The effects of stay-at-home orders and their associated factors in elderly Koreans and Korean-Americans during COVID-19 (preprint)

Objective: The coronavirus pandemic has caused several people to feel isolated. Therefore, we conducted a cross-sectional study on 23 Korean and Korean-Americans to determine their general characteristics, loneliness, depression, and cognitive function. Methods: We investigated the possible relationships between these variables and the duration of staying at home due to coronavirus disease 2019 (COVID-19). Mann–Whitney U and Fisher’s exact tests were used for continuous and categorical variables, respectively. Stepwise multivariate regression analysis was performed to investigate the relationships between the variables. Results: Depression scores were higher in the Korean participants, and no difference was found in loneliness or cognitive function. The multivariate regression analysis results indicated that the duration of staying at home due to COVID-19 negatively associated with depression scores. Conclusions: Depression in the elderly Korean population should be assessed and managed using efficient healthcare approaches. Studies with large cohorts are needed to investigate whether a sense of security affects depression in older adults who stay at home to avoid COVID-19.

Sensing the Pulse of the Pandemic: Geovisualizing the Demographic Disparities of Public Sentiment toward COVID-19 through Social Media (preprint)

Social media offers a unique lens to observe large-scale, spatial-temporal patterns of users reactions toward critical events. However, social media use varies across demographics, with younger users being more prevalent compared to older populations. This difference introduces biases in data representativeness, and analysis based on social media without proper adjustment will lead to overlooking the voices of digitally marginalized communities and inaccurate estimations. This study explores solutions to pinpoint and alleviate the demographic biases in social media analysis through a case study estimating the public sentiment about COVID-19 using Twitter data. We analyzed the pandemic-related Twitter data in the U.S. during 2020-2021 to (1) elucidate the uneven social media usage among demographic groups and the disparities of their sentiments toward COVID-19, (2) construct an adjusted public sentiment measurement based on social media, the Sentiment Adjusted by Demographics (SAD) index, to evaluate the spatiotemporal varying public sentiment toward COVID-19. The results show higher proportions of female and adolescent Twitter users expressing negative emotions to COVID-19. The SAD index unveils that the public sentiment toward COVID-19 was most negative in January and February 2020 and most positive in April 2020. Vermont and Wyoming were the most positive and negative states toward COVID-19.

A Comprehensive Health Effects Assessment of the Use of Sanitizers and Disinfectants during COVID-19 Pandemic: A Global Survey (preprint)

COVID-19 has affected all aspects of human life so far. From the outset of the pandemic, preventing the spread of COVID-19 through the observance of health protocols, especially the use of sanitizers and disinfectants was given more attention. Despite the effectiveness of disinfection chemicals in controlling and preventing COVID-19, there are critical concerns about their adverse effects on human health. This study aims to assess the health effects of sanitizers and disinfectants on a global scale. A total of 91056 participants from 154 countries participated in this cross-sectional study through an electronic questionnaire. Results implied that detergents (67%), alcohol-based materials (56%), and chlorinated compounds (32%) were the most commonly used types of sanitizers and disinfectants. Most frequently reported health issues include skin complications 48.8% and respiratory complications 29.8%. The Chi-square test showed a significant association between chlorinated compounds with all possible health complications under investigation (p-value < 0.001). Examination of risk factors based on multivariate regression analysis showed that alcohols-based materials were associated with skin complications (OR, 1.98; 95%CI, 1.87–2.09), per-chlorine was associated with eye complications (OR, 1.83; 95%CI, 1.74–1.93), and highly likely with itching and throat irritation (OR, 2.00; 95%CI, 1.90–2.11). Furthermore, formaldehyde was associated with a higher prevalence of neurological complications (OR, 2.17; 95%CI, 1.92–2.44). The findings of the current study suggest that health authorities need to implement more awareness programs about the side effects of using sanitizers and disinfectants during viral epidemics.

Tracking community infection dynamics of COVID-19 by monitoring SARS-CoV-2 RNA in wastewater, counting positive reactions by qPCR (preprint)

Wastewater-based epidemiology has proved useful for monitoring the COVID-19 infection dynamics in communities. However, in some countries, low concentrations of SARS-CoV-2 RNA in wastewater make this difficult. Getting meaningful information from wastewater-based epidemiology in regions of low prevalence remains a key challenge. Here we used real-time reverse-transcription PCR (RT-qPCR) to monitor SARS-CoV-2 RNA in wastewater from October 2020 to February 2021 during the third wave of the COVID-19 outbreak in Japan. Viral RNA was below the limit of quantification in all samples. However, by counting the positive reactions in repeated qPCR of each sample, we found that the ratio of positive reactions to all tests in wastewater was significantly correlated with the number of clinically confirmed cases by the date of symptom onset during periods of both increasing and decreasing infection. Time-step analysis indicated that COVID-19 patients excreted large amounts of virus in their feces 2 days either side of symptom onset, which wastewater surveillance could detect. The positive count method is thus useful for tracing COVID-19 dynamics in regions of low prevalence. Highlights Positive ratio by repeated qPCR of low target-molecule numbers correlated with number expected from Poisson distribution. Positive ratio by repeated RT-qPCR of SARS-CoV-2 RNA in wastewater tracked the infection dynamics of COVID-19 in a region of low prevalence. Positive ratios correlated with number of new cases by date of symptom onset. COVID-19 patients might excrete more virus in their feces in the period from 2 days before to 2 days after symptom onset.

