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Mathematical optimization of automated external defibrillator (AED) placement has demonstrated potential to improve survival of out-of-hospital cardiac arrest (OHCA). Existing models mostly aim to improve accessibility based on coverage radius and do not account for detailed impact of delayed defibrillation on survival. We aimed to predict OHCA survival based on time to defibrillation and developed an AED placement model to directly maximize the expected survival rate.We stratified OHCAs occurring in Singapore (2010-2017) based on time to defibrillation and developed a regression model to predict the Utstein survival rate. We then developed a novel AED placement model, the maximum expected survival rate (MESR) model. We compared the performance of MESR with a maximum coverage model developed for Canada that was shown to be generalizable to other settings (Denmark). The survival gain of MESR was assessed through 10-fold cross-validation for placement of 20 to 1000 new AEDs in Singapore. Statistical analysis was performed using χDuring the study period, 15,345 OHCAs occurred. The power-law approximation with RWe developed a novel AED placement model based on the impact of time to defibrillation on OHCA outcomes. Mathematical optimization can improve OHCA survival.

Background Accurate and timely prognostication of patients with out-of-hospital cardiac arrest (OHCA) who achieved the return of spontaneous circulation (ROSC) is crucial in clinical decision-making, resource allocation, and communications with next-of-kins. We aimed to develop the Survival After ROSC in Cardiac Arrest (SARICA), a practical clinical decision tool to predict survival in OHCA patients who attained ROSC. Methods We utilized real-world Singapore data from the population-based Pan-Asian Resuscitation Outcomes Study between 2010-2018. We excluded patients without ROSC. The dataset was segmented into training (60%), validation (20%) and testing (20%) cohorts. The primary endpoint was survival (to 30-days or hospital discharge). AutoScore, an interpretable machine-learning based clinical score generation algorithm, was used to develop SARICA. Candidate factors were chosen based on objective demographic and clinical factors commonly available at the time of admission. Performance of SARICA was evaluated based on receiver-operating curve (ROC) analyses. Results 5970 patients were included, of which 855 (14.3%) survived. A three-variable model was determined to be most parsimonious. Prehospital ROSC, age, and initial heart rhythm were identified for inclusion via random forest selection. Finally, SARICA consisted of these 3 variables and ranged from 0 to 10 points, achieving an area under the ROC (AUC) of 0.87 (95% confidence interval: 0.84-0.90) within the testing cohort. Conclusion We developed and internally validated the SARICA score to accurately predict survival of OHCA patients with ROSC at the time of admission. SARICA is clinically practical and developed using an interpretable machine-learning framework. SARICA has unknown generalizability pending external validation studies.

To develop an effective Management Information System (MIS) that is empowered by predictive models that can forecast the demand of end-stage cancer home hospitalized patients in individual and population levels, and help palliative care service systems operate smoothly where the demand is highly fluctuating, resources are limited, expensive, and hardly adjustable in a short time, and the backlog and shortage costs are high.Inspired by real problems faced by a palliative care center providing various medical, nursing, psychological, and social services in a home-based setting, two Long Short-Term Memory (LSTM) based deep learning models are proposed for demand forecasting at both individual and population levels. The individual-level model can predict the type and time of the next service required for a specific patient with a given demographic and health profile, and the population-level model helps with the prediction of next week's demand for various services in a center supporting a specific patient population. Predicted demand informs on optimal resource and operations plan through a well designed MIS.Experiments were conducted on a dataset consisting of more than 4000 cancer patients with a Palliative Performance Scale (PPS) of 40 and below discharged from hospital to home under a national palliative care center's home hospitalization service in Iran from September 2012 to July 2019. The models outperformed conventional time-series forecasting methods where applicable. Results indicate that the proposed models were capable of forecasting patients' demand with astonishing performances both individually and on larger scales.Intelligent demand forecasting can help palliative care home hospitalization systems to overcome the challenge of progressive demand growth when a considerable portion of patients are approaching death, followed by a sudden drop in demand when those patients pass away. It helps to improve resource utilization and quality of care concurrently.

