Digital Contact Tracing for COVID-19: Overview of Existing Systems and Framework for Future Innovation

Background: The COVID-19 pandemic catalyzed innovation in infection control measures, including widespread deployment of digital contact tracing systems. However, these technologies were not well understood by the general public and were complex for the public health community to implement, hampering adoption. Objective(s): To provide an overview of existing digital contact tracing systems, creating a framework for understanding design elements that impact their effectiveness as public health tools and offering a rubric for decision-makers to evaluate different systems for selection and implementation. Method(s): Scientific literature and publicly available information from relevant health authorities and other stakeholders was reviewed. Information was synthesized to develop a conceptual framework explaining how key design elements impact effectiveness of digital contact tracing systems and highlighting opportunities for future improvement. Result(s): A range of digital contact tracing interventions were deployed by governments worldwide and several professional sports leagues. Key design elements of the systems include: (1) data architecture (i.e., centralized versus decentralized systems, impacting privacy guarantees and data availability);(2) proximity detection technology (e.g., type of device signaling);(3) alert logic and timing (e.g., time- and distance-based criteria affecting sensitivity and specificity of alerts;real-time proximity alerts and/or bidirectional contact tracing, determining scope of infection prevention);(4) population (eligibility and availability);and (5) the structural and public health context of intervention (e.g., availability and timeliness of testing). Several systems demonstrated effectiveness in preventing transmission during COVID-19, though numerous limitations have also been documented in the literature. Conclusion(s): Digital contact tracing systems have the potential to mitigate the economic and public health impact of future infectious disease outbreaks, reducing community transmission and detecting potential cases earlier in the disease course. Lessons learned from solutions deployed during the COVID-19 pandemic provide an opportunity to improve multiple aspects of these systems, enhancing preparedness for future outbreaks.Copyright © 2023

EMPLOYING ARTIFICIAL NEURAL NETWORKS AND MULTIPLE DISCRIMINANT ANALYSIS TO EVALUATE THE IMPACT OF THE COVID-19 PANDEMIC ON THE FINANCIAL STATUS OF JORDANIAN COMPANIES

Aim/Purpose This paper aims to empirically quantify the financial distress caused by the COVID-19 pandemic on companies listed on Amman Stock Exchange (ASE). The paper also aims to identify the most important predictors of financial distress pre- and mid-pandemic. Background The COVID-19 pandemic has had a huge toll, not only on human lives but also on many businesses. This provided the impetus to assess the impact of the pandemic on the financial status of Jordanian companies. Methodology The initial sample comprised 165 companies, which was cleansed and reduced to 84 companies as per data availability. Financial data pertaining to the 84 companies were collected over a two-year period, 2019 and 2020, to empirically quantify the impact of the pandemic on companies in the dataset. Two approaches were employed. The first approach involved using Multiple Discriminant Analysis (MDA) based on Altman's (1968) model to obtain the Z-score of each company over the investigation period. The second approach involved developing models using Artificial Neural Networks (ANNs) with 15 standard financial ratios to find out the most important variables in predicting financial distress and create an accurate Financial Distress Prediction (FDP) model. Contribution This research contributes by providing a better understanding of how financial distress predictors perform during dynamic and risky times. The research confirmed that in spite of the negative impact of COVID-19 on the financial health of companies, the main predictors of financial distress remained relatively steadfast. This indicates that standard financial distress predictors can be regarded as being impervious to extraneous financial and/or health calamities. Findings Results using MDA indicated that more than 63% of companies in the dataset have a lower Z-score in 2020 when compared to 2019. There was also an 8% increase in distressed companies in 2020, and around 6% of companies came to be no longer healthy. As for the models built using ANNs, results show that the most important variable in predicting financial distress is the Return on Capital. The predictive accuracy for the 2019 and 2020 models measured using the area under the Receiver Operating Characteristic (ROC) graph was 87.5% and 97.6%, respectively. Recommendations Decision makers and top management are encouraged to focus on the identified for Practitioners highly liquid ratios to make thoughtful decisions and initiate preemptive actions to avoid organizational failure. Recommendations This research can be considered a stepping stone to investigating the impact of for Researchers COVID-19 on the financial status of companies. Researchers are recommended to replicate the methods used in this research across various business sectors to understand the financial dynamics of companies during uncertain times. Impact on Society Stakeholders in Jordanian-listed companies should concentrate on the list of most important predictors of financial distress as presented in this study. Future Research Future research may focus on expanding the scope of this study by including other geographical locations to check for the generalisability of the results. Future research may also include post-COVID-19 data to check for changes in results. © 2023 Informing Science Institute. All rights reserved.

