SARS-CoV-2 infection elucidates unique features of pregnancy-specific immunity (preprint)

Pregnancy is a risk factor for increased severity of SARS-CoV-2 and other respiratory infections. The mechanisms underlying this risk have not been well-established, partly due to a limited understanding of how pregnancy shapes immune responses. To gain insight into the role of pregnancy in modulating immune responses at steady state and upon perturbation, we collected peripheral blood mononuclear cells (PBMC), plasma, and stool from 226 women, including 152 pregnant individuals (n = 96 with SARS-CoV-2 infection and n = 56 healthy controls) and 74 non-pregnant women (n = 55 with SARS-CoV-2 and n = 19 healthy controls). We found that SARS-CoV-2 infection was associated with altered T cell responses in pregnant compared to non-pregnant women. Differences included a lower percentage of memory T cells, a distinct clonal expansion of CD4-expressing CD8+ T cells, and the enhanced expression of T cell exhaustion markers, such as programmed cell death-1 (PD-1) and T cell immunoglobulin and mucin domain-3 (Tim-3), in pregnant women. We identified additional evidence of immune dysfunction in severely and critically ill pregnant women, including a lack of expected elevation in regulatory T cell (Treg) levels, diminished interferon responses, and profound suppression of monocyte function. Consistent with earlier data, we found maternal obesity was also associated with altered immune responses to SARS-CoV-2 infection, including enhanced production of inflammatory cytokines by T cells. Certain gut bacterial species were altered in pregnancy and upon SARS-CoV-2 infection in pregnant individuals compared to non-pregnant women. Shifts in cytokine and chemokine levels were also identified in the sera of pregnant individuals, most notably a robust increase of interleukin-27 (IL-27), a cytokine known to drive T cell exhaustion, in the pregnant uninfected control group compared to all non-pregnant groups. IL-27 levels were also significantly higher in uninfected pregnant controls compared to pregnant SARS-CoV-2-infected individuals. Using two different preclinical mouse models of inflammation-induced fetal demise and respiratory influenza viral infection, we found that enhanced IL-27 protects developing fetuses from maternal inflammation but renders adult female mice vulnerable to viral infection. These combined findings from human and murine studies reveal nuanced pregnancy-associated immune responses, suggesting mechanisms underlying the increased susceptibility of pregnant individuals to viral respiratory infections.

Knowledge barriers in a national symptomatic-COVID-19 testing programme

Symptomatic testing programmes are crucial to the COVID-19 pandemic response. We sought to examine United Kingdom (UK) testing rates amongst individuals with test-qualifying symptoms, and factors associated with not testing. We analysed a cohort of untested symptomatic app users (N = 1,237), nested in the Zoe COVID Symptom Study (Zoe, N = 4,394,948);and symptomatic respondents who wanted, but did not have a test (N = 1,956), drawn from a University of Maryland survey administered to Facebook users (The Global COVID-19 Trends and Impact Survey [CTIS], N = 775,746). The proportion tested among individuals with incident test-qualifying symptoms rose from ~20% to ~75% from April to December 2020 in Zoe. Testing was lower with one vs more symptoms (72.9% vs 84.6% p<0.001), or short vs long symptom duration (69.9% vs 85.4% p<0.001). 40.4% of survey respondents did not identify all three test-qualifying symptoms. Symptom identification decreased for every decade older (OR = 0.908 [95% CI 0.883–0.933]). Amongst symptomatic UMD-CTIS respondents who wanted but did not have a test, not knowing where to go was the most cited factor (32.4%);this increased for each decade older (OR = 1.207 [1.129–1.292]) and for every 4-years fewer in education (OR = 0.685 [0.599–0.783]). Despite current UK messaging on COVID-19 testing, there is a knowledge gap about when and where to test, and this may be contributing to the ~25% testing gap. Risk factors, including older age and less education, highlight potential opportunities to tailor public health messages. The testing gap may be ever larger in countries that do not have extensive, free testing, as the UK does.

