Viral and host factors associated with SARS-CoV-2 disease severity in Georgia, USA (preprint)

While SARS-CoV-2 vaccines have shown strong efficacy, their suboptimal uptake combined with the continued emergence of new viral variants raises concerns about the ongoing and future public health impact of COVID-19. We investigated viral and host factors, including vaccination status, that were associated with SARS-CoV-2 disease severity in a setting with low vaccination rates. We analyzed clinical and demographic data from 1,957 individuals in the state of Georgia, USA, coupled with viral genome sequencing from 1,185 samples. We found no difference in disease severity between individuals infected with Delta and Omicron variants among the participants in this study, after controlling for other factors, and we found no specific mutations associated with disease severity. Compared to those who were unvaccinated, vaccinated individuals experienced less severe SARS-CoV-2 disease, and the effect was similar for both variants. Vaccination within 270 days before infection was associated with decreased odds of moderate and severe outcomes, with the strongest association observed at 91-270 days post-vaccination. Older age and underlying health conditions, especially immunosuppression and renal disease, were associated with increased disease severity. Overall, this study provides insights into the impact of vaccination status, variants/mutations, and clinical factors on disease severity in SARS-CoV-2 infection when vaccination rates are low. Understanding these associations will help refine and reinforce messaging around the crucial importance of vaccination in mitigating the severity of SARS-CoV-2 disease.

Hospital and laboratory outcomes of patients with COVID-19 who received vitamin D supplementation: a systematic review and meta-analysis of randomized controlled trials.

Coronavirus disease 2019 (COVID-19) has a wide-ranging spectrum of clinical symptoms, from asymptomatic/mild to severe. Recent research indicates that, among several factors, a low vitamin D level is a modifiable risk factor for COVID-19 patients. This study aims to evaluate the effect of vitamin D on hospital and laboratory outcomes of patients with COVID-19.Five databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) and clinicaltrials.gov were searched until July 2022, using relevant keywords/Mesh terms. Only randomized clinical trials (RCTs) that addressed the topic were included. The Cochrane tool was used to assess the studies' risk of bias, and the data were analyzed using the review manager (RevMan 5.4).We included nine RCTs with 1586 confirmed COVID-19 patients. Vitamin D group showed a significant reduction of intensive care unit (ICU) admission (risk ratio = 0.59, 95% confidence interval (CI) [0.41, 0.84], P = 0.003), and higher change in vitamin D level (standardized mean difference = 2.27, 95% CI [2.08, 2.47], P < 0.00001) compared to the control group. Other studied hospital and laboratory outcomes showed non-significant difference between vitamin D and the control group (P ≥ 0.05).In conclusion, vitamin D reduced the risk of ICU admission and showed superiority in changing vitamin D level compared to the control group. However, other outcomes showed no difference between the two groups. More RCTs are needed to confirm these results.

Effect of post-COVID-19 condition on sleep: A case report.

Post-COVID-19 condition affects patients on various aspects. This 41-year-old female presented to the outpatient clinic complaining of severe insomnia characterized by inconsistent 2 h of sleep per day despite taking sleep aid pills after being infected with COVID-19 and persisting for 6 months after recovery.

Effect of COVID-19 vaccine on menstrual experience among females in six Arab countries: A cross sectional study.

BACKGROUND: There have been varying reports on the potential occurrence and severity of changes to menstruation including the median cycle length, days of bleeding, bleeding heaviness, and menstrual pain, following receipt of COVID-19 vaccinations. We aimed to assess potential postvaccination menstrual changes in women residing in the Middle East. METHODS: We implemented a cross-sectional online survey-based study. Data about the participants' demographic characteristics, menstruation experience, and vaccination status were collected and analyzed among six Arab countries. RESULTS: Among 4942 menstruating females included in this study, females who had received one or more doses of COVID-19 vaccination reported a higher frequency of back pain, nausea, tiredness, pelvic pain with periods, unprescribed analgesics use, and passage of loose stools. They also reported higher scores describing average and worst menstrual pain. Fully vaccinated females reported heavier flow and more days of bleeding. CONCLUSION: Our findings indicate that COVID-19 vaccine may have an effect on menstruation in terms of menstrual pain and bleeding heaviness. The evidence needs to be further investigated in longitudinal studies.