Measuring Disparities in Cost and Spending across Connecticut School Districts (preprint)

Despite multiple court cases and repeated efforts at reform, there are still significant concerns about the equity and the adequacy in Connecticut’s public K–12 education funding. One vital component of any attempt to reform education finance is a methodologically rigorous evaluation of what it would cost school districts across the state to achieve target performance standards given their student characteristics. This report addresses that need, evaluating the equity and the adequacy of school spending in Connecticut based on education costs. Different from actual school expenditure, a district’s education cost is an estimation based on its cost factors that are outside the direct control of local officials at any given point in time; efficiency levels are held constant across school districts in the estimation. This report finds large disparities in education costs due to differences among school districts in cost factors. It also finds that, despite existing state aid programs, disparities in cost-adjusted spending across the state remain large. Spending in some districts is well below the levels needed to achieve common performance goals. Among the specific findings of this report is that in the last year for which data were analyzed, the average costs of school districts with the lowest socioeconomic status and highest level of student need were 62 percent greater than those of districts with the highest socioeconomic status and the lowest level of student need. When this report holds every district’s efficiency at the statewide average level, it finds that more than half of Connecticut’s public school students attended districts where spending was insufficient to meet the “predicted costs” to achieve the statewide average student test performance level. A direct, negative consequence of spending inadequacy is student underperformance relative to the common student performance target. This report recommends that the state consider adopting the cost measure as the basis of a new, scientifically grounded, equitable, and adequate formula that allocates more state aid to districts with higher costs. It also suggests that many districts need to increase their spending to meet their predicted costs and close the gap between student performance and the common goal. The exact amount of the additional spending needed partly depends on the state’s choices for the student performance target and the common level of district efficiency. For example, this report estimates that in the last year analyzed, with district efficiency held at the statewide average level, an additional $940 million, or an increase of 12.3 percent from statewide public K–12 school spending, would have been needed to fully fund the predicted costs required to achieve the statewide average student test performance level in every district. While the state and local governments now face great fiscal difficulties induced by the COVID-19 pandemic, they should remain committed to the investment in public education, because it will affect Connecticut’s economic growth in the long run.

Knowledge, Preventive Practices, and Depression Among Chinese University Students in Korea and China During the COVID-19 Pandemic: An Online Cross-sectional Study (preprint)

Background: To investigate the knowledge, preventive practices, and depression of Chinese university students living in South Korea (International Group) and Mainland China (Mainland Group) regarding the Coronavirus Disease 2019 (COVID-19) outbreak and explore the determinants of depression among these students. Methods: An online cross-sectional questionnaire survey was conducted both in China and South Korea from March 23 to April 12, 2020. The online questionnaire included questions on knowledge and preventive practices related to COVID-19, and PHQ-9 which was used to diagnose depression in target groups. A total of 420 Chinese university students were finally included in the study (171 students from South Korea and 249 students from mainland China). Results: Majority of these students had a good level of knowledge of COVID-19. The International Group performed better than the Mainland Group regarding preventive practices; however, the percentage of students with moderate-to-severe depression was higher among the International Group. More severe depression was related to high levels of concern about family members, about getting COVID-19, and suspecting themselves of having come into contact with patients. Meanwhile, taking preventive behaviors more comprehensively decreased the depression state of both the groups. Conclusions: During the COVID-19 pandemic, the depression status of students of the International Group was significantly more severe than that of the Mainland Group (χ2 = 5.50, p < 0.05). The level of depression among students in both the groups was affected by their concern regarding their family members, getting COVID-19, and whether they suspected themselves of having come into contact with patients. Therefore, psychological counseling and education programs are necessary in order to support and improve the mental health of International Group students.

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model (preprint)

Background The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that applied the traditional epidemic models or machine learning models were subject to underfitting or overfitting problems.Methods We propose a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. We used the model to predict the long-term reported cumulative numbers of COVID-19 cases in China from 27 January, 2020.Results We evaluated our model on officially reported confirmed cases from three different regions in China, and the results proved the effectiveness of our model in terms of simulating and predicting the trend of COVID-19 outbreak. In China-Excluding-Hubei area within 7 days after the first public report, our model successfully and accurately predicted the 40 days long trend and the exact date of turning point. The predicted cumulative number (12,506) by 10, March 2020 was only 3·8% different with the actual number (13,005). The parameters obtained by our model proved the effectiveness of prevention and intervention strategies on epidemic control in China.Conclusions The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of COVID-19 outbreak. The learned parameters suggested the effectiveness of intervention measures taken in China.

