Food Insecurity and Health-Related Concerns Among Elementary Schoolteachers During the COVID-19 Pandemic.

INTRODUCTION: US school systems underwent major upheaval, including closures, implementation of virtual and/or hybrid learning, and stringent infection mitigation protocols, during the initial phase of the COVID-19 pandemic. We aimed to examine the association between food insecurity and perceived health, perceived stress, and social determinants of health concerns among elementary schoolteachers serving predominantly low-income children during the COVID-19 pandemic. METHODS: Brighter Bites, a nonprofit organization that weekly distributes fresh fruits and vegetables and nutrition education materials to more than 300 schools serving racial and ethnic minority populations with low income, conducts annual surveys of participating teachers to help determine subsequent efforts to support schools and families during the school year. We analyzed self-reported data collected electronically by the Brighter Bites teachers survey in 76 elementary schools during summer 2020. We used generalized linear mixed models to measure the association between food insecurity and health-related concerns. RESULTS: Of 862 teachers who responded to the survey, 685 answered the 2 questions about food insecurity status; of these, 199 (29.1%) reported experiencing food insecurity. Food insecurity was positively associated with poor perceived general health, greater perceived stress, concerns about various social determinants of health, and changes in fruit and vegetable consumption during the COVID-19 pandemic. CONCLUSION: Our study demonstrated the high prevalence of food insecurity and highlights its associated factors among elementary schoolteachers during the COVID-19 pandemic. It calls attention to the high correlation of various concerns among elementary schoolteachers during the COVID-19 pandemic. Further intervention and policy efforts are needed to relieve food insecurity-related concerns and enhance well-being among teachers.

A Cross-Sectional Study Analyzing Predictors of Perceived Stress Among Elementary School Teachers During the COVID-19 Pandemic.

INTRODUCTION: Teaching is a stressful occupation due to high-stake job demands and limited resources, which were exacerbated during the initial phase of the COVID-19 pandemic. Our study assessed the prevalence of perceived stress and explored its predictors among elementary school teachers employed at schools serving predominantly low-income populations in five cities in the United States. METHOD: Our study analyzed the data among selected schools that were collected through the Brighter Bites teacher survey which comprised items measuring sociodemographic characteristics, perceived stress, perceived general health, food insecurity, and concerns regarding social determinants of health needs. The predictors of perceived stress were examined using generalized linear mixed models (GLMMs) with schools as the random variable. FINDINGS: A total of 685 teachers were included in the analysis (84.9% female, 38.1% Hispanic, 57.6% <5 years of teaching experience). Most (85.4%) of the teachers stated they were stressed "sometimes"/"often." Results from adjusted GLMM showed that teachers who were food insecure (adjusted odds ratio [AOR]: 2.33, confidence interval [CI]: [1.63, 3.35]), those who had concerns regarding financial stability (2.68 [1.91, 3.75]), food availability (1.69 [1.15, 2.48]), food affordability (2.27 [1.57, 3.28]), availability/affordability of housing (2.21 [1.33, 3.67]), access to childcare (1.76 [1.06, 2.92]), and access to a clinic/doctor (1.60 [1.10, 2.33]) were at significantly greater odds of reporting perceived stress. CONCLUSION/APPLICATION FOR PRACTICE: Our study demonstrates the heightened impact of COVID-19 on the mental well-being of teachers across a wide range of social needs. Stress management and additional social service programs are suggested to support teachers to mitigate pandemic impact.

COVID-19 Transmission Dynamics Among Close Contacts of Index Patients With COVID-19: A Population-Based Cohort Study in Zhejiang Province, China.

Importance: Much remains unknown about the transmission dynamics of COVID-19. How the severity of the index case and timing of exposure is associated with disease in close contacts of index patients with COVID-19 and clinical presentation in those developing disease is not well elucidated. Objectives: To investigate the association between the timing of exposure and development of disease among close contacts of index patients with COVID-19 and to evaluate whether the severity of the index case is associated with clinical presentation in close contacts who develop COVID-19. Design, Setting, and Participants: This study used a large, population-based cohort of 730 individuals (index patients) who received a diagnosis of COVID-19 in Zhejiang Province, China, from January 8 to July 30, 2020, along with a contact tracing surveillance program. Field workers visited 8852 close contacts of the index patients and evaluated them for COVID-19 through August 2020. A timeline was constructed to characterize different exposure periods between index patients and their contacts. Main Outcomes and Measures: The primary outcome was the attack rate of COVID-19, defined as the total number of new COVID-19 cases diagnosed among contacts of index patients divided by the total number of exposed contacts. A secondary outcome was asymptomatic clinical presentation among infected contacts. Relative risks were calculated to investigate risk factors for COVID-19 among contacts and asymptomatic clinical presentation among infected contacts. Results: Among 8852 close contacts (4679 male contacts [52.9%]; median age, 41 years [interquartile range, 28-54 years]) of 730 index patients (374 male patients [51.2%]; median age, 46 years [interquartile range, 36-56 years]), contacts were at highest risk of COVID-19 if they were exposed between 2 days before and 3 days after the index patient's symptom onset, peaking at day 0 (adjusted relative risk [ARR], 1.3; 95% CI, 1.2-1.5). Compared with being exposed to an asymptomatic index patient, the risk of COVID-19 among contacts was higher when they were exposed to index patients with mild (ARR, 4.0; 95% CI, 1.8-9.1) and moderate (ARR, 4.3; 95% CI, 1.9-9.7) cases of COVID-19. As index case severity increased, infected contacts were less likely to be asymptomatic (exposed to patient with mild COVID-19: ARR, 0.3; 95% CI, 0.1-0.9; exposed to patient with moderate COVID-19: ARR, 0.3; 95% CI, 0.1-0.8). Conclusions and Relevance: This cohort study found that individuals with COVID-19 were most infectious a few days before and after symptom onset. Infected contacts of asymptomatic index patients were less likely to present with COVID-19 symptoms, suggesting that quantity of exposure may be associated with clinical presentation in close contacts.

Machine learning research towards combating COVID-19: Virus detection, spread prevention, and medical assistance.

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.

Wi-COVID: A COVID-19 symptom detection and patient monitoring framework using WiFi.

The current SARS-CoV-2, better know as COVID-19, has emerged as a serious pandemic with life-threatening clinical manifestations and a high mortality rate. One of the major complications of this disease is the rapid and dangerous pulmonary deterioration that can lead to critical pneumonia conditions, resulting in death. The current healthcare system around the world faces the potential problem of lacking resources to assist a large number of patients at the same time; then, the non-critical patients are mostly referred to perform self-isolation/quarantine at home. This pandemic has placed new demands on the health systems world, asking for novel, rapid and secure ways to monitor patients in order to detect and quickly report patient's symptoms to the healthcare provider, even if they are not in the hospital. While tremendous efforts have been done to develop technologies to detect the virus, create the vaccine, and stop the spread of the disease, it is also important to develop IoT technologies that can help track and monitor diagnosed COVID-19 patients from their homes. In this paper, we explore the possibility of monitoring respiration rates (RR) of COVID-19 patients using a widely-available technology at home - WiFi. Using the at-home WiFi signals, we propose Wi-COVID, a non-invasive and non-wearable technology to monitor the patient and track RR for the healthcare provider. We first introduce the currently available applications that can be done using WiFi signals. Then, we propose the framework scheme for an end-to-end non-invasive monitoring platform of the COVID-19 patients using WiFi. Finally, we present some preliminary results of the proposed framework. We envision the proposed platform as a life-changing technology that leverages WiFi technology as a non-wearable and non-invasive way to monitor COVID-19 patients at home.