New Short RNA Motifs Potentially Relevant in the SARS-CoV-2 Genome

Background: The coronavirus disease has led to an exhaustive exploration of the SARS-CoV-2 genome. Despite the amount of information accumulated, the prediction of short RNA motifs encoding peptides mediating protein-protein or protein-drug interactions has received limited attention. Objective(s): The study aims to predict short RNA motifs that are interspersed in the SARS-CoV-2 genome. Method(s): A method in which 14 trinucleotide families, each characterized by being composed of triplets with identical nucleotides in all possible configurations, was used to find short peptides with biological relevance. The novelty of the approach lies in using these families to search how they are distributed across genomes of different CoV genera and then to compare the distributions of these families with each other. Result(s): We identified distributions of trinucleotide families in different CoV genera and also how they are related, using a selection criterion that identified short RNA motifs. The motifs were reported to be conserved in SARS-CoVs;in the remaining CoV genomes analysed, motifs contained, exclusively, different configurations of the trinucleotides A, T, G and A, C, G. Eighty-eight short RNA motifs, ranging in length from 12 to 49 nucleotides, were found: 50 motifs in the 1a polyprotein-encoding orf, 27 in the 1b polyprotein-encoding orf, 5 in the spike-encoding orf, and 6 in the nucleocapsid-encoding orf. Although some motifs (~27%) were found to be intercalated or attached to functional peptides, most of them have not yet been associated with any known functions. Conclusion(s): Some of the trinucleotide family distributions in different CoV genera are not random;they are present in short peptides that, in many cases, are intercalated or attached to functional sites of the proteome.Copyright © 2022 Bentham Science Publishers.

Detecting Drift in Healthcare AI Models Based on Data Availability

There is an increasing interest in the use of AI in healthcare due to its potential for diagnosis or disease prediction. However, healthcare data is not static and is likely to change over time leading a non-adaptive model to poor decision-making. The need of a drift detector in the overall learning framework is therefore essential to guarantee reliable products on the market. Most drift detection algorithms consider that ground truth labels are available immediately after prediction since these methods often work by monitoring the model performance. However, especially in real-world clinical contexts, this is not always the case as collecting labels is often more time consuming as requiring experts' input. This paper investigates methodologies to address drift detection depending on which information is available during the monitoring process. We explore the topic within a regulatory standpoint, showing challenges and approaches to monitoring algorithms in healthcare with subsequent batch updates of data. This paper explores three different aspects of drift detection: drift based on performance (when labels are available), drift based on model structure (indicating causes of drift) and drift based on change in underlying data characteristics (distribution and correlation) when labels are not available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

The role of biostatistics in the response to COVID-19: a Belgian and international perspective.

In this commentary to Dattner et al. (Israel J Health Policy Res. 11:22, 2022), we highlight similarities and differences in the role that biostatistics and biostatisticians have been playing in the COVID-19 response in Belgium and Israel. We bring out implications and opportunities for our field and for science. We argue that biostatistics has an important place in the multidisciplinary COVID-19 response, in terms of research, policy advice, and science and public communication. In Belgium, biostatisticians located in various institutes, collaborated with epidemiologists, vaccinologists, infectiologists, immunologists, social scientists, and government policy makers to provide rapid and science-informed policy advice. Biostatisticians, who can easily be mobilized to work together in pandemic response, also played a role in public communication.

Primary and Secondary Data Collection to Conduct Researches, Write Thesis and Dissertation Amidst COVID-19 Pandemic: A Guidepost

Research has always been regarded by many as tedious because of the difficulties and challenges associated with doing research such as having to forego certain habits like social life. Doing research became even more difficult, especially with regard to limitation on collecting applicable primary and secondary data due to the COVID-19 pandemic lockdowns. It is to be noted that substantive, thorough, sophisticated literature review and intensive pertinent primary data availability are ncessary for doing quality research relevant to the status quo. Various novel approaches have been adopted by scholars through their diverse academic spheres in conducting internationally acceptable research amidst the COVID-19 pandemic. This research aims to come up with a guidepost to facilitate researchers and other stakeholders with fundamental knowledge and skills in conducting substantive, thorough, sophisticated researches that are of international standards. A comparative and diagnostic analysis method is used for analyzing existing literature and policies developed by higher education institutions and schools for doing research in the advent of the COVID-19 pandemic. The output allowed authors to develop a guidepost with rules on using limited primary and extensive secondary data in doing research. The guidepost consists of various sections explaining on how to do research and write theses and dissertations. These sections include among others research title, statement of the problem, research objectives, theoretical and conceptual frameworks, review of related literature, research methodology, analysis and interpretation of data, and conclusion and recommendations. The guidepost is very significant in doing researches and aids researchers in conducting internationally accepted researches with limited primary data and extensive secondary data in the advent of the COVID-19 Pandemic. The guidepost is flexible and can easily be used by local and international institutions’ researchers through little modification in context of their research fields. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19-Related manuscripts: lag from preprint to publication.

