Varying numbers of acute wheezing cases presenting to paediatric emergency during the COVID-19 pandemic

Acute wheezing in children due to underlying asthma or airways hypersensitivity (including allergic rhinitis) can be exacerbated by infectious and non-infectious causes. Of the infectious causes, seasonal rhinoviruses are the most common. Particulate airborne pollutants (PM2.5, PM10) can also play a role. During the COVID-19 pandemic, we observed changes in the pattern of paediatric emergency department (PED) presentations for acute wheezing. In this retrospective observational cohort study, data was extracted for children (0-18 years) presenting to the PED during 2018-2021, whose illness episodes were coded as 'asthma' or 'viral induced wheeze', along with their age, ethnicity, gender, and clinical outcomes, from hospital databases. The Figure shows the number of PED presentations for acute wheezing during 2018-2021, with annotations to explain the changing patterns. The number of cases presenting during the pandemic years 2020-2021 were similar to those in 2018-2019 in the same months, except for April-June 2020, July-August 2020, November 2020 and January-March 2021. Decreases in PED presentations coincided with periods of enforced national and local lockdowns, likely due to parental reluctance to expose their children to SARS-CoV-2 in a hospital setting. In addition, fluctuations in the incidence of rhinovirus infections, as shown by national sentinel surveillance data, likely contributed to changes in case numbers. Higher and lower incidence of rhinovirus infections tended to increase and decrease the number of presentations, respectively. Surprisingly, the level of airborne particulates (PM2.5, PM10) showed no correlation with these PED presentations for acute wheezing.

Aerosol Transmission and Control of SARS-CoV-2

One fundamental question about any novel pathogen is: how does it transmit? Answering this question will help to protect ourselves from the agent, at least until effective vaccines and antiviral therapies can be developed, especially if it is an agent of moderate to high lethality. Initially, at the start of the Coronavirus disease 2019 (COVID-19) pandemic, more emphasis was placed on handwashing rather than on droplet and aerosol transmission. Although severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2)-infected secretions such as saliva can spread the virus to hands, it became increasing evident that the virus mostly transmitted through close contact (though not necessarily touching), whilst people were breathing, talking, laughing, singing, coughing and sneezing near one another. During such respiratory activities, droplets and aerosols are produced together, and the amount of transmission due to these differentsized liquid particles will likely vary between individuals at different stages of their infection and illness. This question became even more complex as it emerged that viral transmission can occur for several days before symptom onset, and that asymptomatic cases can also shed just as much virus and potentially transmit it just as well as symptomatic cases. This chapter summarizes our understanding of how SARS-CoV-2 transmits and the infection control precautions to reduce this. © 2023 by World Scientific Publishing Co. Pte. Ltd.

Sevi: Speech-to-Visualization through Neural Machine Translation

Data visualization is a powerful tool for understating information through visual cues. However, allowing novices to create visualization artifacts for what they want to see is not easy, just as not everyone can write SQL queries. Arguably, the most natural way to specify what to visualize is through natural language or speech, similar to our daily search on Google or Apple Siri, leaving to the system the task of reasoning about what to visualize and how. In this demo, we present Sevi an end-to-end data visualization system that acts as a virtual assistant to allow novices to create visualizations through either natural language or speech. Sevi is powered by two main components: Speech2Text which is based on Google Cloud Speech-to-Text Rest API, and Text2VIS, which uses an end-to-end neural machine translation model called ncNet trained using a cross-domain benchmark called nvBench. Both ncNet and nvBench have been developed by us. We will walk the audience through two general domain datasets, one related to COVID-19 and the other on NBA player statistics, to highlight how Sevi enables novices to easily create data visualizations. Because nvBench contains Text2VIS training samples from 105 domains (e.g., sport, college, hospital, etc.), the audience can play with speech or text input with any of these domains.

FEASIBILITY AND UTILITY OF THE H&P 360 IN UNCOVERING PATIENT GOALS AND SOCIAL INFLUENCES OF HEALTH: EXPERIENCES AT THREE ACADEMIC MEDICAL CENTERS

