A Data-Driven Context-Aware Health Inference System for Children during School Closures

Many countries have implemented school closures due to the outbreak of the COVID-19 pandemic, which has inevitably affected children's physical and mental health. It is vital for parents to pay special attention to their children's health status during school closures. However, it is difficult for parents to recognize the changes in their children's health, especially without visible symptoms, such as psychosocial functioning in mental health. Moreover, healthcare resources and understanding of the health and societal impact of COVID-19 are quite limited during the pandemic. Against this background, we collected real-world datasets from 1,172 children in Hong Kong during four time periods under different pandemic and school closure conditions from September 2019 to January 2022. Based on these data, we first perform exploratory data analysis to explore the impact of school closures on six health indicators, including physical activity intensity, physical functioning, self-rated health, psychosocial functioning, resilience, and connectedness. We further study the correlation between children's contextual characteristics (i.e., demographics, socioeconomic status, electronic device usage patterns, financial satisfaction, academic performance, sleep pattern, exercise habits, and dietary patterns) and the six health indicators. Subsequently, a health inference system is designed and developed to infer children's health status based on their contextual features to derive the risk factors of the six health indicators. The evaluation and case studies on real-world datasets show that this health inference system can help parents and authorities better understand key factors correlated with children's health status during school closures. © 2023 ACM.

A Risk Factor Analysis of SARS-CoV-2 Infection in Animals in COVID-19-Affected Households.

A higher prevalence of SARS-CoV-2 infections in animals that have close contact with SARS-CoV-2-positive humans ("COVID-19 households") has been demonstrated in several countries. This prospective study aimed to determine the SARS-CoV-2 prevalence in animals from Swiss COVID-19 households and to assess the potential risk factors for infection. The study included 226 companion animals (172 cats, 76.1%; 49 dogs, 21.7%; and 5 other animals, 2.2%) from 122 COVID-19 households with 336 human household members (including 230 SARS-CoV-2-positive people). The animals were tested for viral RNA using an RT-qPCR and/or serologically for antibodies and neutralizing activity. Additionally, surface samples from animal fur and beds underwent an RT-qPCR. A questionnaire about hygiene, animal hygiene, and contact intensity was completed by the household members. A total of 49 of the 226 animals (21.7%) from 31 of the 122 households (25.4%) tested positive/questionably positive for SARS-CoV-2, including 37 of the 172 cats (21.5%) and 12 of the 49 dogs (24.5%). The surface samples tested positive significantly more often in households with SARS-CoV-2-positive animals than in households with SARS-CoV-2-negative animals (p = 0.011). Significantly more animals tested positive in the multivariable analysis for households with minors. For cats, a shorter length of outdoor access and a higher frequency of removing droppings from litterboxes were factors that were significantly associated with higher infection rates. The study emphasizes that the behavior of owners and the living conditions of animals can influence the likelihood of a SARS-CoV-2 infection in companion animals. Therefore, it is crucial to monitor the infection transmission and dynamics in animals, as well as to identify the possible risk factors for animals in infected households.

Computational network analysis of host genetic risk variants of severe COVID-19.

BACKGROUND: Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. RESULTS: We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. CONCLUSIONS: This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

Understanding comorbidities and health disparities related to COVID-19: a comprehensive study of 776 936 cases and 1 362 545 controls in the state of Indiana, USA.

Objective: To characterize COVID-19 patients in Indiana, United States, and to evaluate their demographics and comorbidities as risk factors to COVID-19 severity. Materials and Methods: EHR data of 776 936 COVID-19 cases and 1 362 545 controls were collected from the COVID-19 Research Data Commons (CoRDaCo) in Indiana. Data regarding county population and per capita income were obtained from the US Census Bureau. Statistical analysis was conducted to determine the association of demographic and clinical variables with COVID-19 severity. Predictive analysis was conducted to evaluate the predictive power of CoRDaCo EHR data in determining COVID-19 severity. Results: Chronic obstructive pulmonary disease, cardiovascular disease, and type 2 diabetes were found in 3.49%, 2.59%, and 4.76% of the COVID-19 patients, respectively. Such COVID-19 patients have significantly higher ICU admission rates of 10.23%, 14.33%, and 11.11%, respectively, compared to the entire COVID-19 patient population (1.94%). Furthermore, patients with these comorbidities have significantly higher mortality rates compared to the entire COVID-19 patient population. Health disparity analysis suggests potential health disparities among counties in Indiana. Predictive analysis achieved F1-scores of 0.8011 and 0.7072 for classifying COVID-19 cases versus controls and ICU versus non-ICU cases, respectively. Discussion: Black population in Indiana was more adversely affected by COVID-19 than the White population. This is consistent to findings from existing studies. Our findings also indicate other health disparities in terms of demographic and economic factors. Conclusion: This study characterizes the relationship between comorbidities and COVID-19 outcomes with respect to ICU admission across a large COVID-19 patient population in Indiana.