OBJECTIVE: Preprints have had a prominent role in the swift scientific response to COVID-19. Two years into the pandemic, we investigated how much preprints had contributed to timely data sharing by analyzing the lag time from preprint posting to journal publication. RESULTS: To estimate the median number of days between the date a manuscript was posted as a preprint and the date of its publication in a scientific journal, we analyzed preprints posted from January 1, 2020, to December 31, 2021 in the NIH iSearch COVID-19 Portfolio database and performed a Kaplan-Meier (KM) survival analysis using a non-mixture parametric cure model. Of the 39,243 preprints in our analysis, 7712 (20%) were published in a journal, after a median lag of 178 days (95% CI: 175-181). Most of the published preprints were posted on the bioRxiv (29%) or medRxiv (65%) servers, which allow authors to choose a subject category when posting. Of the 20,698 preprints posted on these two servers, 7358 (36%) were published, including approximately half of those categorized as biochemistry, biophysics, and genomics, which became published articles within the study interval, compared with 29% categorized as epidemiology and 26% as bioinformatics.

Evaluating Availability of Glucose Data During In-person versus Telemedicine Visits in an Endocrinology Specialty Clinic

Introduction: Blood glucose (BG) data are essential for diabetes management. Before Coronavirus Disease 2019 (COVID-19) pandemic, BG data would be obtained as patient fingerstick BG logs, or insulin pump and/or continuous glucose monitor (CGM) data downloaded from patients' devices during in-person visits. Transition to telemedicine during the pandemic altered clinic workflow and challenged access to BG data. This study compares availability and sources of BG data in telemedicine versus in-person endocrinology visits. Research Question or Hypothesis: Hypotheses: 1) BG data availability was higher for in-person versus telemedicine visits. 2) More fingerstick BG logs were available for in-person visits. 3) Availability of pump and/or CGM data was higher during in-person versus in-person visits. Study Design: This was an observational retrospective study conducted via chart review. Methods: We randomly screened adult diabetes management clinic visits at Banner 'University Medicine Endocrinology Clinic from 6/1/2019 to 12/13/2019 (in-person, Group A) and 6/1/2020 to 12/31/2020 (telemedicine, Group B). Incomplete visits were excluded. Chi-square test was used for between group comparison. Results: Out of the 766 screened visits, 200 were included in Group A and 199 in Group B. Overall, availability of BG data (from all noted sources) was higher for Group A (79%) than Group B (46.2%), P<0.001. More fingerstick BG logs were available for Group A (78.5%) than Group B (21.5%), P<0.001. Availability of insulin pump and/or CGM data was not statistically significant between the two groups (54.1% vs 45.9%, P=0.210). Conclusion: The higher overall BG data availability for in-person visits was driven by that of fingerstick BG logs. Pump and CGM data availability did not differ between groups suggesting that those data were successfully shared with the clinic for telemedicine visits. Enhancing ability to share fingerstick BG data for telemedicine visits should be considered. Future studies are needed to assess availability of clinically relevant data.

Clinical trial transparency regulations: Implications to various scholarly publishing stakeholders

As thousands of 2019 Corona virus disease (Covid-19) clinical trials are continuously getting added to various registries these days, good practices on data sharing and transparency have become one of the prime topics of discussion than ever before. Although trial registration is considered a crucial step, there is a lack of integration between registration and published literature. Trial outcomes are a matter of public interest, but sponsor compliances are not adequate with the recommended guidelines. Although the global recognition of data transparency increases day by day, there is still a long journey to travel. It is high time that scholarly publishing stakeholders should put in a collaborative effort to check author compliance. In this article, we aimed to comprehend and discuss the imperative roles of various scholarly publishing stakeholders in improving clinical trial transparency during this pandemic situation and highlight the changing paradigm towards the pressing need for reporting clinical trial data more effectively.

