Real-World Evidence on the Use of Ecmo in Hospitalized COVID-19 Patients: Results from a Linked Closed and Open Claims Database in the US

Objectives: To describe the use of extracorporeal membrane oxygenation (ECMO) among hospitalized coronavirus disease 2019 (H-COVID-19) patients in a linked closed claims (CC) and open claims (OC) database. Method(s): This analysis identified H-COVID-19 patients between April 2020 (Q2 2020) and June 2022 (Q2 2022) in CHRONOS, a linked CC and OC database. The index event was the date of hospitalization, defined as an inpatient claim within 21 days of a COVID-19 diagnosis in the CC. The occurrence of ECMO 30 days after index was identified using CC data alone and then CC and OP data in combination to assess missing data. Study exclusions included patients under the age of 18, a first COVID-19 diagnosis that did not result in hospitalization, and less than 12-months of continuous enrollment in the CC before index. Study criteria were defined by the presence of an ICD-10-CM, ICD-10-PCS, or CPT code on a claim. Results are reported as percentages and 95% confidence intervals. Result(s): Of 321,687 patients with H-COVID-19, the mean age was 50.1 (SD:12.8) with the highest proportion of hospitalizations occurring in Q3 2021 (19.4%). Overall, 0.50% (0.48%-0.52%) of patients in the CC data received ECMO, increasing to 0.61% (0.58%-0.64%) with the inclusion of OC data. The use of ECMO to treat H-COVID-19 patients decreased between Q2 2021 and Q2 2022, with the highest rates occurring in Q2 of 2020 (0.78%) and Q2 2021 (0.80%). The addition of OC data increased rates to 1.12% and 0.89% in Q2 of 2020 and Q2 2021. Conclusion(s): Although use of ECMO decreased in the later months of the pandemic, it represents a substantial burden. The current analysis demonstrates that CC data, often sourced from payers, may underestimate the use of ECMO in real-world settings. Opportunities exist to mitigate issues of missing data by linking CC, OC, and other real-world data sources.Copyright © 2023

The Future of COVID-19: What Is Next in the Drug Pipeline

Objectives: Development of new and repurposed medicines in response to the COVID-19 pandemic has occurred at an unprecedented rate, resulting in a dynamic pipeline marked by significant challenges and successes. This analysis provides an overview of the vaccines and therapies undergoing clinical evaluation or with recent approval for the treatment and prevention of COVID-19 in global markets. Method(s): For this analysis, COVID-19 pipeline medicines are defined in three categories: vaccines, new treatments and repurposed medicines. GlobalData is the primary data source for this study, in addition to online databases from Health Canada, the US FDA, and the EMA. International markets examined include the US and geographic Europe (excluding Russia and Turkey). Result(s): As of November 2022, the global pipeline contained over 600 therapies and vaccines undergoing Phase I, II, III clinical trials or pre-registration for the prevention and treatment of COVID-19. Preventive and repurposed medicines include antivirals, immunoglobulins, monoclonal antibodies, cellular therapies, and convalescent plasma. In Canada, twelve medicines, including six vaccines, have been approved for COVID-19. The number of global approvals is greater, with approximately 9 vaccines on the market in OECD countries. In addition to pre-exposure preventative therapies, manufacturers are also developing COVID-19 drugs to be used as prophylactic therapy. The analysis identifies new oral antiviral treatments and preventative therapies in the pipeline and under review in various jurisdictions globally. Conclusion(s): This research provides a clearer picture of the characteristics and evolution of the market for new and emerging COVID-19 medicines, which will help policy-makers and other stakeholders understand and anticipate the unique pressures of the COVID-19 pandemic.Copyright © 2023

Decline in Colorectal Cancer Screening Rates in Federally Qualified Health Centers in Los Angeles County From 2019 to 2020