D-Dimer Elevation Matters to Predict COVID-19 Severity: A Machine Learning Approach

Although previous studies using limited data have documented an association of D-dimer levels with COVID-19 severity, the role of D-dimer in the progression of COVID-19 remains unclear and requires further investigation using data from larger cohorts. We used traditional statistical modeling and machine learning methods to examine critical factors influencing the D-dimer elevation and to characterize associated risk factors of D-dimer elevation over the course of inpatient admission. We identified 20 important features to predict D-dimer levels, some of which could be used to predict and prevent the D-dimer elevation. Laboratory monitoring of D-dimer level and its risk factors at early stage can mitigate severe or death cases in COVID-19. © 2022 IEEE.

US County-level Risk Factors Associated with COVID-19 Exacerbation During the Vaccination Era

The COVID-19 pandemic has caused unprecedented challenges to public health and disruption to everyday life. The news in 2020 was dominated by the worldwide spread of COVID-19, overwhelming healthcare providers and drastically changing people's lives. In 2021, the release of vaccines from multiple pharmaceutical companies changed the focus to ending the pandemic through mass inoculation. Nevertheless, the vaccine acceptance rate differs significantly across US counties, ranging from 99% to 0.1%. Our study investigates the principal risk factors in predicting COVID-19 infection and mortality rates at the county level during the early vaccination era. We are particularly interested in the role of vaccination in curbing the exacerbation of COVID-19. To this end, we first compare the efficacy of six established machine learning algorithms to predict county-level infection and mortality rates. Next, we perform risk factor analysis by identifying common principal predictors revealed by the models. Our experimental results suggest that vaccination plays an essential role in limiting COVID-19 infection and mortality. Furthermore, socioeconomic factors (e.g., severe housing problems and median household income) are more predictive of county-level mortality rate than intuitive features such as availability of healthcare resources (e.g., total numbers of hospitals/ICU beds/MDs). Our findings could provide additional insights to assist in COVID-19 resource allocation and priority setting. © 2022 IEEE.

A prospective study of risk factors associated with seroprevalence of SARS-CoV-2 antibodies in healthcare workers at a large UK teaching hospital.

OBJECTIVES: To describe the risk factors for SARS-CoV-2 infection in UK healthcare workers (HCWs). METHODS: We conducted a prospective sero-epidemiological study of HCWs at a major UK teaching hospital using a SARS-CoV-2 immunoassay. Risk factors for seropositivity were analysed using multivariate logistic regression. RESULTS: 410/5,698 (7·2%) staff tested positive for SARS-CoV-2 antibodies. Seroprevalence was higher in those working in designated COVID-19 areas compared with other areas (9·47% versus 6·16%) Healthcare assistants (aOR 2·06 [95%CI 1·14-3·71]; p=0·016) and domestic and portering staff (aOR 3·45 [95% CI 1·07-11·42]; p=0·039) had significantly higher seroprevalence than other staff groups after adjusting for age, sex, ethnicity and COVID-19 working location. Staff working in acute medicine and medical sub-specialities were also at higher risk (aOR 2·07 [95% CI 1·31-3·25]; p<0·002). Staff from Black, Asian and minority ethnic (BAME) backgrounds had an aOR of 1·65 (95% CI 1·32 - 2·07; p<0·001) compared to white staff; this increased risk was independent of COVID-19 area working. The only symptoms significantly associated with seropositivity in a multivariable model were loss of sense of taste or smell, fever, and myalgia; 31% of staff testing positive reported no prior symptoms. CONCLUSIONS: Risk of SARS-CoV-2 infection amongst HCWs is highly heterogeneous and influenced by COVID-19 working location, role, age and ethnicity. Increased risk amongst BAME staff cannot be accounted for solely by occupational factors.