SETTING AND PARTICIPANTS: The H&P 360 is a modified medical history prompting students to routinely and systematically gather information from 7 domains - Biomedical Conditions, Patients' Goals and Values, Mental Health, Behavioral Health, Social Support, Living Environment and Resources, and Function. Initial studies in inpatient and standardized patient settings showed the H&P 360 enhances data gathering, patient rapport, and team care planning. Our goal was to examine the feasibility and usefulness of the H&P 360 in routine teaching contexts at three institutions. Setting and Participants: M3 and M4 medical students at the University of Chicago (UC;n=24), the University of Michigan (UM;n=38), and Florida International University (FIU;n=118) in 2020-21. Clinical settings included subinternships (UC), longitudinal clinics (UM), and community-based home visits (FIU). DESCRIPTION: All sites introduced students to the H&P 360 via a nonstandardized 1-2 hour introductory seminar. 3-6 months after implementation a previously developed student survey with 13 Likert-type and 3 text-response items querying implementation of the H&P 360 was administered. A parallel survey was developed for teaching faculty. EVALUATION: A total of 180 students participated. Response rates at UC, UM, and FIU were 46%, 34%, and 16%, respectively. Percent of students (UC, UM, FIU;Overall), responding 'Agree' or 'Strongly agree': 1. The H&P 360: - Was easy to use (67, 85, 89;81) - Helped me better understand patient goals (89, 100, 77;88) - Added valuable information that I woul otherwise not know (78, 85, 63;79) - Facilitated care planning that included other health professionals(56, 85, 71, 70) 2. Presentations using the H&P 360 were well received by my clinical team. (33, 46, 82;60) 3. Elements of the H&P 360 are potentially useful in all patient interactions. (67, 100, 95;88) Overall faculty response rates were low;most described unfamiliarity with the H&P 360 with limited incorporation into teaching. DISCUSSION / REFLECTION / LESSONS LEARNED: Discussion: Students in clinical teaching settings found the H&P 360 feasible, easy to use, and helpful for understanding patient priorities. Most reported it facilitated interprofessional planning. Potentially significant variation occurred across sites. Faculty incorporation into clinical teaching was limited. Overall survey response rates were low, partially due to COVID. Conclusions: The H&P 360 promotes information gathering critical for chronic disease management: clinical diagnosis, social determinants of health, and patient priorities. Such information should prove useful for fostering health equity. Future work should measure effects on patient care and outcomes. Our team is developing methods to better engage faculty with integrating the H&P 360 into clinical skills teaching throughout medical school and has a toolkit to facilitate implementation and additional studies.

The role of SARS-CoV-2 aerosol transmission during the COVID-19 pandemic

The COVID-19 pandemic, caused by the virus SARS-CoV-2, has touched most parts of the world and devastated the lives of many. The high transmissibility coupled with the initial poor outcome for the elderly led to crushingly high fatalities. The scientific response to the pandemic has been formidable, aided by advancements in virology, computing, data analysis, instrumentation, diagnostics, engineering and infection control. This has led to improvements in understanding and has helped to challenge some established orthodoxies. Sufficient time has elapsed since the start of the COVID-19 pandemic that a clearer view has emerged about transmission and infection risks, public health responses and related societal and economic impacts. This timely volume has provided an opportunity for the science community to report on these new developments. © 2022 The Authors.

Practical Indicators for Risk of Airborne Transmission in Shared Indoor Environments and Their Application to COVID-19 Outbreaks.

Some infectious diseases, including COVID-19, can undergo airborne transmission. This may happen at close proximity, but as time indoors increases, infections can occur in shared room air despite distancing. We propose two indicators of infection risk for this situation, that is, relative risk parameter (Hr) and risk parameter (H). They combine the key factors that control airborne disease transmission indoors: virus-containing aerosol generation rate, breathing flow rate, masking and its quality, ventilation and aerosol-removal rates, number of occupants, and duration of exposure. COVID-19 outbreaks show a clear trend that is consistent with airborne infection and enable recommendations to minimize transmission risk. Transmission in typical prepandemic indoor spaces is highly sensitive to mitigation efforts. Previous outbreaks of measles, influenza, and tuberculosis were also assessed. Measles outbreaks occur at much lower risk parameter values than COVID-19, while tuberculosis outbreaks are observed at higher risk parameter values. Because both diseases are accepted as airborne, the fact that COVID-19 is less contagious than measles does not rule out airborne transmission. It is important that future outbreak reports include information on masking, ventilation and aerosol-removal rates, number of occupants, and duration of exposure, to investigate airborne transmission.

Dismantling myths on the airborne transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).

The coronavirus disease 2019 (COVID-19) pandemic has caused untold disruption throughout the world. Understanding the mechanisms for transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is key to preventing further spread, but there is confusion over the meaning of 'airborne' whenever transmission is discussed. Scientific ambivalence originates from evidence published many years ago which has generated mythological beliefs that obscure current thinking. This article collates and explores some of the most commonly held dogmas on airborne transmission in order to stimulate revision of the science in the light of current evidence. Six 'myths' are presented, explained and ultimately refuted on the basis of recently published papers and expert opinion from previous work related to similar viruses. There is little doubt that SARS-CoV-2 is transmitted via a range of airborne particle sizes subject to all the usual ventilation parameters and human behaviour. Experts from specialties encompassing aerosol studies, ventilation, engineering, physics, virology and clinical medicine have joined together to produce this review to consolidate the evidence for airborne transmission mechanisms, and offer justification for modern strategies for prevention and control of COVID-19 in health care and the community.

Inferring super-spreading from transmission clusters of COVID-19 in Hong Kong, Japan, and Singapore.

Super-spreading events in an outbreak can change the nature of an epidemic. Therefore, it is useful for public health teams to determine whether an ongoing outbreak has any contribution from such events, which may be amenable to interventions. We estimated the basic reproductive number (R0) and the dispersion factor (k) from empirical data on clusters of epidemiologically linked coronavirus disease 2019 (COVID-19) cases in Hong Kong, Japan and Singapore. This allowed us to infer the presence or absence of super-spreading events during the early phase of these outbreaks. The relatively large values of k implied that large cluster sizes, compatible with super-spreading, were unlikely.