Aim To understand the impact of COVID‐19 on isolation bed capacity requirements, nursing workforce requirements and nurse:patient ratios. Background COVID‐19 created an increased demand for isolation beds and nursing workforce globally. Methods This was a retrospective review of bed capacity, bed occupancy and nursing workforce data from the isolation units of a tertiary hospital in Singapore from 23 January 2020 to 31 May 2020. R v4.0.1 and Tidyverse 1.3.0 library were used for data cleaning and plotly 4.9.2.1 library for data visualization. Results In January to March 2020, isolation bed capacity was low (=

Evacuation planning for emergency incidents is an essential preparedness for Emergency Departments (ED) which normally contains patients with severe illness and limited mobility. However, the preparedness can be challenging due to a lack of empirical data and difficulties conducting physical drills. We propose an agent-based model to simulate the evacuation process in the EDs containing medical staff, rescuers, visitors and various types of patients. In a case study, we apply the model to a peak hour scenario of the ED of the largest hospital in Singapore. Two rescue strategies with different behavior sequences of medical staff as suggested by the practitioners are evaluated. The simulation results show that prioritizing preparation of all the patients generates less total evacuation time but leads to fewer evacuated cases in the first 20 minutes and more serious congestion compared to one-by-one transfer of individual patients. National Research Foundation (NRF) Submitted/Accepted version This research is supported by National Research Foundation (NRF) Singapore, GOVTECH under its Virtual Singapore program Grant No. NRF2017VSG-AT3DCM001-031.

Emergency evacuation of patients from a hospital can be challenging in the event of a fire. Most emergency evacuation studies are based on the assumption that pedestrians are ambulant and can egress by themselves. However, this is often not the case during emergency evacuations in healthcare facilities such as hospitals and nursing homes. To investigate emergency evacuations in such healthcare facilities, we performed a series of controlled experiments to study the dynamics of patient beds in horizontal movement. We considered a patient bed because it is one of the commonly used devices to transport patients within healthcare facilities. Through a series of controlled experiments, we examined the change of velocity in corner turning movements and speed reductions in multiple trips between both ends of a straight corridor. Based on the experimental results, we then developed a mathematical model of total evacuation time prediction for a patient bed horizontally moving in a healthcare facility. Factoring uncertainty in the horizontal movement, we produced the probability distribution of movement duration and estimated the probability that an evacuation can be safely performed within certain amount of time. In addition, we predicted that the evacuation time would be longer than the prediction results from an existing model which assumes constant movement speed. Our results from the model demonstrated good agreement with our experimental results., 14 pages, 13 figures, 10 tables

Background: We describe the development of a dynamic simulation modelling framework to support agile resource planning during the COVID-19 pandemic. The framework takes into consideration the dynamic evolution of the pandemic and the rapidly evolving policies and processes to deal with the ever-changing outbreak scenarios.Methods: A specific use case based on short-term bed resource planning is described within the proposed framework. The simulation model was calibrated against historical data for the Singapore COVID-19 situation. The time period for model calibration was from 1st April till 30th April 2020. The model was used to project for bed resource needs over the period from 1st May 2020 till 31st May 2020. Multivariate sensitivity analysis was also conducted for ICU and general isolation bed demand, length-of-stay (LOS), and age-adjusted conversion rates across different care needs. The unmet needs under various scenarios were also evaluated for planning purposes.Results: Several variants of the agile resource planning model were developed to adapt to the fast-changing COVID-19 situation in Singapore. The use case demonstrated an agile adaptation of the model to account for previously unexpected scenarios. The rapid evolution of the pandemic locally revealed streams of new infections that arose from two distinct sources. The model projections were calibrated with the latest data for short-term projections. The agility in flexing plans and collaborative management structures to rapidly deploy human and capital resources to surge the level of care during the COVID-19 pandemic have proven utility in guiding the allocation of scarce healthcare resources and helped system resiliency.Conclusions: The rapidly evolving COVID-19 pandemic in Singapore has necessitated the development of an agile and adaptable modelling framework that can be quickly calibrated to changes both from demand and supply. The modelling framework is able to deploy systems modelling concepts in a holistic manner. This facilitates the evaluation of complex cause-and-effect relationships. A robust collaborative framework, coupled with the availability of in-depth domain knowledge and accurate and updated data availability ensures a model is realistic, timely and useful.