Introduction: Federally Qualified Health Centers (FQHCs) are funded by the Health Resources and Services Administration (HRSA) to provide primary care services to low-income and underinsured individuals. Los Angeles County (LAC) is a large, diverse county with greater than 10.2 million residents and 8 distinct Service Planning Areas (SPAs) that represent specific geographic regions with variable resources. We aimed to describe colorectal cancer (CRC) screening rates (CRCSR) and the screening rate change (SRCs) in LAC overall and for each SPA between 2019 and 2020 to determine where resources are most needed for CRCSR recovery following the COVID-19 pandemic. Method(s): Our data source was the Uniform Data System (UDS), which includes quality data for the FQHCs funded by HRSA. We determined 2019 and 2020 CRCSR for LAC FQHCs overall and for each FQHC, including average-risk patients age 50-74. We then separated FQHCs into quartiles based on SRC and performed mixed-effects logistic regression to determine FQHC-level characteristics associated with the largest decline in CRCSR from 2019 to 2020 (i.e., predictors of category SRC Q1). Lastly, we determined SRC for each SPA in LAC. Result(s): In 2019, there were 58 FQHCs in LAC with 326,473 patients eligible for CRC screening. In 2020, there were 59 FQHCs with 350,405 eligible patients. The median 2020 CRCSR in LAC FQHCs was 37.3%, down from 48.0% in 2019 (2020 median SRC= -9.6%) (Table). In the regression model among all LAC FQHCs, those with higher proportions of patients preferring a non-English language had significantly higher odds of having the largest decline in CRCSR from 2019 to 2020 (SRC Q1) (aOR=3.25, 95% CI=1.22-8.65;data not shown). CRCSR decreased from 2019 to 2020 in all SPAs with SRC ranging from -17.0% (South Bay) to -1.4% (West LA) (Figure). Conclusion(s): In Los Angeles County FQHCs, CRC screening rates were higher than the national FQHC average in 2019 however declined considerably between 2019 and 2020. The decline in CRC screening rates was highest in FQHCs serving a higher proportion of patients with a preference for a non-English language and varied by county region. Our findings highlight the need for targeted measures, including language-appropriate resources, to improve CRC screening uptake in FQHCs that provide care to some of the most historically marginalized individuals.

The Impact of the COVID-19 Pandemic on Retrenchment, Vaccinations, and Global Happiness

COVID-19's impacts have spread widely in all directions such as economy, people's lifestyles and well-being. Though existing studies have highlighted such an impact, it remains unclear how the current COVID-19 situation has affected the retrenchment, vaccination and global happiness. In this paper, we present an automated tool enables the public to view various insight. In particular, we integrate and analyze the data from various data sources and show how the COVID19 has impacted Singapore and globally. We employ the regression models to identify the correlation between Human Development Index, Stringency Index, Gross Domestic Product per Capita, Total Deaths from COVID-19, and Total Cases of COVID-19;the rate of vaccination and vaccine hesitancy;and the factors to positively correlate to the global happiness. The insight provided adds values to better fight against the COVID-19 pandemic and future global crisis. © 2023 IEEE.

Prevalence of Potential Drug-Drug Interactions with Ritonavir-Containing Covid-19 Medications among the Adult Patient Population in Australia: Analysis of Pharmaceutical Benefits Scheme 10% Sample (Pbs10) Claims