The Presence of Non-pharmaceutical Interventions Correlates with Decreased Rates of Asthma Exacerbations and RSV infections in Arkansas

Rationale: To reduce transmission of SARS-CoV-2, non-pharmaceutical interventions (NPIs), including school closures, hand hygiene, mask mandates, and social distancing, were enforced in Arkansas from 3/2020-2/2021. We hypothesized that the presence of NPIs would correlate with a decrease in asthma exacerbations and viral infections. Methods: Demographic information was collected on subjects with asthma exacerbations or viral infections from 3/2018-5/2022, including age, race, ethnicity, and sex. To evaluate the effects of NPIs, three periods were considered: pre- (03/2018-02/2020), during (03/2020-02/2021), and post- (03/2021-05/2022) NPIs. ANOVA analysis and generalized linear models were performed to determine statistical significance. The stringency of NPIs was evaluated using publicly available data (Oxford Covid-19 Government Response Tracker), which allows for direct comparison of Arkansas NPI status to exacerbation data during the same time periods. Results: 5055 asthma exacerbations (3322 unique subjects) occurred between 3/2018-5/2022. Asthma exacerbations decreased from 3/2020-3/2021 and returned to pre-pandemic numbers by summer 2021 (p<0.0001). Similar downward trends occurred for respiratory syncytial virus (RSV) with out-of-season return in summer 2021 (p<0.0001). Rhinovirus was present throughout NPIs. The mean age of exacerbations decreased by 0.9 years when comparing the during NPIs and after NPIs periods (p = 0.0002). An increase in the proportion of exacerbations was noted for non-black and other/unknown ethnicity subjects during and after NPIs. Conclusions: Fewer asthma exacerbations occurred during the most significant NPI employment period (03/2020-02/2021), and an increase in exacerbations was seen as mitigation strategies were relaxed, which correlated with timing of increasing RSV infections.

Associations between greenness and predicted COVID-19-like illness incidence in the United States and the United Kingdom.

Green spaces may be protective against COVID-19 incidence. They may provide outdoor, ventilated, settings for physical and social activities and therefore decrease transmission risk. We examined the association between neighborhood greenness and COVID-19-like illness incidence using individual-level data. Methods: The study population includes participants enrolled in the COVID Symptom Study smartphone application in the United Kingdom and the United States (March-November 2020). All participants were encouraged to report their current health condition and suspected risk factors for COVID-19. We used a validated symptom-based classifier that predicts COVID-19-like illness. We estimated the Normalized Difference Vegetation Index (NDVI), for each participant's reported neighborhood of residence for each month, using images from Landsat 8 (30 m2). We used time-varying Cox proportional hazards models stratified by age, country, and calendar month at study entry and adjusted for the individual- and neighborhood-level risk factors. Results: We observed 143,340 cases of predicted COVID-19-like illness among 2,794,029 participants. Neighborhood NDVI was associated with a decreased risk of predicted COVID-19-like illness incidence in the fully adjusted model (hazard ratio = 0.965, 95% confidence interval = 0.960, 0.970, per 0.1 NDVI increase). Stratified analyses showed protective associations among U.K. participants but not among U.S. participants. Associations were slightly stronger for White individuals, for individuals living in rural neighborhoods, and for individuals living in high-income neighborhoods compared to individuals living in low-income neighborhoods. Conclusions: Higher levels of greenness may reduce the risk of predicted COVID-19-like illness incidence, but these associations were not observed in all populations.

Anosmia, ageusia, and other COVID-19-like symptoms in association with a positive SARS-CoV-2 test, across six national digital surveillance platforms: an observational study.

