Materials and Methods: A retrospective cohort study was conducted in the ED of the largest tertiary medical center in Taiwan. Trends of adult, non-trauma patients who visited the ED during February-April 2019 were compared with those during February-April 2020. The number of visits, their dispositions, crowding parameters, and turnover rates were analyzed. The primary outcome was the change in ED attendance between the two periods. The secondary outcomes were changes in hospital admission rates, crowding parameters, and turnover rates.
Results: During the outbreak, there were decreased non-trauma ED visits by 33.45% (p < 0.001) and proportion of Taiwan Triage and Acuity Scale (TTAS) 3 patients (p=0.02), with increased admission rates by 4.7% (p < 0.001). Crowding parameters and turnover rate showed significant improvements.
Conclusion: Comparison of periods before and during the COVID-19 outbreak showed an obvious decline in adult, non-trauma ED visits. The reduction in TTAS 3 patient visits and the increased hospital admission rates provide references for future public-health policy-making to optimise emergency medical resource allocations globally.
METHODS: We downloaded COVID-19 outbreak data of the number of confirmed cases in all countries as of October 19, 2020. The IRT-based predictive model was built to determine the pandemic IP for each country. A model building scheme was demonstrated to fit the number of cumulative infected cases. Model parameters were estimated using the Solver add-in tool in Microsoft Excel. The absolute advantage coefficient (AAC) was computed to track the IP at the minimum of incremental points on a given ogive curve. The time-to-event analysis (a.k.a. survival analysis) was performed to compare the difference in IPs among continents using the area under the curve (AUC) and the respective 95% confidence intervals (CIs). An online comparative dashboard was created on Google Maps to present the epidemic prediction for each country.
RESULTS: The top 3 countries that were hit severely by COVID-19 were France, Malaysia, and Nepal, with IP days at 263, 262, and 262, respectively. The top 3 continents that were hit most based on IP days were Europe, South America, and North America, with their AUCs and 95% CIs at 0.73 (0.61-0.86), 0.58 (0.31-0.84), and 0.54 (0.44-0.64), respectively. An online time-event result was demonstrated and shown on Google Maps, comparing the IP probabilities across continents.
CONCLUSION: An IRT modeling scheme fitting the epidemic data was used to predict the length of IP days. Europe, particularly France, was hit seriously by COVID-19 based on the IP days. The IRT model incorporated with AAC is recommended to determine the pandemic IP.