Effectiveness of the booster dose of COVID-19 vaccine in the Basque Country during the sixth wave: A nationwide cohort study.

The aim of the study was to assess the effect of a booster dose of COVID-19 vaccine on the rates of hospital ward and intensive care unit (ICU) admissions around the time of emergence of the Omicron variant in the Basque Country. A retrospective cohort population-based study was conducted. The population with any records related to COVID-19 vaccination up to 28 February 2022 was classified into four cohorts by vaccination status. For every cohort, the hospital ward and ICU admission rates were calculated for each day between November 2021 and February 2022. Generalized linear models with a negative binomial distribution were used to estimate the age-adjusted hospitalization rate ratio of the cohort of individuals who had received a booster compared to the other cohorts. The age-adjusted rates of hospital ward and ICU admissions were 70.4 % and 72.0 % lower, respectively, in the fully vaccinated plus booster group compared to the fully vaccinated but no booster group. Analysing changes in the 14-day admission incidence rates showed that as the prevalence of the Omicron variant increased, the corresponding rate ratios decreased. The immunity acquired with the booster dose allowed the hospital network to meet all the demand for hospitalization during a period of high incidence of COVID-19, despite the fact that vaccine protection decreased as the prevalence of the Omicron variant increased.

Decrease of visits and hospital admissions for cancer patients during the COVID-19 pandemic. A systematic review and meta-analysis.

Background: During the COVID-19 pandemic, many nonurgent oncologic services were postponed. The aim of the present study was to estimate the impact of the pandemic on visits and hospital admissions for cancer patients worldwide. Methods: In our systematic review and meta-analysis, databases such as Pubmed, Proquest, and Scopus were searched comprehensively for articles published between January 1, 2020, and December 12, 2021. We included articles reporting data comparing the number of visits and hospital admissions for oncologic patients performed before and during the pandemic. Two pairs of independent reviewers extracted data from the selected studies. The weighted average of the percentage change was calculated and compared between pandemic and pre-pandemic periods. Stratified analysis was performed by geographic area, time interval, and study setting. Findings: We found a mean relative change throughout January-October 2020 of -37.8% (95% CI -42.6; -32.9) and -26.3% (95% CI -31.4; -21.1) compared to pre-pandemic periods for oncologic visits and hospital admission, respectively. The temporal trend showed a U-shaped curve with nadir in April for cancer visits and in May 2020 for hospital admissions. All geographic areas showed a similar pattern and the same was observed when stratifying the studies as clinic-based and population-based. Interpretation: Our results showed a decrease in the number of visits and hospital admission during the January-October 2020 period after the outbreak of the COVID-19 pandemic. The postponement or cancellation of these oncologic services may negatively affect the patient's outcome and the future burden of disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s10389-023-01857-w.

Trends in Cases, Hospitalization and Mortality Related to the Omicron BA.4/BA.5 Sub-Variants in South Africa.

BACKGROUND: This study compared admission incidence risk across waves, and the risk of mortality in the Omicron BA.4/BA.5 wave, to the Omicron BA.1/BA.2 and Delta waves. METHODS: Data from South Africa's national hospital surveillance system, SARS-CoV-2 case linelist and Electronic Vaccine Data System were linked and analysed. Wave periods were defined when the country passed a weekly incidence of 30 cases/100,000 people. In-hospital case fatality ratios (CFR) in the Delta, Omicron BA.1/BA.2 and Omicron BA.4/BA.5 wave periods were compared by post-imputation random effect multivariable logistic regression models. RESULTS: The CFR was 25.9% (N = 37,538/144,778), 10.9% (N = 6,123/56,384) and 8.2% (N = 1,212/14,879) in the Delta, Omicron BA.1/BA.2, and Omicron BA.4/BA.5 waves respectively. After adjusting for age, sex, race, comorbidities, health sector and province, compared to the Omicron BA.4/BA.5 wave, patients had higher risk of mortality in the Omicron BA.1/BA.2 wave (adjusted odds ratio [aOR] 1.3; 95% confidence interval [CI] 1.2-1.4) and Delta (aOR 3.0; 95% CI 2.8-3.2) wave. Being partially vaccinated (aOR 0.9, CI 0.9-0.9), fully vaccinated (aOR 0.6, CI 0.6-0.7) and boosted (aOR 0.4, CI 0.4-0.5); and prior laboratory-confirmed infection (aOR 0.4, CI 0.3-0.4) were associated with reduced risks of mortality. CONCLUSION: Overall, admission incidence risk and in-hospital mortality, which had increased progressively in South Africa's first three waves, decreased in the fourth Omicron BA.1/BA.2 wave and declined even further in the fifth Omicron BA.4/BA.5 wave. Mortality risk was lower in those with natural infection and vaccination, declining further as the number of vaccine doses increased.

