Coronavirus-mimicking nanoparticles (CorNPs) in artificial saliva droplets and nanoaerosols: Influence of shape and environmental factors on particokinetics/particle aerodynamics.

Severe acute respiratory syndrome coronavirus 2, abbreviated as SARS-CoV-2, has been associated with the transmission of infectious COVID-19 disease through breathing and speech droplets emitted by infected carriers including asymptomatic cases. As part of SARS-CoV-2 global pandemic preparedness, we studied the transmission of aerosolized air mimicking the infected person releasing speech aerosol with droplets containing CorNPs using a vibrating mesh nebulizer as human patient simulator. Generally speech produces nanoaerosols with droplets of <5 µm in diameter that can travel distances longer than 1 m after release. It is assumed that speech aerosol droplets are a main element of the current Corona virus pandemic, unlike droplets larger than 5 m, which settle down within a 1 m radius. There are no systemic studies, which take into account speech-generated aerosol/droplet experimental validation and their aerodynamics/particle kinetics analysis. In this study, we cover these topics and explore role of residual water in aerosol droplet stability by exploring drying dynamics. Furthermore, a candle experiment was designed to determine whether air pollution might influence respiratory virus like nanoparticle transmission and air stability.

The Johns Hopkins University Center for Systems Science and Engineering COVID-19 Dashboard: data collection process, challenges faced, and lessons learned.

On Jan 22, 2020, a day after the USA reported its first COVID-19 case, the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) launched the first global real-time coronavirus surveillance system: the JHU CSSE COVID-19 Dashboard. As of June 1, 2022, the dashboard has served the global audience for more than 30 consecutive months, totalling over 226 billion feature layer requests and 3·6 billion page views. The highest daily record was set on March 29, 2020, with more than 4·6 billion requests and over 69 million views. This Personal View reveals the fundamental technical details of the entire data system underlying the dashboard, including data collection, data fusion logic, data curation and sharing, anomaly detection, data corrections, and the human resources required to support such an effort. The Personal View also covers the challenges, ranging from data visualisation to reporting standardisation. The details presented here help develop a framework for future, large-scale public health-related data collection and reporting.

Association of Cancer Screening Deficit in the United States With the COVID-19 Pandemic.

Importance: The COVID-19 pandemic led to sharp declines in cancer screening. However, the total deficit in screening in the US associated with the pandemic and the differential impact on individuals in different geographic regions and by socioeconomic status (SES) index have yet to be fully characterized. Objectives: To quantify the screening rates for breast, colorectal, and prostate cancers associated with the COVID-19 pandemic in different geographic regions and for individuals in different SES index quartiles and estimate the overall cancer screening deficit in 2020 across the US population. Design, Setting, and Participants: This retrospective cohort study uses the HealthCore Integrated Research Database, which comprises single-payer administrative claims data and enrollment information covering approximately 60 million people in Medicare Advantage and commercial health plans from across geographically diverse regions of the US. Participants were individuals in the database in January through July of 2018, 2019, and 2020 without diagnosis of the cancer of interest prior to the analytic index month. Exposures: Analytic index month and year. Main Outcomes and Measures: Receipt of breast, colorectal, or prostate cancer screening. Results: Screening for all 3 cancers declined sharply in March through May of 2020 compared with 2019, with the sharpest decline in April (breast, -90.8%; colorectal, -79.3%; prostate, -63.4%) and near complete recovery of monthly screening rates by July for breast and prostate cancers. The absolute deficit across the US population in screening associated with the COVID-19 pandemic was estimated to be 3.9 million (breast), 3.8 million (colorectal), and 1.6 million (prostate). Geographic differences were observed: the Northeast experienced the sharpest declines in screening, while the West had a slower recovery compared with the Midwest and South. For example, percentage change in breast cancer screening rate (2020 vs 2019) for the month of April ranged from -87.3% (95% CI, -87.9% to -86.7%) in the West to -94.5% (95% CI, -94.9% to -94.1%) in the Northeast (decline). For the month of July, it ranged from -0.3% (95% CI, -2.1% to 1.5%) in the Midwest to -10.6% (-12.6% to -8.4%) in the West (recovery). By SES, the largest screening decline was observed in individuals in the highest SES index quartile, leading to a narrowing in the disparity in cancer screening by SES in 2020. For example, prostate cancer screening rates per 100 000 enrollees for individuals in the lowest and highest SES index quartiles, respectively, were 3525 (95% CI, 3444 to 3607) and 4329 (95% CI, 4271 to 4386) in April 2019 compared with 1535 (95% CI, 1480 to 1589) and 1338 (95% CI, 1306 to 1370) in April 2020. Multivariable analysis showed that telehealth use was associated with higher cancer screening. Conclusions and Relevance: Public health efforts are needed to address the large cancer screening deficit associated with the COVID-19 pandemic, including increased use of screening modalities that do not require a procedure.

Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations.

BACKGROUND: In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing policies as states "re-open." METHODS: We developed a COVID-19 social distancing intensity framework that is sufficiently specific and sensitive to capture this gradient. Based on a review of policies from a 12 U.S. state sample, we developed a social distancing intensity framework consisting of 16 domains and intensity scales of 0-5 for each domain. RESULTS: We found that the states with the highest average daily intensity from our sample were Pennsylvania, Washington, Colorado, California, and New Jersey, with Georgia, Florida, Massachusetts, and Texas having the lowest. While some domains (such as restaurants and movie theaters) showed bimodal policy intensity distributions compatible with binary (yes/no) coding, others (such as childcare and religious gatherings) showed broader variability that would be missed without more granular coding. CONCLUSION: This detailed intensity framework reveals the granularity and nuance between social distancing policy responses. Developing standardized approaches for constructing policy taxonomies and coding processes may facilitate more rigorous policy analysis and improve disease modeling efforts.