Intro: Oral antiviral agents with differing modes of action are now available for the treatment of COVID-19. However, potentially life-threatening drug-drug interactions (DDIs) may occur if patients' underlying co-morbidities are treated with medications that are contraindicated with ritonavir-containing antivirals. This study evaluated the prevalence and severity of potential DDIs (pDDIs) with ritonavir-containing COVID-19 oral antiviral therapy among the Australian population. Method(s): Adult patients supplied with >=1 medication between January 1, 2019, and December 31, 2019, were identified in the PBS10 dataset, a longitudinal, random 10% sample of the national Pharmaceutical Benefits Scheme (PBS) data for supplied prescriptions. Patients receiving medications that have pDDIs with a ritonavir-containing COVID-19 antiviral treatment were classified as the pDDI group, using data sources from University of Liverpool, Lexicomp, or the US Food and Drugs Administration. Finding(s): Over 1,434,000 patients in the PBS10 were supplied with >=1 medication during the study period. The majority (58.8%) had been prescribed at least one medication with pDDI with ritonavir-containing treatment. Among all patients with pDDIs, 43.3% of them were major or contraindicated, followed by moderate (15.1%), and minor pDDIs (1.9%). Patients with cancer had the highest prevalence of contraindicated or major pDDIs (79.5%), followed by dementia and/or Alzheimer's (77.2%), and diabetes (73.8%). Elderly patients (>=60 years old) also had a higher prevalence of contradicted or major pDDI (65.4%) than the general patient population. Conclusion(s): Our results demonstrated that one-third of the Australian adult population in the PBS10 dataset may be classified as contraindicated with ritonavir-containing COVID-19 therapies. The prevalence of pDDI is much higher in elderly patients and in patients with certain co-morbidities. Health care providers will need to evaluate patients carefully should they be eligible for COVID-19 oral antiviral treatments. Alternative therapies should be considered as patients may be precluded from being treated with ritonavir-containing therapies owing to pDDIs.Copyright © 2023

Safety surveillance of covid-19 vaccines using large-linked database

In the global drive to vaccinate against SARS-CoV-2, millions of people have received at least one dose of a COVID- 19 vaccine. Vaccination safety is the key to the success of immunisation programs and in combating vaccine hesitancy among the public. Post-licensure safety monitoring of COVID-19 vaccines is essential to detect rare or severe vaccine-associated adverse events in the population and provide ongoing data of safety issues. Passive surveillance is the primary method most widely used to collect adverse events following immunisation (AEFI) via voluntary reporting. Monitoring through active surveillance is strongly encouraged to improve vaccine safety monitoring and provide more robust data. The SAFECOVAC project was initiated to evaluate risk of serious adverse events following COVID-19 vaccination. It leverages on the availability of nationwide COVID- 19 vaccine registry, hospital admission database, and other data sources to create a large-linked database. Uniquely for Malaysia, diverse vaccine portfolio was used and we are able to compare the risk estimate for the three major vaccine types of different platform i.e., mRNA-based vaccine (BNT162b2), inactivated vaccine (CoronaVac), and adenovirus vector-based vaccine (ChAdOx1). Current data shows that safety of COVID-19 vaccine is assured and findings are fairly consistent with data from other countries.

Global Status of Covid-19 Vaccination

Background: Covid 19 vaccination has substantially altered the course of the pandemic, saving tens of millions of lives globally. However, inadequate access to vaccines in low income countries has limited the impact in these settings, reinforcing the need for global vaccine equity and coverage. The present study was aimed to assess the global data of covid 19 vaccination from a secondary data source. Material(s) and Method(s): It is a secondary data analysis of worldwide covid 19 vaccination data obtained from World Health Organization Website. Data updated upto October 2022 on WHO website was collected. Result(s): Variables included name of countries, total vaccinations, total vaccinations per 100, person vaccinated one plus dose, booster dose, booster dose per 100, type of vaccine etc. Afghnaistan reported a total vaccinations of 11951964 till 11-10-2022 whereas India reported vaccinations of 2190969572 till 22 October 2022. Conclusion(s): Total vaccination per 100 in Afghnaistan is 30.702 and for India it is 158.7.

The impact of prone episodes on oxygenation in COVID-19 mechanically ventilated patients