The Effect of CoViD-19 Pandemic on the Hospitalization of a Department of Oncology of an Italian Hospital

In the last years, the entire world has been affected by the SARS-COV-2 pandemic, that represents the etiologic agent of Coronavirus disease 2019 (CoViD-19), which degenerated into a global pandemic in 2020. CoViD-19 has also had a strong impact on cancer patients. Our analysis has been performed at the Department of Oncology of the AORN "Cardarelli” in Naples, collecting data from all patients who had access in 2019–2020. We aim to understand how CoViD-19 affected hospital admissions. The statistical analysis showed that between 2019 and 2020 there was an increase in urgent hospitalizations and a decrease in scheduled hospitalization, probably to decrease the risk of infection, particularly in this category of susceptible patients. Indeed, as recommended by the European Society of Medical Oncology, during the pandemic, it was necessary to reorganize healthcare activities, ensure adequate care for patients infected with CoViD-19. Therefore telemedicine services were implemented and clinic visits were reduced. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analysis of Hospital Admissions of Neurological Patients in the COVID-19 Era: Comparison Between Hospitals

In the last few years, the COVID-19 pandemic has strongly affected different hospital departments, revealing their major weaknesses. For this reason, this emergency has been a driver for healthcare transformation in a very short interval of time in order to optimize the resources, minimize costs and simultaneously increase the caring services, also limiting over-occupancy in wards, especially emergency ones. One of the main factors for assessing the efficiency of a department is associated with how long a patient stays in the facility (LOS). This bicentric study investigated how COVID-19 has modified the activity of the Complex Operative Unit (C.O.U.) of Neurology and Stroke of the University Hospital "San Giovanni di Dio e Ruggi d'Aragona” of Salerno (Italy) and the hospital A.O.R.N. "Antonio Cardarelli” of Naples (Italy). In the work data for the year 2019 (in the absence of Covid-19) and in the year of Covid-19 pandemic 2020 were considered. This work used the logistic regression technique to study the following variables: age, gender, length of stay (LOS), relative weight of DRG and mode of discharge. The analysis shows that in 2020 there was a greater adequacy of admissions, with an increase in the relative weight of DRG. And the statistical analysis obtained the following significant variables: gender, age, relative weight of DRG and discharge mode. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Impact of COVID-19 in a Surgery Department: Comparison Between Two Italian Hospitals

The main phenomenon that impacted people's lives was the COVID-19 pandemic, having strong consequences on national health systems. Since the beginning of the Covid-19 pandemic, hospital admissions dropped precipitously in 2020. Our aim concerns the analysis about how the COVID-19 affects the activity of the Department of General Surgery, Day Surgery and Breast Unit in the University Hospital "San Giovanni di Dio and Ruggi d'Aragona” of Salerno and the hospital "A.O.R.N. Antonio Cardarelli” of Naples (Italy). In the work data for the year 2019 (in the absence of pandemic) and in the year of pandemic 2020 were considered. This work used the logistic regression technique to study the following variables: age, gender, length of stay (LOS), relative weight of DRG, admission procedure, mode of discharge and the results about both hospitals were used to make a comparison. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Quantifying changes in respiratory syncytial virus-associated hospitalizations among children in Texas during COVID-19 pandemic using records from 2006 to 2021.