MAPPING DATA OPPORTUNITIES RELATING TO FOOD, NUTRITION AND HEALTH IN THE COVID-19 PANDEMIC

Background The COVID-19 pandemic has impacted the nutrition and health of individuals, households, and populations globally. Through exposing fragilities in food, health, and social welfare systems, the negative influence of COVID-19 continues to affect the global burden of malnutrition. The nature and scale of these impacts are not yet well understood thus the body of evidence for informing policy is limited. Collating and monitoring relevant data in real-time from multiple levels, sectors and sources is essential in preparing and responding to the ongoing COVID-19 pandemic. Objectives To identify key data sources related to food, nutrition, and health indicators in the context of the COVID-19 pandemic. Methods A COVID-19, food, nutrition and health framework was developed through multiple iterative rounds of online multidisciplinary discussions including the NNEdPro COVID- 19 taskforce and the Swiss Re Institute's Republic of Science, which comprised researchers and clinicians with expertise in data science, food, nutrition, and health. Results The proposed framework encompasses five socio-ecological levels which were further sub-divided by six categories of the food and nutrition ecosystem, including food production & supply, food environment & access, food choices & dietary patterns, nutritional status & comorbidities, health & disease outcomes, health & nutrition services. A limited number of exemplar variables for the assessment of global status of food, nutrition and health are identified under each category. Discussion/Conclusion This collaborative framework is the first step towards the development of a better understanding of the impact of COVID-19 on food, nutrition, and health systems. Limited data availability and disruption in routine data collection as well as other nutrition assessments during the pandemic are challenges that might limit the potential of the proposed framework. Next steps will include formal research and data gap analysis and the identification, as well as utilisation, of other indicators that could be used as proxies of the variables identified. (Table Presented).

Challenges of Data Availability and Use in Conducting Health-EDRM Research in a Post-COVID-19 World.

Disasters disrupt communication channels, infrastructure, and overburden health systems. This creates unique challenges to the functionality of surveillance tools, data collection systems, and information sharing platforms. The WHO Health Emergency and Disaster Risk Management (Health-EDRM) framework highlights the need for appropriate data collection, data interpretation, and data use from individual, community, and global levels. The COVID-19 crisis has evolved the way hazards and risks are viewed. No longer as a linear event but as a protracted hazard, with cascading and compound risks that affect communities facing complex risks such as climate-related disasters or urban growth. The large-scale disruptions of COVID-19 show that disaster data must evolve beyond mortality and frequency of events, in order to encompass the impact on the livelihood of communities, differentiated between population groups. This includes relative economic losses and psychosocial damage. COVID-19 has created a global opportunity to review how the scientific community classifies data, and how comparable indicators are selected to inform evidence-based resilience building and emergency preparedness. A shift into microlevel data, and regional-level information sharing is necessary to tailor community-level interventions for risk mitigation and disaster preparedness. Real-time data sharing, open governance, cross-organisational, and inter-platform collaboration are necessary not just in Health-EDRM and control of biological hazards, but for all natural hazards and man-made disasters.

SARS-CoV-2 variants classification and characterization

As of late 2019, the SARS-CoV-2 virus has spread globally, giving several variants over time. These variants, unfortunately, differ from the original sequence identified in Wuhan, thus risking compromising the efficacy of the vaccines developed. Some software has been released to recognize currently known and newly spread variants. However, some of these tools are not entirely automatic. Some others, instead, do not return a detailed characterization of all the mutations in the samples. Indeed, such characterization can be helpful for biologists to understand the variability between samples. This paper presents a Machine Learning (ML) approach to identifying existing and new variants completely automatically. In addition, a detailed table showing all the alterations and mutations found in the samples is provided in output to the user. SARS-CoV-2 sequences are obtained from the GISAID database, and a list of features is custom designed (e.g., number of mutations in each gene of the virus) to train the algorithm. The recognition of existing variants is performed through a Random Forest classifier while identifying newly spread variants is accomplished by the DBSCAN algorithm. Both Random Forest and DBSCAN techniques demonstrated high precision on a new variant that arose during the drafting of this paper (used only in the testing phase of the algorithm). Therefore, researchers will significantly benefit from the proposed algorithm and the detailed output with the main alterations of the samples. Data availability: the tool is freely available at https://github.com/sofiaborgato/-SARS-CoV-2-variants-classification-and-characterization. © 2022, EasyChair. All rights reserved.

