of how and where Omicron spreads. One positive development is that researchers are sequencing more SARS-CoV-2 genomes than ever before. This is what enabled them to notice Omicron relatively swiftly. Last April — about 16 months into the pandemic — an online database belonging to the GISAID data-science initiative contained one million SARS-CoV-2 genomic sequences. Since then, researchers have submitted another five million sequences to GISAID in about eight months —

Israeli data: How can efficacy vs. severe disease be strong when 60% of hospitalized are vaccinated?
J Morris, COVID-19 Data Science, August 17, 2021 (Posted: Aug 18, 2021 6AM)

As long as there is a major age disparity in vaccination rates, with older individuals being more highly vaccinated, then the fact that older people have an inherently higher risk of hospitalization when infected with a respiratory virus means that it is always important to stratify results by age; if not the overall efficacy will be biased downwards and a poor representation of how well the vaccine is working in preventing serious disease (the same holds for efficacy vs. death). Even more fundamentally, it is important to use infection and disease rates (per 100k, e.g.) and not raw counts to compare unvaccinated and vaccinated groups to adjust for the proportion vaccinated.

Genomic Data Science
NIH, July 2021 Brand (Posted: Jul 22, 2021 7AM)

As humans dig deeper into the genome, the analysis and interpretation of the genomic data collected are helping to better understand human health and disease, while also bringing up questions about privacy and ethics.

Is “bioinformatics” dead?
P Bourne, PLOS Biology, March 2021 (Posted: Mar 22, 2021 7AM)

Now that I have your attention, clearly, bioinformatics as a field is very much alive. The name, however, no longer applies to what we actually do in the field. It is not what forward-thinking scientists should be calling themselves in this era of the fourth paradigm of data science [1], where data sharing lies at the core of biology. If you’re asking why anyone should care, let me explain.

Challenging racism in the use of health data
HE Knight et al, Lancet Digital Health, February 4, 2021 (Posted: Feb 06, 2021 7AM)

The authors examine how structural inequalities, biases, and racism in society are easily encoded in datasets and in the application of data science, and how this practice can reinforce existing social injustices and health inequalities.

Commentary: Towards machine learning-enabled epidemiology
LR Jorn, Int J Epi, December 2020 (Posted: Dec 11, 2020 9AM)

Most training programs in epidemiology do not teach the primary skills that healthcare organizations seek in data scientists, which include machine learning (ML) and the open-source programming. Indeed, a course in data science is a mandatory component of only 18% of epidemiology programs offered by the top 20-ranked public health schools in the US.

Data Are Not Enough to Reimagine Public Health.
Chiolero Arnaud et al. American journal of public health 2020 Nov (11) 1614 (Posted: Oct 22, 2020 8AM)

Data, however, will not be enough. Improving our public health surveillance systems requires policymakers and health data scientists to work together; they have to develop a common culture and agree on surveillance goals. Policymakers must be trained in surveillance principles and methods in this age of data science.

Data science and machine learning, mathematical and statistical methods
J Rocklov et al, IJE, June 2020 (Posted: Jul 01, 2020 8AM)

Social determinants of health, data science, and decision-making: Forging a transdisciplinary synthesis
S Galea et al, PLOS Medicine, June 2020 (Posted: Jun 18, 2020 2PM)

It is time to bring about a transdisciplinary synthesis of different streams. The social determinants of health represent an agenda for action to improve health that can be enhanced by a serious engagement with the role that data and technology are beginning to play in improving population health.

Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory
T Alamo et al, ARXIV, June 1, 2020 (Posted: Jun 04, 2020 7AM)

The National Microbiome Data Collaborative: enabling microbiome science
EM Wood-Charlson et al Nat Rev Micro, April 2020 (Posted: May 27, 2020 9AM)

To harness the potential of microbiome science across the broad range of relevant disciplines, new approaches to data infrastructure and transdisciplinary collaboration are necessary. The National Microbiome Data Collaborative is a new initiative to support microbiome data exploration and discovery through a collaborative, integrative data science ecosystem.

AI Can Help Us Fight Infectious Diseases In A More Effective Way
M Colangelo et al, Forbes, March 27, 2020 (Posted: Mar 28, 2020 8AM)

There are several approaches that can be taken with technology. One approach is to apply data science techniques to personalized vaccination on a massive scale. Another is to optimize immunization management using AI. By using AI, big data, and small data techniques together, vaccines could be distributed on a massive scale in a more precise way.

Coronavirus: How Artificial Intelligence, Data Science And Technology Is Used To Fight The Pandemic
B Marr, Forbes, March 2020 (Posted: Mar 16, 2020 8AM)

As China initiated its response to the virus, it leaned on its strong technology sector and specifically artificial intelligence (AI), data science, and technology to track and fight the pandemic. Here are 10 ways artificial intelligence, data science, and technology are being used to manage and fight COVID-19.

STRIDES initiative
NIH Office of Data Science, 2020 Brand (Posted: Jan 23, 2020 11AM)

The NIH Science and Technology Research Infrastructure for Discovery, Experimentation, and Sustainability (STRIDES) Initiative allows NIH to explore the use of cloud environments to streamline NIH data use by partnering with commercial providers.

