Impact of COVID-19 pandemic on emergency department patient volume and flow: Two countries, two hospitals.

OBJECTIVES: COVID-19 greatly disrupted the provision of emergency care across the globe. ED service delivery was urgently redesigned as human and material resources were mobilised, and patients with respiratory symptoms were isolated. This study aimed to compare ED patient volume and flow metrics before and during the COVID-19 pandemic. METHODS: An observational study was conducted in two large urban EDs in Brisbane, Australia and Seoul, Republic of Korea. Patient volume and flow were quantified using ED presentation numbers and service times, respectively. Daily case numbers, waiting, treatment and admission delay times were compared between 2019 and 2020/2021 using time series plots. Outcomes were further classified by triage category and age group. Trends were examined alongside a timeline of health service and government policies. RESULTS: There were reductions in daily presentations for the least urgent triage categories during the early phase of the pandemic. The caseloads for the most urgent triage categories were unaffected. The trends were similar in both EDs. A reduction in waiting and admission delay times but not treatment times coincided with reduced presentations in Brisbane. This pattern gradually reversed as presentations returned to baseline. In Seoul, admission delay times returned to pre-pandemic levels despite a persistent reduction in presentation numbers. CONCLUSIONS: Total daily presentations varied considerably according to government mandated social restrictions and testing requirements in both EDs. The reductions in waiting and admission delay times corresponded with improvements in hospital capacity.

Understanding the Impact of Walkability, Population Density, and Population Size on COVID-19 Spread: A Pilot Study of the Early Contagion in the United States.

The novel coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global event that has been challenging governments, health systems, and communities worldwide. Available data from the first months indicated varying patterns of the spread of COVID-19 within American cities, when the spread was faster in high-density and walkable cities such as New York than in low-density and car-oriented cities such as Los Angeles. Subsequent containment efforts, underlying population characteristics, variants, and other factors likely affected the spread significantly. However, this work investigates the hypothesis that urban configuration and associated spatial use patterns directly impact how the disease spreads and infects a population. It follows work that has shown how the spatial configuration of urban spaces impacts the social behavior of people moving through those spaces. It addresses the first 60 days of contagion (before containment measures were widely adopted and had time to affect spread) in 93 urban counties in the United States, considering population size, population density, walkability, here evaluated through walkscore, an indicator that measures the density of amenities, and, therefore, opportunities for population mixing, and the number of confirmed cases and deaths. Our findings indicate correlations between walkability, population density, and COVID-19 spreading patterns but no clear correlation between population size and the number of cases or deaths per 100 k habitants. Although virus spread beyond these initial cases may provide additional data for analysis, this study is an initial step in understanding the relationship between COVID-19 and urban configuration.

A critical overview of computational approaches employed for COVID-19 drug discovery.

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.