BACKGROUND: Multiple voluntary surveillance platforms were developed across the world in response to the COVID-19 pandemic, providing a real-time understanding of population-based COVID-19 epidemiology. During this time, testing criteria broadened and health-care policies matured. We aimed to test whether there were consistent associations of symptoms with SARS-CoV-2 test status across three surveillance platforms in three countries (two platforms per country), during periods of testing and policy changes. METHODS: For this observational study, we used data of observations from three volunteer COVID-19 digital surveillance platforms (Carnegie Mellon University and University of Maryland Facebook COVID-19 Symptom Survey, ZOE COVID Symptom Study app, and the Corona Israel study) targeting communities in three countries (Israel, the UK, and the USA; two platforms per country). The study population included adult respondents (age 18-100 years at baseline) who were not health-care workers. We did logistic regression of self-reported symptoms on self-reported SARS-CoV-2 test status (positive or negative), adjusted for age and sex, in each of the study cohorts. We compared odds ratios (ORs) across platforms and countries, and we did meta-analyses assuming a random effects model. We also evaluated testing policy changes, COVID-19 incidence, and time scales of duration of symptoms and symptom-to-test time. FINDINGS: Between April 1 and July 31, 2020, 514 459 tests from over 10 million respondents were recorded in the six surveillance platform datasets. Anosmia-ageusia was the strongest, most consistent symptom associated with a positive COVID-19 test (robust aggregated rank one, meta-analysed random effects OR 16·96, 95% CI 13·13-21·92). Fever (rank two, 6·45, 4·25-9·81), shortness of breath (rank three, 4·69, 3·14-7·01), and cough (rank four, 4·29, 3·13-5·88) were also highly associated with test positivity. The association of symptoms with test status varied by duration of illness, timing of the test, and broader test criteria, as well as over time, by country, and by platform. INTERPRETATION: The strong association of anosmia-ageusia with self-reported positive SARS-CoV-2 test was consistently observed, supporting its validity as a reliable COVID-19 signal, regardless of the participatory surveillance platform, country, phase of illness, or testing policy. These findings show that associations between COVID-19 symptoms and test positivity ranked similarly in a wide range of scenarios. Anosmia, fever, and respiratory symptoms consistently had the strongest effect estimates and were the most appropriate empirical signals for symptom-based public health surveillance in areas with insufficient testing or benchmarking capacity. Collaborative syndromic surveillance could enhance real-time epidemiological investigations and public health utility globally. FUNDING: National Institutes of Health, National Institute for Health Research, Alzheimer's Society, Wellcome Trust, and Massachusetts Consortium on Pathogen Readiness.

Physical Activity, Sedentary Behavior, and Risk of Coronavirus Disease 2019.

INTRODUCTION: Data on the associations of prepandemic physical activity and sedentary behavior with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) severity, particularly milder illness, have been limited. METHODS: We used data from 43,913 participants within the Nurses' Health Study II and Health Professionals Follow-Up Study who responded to periodic COVID-related surveys from May 2020 through March 2021. History of physical activity from the prepandemic period was assessed as the metabolic equivalents of task (MET)-hours per week of various activities of different intensity and sedentary behavior assessed from reports of time spent sitting from questionnaires completed 2016-2017. Multivariable logistic regression models were fitted to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for risk of SARS-CoV-2 infection and COVID-19 severity, as well as predicted COVID-19 defined using a validated symptom-based algorithm. RESULTS: Higher levels of prepandemic physical activity were associated with a lower risk for SARS-CoV-2 infection. Compared to participants with <3 MET-hours per week, the multivariable-adjusted OR was 0.86 (95% CI: 0.74, 0.99; P trend =.07) for those with ≥27 MET-hours per week. Higher physical activity levels were also associated with lower risk of symptomatic SARS-CoV-2 infection (OR: 0.84; 95% CI: 0.72, 0.99; P trend = .05) and predicted COVID-19 (OR: 0.87; 95% CI: 0.78, 0.97; P trend = .01). Longer time sitting at home watching TV (OR: 0.85; 95% CI: 0.73, 0.97) or for other tasks (OR: 0.78; 95% CI: 0.66, 0.92) was associated with a lower risk of SARS-CoV-2 infection. CONCLUSIONS: Our findings support a protective association between prepandemic physical activity and lower risk and severity of COVID-19.

A COVID-19 Control Policy Evaluation and Prediction Method Based on Clustering

COVID-19 has brought huge losses to the economy all over the world. To solve this problem, we delivered an epidemic situation evaluation and prediction system based on dynamic data clustering, which was established to cluster the epidemic data in different regions and make evaluations and predictions through the Markov chain. Using the method of streaming data to cluster, we set the data at the same cluster as the sampling results from the same distribution to classify the epidemic situation. We used the Markov chain model to estimate the future development of the epidemic situation. According to the characteristics of stream data, the system can avoid the impact of epidemic data not meeting the assumption of independent homodistribution and only assess the epidemic situation based on local areas. © 2022 IEEE.

Fruit-CoV: An efficient vision-based framework for speedy detection and diagnosis of SARS-CoV-2 infections through recorded cough sounds.