Health initiative aims to democratize data science
C Cheney, Devex, September 2019 (Posted: Oct 08, 2019 8AM)

Launched on the sidelines of the United Nations General Assembly, the Rockefeller foundation Precision Public Health initiative aims to ensure that frontline health workers have access to data science tools such as predictive analytics, artificial intelligence, and machine learning.

Data Science and Machine Learning in Public Health: Promises and Challenges
C Patel et al, CDC Blog Post, September 20, 2019 Brand (Posted: Sep 23, 2019 8AM)

Big data include large amount of information becoming available to researchers. What makes them really “big” is the sheer number of individuals and/or the massive amount of information. Given that the primary use for these datasets is often not research, the question is, “are these data helpful for health-related discoveries and public health surveillance?”

Harnessing digital data and data science to achieve 90-90-90 goals to end the HIV epidemic.
Strathdee Steffanie A et al. Current opinion in HIV and AIDS 2019 Aug (Posted: Aug 27, 2019 6PM)

Can Big Data Science Deliver Precision Public Health?
MJ Khoury et al, CDC Blog Post, July 23, 2019 Brand (Posted: Jul 24, 2019 10AM)

In the age of big data, more extensive information by place, person and time are becoming available to measure public health impact and implementation needs. In principle, big data could point to implementation gaps and disparities and accelerate the evaluation of implementation strategies to reach population groups in most need for interventions. However, major challenges need to be overcome.

Geneva Health Forum: The First International Conference on Precision Global Health.
Richard Aude et al. American journal of public health 2019 Jun (6) 863-865 (Posted: Jun 28, 2019 8AM)

The Seventh Edition of the Geneva Health Forum: Precision Global Health in the Digital Age was the first conference entirely dedicated to the newly emerging concept of precision global health. Precision global health aims to more precisely bring the right interventions to the right people at the right time. It does this by integrating digital tools into innovative solutions, all of which are rooted in a unique synergy between life science, social science, and data science

Genomics, Big Data and Data Science in Public Health
CDC Webinar, August 9, 2019 Brand (Posted: Jun 28, 2019 8AM)

Big Data Scientist Training Enhancement Program (BD-STEP)
NCI, 2019 Brand (Posted: Jun 24, 2019 8AM)

A two-year fellowship opportunity that uses data science to advance cancer research and care.

Top 10 Statistics Mistakes Made by Data Scientists
N Niemer, Towards Data Science Blog, June 17, 2019 (Posted: Jun 18, 2019 8AM)

Harnessing the Power of Collaboration and Training Within Clinical Data Science to Generate Real]World Evidence in the Era of Precision Oncology
DR Rivera et al, Clinical Pharmacology and Therapeutics, June 5, 2019 (Posted: Jun 05, 2019 11AM)

Systems modeling to advance the promise of data science in epidemiology.
Cerdá Magdalena et al. American journal of epidemiology 2019 Mar (Posted: Mar 21, 2019 8AM)

The Data Science for Social Impact Collaborative
Rockefeller Foundation, 2019 (Posted: Jan 30, 2019 1PM)

How data science can advance mental health research
TC Russ et al, Nature Human Behavior, December 10, 2018 (Posted: Dec 11, 2018 10AM)

From hype to reality: data science enabling personalized medicine.
Fröhlich Holger et al. BMC medicine 2018 Aug 16(1) 150 (Posted: Aug 29, 2018 9AM)

NIH releases strategic plan for data science
NIH, June 4, 2018 Brand (Posted: Jun 04, 2018 3PM)

Revolution in Health Care: How Will Data Science Impact Doctor–Patient Relationships?
I Lerner, Front Public Health, Apr 2018 (Posted: May 03, 2018 7AM)

Next-Generation Data Science Research Challenges
P Brennan, NLM, Mar 20, 2018 Brand (Posted: Mar 20, 2018 8PM)

Strategic Plan for Data Science
Request for Information (RFI): Soliciting Input for the National Institutes of Health (NIH), March 2018 (Posted: Mar 05, 2018 1PM)

Advancing stroke genomic research in the age of Trans-Omics big data science: Emerging priorities and opportunities.
Owolabi Mayowa et al. Journal of the neurological sciences 2017 Nov 18-28 (Posted: Nov 15, 2017 9AM)

Big data yields surprising connections between diseases
Science Mag, August 7, 2017 (Posted: Aug 07, 2017 5PM)

Building the biomedical data science workforce
MC Dunn et al, PLOS Comp Biol, July 2017 (Posted: Jul 25, 2017 8AM)

How data science will change public health
ME Black, BMJ Blogs, November 13, 2015 (Posted: Nov 17, 2015 2PM)

Public Health England Data Week: Big data, data science and public health
J Flowers, November 9, 2015 (Posted: Nov 09, 2015 7AM)

Data Science in Simple Terms
V Granville, Blog Post, October 2, 2015 (Posted: Oct 05, 2015 9AM)

NIH Data Science Portal: Enabling Biomedical Scientists to capitalize more fully on the Big Data being generated by the research communities
Brand (Posted: Apr 14, 2015 0PM)


Disclaimer: Articles listed in Hot Topics of the Day are selected by Public Health Genomics Branch to provide current awareness of the scientific literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the Clips, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
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