Aim: To quantify changes on RSV- associated hospitalizations during COVID-19 pandemic, among children four years of age or younger at the state and county levels of Texas using routinely acquired hospital admission records. Methods: We used the Texas Public Use Data Files (PUDF) of the Department of State Human Services (DSHS) to obtain hospital admissions and healthcare outcomes from 2006 to 2021. We used the 2006-2019 period to estimate a long-term temporal trend and predict expected values for 2020-2021. Actual and predicted values were used to quantify changes in seasonal trends of the number of hospital admissions and mean length of hospital stay. Additionally, we calculated hospitalization rates and assessed their similarity to rates reported in the RSV Hospitalization Surveillance Network (RSV-NET). Results: An unusually low number of hospitalizations in 2020 was followed by an unusual peak in the third quarter of 2021. Hospital admissions in 2021 were approximately twice those in a typical year. The mean length of hospital stay typically followed a seasonal trend before COVID-19, but increased by a factor of ∼6.5 during the pandemic. Spatial distribution of hospitalization rates revealed localized healthcare infrastructure overburdens during COVID-19. RSV associated hospitalization rates were, on average, two times higher than those of RSV-NET. Conclusion: Hospital admission data can be used to estimate long-term temporal and spatial trends and quantify changes during events that exacerbate healthcare systems, such as pandemics. Using the mean difference between hospital rates calculated with hospital admissions and hospital rates obtained from RSV-NET, we speculate that state-level hospitalization rates for 2022 could be at least twice those observed in the two previous years, and the highest in the last 17 years.

COVID-19 (Omicron strain) hospital admissions from a virtual ward - who required further care?

BACKGROUND: The COVID-19 virtual ward was created to provide care for people at home with COVID-19. Given this was a new model of care, little was known about the clinical characteristics and outcomes of patients requiring admission to hospital from the virtual ward platform. The aims were to characterise hospital admission volume, patient epidemiology, clinical characteristics, and outcome from a virtual ward in the setting of an Omicron (BA.1, BA.2) outbreak. METHODS: A retrospective observational study was performed for all virtual ward patients admitted from 1st January 2022 to 25th March 2022 (over 16 years old). Epidemiological, clinical and laboratory data was reviewed on all patients who required hospital admission. RESULTS: A total of 7021 patients were cared for on the virtual ward over the study period with 473 referred to hospital for assessment. Twenty-six (0.4%) patients were admitted to hospital during their care on the ward. Twenty-two (84.6%) admissions were COVID-19 related. Fifty three percent of the hospitalised patients were fully vaccinated and 11 had received prior therapeutics for COVID-19. Shortness of breath was the most common reason for escalation to hospital. Chest pain was the second most common reason and the most common diagnosis after investigation was non-cardiac chest pain. CONCLUSIONS: Few patients required admission from the virtual ward in the setting of the Omicron variant (BA.1, BA.2) as a direct result of COVID-19 disease and virtual ward care. Shortness of breath and chest pain were the most common symptoms driving further clinical care.

Air pollution and meteorological variables' effects on COVID-19 first and second waves in Spain.

The aim of this research is to study the influence of atmospheric pollutants and meteorological variables on the incidence rate of COVID-19 and the rate of hospital admissions due to COVID-19 during the first and second waves in nine Spanish provinces. Numerous studies analyze the effect of environmental and pollution variables separately, but few that include them in the same analysis together, and even fewer that compare their effects between the first and second waves of the virus. This study was conducted in nine of 52 Spanish provinces, using generalized linear models with Poisson link between levels of PM10, NO2 and O3 (independent variables) and maximum temperature and absolute humidity and the rates of incidence and hospital admissions of COVID-19 (dependent variables), establishing a series of significant lags. Using the estimators obtained from the significant multivariate models, the relative risks associated with these variables were calculated for increases of 10 µg/m3 for pollutants, 1 °C for temperature and 1 g/m3 for humidity. The results suggest that NO2 has a greater association than the other air pollution variables and the meteorological variables. There was a greater association with O3 in the first wave and with NO2 in the second. Pollutants showed a homogeneous distribution across the country. We conclude that, compared to other air pollutants and meteorological variables, NO2 is a protagonist that may modulate the incidence and severity of COVID-19, though preventive public health measures such as masking and hand washing are still very important. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04190-z.

Using a Dynamic Causal Model to validate previous predictions and offer a 12-month forecast of the long-term effects of the COVID-19 epidemic in the UK.