Comparing COVID-19 risk factors in Brazil using machine learning: the importance of socioeconomic, demographic and structural factors.

The COVID-19 pandemic continues to have a devastating impact on Brazil. Brazil's social, health and economic crises are aggravated by strong societal inequities and persisting political disarray. This complex scenario motivates careful study of the clinical, socioeconomic, demographic and structural factors contributing to increased risk of mortality from SARS-CoV-2 in Brazil specifically. We consider the Brazilian SIVEP-Gripe catalog, a very rich respiratory infection dataset which allows us to estimate the importance of several non-laboratorial and socio-geographic factors on COVID-19 mortality. We analyze the catalog using machine learning algorithms to account for likely complex interdependence between metrics. The XGBoost algorithm achieved excellent performance, producing an AUC-ROC of 0.813 (95% CI 0.810-0.817), and outperforming logistic regression. Using our model we found that, in Brazil, socioeconomic, geographical and structural factors are more important than individual comorbidities. Particularly important factors were: The state of residence and its development index; the distance to the hospital (especially for rural and less developed areas); the level of education; hospital funding model and strain. Ethnicity is also confirmed to be more important than comorbidities but less than the aforementioned factors. In conclusion, socioeconomic and structural factors are as important as biological factors in determining the outcome of COVID-19. This has important consequences for policy making, especially on vaccination/non-pharmacological preventative measures, hospital management and healthcare network organization.

How artificial intelligence and machine learning can help healthcare systems respond to COVID-19.

The COVID-19 global pandemic is a threat not only to the health of millions of individuals, but also to the stability of infrastructure and economies around the world. The disease will inevitably place an overwhelming burden on healthcare systems that cannot be effectively dealt with by existing facilities or responses based on conventional approaches. We believe that a rigorous clinical and societal response can only be mounted by using intelligence derived from a variety of data sources to better utilize scarce healthcare resources, provide personalized patient management plans, inform policy, and expedite clinical trials. In this paper, we introduce five of the most important challenges in responding to COVID-19 and show how each of them can be addressed by recent developments in machine learning (ML) and artificial intelligence (AI). We argue that the integration of these techniques into local, national, and international healthcare systems will save lives, and propose specific methods by which implementation can happen swiftly and efficiently. We offer to extend these resources and knowledge to assist policymakers seeking to implement these techniques.

CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19.

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)-a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic-we conclude the paper with a summary of the lessons learned from this experience.

Retrospective cohort study of admission timing and mortality following COVID-19 infection in England.

OBJECTIVES: We investigated whether the timing of hospital admission is associated with the risk of mortality for patients with COVID-19 in England, and the factors associated with a longer interval between symptom onset and hospital admission. DESIGN: Retrospective observational cohort study of data collected by the COVID-19 Hospitalisation in England Surveillance System (CHESS). Data were analysed using multivariate regression analysis. SETTING: Acute hospital trusts in England that submit data to CHESS routinely. PARTICIPANTS: Of 14 150 patients included in CHESS until 13 May 2020, 401 lacked a confirmed diagnosis of COVID-19 and 7666 lacked a recorded date of symptom onset. This left 6083 individuals, of whom 15 were excluded because the time between symptom onset and hospital admission exceeded 3 months. The study cohort therefore comprised 6068 unique individuals. MAIN OUTCOME MEASURES: All-cause mortality during the study period. RESULTS: Timing of hospital admission was an independent predictor of mortality following adjustment for age, sex, comorbidities, ethnicity and obesity. Each additional day between symptom onset and hospital admission was associated with a 1% increase in mortality risk (HR 1.01; p<0.005). Healthcare workers were most likely to have an increased interval between symptom onset and hospital admission, as were people from Black, Asian and minority ethnic (BAME) backgrounds, and patients with obesity. CONCLUSION: The timing of hospital admission is associated with mortality in patients with COVID-19. Healthcare workers and individuals from a BAME background are at greater risk of later admission, which may contribute to reports of poorer outcomes in these groups. Strategies to identify and admit patients with high-risk and those showing signs of deterioration in a timely way may reduce the consequent mortality from COVID-19, and should be explored.