Forecasting the long-term trend of COVID-19 epidemic using a dynamic model (preprint)

The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. This protocol introduced a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. The model could be applied to forecast the long-term trend of the current COVID-19 epidemic. The parameters obtained by the model could help assess the effectiveness of prevention and intervention strategies on epidemic control in different countries.

Clinical characteristics of 106 patients with neurological diseases and co-morbid coronavirus disease 2019: a retrospective study (preprint)

Objectives:To describe the clinical characteristics of patients with coronavirus disease 2019 (COVID-19) with co-morbid neurological symptoms. Design:Retrospective case series. Setting:Huoshenshan Hospital in Wuhan, China. Participants:From 4 February to 14 April 2020, 106 patients with neurological diseases were enrolled from all patients in the hospital with confirmed COVID-19 and divided into a severe group and a nonsevere group according to their COVID-19 diagnosis. Main outcome measures:Clinical characteristics, laboratory results, imaging findings, and treatment methods were all retrieved through an electronic medical records system and recorded in spreadsheets. Results:The mean (standard deviation, SD) age of patients was 72.7 (11.8) years, and 64 patients were male (60.4%). Among patients with co-morbid neurological diseases, 81 had a previous cerebral infarction (76.4%), 20 had dementia (18.9%), 10 had acute cerebral infarction (9.4%), 5 had sequelae of cerebral haemorrhage (4.7%), 4 had intracranial mass lesions (3.8%), 3 had epilepsy (2.8%), 2 had Parkinsons disease (1.9%), and 1 had myelopathy (0.9%). Fever (n = 62, 58.5%) was the most common symptom. The most common neurological symptoms were myalgia (n = 26, 24.5%), followed by extremity paralysis (n = 20, 18.9%), impaired consciousness (n = 17, 16%), and positive focal neurological signs (n = 42, 39.6%). Eight patients (7.5%) died. There were more patients with altered mental status in the severe group than in the non-severe group (6 [10.2%] vs. 0, P = 0.033). The inflammatory response in the severe group was more significant than that in the non-severe group. There were more patients taking anticoagulant drugs (25 [42.4%] vs. 4 [8.5%], P < 0.001) and sedative drugs (22 [37.3%] vs. 9 [19.1%], P = 0.041) in the severe group than in the non-severe group. Amid all 93 patients with cerebrovascular diseases, only 32 (34.4%) were taking aspirin, 13 (14%) taking clopidogrel, and 33 (35.5%) taking statins. Conclusions:Patients with COVID-19 with co-morbid neurological diseases had an advanced age, a high rate of severe illness, and a high mortality rate. Among the neurological symptoms, altered mental status was more common in patients with severe COVID-19 with co-morbid neurological diseases.

Predicting the Angiotensin Converting Enzyme 2 (ACE2) Utilizing Capability as the Receptor of SARS-CoV-2 (preprint)

SARS-CoV-2, the newly identified human coronavirus causing severe pneumonia epidemic, was probably originated from Chinese horseshoe bats. However, direct transmission of the virus from bats to humans is unlikely due to lack of direct contact, implying the existence of unknown intermediate hosts. Angiotensin converting enzyme 2 (ACE2) is the receptor of SARS-CoV-2, but only ACE2s of certain species can be utilized by SARS-CoV-2. Here, we evaluated and ranked the receptor-utilizing capability of ACE2s from various species by phylogenetic clustering and sequence alignment with the currently known ACE2s utilized by SARS-CoV-2, predicting potential intermediate hosts of SARS-CoV-2.

The Impact of the COVID-19 Outbreak on the Medical Treatment of Chinese Children with Chronic Kidney Disease (CKD)：A Multicenter Cross-section Study in the Context of a Public Health Emergency of International Concern (preprint)

Objective: To investigate the impact of the COVID-19 outbreak on the medical advice seeking of Chinese children with chronic kidney disease (CKD). Materials and Methods: An anonymous online questionnaire survey was conducted in 17 pediatric nephropathy diagnosis and treatment centers in China. The questions collected basic information on the patients and their parents and data on changes in the approach to medical treatment and their needs in the context of the outbreak etc. This is a Multicenter Cross-section Study. Results: A total of 735 valid questionnaires were collected. 555 patients (75.5%) and their parents said that the outbreak had a significant influence on their medical treatment: 264 patients (47.6%) said that it would be delayed by 2 to 4 weeks and 199 patients (35.9%) by 4 to 8 weeks. 510 patients (84.16%) hoped to get in touch with specialists through online consultation, and 528 patients (84.5%) hoped that online consultation could be implemented and that medication could be delivered to them.. A total of 458 patients (62.3%) said that their greatest concern was that the CKD would be aggravated or that they would experience a relapse; only 203 patients were infected by 2019-nCoV. A total of 313 patients (42.5%) experienced anxiety and thus required the intervention of psychologists. Conclusion: The COVID-19 outbreak has affected the medical treatment of children with CKD. Online consultation, medication delivery and psychological counselling are the greatest needs reported by patients and their families and could especially provide solutions for the management of low income children with CKD in remote rural areas in the context of the COVID-19 epidemic.