Background: Predicting the future UK COVID-19 epidemic provides a baseline of a vaccine-only mitigation policy from which to judge the effects of additional public health interventions. A previous 12-month prediction of the size of the epidemic to October 2022 underestimated its sequelae by a fifth. This analysis seeks to explain the reasons for the underestimation before offering new long-term predictions. Methods: A Dynamic Causal Model was used to identify changes in COVID-19 transmissibility and the public's behavioral response in the 12-months to October 2022. The model was then used to predict the future trends in infections, long-COVID, hospital admissions and deaths over 12-months to October 2023. Findings: The model estimated that the secondary attack rate increased from 0.4 to 0.5, the latent period shortened from 2.7 to 2.6 and the incubation period shortened from 2.0 to 1.95 days between October 2021 and October 2022. During this time the model also estimated that antibody immunity waned from 177 to 160 days and T-cell immunity from 205 to 180 days. This increase in transmissibility was associated with a reduction in pathogenicity with the proportion of infections developing acute respiratory distress syndrome falling for 6-2% in the same twelve-month period. Despite the wave of infections, the public response was to increase the tendency to expose themselves to a high-risk environment (e.g., leaving home) each day from 33-58% in the same period.The predictions for October 2023 indicate a wave of infections three times larger this coming year than last year with significant health and economic consequences such as 120,000 additional COVID-19 related deaths, 800,000 additional hospital admissions and 3.5 million people suffering acute-post-COVID-19 syndrome lasting more than 12 weeks. Interpretation: The increase in transmissibility together with the public's response provide plausible explanations for why the model underestimated the 12-month predictions to October 2022. The 2023 projection could well-underestimate the predicted substantial next wave of COVID-19 infection. Vaccination alone will not control the epidemic. The UK COVID-19 epidemic is not over. The results call for investment in precautionary public health interventions.

Small rural hospitals in the United States and the impact of COVID: A cross-sectional study.

BACKGROUND: Rural hospitals in the United States face staffing and financial challenges, low patient volumes, and aging infrastructures among others. In addition, they deal with such crises as the opioid epidemic, natural disasters, and the coronavirus. METHODS: The analyses presented in this study are based on two databases: (1) the 2019 annual survey data published by the American Hospital Association (AHA) and (2) US Department of Health and Human Services (HHS) database of US hospitals containing information related to COVID-19 for the week of November 27, 2020. Using a subset of the 2019 AHA annual survey data to which the authors acquired access, this study develops a profile of rural hospitals in America. The data are proprietary property of AHA. The authors are permitted to use the data in published research but only in aggregate form. No individual hospital metrics can be used in this report. The HHS database is public data and as such is available to all. HHS recognizes the importance of providing high-quality, accessible, and timely information for entrepreneurs, researchers, and policy makers to help drive insights and better health outcomes for all. Employing this HHS database, a cross-sectional view of the impact of COVID on small, rural hospitals in the United States is undertaken. In this study, data found in the HHS database are presented only in the aggregate form. RESULTS AND DISCUSSION: The average small, rural hospital has 20.8 beds, 10 weekly admissions, a daily census of 6.6 patients, 145 full-time personnel, 67 part-time personnel, and a total facility expense of $27 million of which payroll expense was 41%. Due to COVID, there was an increase in admissions and outpatient visits.

Improving diabetes care in Ealing, London: an analysis of the NDA and diabetes‐related admissions data during first year of COVID‐19

In this paper, we present and analyse National Diabetes Audit (NDA) and diabetes‐related emergency admissions data in Ealing during the period 1 January 2020 to 31 March 2021. Care for diabetes and other long‐term conditions was disrupted and significantly impacted during this initial period of the COVID‐19 pandemic.The NDA analysis shows that for type 1 and type 2 diabetes Key Care Processes (KCPs), both the eight and nine KCPs fell between 2019/20 and 2020/21, but the relative fall for NHS Ealing was lower than that seen for England. Type 1 diabetes Three Treatment Targets (3TTs) for NHS Ealing increased from 22.6% in 2019/20 to 26.4% in 2020/21;in contrast the 3TTs for England increased slightly from 19.8% in 2019/20 to 20.8% in 2020/21. Type 2 diabetes 3TTs for NHS Ealing changed from 40.9% in 2019/20 to 38.9% in 2020/21, while in England it was 40.3% in 2019/20 and 35.5% in 2020/21.The diabetes‐related emergency admissions analysis shows that there were reductions in the number and rate of emergency admissions for cerebrovascular accident and myocardial infarction;admissions and rates for diabetic ketoacidosis and amputations were the same;those for diabetes mellitus and hypoglycaemia increased. There were overall cost savings of £874,147 due to estimated avoided admissions.In Ealing, the NDA data, diabetes‐related emergency admissions and estimated avoided admissions data show that improvements in diabetes care achieved in previous years in Ealing, faltered, but were broadly sustained in the first pandemic year. Support from the Ealing diabetes care teams, improved self‐management of diabetes and the empowering of people with diabetes through digital technologies could explain these trends in Ealing. Continued access to health care practitioners during the COVID‐19 pandemic is important to ensure the appropriate management of long‐term conditions such as type 1 diabetes mellitus and type 2 diabetes mellitus. Copyright © 2023 John Wiley & Sons.