COVID-19 is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly virus has spread worldwide, leading to a global pandemic since March 2020. A recent variant of SARS-CoV-2 named Delta is intractably contagious and responsible for more than four million deaths globally. Therefore, developing an efficient self-testing service for SARS-CoV-2 at home is vital. In this study, a two-stage vision-based framework, namely Fruit-CoV, is introduced for detecting SARS-CoV-2 infections through recorded cough sounds. Specifically, audio signals are converted into Log-Mel spectrograms, and the EfficientNet-V2 network is used to extract their visual features in the first stage. In the second stage, 14 convolutional layers extracted from the large-scale Pretrained Audio Neural Networks for audio pattern recognition (PANNs) and the Wavegram-Log-Mel-CNN are employed to aggregate feature representations of the Log-Mel spectrograms and the waveform. Finally, the combined features are used to train a binary classifier. In this study, a dataset provided by the AICovidVN 115M Challenge is employed for evaluation. It includes 7,371 recorded cough sounds collected throughout Vietnam, India, and Switzerland. Experimental results indicate that the proposed model achieves an Area Under the Receiver Operating Characteristic Curve (AUC) score of 92.8% and ranks first on the final leaderboard of the AICovidVN 115M Challenge. Our code is publicly available.

Design of a predictor for COVID-19 misinformation prediction

Due to the increase of social media usage, the online sharing of content has been extremely increased. As a result, the spread of misinformation on social media platforms has also increased. To address this issue, we proposed an approach that predicts the news is fake or real. In our approach, we select the top k ranked features through a filter base algorithm and feed them to the classifier. The main objective of this research is to provide two things. First, to provide an approach, which compares the benchmark performance results of the evolutionary detection approach on the Koirala dataset. The second is to build, publicly available dataset through web scraping for the classification of COVID-19 fake news articles. Our method significantly uplifts the F1-score with 14.88 percent for the same number of features selected 605 for the already existing approach. Also, stated the number of features 5000 on which the approach showed the best results with a margin of F1-score of 20.4 percent, respectively. Similarly, on the self-build dataset, this approach also outshines and achieved 99.66 percent of F1-score, respectively. Our experimental results show that our robust approach by comparing with other classifiers and existing approach, Max-Min Ratio (MMR) along with support vector machine (SVM) outperformed on both of these datasets. Hence, feature selection plays a vital role in the performance of the model rather than deeply tuning and training the classifier.

The mental health burden of racial and ethnic minorities during the COVID-19 pandemic.

Racial/ethnic minorities have been disproportionately impacted by COVID-19. The effects of COVID-19 on the long-term mental health of minorities remains unclear. To evaluate differences in odds of screening positive for depression and anxiety among various racial and ethnic groups during the latter phase of the COVID-19 pandemic, we performed a cross-sectional analysis of 691,473 participants nested within the prospective smartphone-based COVID Symptom Study in the United States (U.S.) and United Kingdom (U.K). from February 23, 2021 to June 9, 2021. In the U.S. (n=57,187), compared to White participants, the multivariable odds ratios (ORs) for screening positive for depression were 1·16 (95% CI: 1·02 to 1·31) for Black, 1·23 (1·11 to 1·36) for Hispanic, and 1·15 (1·02 to 1·30) for Asian participants, and 1·34 (1·13 to 1·59) for participants reporting more than one race/other even after accounting for personal factors such as prior history of a mental health disorder, COVID-19 infection status, and surrounding lockdown stringency. Rates of screening positive for anxiety were comparable. In the U.K. (n=643,286), racial/ethnic minorities had similarly elevated rates of positive screening for depression and anxiety. These disparities were not fully explained by changes in leisure time activities. Racial/ethnic minorities bore a disproportionate mental health burden during the COVID-19 pandemic. These differences will need to be considered as health care systems transition from prioritizing infection control to mitigating long-term consequences.

Knowledge barriers in a national symptomatic-COVID-19 testing programme.

