Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study.

BackgroundReal-time prediction is key to prevention and control of infections associated with health-care settings. Contacts enable spread of many infections, yet most risk prediction frameworks fail to account for their dynamics. We developed, tested, and internationally validated a real-time machine-learning framework, incorporating dynamic patient-contact networks to predict hospital-onset COVID-19 infections (HOCIs) at the individual level.MethodsWe report an international retrospective cohort study of our framework, which extracted patient-contact networks from routine hospital data and combined network-derived variables with clinical and contextual information to predict individual infection risk. We trained and tested the framework on HOCIs using the data from 51 157 hospital inpatients admitted to a UK National Health Service hospital group (Imperial College Healthcare NHS Trust) between April 1, 2020, and April 1, 2021, intersecting the first two COVID-19 surges. We validated the framework using data from a Swiss hospital group (Department of Rehabilitation, Geneva University Hospitals) during a COVID-19 surge (from March 1 to May 31, 2020; 40 057 inpatients) and from the same UK group after COVID-19 surges (from April 2 to Aug 13, 2021; 43 375 inpatients). All inpatients with a bed allocation during the study periods were included in the computation of network-derived and contextual variables. In predicting patient-level HOCI risk, only inpatients spending 3 or more days in hospital during the study period were examined for HOCI acquisition risk.FindingsThe framework was highly predictive across test data with all variable types (area under the curve [AUC]-receiver operating characteristic curve [ROC] 0·89 [95% CI 0·88-0·90]) and similarly predictive using only contact-network variables (0·88 [0·86-0·90]). Prediction was reduced when using only hospital contextual (AUC-ROC 0·82 [95% CI 0·80-0·84]) or patient clinical (0·64 [0·62-0·66]) variables. A model with only three variables (ie, network closeness, direct contacts with infectious patients [network derived], and hospital COVID-19 prevalence [hospital contextual]) achieved AUC-ROC 0·85 (95% CI 0·82-0·88). Incorporating contact-network variables improved performance across both validation datasets (AUC-ROC in the Geneva dataset increased from 0·84 [95% CI 0·82-0·86] to 0·88 [0·86-0·90]; AUC-ROC in the UK post-surge dataset increased from 0·49 [0·46-0·52] to 0·68 [0·64-0·70]).InterpretationDynamic contact networks are robust predictors of individual patient risk of HOCIs. Their integration in clinical care could enhance individualised infection prevention and early diagnosis of COVID-19 and other nosocomial infections.FundingMedical Research Foundation, WHO, Engineering and Physical Sciences Research Council, National Institute for Health Research (NIHR), Swiss National Science Foundation, and German Research Foundation

Green Fleet Consulting : a strategy for UBC Fleet Management

ABOUT THE CLIENT UBC ‘s Fleet Management Department manages two-thirds of UBC’s vehicles. They procure, maintain, and manage their inventory of assets while optimizing costs through established connections with suppliers. One of UBC Fleet Management’s highest priorities is to minimize GHB emissions and in 2014 they received the E3 (Energy, Emissions, and Excellence) Fleet Platinum Ranking. However, the Fleet Management department needs to continue to improve to hit their 2020 goal of a 67% reduction of emissions and a 100% reduction by 2050. With their “Project Pegasus”, UBC Fleet Management took several steps towards hitting its emission goals. This project put in place several successful polices including; rightsizing, standardizing the feet, alternative fuels, and a fuel-efficient driving policy. Our recommendations will build upon the Pegasus Project instead of trying to radically change it. We believe that UBC Fleet Management is already very strong with their management of fuel emissions but we have identified several areas where they can still improve. ANALYSIS Before developing our strategy, we did an analysis on the Fleet Management department’s current position and the surrounding macro environment. We evaluated the strengths of UBC Fleet Management and listed some of the opportunities that the department could take advantage of. We found that car share technologies are not a viable option because they are too high of a cost to operate when compared to owning or leasing a vehicle and they do not fit the operational requirements of UBC. We also identified and evaluated several emission reduction technologies that are available. THE STRATEGY Green Fleet Consulting has developed a short and long term strategy that we believe will allow the Fleet Management Department to meet and exceed these emission goals while simultaneously reducing fuel costs. Since Fleet Management has a set budget any recommendation we considered needed to be cost neutral meaning the initial cost needed to be completely offset by the reduction in fuel costs. We are recommending two pieces of technologies to adopt in the short term and a switch to a fully electric fleet in the long term. The first piece of technology is direct fired heaters which is an example of anti-idling technology. The direct fired heaters keep the cabin of the vehicle warm without using the engine of the vehicle. This dramatically reduces fuel costs and would be most effective when installed on large vehicles like garbage trucks. The second technology is electrically assisted diesel particulate filters. This technology uses electricity rather than fuel to filter fuel in diesel vehicles. While this piece of technology has not yet come to market, it could dramatically reduce emissions and would be effective on any diesel vehicle. We identified several case studies which corroborate our findings and lead us to believe that our technologies would be very effective if implemented. We also performed financial analysis to show how this change could be done on a cost neutral basis, and an environmental analysis to see what our tactics could do to reduce emissions. We also looked at some vehicles that might be better options for each vehicle category. Keeping in mind the need to make changes on a cost neutral basis, we developed a decision-making process that will identify when electric vehicle technology has advanced to the point where electric vehicles can meet the operational requirements of UBC, and when the reduction of fuel costs offset the higher initial price compared to a traditional gasoline vehicle. We tested this decision-making process on a new electric van that will be introduced to North America and determined that this van does not meet our decision-making criteria. IMPLEMENTATION We provided a timeline to show how and when our recommendations could be implemented and we based our timeline around the two future emission goals. We understand that any strategy is not without risks, so we have identified several possible risks and show how the Fleet Management department could mitigate these potential pitfalls. Finally, we have identified several financial and environmental metrics that should be monitored to determine the success of our strategy. Ultimately, we believe that our recommendations will make a meaningful impact on UBC Fleet Management’s emission footprint, and the success of our initiatives will allow UBC Fleet Management to hit its ambitious future goals. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”Other UBCUnreviewedUndergraduat