Time Trends in Clinical Characteristics and Hospital Outcomes of Hospitalizations for Lung Transplantation in COPD Patients in Spain from 2016 to 2020-Impact of the COVID-19 Pandemic.

(1) Background: To examine the clinical characteristics and hospital outcomes of hospitalization for lung transplantation in COPD patients in Spain from 2016 to 2020; and to assess if the COVID-19 pandemic has affected the number or the outcomes of lung transplantations in these patients. (2) Methods: We used the Spanish National Hospital Discharge Database to select subjects who had a code for COPD (ICD-10: J44) and had undergone a lung transplantation (ICD-10 codes OBYxxxx). (3) Results: During the study period, 704 lung transplants were performed among COPD patients (single 31.68%, bilateral 68.32%). The absolute number of transplants increased with raising rates of 8%, 14% and 19% annually from 2016 to 2019. However, a marked decrease of -18% was observed from 2019 to year 2020. Overall, 47.44% of the patients suffered at least one complication, being the most frequent lung transplant rejection (24.15%), followed by lung transplant infection (13.35%). The median length of hospital stay (LOHS) was 33 days and the in-hospital-mortality (IHM) was 9.94%. Variables associated with increased risk of mortality were a Comorbidity Charlson Index ≥ 1 (OR 1.82; 95%CI 1.08-3.05) and suffering any complication of the lung transplantation (OR 2.14; 95%CI 1.27-3.6). COPD patients in 2020 had a CCI ≥ 1 in a lower proportion than 2019 patients (29.37 vs. 38.51%; p = 0.015) and less frequently suffered any complications after the lung transplantation (41.26 vs. 54.6%; p = 0.013), no changes in the LOHS or the IHM were detected from 2019 to 2020. (4) Conclusions: Our study showed a constant increase in the number of lung transplantations from 2016 to 2019 in COPD patients, with a drop from 2019 to 2020, probably related to the COVID-19 pandemic. However, no changes in LOHS or IHM were detected over time.

The Impact of COVID-19 National Lockdown on Myocardial Infarction (MI) Hospitalizations in Northern Jordan.

Background: During COVID-19 lockdown periods, several studies reported decreased numbers of myocardial infarction (MI) admissions. The lockdown impact has not yet been determined in developing countries. The aim of this study was to investigate the impact that of the lockdown measures might have had on the mean number of MI hospital admissions in Northern Jordan. Methodology: A single-center study examined consecutive admissions of MI patients during COVID-19 outbreak. Participants' data was abstracted from the medical records of King Abdullah University Hospital between 2018 and 2020. Mean and percentages of monthly admissions were compared by year and by lockdown status (pre-lockdown, lockdown, and post-lockdown time intervals). Results: A total of 1380 participants were admitted with acute MI symptoms: 59.2% of which were STEMI. A decrease in number of MI admissions was observed in 2020, from 43.1 (SD: 8.017) cases per month in 2019 to 40.59 (SD: 10.763) in 2020 (P < 0.0001) while an increase in the numbers during the lockdown was observed. The mean number during the pre-lockdown period was 40.51 (SD: 8.883), the lockdown period was 44.74 (SD: 5.689) and the post-lockdown was 34.66 (SD: 6.026) (P < 0.0001 for all comparisons). Similar patterns were observed when percentages of admissions were used. Conclusion: Upon comparing the lockdown period both to the pre- and post-lockdown periods separately, we found a significant increase in MI admissions during the lockdown period. This suggests that lockdown-related stress may have increased the risk of myocardial infarction.