The Importance of Data Accuracy and Transparency for Policymaking during a Public Health Crisis: A Case Study in the State of Iowa

Background. High-quality data are necessary for decision-making during the SARS-CoV-2 pandemic. Lack of transparency and accuracy in data reporting can erode public confidence, mislead policymakers, and endanger safety. Two major data errors in Iowa impacted critical state- and county-level decision-making. Methods. The Iowa Department of Public Health (IDPH) publishes daily COVID-19 data. Authors independently tracked daily data from IDPH and other publicly available sources (i.e., county health departments, news media, and social networks). Data include: number and type of tests, results, hospitalizations, intensive care unit admissions, and deaths at state/county levels. Results. Discrepancies were identified between IDPH and non-IDPH data, with at least two confirmed by IDPH: (1) The backdating of test results identified on May 28, 2020. IDPH labeled results as occurring up to four months before the actual test date. IDPH confirmed that if a person previously tested for SARS-CoV-2, a new test result was attributed to the initial test's date. Corrections on August 19, 2020 increased positivity rates in 31 counties, but decreased the state's overall rate (9.1% to 7.5%). (2) The selective exclusion of antigen test results noted on August 20, 2020. Antigen testing was included in the total number of tests reported in metric denominators, but their results were being excluded from their respective numerators. Thus, positive antigen results were interpreted as de facto negative tests, artificially lowering positivity rates. Corrections increased Iowa's positivity rate (5.0% to 14.2%). In July 2020, the Iowa Department of Education mandated in-person K-12 learning for counties with < 15% positivity. These data changes occurred during critical decision-making, altering return-to-learn plans in seven counties. The Center for Medicare and Medicaid Services' requirements also caused nursing homes to urgently revise testing strategies. Timeline of changes to Iowa state COVID-19 testing through the end of August 2020. Change in positive and overall test results due to IDPH data corrections. These graphs represent the difference in cumulative total reported test results when pulled from the IDPH website on September 29, 2020 compared to data for the same dates when pulled on August 19, 2020 before the announced adjustment. The adjustment and subsequent daily changes in reported data amount to a dramatic change in the number of reported positive cases (A) with an increase of nearly 3,000 cases by April 25, as well as the loss of tens of thousands of data points when tracking total resulted tests (B). Conclusion. Data availability, quality, and transparency vary widely across the US, hindering science-based policymaking. Independent audit and curations of data can contribute to better public health policies. We urge all states to increase the availability and transparency of public health data.

POSA190 Socio-Economic Influences on County-Level Differences in COVID-19 Incidence During the Second Wave in Germany

Objectives: During fall and winter 2020/2021, before vaccine availability, Germany experienced a severe second wave of the COVID-19 pandemic. Daily cases grew exponentially in October/November (5Oct to 5Nov 2020, phase I), plateaued in November (6Nov to 6Dec 2020, phase II), peaked in late December, and declined in January/February (12Jan to 12Feb 2021, phase III). We investigated whether socio-economic characteristics (population density (inhabitants/km2;2019;“popDens”), household size (average number of persons/household;2011;“hhSize”), average living space (m2/inhabitant;2011;“livSpace”), education level (percentage of inhabitants with university-entrance qualification;2019;“Abitur”) and disposable income (EUR/inhabitant;2018;“income”)) predicted regional differences in incidence (cases per 100,000 inhabitants;“cases/100k”) in each of the three phases. We expected counties with greater living space, education level and disposable income to report lower incidence. Methods: County-level daily COVID-19 cases were extracted from RKI databases. County-level predictor variables were retrieved from public sources. For each phase, we computed a robust linear regression model with popDens, hhSize, livSpace, Abitur, and income as predictor variables for county-level cases/100k. Analyses were performed using statistical software R. Results: For phase I, cases/100k significantly increased with popDens (beta=0.17, p<0.001), hhSize (beta=0.41, p<0.001), and income (beta=0.20, p<0.001), and decreased with livSpace (beta=-0.22, p<0.001), R2=0.32. For phase II, cases/100k significantly increased with popDens (beta=0.12, p<0.001), hhSize (beta=0.39, p<0.001), and income (beta=0.15, p<0.001) and decreased with Abitur (beta=-0.15, p=0.002) and livSpace (beta=-0.24, p<0.001), R2=0.26. For phase III, cases/100k were significantly decreasing with popDens (beta=-0.06, p<0.001), hhSize (beta=-0.14, p=0.031), income (beta=-0.16, p<0.001), and livSpace (beta=-0.21, p<0.001), R2=0.08. Conclusions: Socio-economic regional differences influenced COVID-19 incidence in Germany´s second wave, however the explanatory power was low. The inclusion of influence factors was limited by data availability. The relevant factors differed between phase I/II and phase III. Our research does not claim a causative relationship between variables.