Background: This study conducts a counterfactual analysis, using the case of Singapore, a country fairly successful in suppressing community spread of COVID-19, to explore what the trajectory of COVID-19 infection might have been in Singapore had the government intervention not focused on containment, but rather on mitigation or uninhibited spread. Methods: We developed an SEIR model to estimate the number of COVID-19 infection cases in Singapore and deaths, under different public health intervention compared to the containment interventions implemented in Singapore. Finding: Under the Singapore containment interventions, we would expect a peak in infected cases to occur 3 months from January 23, 2020 [100% confidence range (CR), 2·2 to 4]. The infected cases of COVID-19 over 8·6 months is projected to be 2,488 [1,814 to 4,510]; representing 0·044% of the total population [0·032 to 0·081]. Deaths is estimated to be 25 [9 to 57]. In comparison, a mitigation intervention with 20% quarantine rate, would delay the time to peak infection by an estimated 5·2 months, prolong duration of the infection by 12·4 months, cumulative infected cases 1,388-fold, and deaths 1,371-fold. Likewise, a mitigation intervention with 40% quarantine rate, compared to the Singapore containment intervention could delay the time to peak infection by 7 months, extend the infection surge by 21 months, and increase cumulative infected cases and deaths 727-fold. Finally, a strategy of uninhibited spread would increase the time to peak infection by 2·9 months, duration of infection by 8 months, increase cumulative infected cases 1,863-fold and deaths 1,840-fold. Interpretation: Early public health measures in the context of targeted, aggressive containment — such as swift and effective contact tracing and quarantine – was likely responsible for suppressing the number of COVID-19 infections in Singapore. Funding Statement: The project was funded by the Singapore Ministry of Health’s National Medical Research Council. Declaration of Interests: There is no conflict of interests.

We study how the presence of committed volunteers influences the collective helping behavior in emergency evacuation scenarios. In this study, committed volunteers do not change their decision to help injured persons, implying that other evacuees may adapt their helping behavior through strategic interactions. An evolutionary game theoretic model is developed which is then coupled to a pedestrian movement model to examine the collective helping behavior in evacuations. By systematically controlling the number of committed volunteers and payoff parameters, we have characterized and summarized various collective helping behaviors in phase diagrams. From our numerical simulations, we observe that the existence of committed volunteers can promote cooperation but adding additional committed volunteers is effective only above a minimum number of committed volunteers. This study also highlights that the evolution of collective helping behavior is strongly affected by the evacuation process., 25 pages, 13 figures, 3 tables

This paper proposes a new multi-site production planning model integrating procurement and distribution plans in a multi-echelon supply chain network including routing and fleet sizing problems as well as an advanced discount policy. A two step solution approach including scatter search (SS) algorithm is proposed in order to cope with these issues. The proposed model and solution method are validated through some numerical tests inspired by an automobile manufacturing company.

In this paper the multi-mode resource-constrained project scheduling problem is considered. Activities are non-preemptable, resources are renewable, and the objective is to minimize the project duration (or makespan). A local search metaheuristic-scatter search is proposed to solve this strongly NP-hard problem. A comprehensive computational experiment is described, performed on a set of instances based on standard test problems constructed by the proGen project generator. The performance of this scatter search algorithm is compared with the performance of other published algorithms for multi-mode resource-constrained project scheduling problem, the results are analyzed and discussed and some conclusions are given.

The periodic vehicle routing problem (PVRP) consists in establishing a planning of visits to clients over a given time horizon so as to satisfy some service level while optimizing the routes used in each time period. A new, simple and effective heuristic algorithm has been developed for the PVRP that is a tour construction type procedure with an embedded improvement procedure. Computational results obtained from the test problems taken from the literature indicate that the algorithm compares well in terms of accuracy and execution time with other existing algorithms, finding a larger number of best solutions.