Impact of Victorian COVID-19 restrictions on emergency department presentations and hospital admissions for ophthalmic conditions.

CLINICAL RELEVANCE: Assessing the extent to which COVID-19 impacted hospitals can provide important learnings for future pandemics. BACKGROUND: This study aims to determine the impact of the 7-month duration COVID-19 pandemic-related lockdown orders on ophthalmology-related hospital admissions and emergency department (ED) presentations, during 2020 in Victoria, Australia. METHODS: Analysis was performed on Victorian statewide data from the Victorian Emergency Minimum Dataset (VEMD) and Victorian Admitted Episodes Dataset (VAED), between 1 January 2018 and 31 October 2020. Numbers of presentations and admissions for key ophthalmic conditions were stratified by age, socioeconomic status, location (metropolitan versus rural), and triage category. From the observations occurring in the pre-pandemic period (January 2018 to March 2020), a linear regression prediction model was built for each diagnosis which predicted what the presentation number in the COVID-19 period would have been if the pandemic had not occurred. RESULTS: Based on pre-COVID-19 trends, the largest decreases in expected admissions were for glaucoma (32.9%) and retinal breaks and detachments (21.2%). For the ED data, the most apparent changes were: an increase in presentations for foreign bodies (22.6%); a decrease in retinal detachments (35.5%); and a decrease in keratitis (18.4%) relative to predictions. CONCLUSIONS: Hospital admissions decreased and patterns of ED attendances changed during lockdown. The findings suggest the need for the following: increased safety messaging to avoid eye injuries around the home; improved pathways for safe and rapid triaging of eye conditions in the community to ensure effective use of ED resources; and messaging to ensure that people do not delay care when they notice signs of sight-threatening conditions such as retinal detachment.

Alcohol Use Disorder Admissions During the COVID-19 Pandemic: Findings From a Tertiary Community Hospital.

OBJECTIVES: The first case of Coronavirus disease-19 (COVID-19) in the United States was confirmed by the Centers for Disease Control (CDC) in January 2020. The presence of COVID-19 and the subsequent spread of this disease led to stress, anxiety, grief, and worry. We aimed to study the rate of hospital admission for alcohol use disorder (AUD) before and during the COVID-19 pandemic in a tertiary community hospital in Michigan. METHODS: Two subsets of hospital data were collected for comparison between hospitalized patients before and during the pandemic in a tertiary community hospital. Logistic regression was used to identify the odds ratio of AUD admission rates among all patients in 2020 compared with 2019 while controlling for covariates. RESULTS: Our data showed a statistically significant increase in AUD patients in 2020 compared to 2019 (3.26% versus 2.50%, adjusted OR=1.44 with P=0.002). In addition, females had significantly lower chances of admission for AUD compared with males (OR=0.22 with P<0.001) and African Americans had significantly lower chances of admission for AUD compared to Whites (OR=0.44 with P <0.001). Divorced patients had a higher probability of admission for AUD compared to married patients (OR=2.62 with P<0.001). CONCLUSION: Our study found a significantly higher rate of AUD admissions in 2020 during the COVID-19 Pandemic compared to 2019. Gender, race, age, and marital status are significant risk factors related to AUD admissions.

Study of Ensemble Learners to identify the COVID-19 positive patients

SARS-CoV-2 coronavirus has already attracted substantial attention of the scientific community. Medical Science had never faced a tougher challenge than this pandemic. The rapid spread of the virus has caused a monumental increase in hospital admissions and deaths resulting in availability of data for analysis. Moreover, the disease has now become asymptomatic in most cases yet could be fatal for co-morbid patients. Patients on arrival to hospitals, whatever the case may be, are generally advised to opt for economically reasonable routine blood tests and certain aspects of this blood testing can assist us in determining if a patient is infected with coronavirus or not, at a very early stage. We can utilize ensemble classifiers (i.e., conglomerate of advanced and improved ensemble of learning algorithms) to distinguish between infected and non-infected individuals and rule out the scope of further spreading. In this paper, we have done a comparative study of the diverse ensemble learning techniques that are implemented over different patient's blood test reports and can presage if a patient is infected with coronavirus. © 2022 IEEE.