Symptomatic testing programmes are crucial to the COVID-19 pandemic response. We sought to examine United Kingdom (UK) testing rates amongst individuals with test-qualifying symptoms, and factors associated with not testing. We analysed a cohort of untested symptomatic app users (N = 1,237), nested in the Zoe COVID Symptom Study (Zoe, N = 4,394,948); and symptomatic respondents who wanted, but did not have a test (N = 1,956), drawn from a University of Maryland survey administered to Facebook users (The Global COVID-19 Trends and Impact Survey [CTIS], N = 775,746). The proportion tested among individuals with incident test-qualifying symptoms rose from ~20% to ~75% from April to December 2020 in Zoe. Testing was lower with one vs more symptoms (72.9% vs 84.6% p<0.001), or short vs long symptom duration (69.9% vs 85.4% p<0.001). 40.4% of survey respondents did not identify all three test-qualifying symptoms. Symptom identification decreased for every decade older (OR = 0.908 [95% CI 0.883-0.933]). Amongst symptomatic UMD-CTIS respondents who wanted but did not have a test, not knowing where to go was the most cited factor (32.4%); this increased for each decade older (OR = 1.207 [1.129-1.292]) and for every 4-years fewer in education (OR = 0.685 [0.599-0.783]). Despite current UK messaging on COVID-19 testing, there is a knowledge gap about when and where to test, and this may be contributing to the ~25% testing gap. Risk factors, including older age and less education, highlight potential opportunities to tailor public health messages. The testing gap may be ever larger in countries that do not have extensive, free testing, as the UK does.

Teaching Division Online to Students via a Virtual Manipulative Instructional Sequence

The global Covid-19 pandemic forced teachers to support students online in multiple academic subjects, including mathematics, including students with disabilities and those at-risk. And yet, to date, limited research exists examining teaching mathematics to students with disabilities online. The current study explored the use of the virtual-representational- (VRA) instructional sequence paired with the system of least prompts (SLP) to support three elementary students with either a developmental disability or at-risk in mathematics to solve division with remainder problems. All three students learned to solve the targeted division with remainder problems with at least 75% accuracy and 85% independence across virtual, representational, and sessions. Further, all three students maintained their accuracy for up to two weeks following the completion of intervention. The study holds implications for virtual manipulative-based instructional sequences, which to date focused on secondary students, and for teaching mathematics to students with disabilities online. © Division on Autism and Developmental Disabilities.

Associations between predicted vitamin D status, vitamin D intake, and risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) severity.

BACKGROUND: Vitamin D may have a role in immune responses to viral infections. However, data on the association between vitamin D and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and coronavirus disease 2019 (COVID-19) severity have been limited and inconsistent. OBJECTIVE: We examined the associations of predicted vitamin D status and intake with risk of SARS-CoV-2 infection and COVID-19 severity. METHODS: We used data from periodic surveys (May 2020 to March 2021) within the Nurses' Health Study II. Among 39,315 participants, 1768 reported a positive test for SARS-CoV-2 infection. Usual vitamin D intake from foods and supplements were measured using a semiquantitative, pre-pandemic food-frequency questionnaire in 2015. Predicted 25-hydroxyvitamin D [25(OH)D] concentration were calculated based on a previously validated model including dietary and supplementary vitamin D intake, UV-B, and other behavioral predictors of vitamin D status. RESULTS: Higher predicted 25(OH)D concentrations, but not vitamin D intake, were associated with a lower risk of SARS-CoV-2 infection. Comparing participants in the highest quintile of predicted 25(OH)D concentrations with the lowest, the multivariable-adjusted OR was 0.76 (95% CI: 0.58, 0.99; P-trend = 0.04). Participants in the highest quartile of UV-B (OR: 0.76; 95% CI: 0.66, 0.87; P-trend = 0.002) and UV-A (OR: 0.76; 95% CI: 0.66, 0.88; P-trend < 0.001) also had a lower risk of SARS-CoV-2 infection compared with the lowest. High intake of vitamin D from supplements (≥400 IU/d) was associated with a lower risk of hospitalization (OR: 0.51; 95% CI: 0.29, 0.91; P-trend = 0.04). CONCLUSIONS: Our study provides suggestive evidence on the association between higher predicted circulating 25(OH)D concentrations and a lower risk of SARS-CoV-2 infection. Greater intake of vitamin D supplements was associated with a lower risk of hospitalization. Our data also support an association between exposure to UV-B or UV-A, independently of vitamin D and SARS-CoV-2 infection, so results for predicted 25(OH)D need to be interpreted cautiously.

