An open repository of real-time COVID-19 indicators

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Evaluating the usability and acceptability of a geographical information system (GIS) prototype to visualise socio-economic and public health data

Background Understanding the impact of socio-economic inequality on health outcomes is arguably more relevant than ever before given the global repercussions of Covid-19. With limited resources, innovative methods to track disease, population needs, and current health and social service provision are essential. To best make use of currently available data, there is an increasing reliance on technology. One approach of interest is the implementation and integration of mapping software. This research aimed to determine the usability and acceptability of a methodology for mapping public health data using GIS technology. Methods Prototype multi-layered interactive maps were created demonstrating relationships between socio-economic and health data (vaccination and admission rates). A semi-structured interview schedule was developed, including a validated tool known as the System Usability Scale (SUS), which assessed the usability of the mapping model with five stakeholder (SH) groups. Fifteen interviews were conducted across the 5 SH and analysed using content analysis. A Kruskal-Wallis H test was performed to determine any statistically significant difference for the SUS scores across SH. The acceptability of the model was not affected by the individual use of smart technology among SHs. Results The mean score from the SUS for the prototype mapping models was 83.17 out of 100, indicating good usability. There was no statistically significant difference in the usability of the maps among SH (p = 0.094). Three major themes emerged with respective sub-themes from the interviews including: (1) Barriers to current use of data (2) Design strengths and improvements (3) Multiple benefits and usability of the mapping model. Conclusion Irrespective of variations in demographics or use of smart technology amongst interviewees, there was no significant difference in the usability of the model across the stakeholder groups. The average SUS score for a new system is 68. A score of 83.17 was calculated, indicative of a “good” system, as falling within the top 10% of scores. This study has provided a potential digital model for mapping public health data. Furthermore, it demonstrated the need for such a digital solution, as well as its usability and future utilisation avenues among SH.

Collection of Data on Sex, Sexual Orientation, and Gender Identity by U.S. Public Health Data and Monitoring Systems, 2015–2018

We aimed to assess Centers for Disease Control and Prevention (CDC) data systems on the extent of data collection on sex, sexual orientation, and gender identity as well as on age and race/ethnicity. Between March and September 2019, we searched 11 federal websites to identify CDC-supported or -led U.S. data systems active between 2015 and 2018. We searched the systems’ website, documentation, and publications for evidence of data collection on sex, sexual orientation, gender identity, age, and race/ethnicity. We categorized each system by type (disease notification, periodic prevalence survey, registry/vital record, or multiple sources). We provide descriptive statistics of characteristics of the identified systems. Most (94.1%) systems we assessed collected data on sex. All systems collected data on age, and approximately 80% collected data on race/ethnicity. Only 17.7% collected data on sexual orientation and 5.9% on gender identity. Periodic prevalence surveys were the most common system type for collecting all the variables we assessed. While most U.S. public health data and monitoring systems collect data disaggregated by sex, age, and race/ethnicity, far fewer do so for sexual orientation or gender identity. Standards and examples exist to aid efforts to collect and report these vitally important data. Additionally important is increasing accessibility and appropriately tailored dissemination of reports of these data to public health professionals and other collaborators.

Safe reopening of college campuses during COVID-19: The University of California experience in Fall 2020

Background Epidemics of COVID-19 in student populations at universities were a key concern for the 2020–2021 school year. The University of California (UC) System developed a set of recommendations to reduce campus infection rates. SARS-CoV-2 test results are summarized for the ten UC campuses during the Fall 2020 term. Methods UC mitigation efforts included protocols for the arrival of students living on-campus students, non-pharmaceutical interventions, daily symptom monitoring, symptomatic testing, asymptomatic surveillance testing, isolation and quarantine protocols, student ambassador programs for health education, campus health and safety pledges, and lowered density of on-campus student housing. We used data from UC campuses, the UC Health–California Department of Public Health Data Modeling Consortium, and the U.S. Census to estimate the proportion of each campus’ student populations that tested positive for SARS-CoV-2 and compared it to the fraction individuals aged 20–29 years who tested positive in their respective counties. Results SARS-CoV-2 cases in campus populations were generally low in September and October 2020, but increased in November and especially December, and were highest in early to mid-January 2021, mirroring case trajectories in their respective counties. Many students were infected during the Thanksgiving and winter holiday recesses and were detected as cases upon returning to campus. The proportion of students who tested positive for SARS-CoV-2 during Fall 2020 ranged from 1.2% to 5.2% for students living on campus and was similar to students living off campus. For most UC campuses the proportion of students testing positive was lower than that for the 20–29-year-old population in which campuses were located. Conclusions The layered mitigation approach used on UC campuses, informed by public health science and augmented perhaps by a more compliant population, likely minimized campus transmission and outbreaks and limited transmission to surrounding communities. University policies that include these mitigation efforts in Fall 2020 along with SARS-CoV-2 vaccination, may alleviate some local concerns about college students returning to communities and facilitate resumption of normal campus operations and in-person instruction.

