Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions.

During the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul's Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.

Cultural similarity and impartiality on voting bias: The case of FIFA's World's Best Male Football Player Award.

Previous studies on voting bias in competitive awards have not fully considered the role of cultural similarity. Using data for the Best FIFA Men's Player Award, we evaluate the extent of voting bias in this Award using three cultural similarity factors (cultural distance, cultural clusters, and collectivism), six established in-group factors (nationality, club, league, geography, ethnicity, religion, and language) and the impartiality of the voter's country. Using statistical and econometric methods, we find that voter-player cultural similarity is positively associated with voting bias and find no evidence of impartiality when it comes to cultural or national ties. We also find that media voters are less biased than captain voters and coach voters, and that coaches are less biased than captains.

Learning from the first wave: Lessons about managing patient flow and resource utilization on medical wards at providence health during the COVID-19 pandemic.

We report the successful implementation of a modified Traffic Control Bundling (TCB) protocol called "Red, Yellow and Green" on the inpatient medical units at St. Paul's Hospital in Vancouver, Canada during the first wave of the coronavirus disease 2019 (COVID-19) pandemic. The modified TCB protocol demonstrates an important example on how hospitals can rapidly reorganize operational and clinical processes to reallocate existing capacity to minimize exposure, improve traffic flow and reduce nosocomial transmissions of COVID-19 to health care workers (HCWs) and other patients. Preliminary evidence demonstrates the benefits on how an existing facility can be redesigned for adjustable ward capacity to provide disease containment under a context of uncertainty of disease transmission and varying patient load. Important lessons in preparation for the evolution of the pandemic fall into categories of risk management, capacity and demand management.