Thermal conductivity and phonon scattering of AlGaN nanofilms by elastic theory and Boltzmann transport equation

Aluminum gallium nitride (AlGaN) plays an essential role in deep ultra-violet light emitting diodes and high electron mobility transistors etc. For example, 2 nm - 5 nm AlGaN nanofilms consist of the quantum wells in ultra-violet light emitting diodes, which have been attracting extensive attention since the rise of COVID 2019. Since most photons and heat are generated in these AlGaN nanofilms, the thermal properties of AlGaN nanofilms are strongly influenced by the heat dissipation of devices. In this paper, utilizing elastic theory and the Boltzmann transport equation, the phonon dispersion relations, density of states, specific heat capacities and thermal conductivities of 2 nm Al (delta) Ga1-delta N nanofilms with various delta are theoretically calculated at different temperatures. The thermal conductivity of nanofilm is significantly smaller than that of its bulk counterpart. In contrast with bulk AlGaN, due to the dominance of boundary scattering and alloy disorder scattering, the thermal conductivity of Al (delta) Ga1-delta N exhibits a similar dependence on Al concentration to bulk Al (delta) Ga1-delta N. Meanwhile, since the screening of Umklapp scattering, the saturation temperature of thermal conductivity is delayed from 50 to 100 K in bulks to about 300 K in nanofilms. The shrinkage of nanofilms' thermal conductivity is also slower than for bulks. We believe that our work will be helpful in controlling the self-heating effect of devices based on AlGaN nanofilms.

Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom.

Worldwide, racial and ethnic minorities have been disproportionately impacted by COVID-19 with increased risk of infection, its related complications, and death. In the initial phase of population-based vaccination in the United States (U.S.) and United Kingdom (U.K.), vaccine hesitancy may result in differences in uptake. We performed a cohort study among U.S. and U.K. participants who volunteered to take part in the smartphone-based COVID Symptom Study (March 2020-February 2021) and used logistic regression to estimate odds ratios of vaccine hesitancy and uptake. In the U.S. (n = 87,388), compared to white participants, vaccine hesitancy was greater for Black and Hispanic participants and those reporting more than one or other race. In the U.K. (n = 1,254,294), racial and ethnic minority participants showed similar levels of vaccine hesitancy to the U.S. However, associations between participant race and ethnicity and levels of vaccine uptake were observed to be different in the U.S. and the U.K. studies. Among U.S. participants, vaccine uptake was significantly lower among Black participants, which persisted among participants that self-reported being vaccine-willing. In contrast, statistically significant racial and ethnic disparities in vaccine uptake were not observed in the U.K sample. In this study of self-reported vaccine hesitancy and uptake, lower levels of vaccine uptake in Black participants in the U.S. during the initial vaccine rollout may be attributable to both hesitancy and disparities in access.

Feasibility of Telemedicine Home Assessments for Identification of Asthma Triggers in a Rural State

Rationale: Identification and control of environmental triggers is one of the cornerstones of asthma management. Access to homes, underscored during the COVID-19 pandemic, frequently limits mitigation efforts. We sought to determine the feasibility of telemedicine for identification of home asthma triggers. Methods: Patients age 5-18 years with persistent asthma, recent exacerbation, home Internet access, and stable residency were eligible. Families were randomized to standard of care (SOC) or telemedicine (TELE);virtual assessments were performed at 2, 4, and 6 months. Data included demographics and standardized assessment of the home environment. Results: Eighteen participants were enrolled (9 TELE, 9 SOC). There were no significant differences in baseline demographics between groups. Sensitization to pollens and dust mites was most common in both groups. Housing conditions were similar. In both groups, 89% lived in detached homes with forced air (standard filter) being the most common heating source (33%). Visible signs of pests/mold were seen in <12%. 89% use scented air fresheners/candles/potpourri. The SOC group had more pets (67% versus 33%) and smoking in the home (22% versus 11%). There was less evidence of mold in the TELE group (0 versus 22%). All SOC participants use bleach/ammonia cleaning products (78% in TELE). Thirteen participants (72%) completed at least one virtual home visit. No significant barriers were identified to telemedicine encounters. All participants reported “completely” or “very” satisfied with telemedicine visits. Conclusions: Virtual home assessments for identification of asthma triggers is a feasible alternative to in-person home visits, and it is well accepted by patients.