Introduction: Prone positions have been used extensively to improve oxygenation in patients with acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic there was widespread adoption of proning in patients with acute severe hypoxic respiratory failure. Few studies explore the use of prone positioning in mechanically ventilated COVID-19 patients. Method(s): This study was part of the REACT COVID observational study at University Hospital Southampton (UHS) [1]. Eligibility included admission to UHS with a positive COVID-19 RT-PCR between 03/2020 and 03/2022. Data was collected from all available electronic clinical data sources using semi-automated and manual data extraction. Result(s): 184 patients received invasive mechanical ventilation with documented evidence for 931 prone episodes. We performed detailed analysis for 763 prone episodes. The rest were excluded due to insufficient data. The median duration of each cycle was 16 h (IQR 15-17 h). 459 cycles were done within 7 days of intubation (early), 202 in 7-14 days (intermediate) and 102 after 14 days (late). The change in oxygenation defined as delta PaO2/ FiO2 ratio (DELTAPF) for early, intermediate, and late cycles were 2.4 +/- 5.2 kPa, 1.6 +/- 3.7 kPa and 1.4 +/- 4.0 kPa, (p = 0.03) respectively. The overall DELTAPF for all groups after a cycle was 2.1 +/- 4.7 kPa. There was an increase in PaCO2 following proning with an overall change of 0.30 +/- 1.0, however, this was not statistically significant (p = 0.30). Conclusion(s): Following proning, there was significant improvement in oxygenation. Cycles lasted for 16 h consistent with current ARDS guidelines [2]. Although the results suggest a diminishing response in those proned at later times, the DELTAPF ratio was still significant. Overall, this suggests a beneficial effect on oxygenation. However, findings cannot be translated into survival benefit. Further research including randomised controlled trials is recommended.

EPH118 Healthcare Systems Sustainability and Resilience: Creating an Index

Objectives: The COVID-19 pandemic has highlighted the need for sustainable and resilient healthcare systems to protect population health. This requires measuring the relative progress of health systems towards becoming more sustainable and resilient. In this research, we design, construct and estimate a country-level healthcare system sustainability and resilience index (HSSRI) that reflects and combines the two dimensions. Method(s): The HSSRI aims to summarise the performance of a health system in the different domains contributing to its sustainability and resilience. These domains are: i) health system governance, ii) health system financing, iii) health system workforce, iv) medicines and technologies, v) health service delivery, vi) population health and social determinants, and vii) environmental sustainability. As part of our analyses, we conduct a rapid evidence assessment to identify indicators reflecting the domains included in the sustainability and resilience dimensions. We assess the domain indicators' suitability by the quantity and quality of the literature supporting their inclusion. The variables in each indicator are extracted from publicly available data sources, such as the OECD, World Bank, and others. The period covered is from 2000 to 2020. Weighted means of the indicators are used to construct the domains' indices in each dimension. We apply a geometric mean to combine the domain indices into one final index. Result(s): The HSSRI is piloted using data from five high-income countries, providing a credible instrument for measuring and reporting healthcare system sustainability and resilience. The results enable policy-makers and stakeholders to observe how different domains of sustainability and resilience have evolved across countries and time. Conclusion(s): The HSSRI will facilitate better understanding and monitoring of the healthcare system's relative weaknesses and strengths, and empower policy-makers to design interventions that improve its resilience and sustainability.Copyright © 2022

The Implications of Covid-19 on Health Economics - an Overview

Purpose- This paper is an analysis of the current COVID-19 scenario and its inevitable effect on the economies around the world. The aim of this study is to provide an overview and the implications of a pandemic to the health economics of any country irrespective of being developed or otherwise developing. Design/Methodology/Approach-For the same, data has been collected through secondary sources likes articles, reports including online and offline data sources. The implications of the pandemic under political and economic repercussions, and its implications to the policy makers in the immediate future were studied and complied for the current study. Findings- The study has brought forward a long-term assessments and speculations of the effects of the on going pandemic.Copyright © 2020 Ubiquity Press. All rights reserved.