Understanding the dynamic relation between wastewater SARS-CoV-2 signal and clinical metrics throughout the pandemic.

Wastewater surveillance (WWS) of SARS-CoV-2 was proven to be a reliable and complementary tool for population-wide monitoring of COVID-19 disease incidence but was not as rigorously explored as an indicator for disease burden throughout the pandemic. Prior to global mass immunization campaigns and during the spread of the wildtype COVID-19 and the Alpha variant of concern (VOC), viral measurement of SARS-CoV-2 in wastewater was a leading indicator for both COVID-19 incidence and disease burden in communities. As the two-dose vaccination rates escalated during the spread of the Delta VOC in Jul. 2021 through Dec. 2021, relations weakened between wastewater signal and community COVID-19 disease incidence and maintained a strong relationship with clinical metrics indicative of disease burden (new hospital admissions, ICU admissions, and deaths). Further, with the onset of the vaccine-resistant Omicron BA.1 VOC in Dec. 2021 through Mar. 2022, wastewater again became a strong indicator of both disease incidence and burden during a period of limited natural immunization (no recent infection), vaccine escape, and waned vaccine effectiveness. Lastly, with the populations regaining enhanced natural and vaccination immunization shortly prior to the onset of the Omicron BA.2 VOC in mid-Mar 2022, wastewater is shown to be a strong indicator for both disease incidence and burden. Hospitalization-to-wastewater ratio is further shown to be a good indicator of VOC virulence when widespread clinical testing is limited. In the future, WWS is expected to show moderate indication of incidence and strong indication of disease burden in the community during future potential seasonal vaccination campaigns.

COVID-19 hospital admissions and mortality among healthcare workers in South Africa, 2020-2021.

Objectives: This study describes the characteristics of admitted HCWs reported to the DATCOV surveillance system, and the factors associated with in-hospital mortality in South African HCWs. Methods: Data from March 5, 2020 to April 30, 2021 were obtained from DATCOV, a national hospital surveillance system monitoring COVID-19 admissions in South Africa. Characteristics of HCWs were compared with those of non-HCWs. Furthermore, a logistic regression model was used to assess factors associated with in-hospital mortality among HCWs. Results: In total, there were 169 678 confirmed COVID-19 admissions, of which 6364 (3.8%) were HCWs. More of these HCW admissions were accounted for in wave 1 (48.6%; n = 3095) than in wave 2 (32.0%; n = 2036). Admitted HCWs were less likely to be male (28.2%; n = 1791) (aOR 0.3; 95% CI 0.3-0.4), in the 50-59 age group (33.1%; n = 2103) (aOR 1.4; 95% CI 1.1-1.8), or accessing the private health sector (63.3%; n = 4030) (aOR 1.3; 95% CI 1.1-1.5). Age, comorbidities, race, wave, province, and sector were significant risk factors for COVID-19-related mortality. Conclusion: The trends in cases showed a decline in HCW admissions in wave 2 compared with wave 1. Acquired SARS-COV-2 immunity from prior infection may have been a reason for reduced admissions and mortality of HCWs despite the more transmissible and more severe beta variant in wave 2.

Defining factors in hospital admissions during COVID-19 using LSTM-FCA explainable model.

Outbreaks of the COVID-19 pandemic caused by the SARS-CoV-2 infection that started in Wuhan, China, have quickly spread worldwide. The current situation has contributed to a dynamic rate of hospital admissions. Global efforts by Artificial Intelligence (AI) and Machine Learning (ML) communities to develop solutions to assist COVID-19-related research have escalated ever since. However, despite overwhelming efforts from the AI and ML community, many machine learning-based AI systems have been designed as black boxes. This paper proposes a model that utilizes Formal Concept Analysis (FCA) to explain a machine learning technique called Long-short Term Memory (LSTM) on a dataset of hospital admissions due to COVID-19 in the United Kingdom. This paper intends to increase the transparency of decision-making in the era of ML by using the proposed LSTM-FCA explainable model. Both LSTM and FCA are able to evaluate the data and explain the model to make the results more understandable and interpretable. The results and discussions are helpful and may lead to new research to optimize the use of ML in various real-world applications and to contain the disease.