Public Health Infographic Design for the public – the evidence base

In order to make effective infographics, one needs to understand the science behind public health infographic design. This presentation introduces guidelines for public health infographic design based on gathered academic evidence of effectiveness as well as information design principles. We tackle the topic from a variety of angles exploring issues of attention, comprehension, recall and behavioral change and focuses on infographics designed for a lay audience. Despite the exhaustive research conducted on say, graph comprehension, there remains a gap in how we account for the effectiveness of public health infographic design more broadly. The presentation also covers a brief examination of ‘hidden' historical precedents for the design of engaging health infographics, beyond the oft-cited visual work of John Snow or Florence Nightingale. We argue that notions of data spectacle and the need to grab attention remain vital today. The presentation concludes by reflecting on the future of infographics for displaying public health data, particularly with reference to the use of COVID-19 graphics in 2020/21.

COVID-19 Pandemic and Indigenous Representation in Public Health Data

Public Health 3.0 calls for the inclusion of new partners and novel data to bring systemic change to the US public health landscape. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has illuminated significant data gaps influenced by ongoing colonial legacies of racism and erasure. American Indian and Alaska Native (AI/AN) populations and communities have been disproportionately affected by incomplete public health data and by the COVID-19 pandemic itself. Our findings indicate that only 26 US states were able to calculate COVID-19‒related death rates for AI/AN populations. Given that 37 states have Indian Health Service locations, we argue that public health researchers and practitioners should have a far larger data set of aggregated public health information on AI/AN populations. Despite enormous obstacles, local Tribal facilities have created effective community responses to COVID-19 testing, tracking, and vaccine administration. Their knowledge can lead the way to a healthier nation. Federal and state governments and health agencies must learn to responsibly support Tribal efforts, collect data from AI/AN persons in partnership with Indian Health Service and Tribal governments, and communicate effectively with Tribal authorities to ensure Indigenous data sovereignty. (Am J Public Health. 2021;111(S3): S208–S214. https://doi.org/10.2105/AJPH.2021.306415 )

Successful, Easy to Access, Online Publication of COVID-19 Data During the Pandemic, New York City, 2020

Making public health data easier to access, understand, and use makes it more likely that the data will be influential. Throughout the COVID-19 pandemic, the New York City (NYC) Department of Health and Mental Hygiene’s Web-based data communication became a cornerstone of NYC’s response and allowed the public, journalists, and researchers to access and understand the data in a way that supported the pandemic response and brought attention to the deeply unequal patterns of COVID-19’s morbidity and mortality in NYC. (Am J Public Health. 2021;111(S3):S193–S196. https://doi.org/10.2105/AJPH.2021.306446 )

IoTs, Machine Learning (ML), AI and Digital Transformation Affects Various Industries - Principles and Cybersecurity Risks Solutions

Our purpose to conduct this research is that we would like to present advantages and applications of internet of things (IoTs), Machine learning (ML), AI - Artificial intelligence and digital transformation in Education, Medicine-hospitals, Tourism and Manufacturing Sectors. In this paper authors will use methods such as empirical research and practices and experiences in infrared rays system applications in emerging markets such as Vietnam. Research Results find out that in education sector, ML and IoTs and AI has affected methods of teaching and methods of evaluating students in classroom and from then, teachers or instructors can decide suitable career development path for learners. Last but not least, ML and IoTs and AI together also has certain impacts in hospitals and medicine sector where public health data and patients information and diseases information are recorded and processed faster with Big Data. Till the end, we have enough information to propose implications for future researches on applications of machine learning in each specific sector and also, cybersecurity Risk management also need for implementing and applying ML and IoTs and AI.

A Systematic Literature Review of Ethical Challenges Related to Medical and Public Health Data Sharing in China

Although stakeholders in China have begun promoting medical and public health data sharing, a large portion of data cannot flow freely across research teams and borders and be converted into “big data.” To identify the ethical challenges that are considered to hinder medical and public health data sharing, we performed a systematic literature review pertaining to medical and health data sharing in China. A total of 2959 unique records were retrieved through the database search, 61 of which were included in the final synthesis after full-text screening. This review provides an overview of the current ethical challenges and barriers involved in data sharing for healthcare purposes in China. Through the systematic review of evidence from peer-reviewed literature and dissertations, we identified barriers and ethical challenges grouped in a taxonomy of capacity building needs, balancing different stakeholders’ interests, scientific and social value, and the data subjects’ rights, public trust and engagement. Best practices and educational implications were suggested based on our findings.