Ethnic Differences in Cancer Deaths at Home before and during Pandemic

Introduction Place of death is a metric used for planning and monitoring palliative care (PC). The COVID-19 pandemic has seen a significant increase in cancer deaths at home. Aims To determine whether pandemic increases in the percentage of cancer deaths at home differ by ethnic group Methods Data source: death registrations in England, 2018 to 2021 with underlying cause of death cancer (ICD-10 C00-C97). Ethnic group derived from linked hospital episode data. The age and deprivation distribution across ethnic groups varies and each has a strong independent effect on place of death. so, calculated percentage deaths at home were standardised by these factors to make them comparable. Analysis concentrated on the largest ethnic groups: White, Asian/Asian British (Asian), and Black/African/ Caribbean/Black British (Black). Comparisons were made between time periods by analysis of the ratio of percentages 2020-2021 (COVID-19 Pandemic) vs 2018-2019 (Baseline). Results For each ethnic group the age-standardised percentage of cancer deaths at home significantly increased (P < 0.05) from 2018-2019 to 2020-2021 . Asian: 33.5%, 47.5% . Black: 28.8%, 39.0% . White: 30.7%, 41.2% The ratio of standardised percentage of deaths at home (95% CI) was . Asian: 1.42 (1.36,1.48 ) . Black: 1.35 (1.27, 1.44) . White 1.34 (1.33, 1.35) Conclusions Cancer deaths at home increased by > 10 percentage points during the pandemic for Asians, Blacks and Whites. Significant differences between ethnic groups before the pandemic (2018-19) persisted with Asians more likely than Whites, and Blacks less likely than Whites to die at home. The largest increase was for Asians, the group with the highest pre-pandemic home deaths. Impact These ethnic differences merit investigation regarding cultural preferences, access issues and quality of PC experience. Community health and PC teams need additional resources and training in culturally sensitive care to support the increased number of ethnically diverse cancer patients dying at home.

The Promise and Challenges of Integrating Real-World Evidence in Clinical Research

The role of real-world evidence (RWE) in regulatory, drug development, and healthcare decision-making is rapidly expanding. While RWE cannot substitute the evidence obtained from randomized controlled studies (RCTs), the two can be viewed as complementary sources with the same goal of understanding and improving patient's outcomes. However, the hopes of RWE have been tempered by several critical aspects/ challenges such as quality of data sources, potential for systematic bias, or formulating a research question using causal inference framework. In this session, we will discuss commonly encountered issues and recommend key methodological considerations and potential solutions for (1) assessing representativeness and generalizing results from experimental to non-experimental studies, (2) identifying under-represented groups in clinical trials for pharmacotherapy for opioid use disorder, (3) characterizing and increasing diversity in clinical trials, and (4) assessing biases and constructing valid ''synthetic control'' arms for (oncology) clinical trials. Each speaker will have 15-20 min each, followed by a 10-min discussion. Additional Q&A time will be allocated at the end of the session. The individual s are described in more detail below. (1) Ben Ackerman;Title: Using real-world data to assess representativeness and improve generalizations of study findings Randomized trials are considered the gold standard for estimating causal effects. Trial findings are often used to inform policy and programming efforts, yet their results may not generalize well to a relevant target population if the trial sample is not representative of the population of interest. More specifically, generalizations will be hindered if a trial is not similar to the population with respect to characteristics that moderate the treatment effect. Statistical methods have been developed to assess representativeness and improve generalizability by combining trials with data from non-experimental studies. Real-world datasets derived from electronic health records are promising resources that can supplement trial data when applying such methods. However, identifying the right real-world data source with the appropriate characteristics captured can be challenging in practice. In this talk, we will articulate a framework for combining trial and real-world data to assess representativeness and ultimately addressing concerns of generalizability. Through this framework, we will provide guidance on defining the target population of interest, identifying a suitable real-world data source describing that population, harmonizing across the data sources, and drawing meaningful comparisons between the trial and target population. This work will provide researchers with methods and tools to contextualize trial findings within the target population of interest through the use of real-world data. (2) Kara Rudolph;Title: Characterizing subgroups that are under-represented in clinical trials for pharmacotherapy for opioid use disorder The opioid epidemic in the United States is a public health emergency, exacerbated by the Covid-19 pandemic. Medications for opioid use disorder (MOUD)- injection naltrexone, buprenorphine, and methadone- are the most effective tools for improving outcomes and preventing overdose among persons with opioid use disorder (OUD), but engagement in MOUD, especially long-term engagement typically required for a successful outcome, is unacceptably low. Long-term engagement rates tend to be even lower in real-world settings-what National Institute on Drug Abuse (NIDA) has termed the research-to-practice gap. This discrepancy between trial and real-world MOUD effectiveness could be partially attributable to differences between clinical trial and real-world population characteristics (e.g. in terms of psychiatric and substance use comorbidities, previous treatment experience, and immigration status) if treatment effects are modified (increased/decreased) by some of these characteristics that also relate to trial participation. We identif and characterize clinically meaningful, interpretable subgroups of persons seeking OUD treatment in US usualcare settings who are not represented or underrepresented in MOUD trials based on multiple characteristics simultaneously. This moves us beyond existing approaches for assessing representation that have generally been limited to considering one individual-level characteristic at a time (e.g. race/ethnicity). (3) Madison Stoms;Title: Minority representation in clinical trials: generalizing trial results to diverse populations Since its origin, medical research has persistently lacked minority representation. In 2020 alone, the US Food and Drug Administration (FDA) reported that a mere 6% and 11% of clinical trial participants report Black and Hispanic race, respectively. Along with efforts to directly increase representation, via revised recruitment strategies, methods are being developed to leverage external data containing information on under-represented populations. The field from which these methods arise, real-world evidence (RWE), is rapidly emerging and aims to address clinical questions outside the scope of clinical trials. In this talk, we focus on generalizability and transportability methods, which can be used to extrapolate results from mostly racially homogeneous samples to diverse populations. We will also discuss the current state of diversity in clinical trials, important assumptions of generalizability and transportability methods, and applications relevant to increasing racial/ethnic representation. (4) Elizabeth Garrett-Mayer;Title: Leveraging RWD for new indications for FDA-approved anticancer agents: are we there yet? Vast amounts of health outcome data are available in real-world datasets (RWDs), like electronic health record databases and medical claim databases. In rare disease settings, performing randomized trials may be resource-intensive and inefficient due to accrual challenges. Efforts have been made to derive ''control arms'' from RWDs, representing a standard of care treatment arm, so all (or most) patients prospectively enrolled in a trial can be assigned to an experimental arm. Major challenges exist in ensuring that comparisons between the arms are meaningful, valid, and unbiased. This talk will discuss challenges, including potential for biases, harmonizing outcome measures, and efforts moving forward that will facilitate supplementing clinical trial data with RWD.

