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Abstract Objective: The aim of this study was to investigate the performance of key hospital units associated with emergency care of both routine emergency and pandemic (COVID-19) patients under capacity enhancing strategies. Methods: This investigation was conducted using whole-hospital, resource-constrained, patient-based, stochastic, discrete-event, simulation models of a generic 200-bed urban U.S. tertiary hospital serving routine emergency and COVID-19 patients. Systematically designed numerical experiments were conducted to provide generalizable insights into how hospital functionality may be affected by the care of COVID-19 pandemic patients along specially designated care paths, under changing pandemic situations, from getting ready to turning all of its resources to pandemic care. Results: Several insights are presented. For example, each day of reduction in average ICU length of stay increases intensive care unit patient throughput by up to 24% for high COVID-19 daily patient arrival levels. The potential of 5 specific interventions and 2 critical shifts in care strategies to significantly increase hospital capacity is also described. Conclusions: These estimates enable hospitals to repurpose space, modify operations, implement crisis standards of care, collaborate with other health care facilities, or request external support, thereby increasing the likelihood that arriving patients will find an open staffed bed when 1 is needed. 

Objective This paper investigates the impact on emergency hospital services from initiation through recovery of a ransomware attack affecting the emergency department, intensive care unit and supporting laboratory services. Recovery strategies of paying ransom to the attackers with follow-on restoration and in-house full system restoration from backup are compared. Methods A multi-unit, patient-based and resource-constrained discrete-event simulation model of a typical U.S. urban tertiary hospital is adapted to model the attack, its impacts, and tested recovery strategies. The model is used to quantify the hospital's resilience to cyberattack. Insights were gleaned from systematically designed numerical experiments. Results While paying the ransom was found to result in some short-term gains assuming the perpetrators actually provide the decryption key as promised, in the longer term, the results of this study suggest that paying the ransom does not pay off. Rather, paying the ransom, when considered at the end of the event when services are fully restored, precluded significantly more patients from receiving critically needed care. Also noted was a lag in recovery for the intensive care unit as compared with the emergency department. Such a lag must be considered in preparedness plans. Conclusion Vulnerability to cyberattacks is a major challenge to the healthcare system. This paper provides a methodology for assessing the resilience of a hospital to cyberattacks and analyzing the effects of different response strategies. The model showed that paying the ransom resulted in short-term gains but did not pay off in the longer term. 

Meal delivery has become increasingly popular in past years and of great importance in past months during the COVID-19 pandemic. Sustaining such services depends on maintaining provider profitability and reduced cost to consumers while continuing to support autonomy and independence for customers, restaurants, and delivery drivers (here crowdsourced drivers). This paper investigates the possible enactment of curbside regulations in the U.S. that limit the number of drivers simultaneously waiting at restaurants to pick up meals for delivery on both public safety and delivery efficiency. Curbside regulations would aim to increase safety by enabling social distancing between delivery personnel at pickup locations and have a secondary benefit of improving local traffic flows, which are sometimes impeded in busier, urban locations. Curbside space limits are studied in relation to their impacts on consumer-related performance measures: freshness of the food on delivery and click-to-door time. This investigation is enabled through a proposed hybrid discrete-event and time-advanced simulation platform that replicates meal delivery service calls and pickup and delivery operations across a region built on data from a leading meal delivery company. Embedded within the simulation is an integer program that optimally assigns orders to drivers in a dynamically changing environment. Order assignments are constrained by imposed curbside capacity limits at the restaurants, and potential efficiencies and curbside violation reductions from bundling orders are assessed. Results of analyses from numerical experiments provide insights to state and local communities in designing curbside restrictions that reduce curbside crowding yet enable delivery companies to retain their profitability.