Teledermatology reduces waiting times for skin cancer diagnosis

Teledermatology triages large volumes of general practitioner (GP) referrals for suspicious lesions to outpatient services. A commercial, National Health Service (NHS) compliant teledermatology service was supported and evaluated in 12 GP practices during the COVID-19 pandemic. Images from a GP were transferred for review by a dermatology consultant via a mobile phone, a customized dermatoscope, mobile application and web platform. This platform, untested in the UK, showed benefits to both patients and GP services in reducing waiting times for dermatology diagnosis. Between January 2020 and May 2021, a mixed-method evaluation assessed whether this teledermatology platform was fit for purpose, provided benefits to patients and primary care, and reduced unnecessary referrals to secondary care. Data sources were surveys, patient questionnaires, informant interviews and NHS activity. Activity outcomes during teledermatology implementation were compared to those expected in the original pathway (no teledermatology). Avoided face-to-face activity was used as a surrogate assessment to estimate economic impact. Successful implementation across GP practices diverted patients away from the urgent 2-week wait outpatient appointment straight to biopsy, if necessary. This reduced waiting times from between 10 and 47 days to < 1 day between GP referral and lesion review. Eighty-eight per cent of referrals using teledermatology were associated with avoided face-to-face appointments. Most patients (92%) welcomed the ease, simplicity, speed of diagnosis and follow-up information, finding the new pathway 'excellent' and 'quick'. Some previously treated patients were less happy with photographing their lesion vs. those not previously treated (85% vs. 92%). Some patients still prefer to see a consultant dermatologist. Professionals reported patients were receptive, but not all understood the remote management of their skin lesion. Staff across GP practices found teledermatology worked well, but greater familiarity would normalize use. Technical issues were resolved promptly by the supplier;however, local NHS provider teledermatology configuration requires careful consideration. Pathway adaptions included use of eConsult to triage patient images to an advanced nurse practitioner-led skin clinic, creating additional efficiencies. Images were used at secondary care multidisciplinary team meetings to agree treatment plans. Inexperienced GPs had an opportunity to improve their diagnostic skills. However, teledermatology does not offer the opportunity for a full body scan. An estimated 63% reduction in future outpatient appointments was predicted from the results of this teledermatology. Future economic assessment of teledermatology vs. the benefits achieved, especially avoidance of unnecessary dermatologist consultations, would validate these estimated avoided appointment numbers. Teledermatology had a positive impact on patients and NHS services.

Risk management research in East Asia: a bibliometric analysis

Purpose: The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends, hotspots, and directions for future research. Design/methodology/approach: The data source for this paper is the Web of Science Core Collection, and 7,154 publications and related information have been derived. We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools (VOS Viewer and CiteSpace). Findings: The analysis results show that China is the most productive and influential country/region. East Asia countries have strong cooperation with each other and also have cooperation with other countries. The study shows that risk management has been involved in various fields such as credit, supply chain, health emergency and disaster especially in the background of COVID-19. We also found that machine learning, especially deep learning, has been playing an increasingly important role in risk management due to its excellent performance. Originality/value: This paper focuses on studying risk management in East Asia, exploring its publication's fundamental information, citation and cooperation networks, hotspots, and research trends. It provides some reference value for scholars who are interested or further research in this field. © 2023, Emerald Publishing Limited.

Open science progress: A literature assessment of open access articles

The open science movement has been widely adopted in multiple scientific fields across nations. Its benefit has been proven in many cases, most notably when the practice accelerated the search for solutions to the Covid-19 pandemic both in medical and socio-economic contexts. Still, the movement has faced multiple challenges, including an imbalance in the adoption of its numerous aspects. For example, the open access aspect which indicates the starting point of the movement has been widely practiced. Unfortunately, while open access is essential, an open access practice alone is not enough to pursue open science. In this work, we would like to assess the imbalance of the adoption, especially to measure how open access practice contributes to other practices, namely open data and open source as a sub-aspect of the open reproducibility research. Our assessment is based on descriptive statistic analysis of 300 open access articles from three domains, that is engineering, social and life science. Our findings indicated that the free and open source computer codes were dominantly adopted by the three scientific fields. However, social science has the lowest involvement in public data. © 2022 ACM.

Enhancing COVID-19 Ensemble Forecasting Model Performance Using Auxiliary Data Sources

Real-time forecasting of non-stationary time series is a challenging problem, especially when the time series evolves rapidly. For such cases, it has been observed that ensemble models consisting of a diverse set of model classes can perform consistently better than individual models. In order to account for the nonstationarity of the data and the lack of availability of training examples, the models are retrained in real-time using the most recent observed data samples. Motivated by the robust performance properties of ensemble models, we developed a Bayesian model averaging ensemble technique consisting of statistical, deep learning, and compartmental models for fore-casting epidemiological signals, specifically, COVID-19 signals. We observed the epidemic dynamics go through several phases (waves). In our ensemble model, we observed that different model classes performed differently during the various phases. Armed with this understanding, in this paper, we propose a modification to the ensembling method to employ this phase information and use different weighting schemes for each phase to produce improved forecasts. However, predicting the phases of such time series is a significant challenge, especially when behavioral and immunological adaptations govern the evolution of the time series. We explore multiple datasets that can serve as leading indicators of trend changes and employ transfer entropy techniques to capture the relevant indicator. We propose a phase prediction algorithm to estimate the phases using the leading indicators. Using the knowledge of the estimated phase, we selectively sample the training data from similar phases. We evaluate our proposed methodology on our currently deployed COVID-19 forecasting model and the COVID-19 ForecastHub models. The overall performance of the proposed model is consistent across the pandemic. More importantly, it is ranked second during two critical rapid growth phases in cases, regimes where the performance of most models from the ForecastHub dropped significantly. © 2022 IEEE.

Inequalities in connectivity and academic performance during non-face-to-face teaching due to the Covid-19 Pandemic

The outbreak of the COVID-19 virus has turned our world upside down. Universities were not the exception. Most administrators struggle to redesign higher education for a postCOVID world university. During the pandemic, however, many of the face-face courses turned to virtual, giving rise to or incrementing inequalities among students due to lack of equipment and network connectivity could affect their academic performance. This paper brings together evidence from various data sources and the most recent studies to describe what we know so far about the impacts of the COVID-19 crisis on inequalities across several key domain factors, including the availability of equipment and connectivity could affect their academic performance. The paper explores the effect of connectivity on the academic performance of our chemical and engineering students based on (i) the location of the home, (ii) the equipment, (iii) the type of connectivity, (iv) the number of people with those in which the equipment is shared and (v) online attendance at classes, finding that the ones with the greatest impact are the lack of synchronous attendance at class, the equipment, and internet connectivity. © 2022 IEEE.

Analyzing Access to Health Facilities by Road Using Unconventional Data Sources

The COVID-19 pandemic has led to an urgent need in emerging economies to quickly identify vulnerable populations that do not live within access of a health facility for testing and vaccination. This access information is critical to prioritize investments in mobile and temporary clinics. To meet this need, the World Bank team sought to develop an open-source methodology that could be quickly and easily implemented by government health departments, regardless of technical and data collection capacity. The team explored use of readily available open-source and licensable data, as well as non-intensive computational methodologies. By bringing together population data from Facebook's Data for Good program, travel-time calculations from Mapbox, road network and point-of-interest data from the OpenStreetMap (OSM), and the World Bank's open-source GOSTNets network routing tools, we created a computational framework that supports efficient and granular analysis of road-based access to health facilities in two pilot locations—Indonesia and the Philippines. Our findings align with observed health trends in these countries and support identification of high-density areas that lack sufficient road access to health facilities. Our framework is easy to replicate, allowing health officials and infrastructure planners to incorporate access analysis in pandemic response and future health access planning. © National Academy of Sciences: Transportation Research Board 2022.

RoCoFake – A Romanian Covid-19 Fake News Dataset

The Covid-19 pandemic, managed to shed light onto a neglected problem – that of fake news. Even though lockdowns were imposed in most parts of the world, collaboration between researchers across the globe wasn't impeded. Moreover, the lockdown has deprived people of face-to-face interactions and so they shifted towards online communication. This translated into a massive chatting data, which part was true, but fake information also had its share. Therefore, it is of great interest to develop a dataset to try to spot the fake information. RoCoFake comes to address the lack of resources in this domain, by providing a Romanian Covid-19 Fake News dataset, by aggregating various resources available online, like tweets, news titles and fact-checking news sites like factual.ro. This data provides researchers from the medical domain particularly, but not only, with a valuable, open-access data source useful for various research projects. A benchmark for fake news detection is also provided, so that future investigations can compare against our research. Results suggest that even though the dataset is relatively large, improvements can be made by incorporating retweets and comments. © 2022 IEEE.

Health Informatics on Big COVID-19 Pandemic Data via N-Shot Learning

Nowadays, very large amounts of data are generating at a fast rate from a wide variety of rich data sources. Valuable information and knowledge embedded in these big data can be discovered by data science, data mining and machine learning techniques. Biomedical records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 635 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 3 years since COVID-19 has declared as a pandemic. Hence, effective strategies, solutions, tools and methods - such as artificial intelligence (AI) and/or big data approaches - to tackle the COVID-19 pandemic and possible future pandemics are in demand. In this paper, we present models to analyze big COVID-19 pandemic data and make predictions via N-shot learning. Specifically, our binary model predicts whether patients are COVID-19 or not. If so, the model predicts whether they require hospitalization or not, whereas our multi-class model predicts severity and thus the corresponding levels of hospitalization required by the patients. Our models uses N-shot learning with autoencoders. Evaluation results on real-life pandemic data demonstrate the practicality of our models towards effective allocation of resources (e.g., hospital facilities, staff). These showcase the benefits of AI and/or big data approaches in tackling the pandemic. © 2022 IEEE.