Arching from Function to Form—Important Design Elements of Simulation Exercises (SimEx) in Emergency Response and Disaster Risk Management

With Rasmussen’s abstraction hierarchy as starting point, the present article focuses on understanding some of the aspects guiding the development of a simulation exercise (SimEx) from a multi-faceted perspective, based on interviews and post-exercise evaluations conducted with both exercise designers and participants. The results show that, in order to achieve its overarching objective, an exercise must fulfill a wide range of “functions”, which in turn can take various kinds of “forms” or actual representations in the physical world. The paper discusses a number of identified required functions of a SimEx, sometimes labeled as design elements, and furthermore elaborates on differences and specific requirements at form level, e.g., virtual vs. physical exercises.

Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study.

BACKGROUND: The dynamics of the COVID-19 pandemic vary owing to local population density and policy measures. During decision-making, policymakers consider an estimate of the effective reproduction number Rt, which is the expected number of secondary infections spread by a single infected individual. OBJECTIVE: We propose a simple method for estimating the time-varying infection rate and the Rt. METHODS: We used a sliding window approach with a Susceptible-Infectious-Removed (SIR) model. We estimated the infection rate from the reported cases over a 7-day window to obtain a continuous estimation of Rt. A proposed adaptive SIR (aSIR) model was applied to analyze the data at the state and county levels. RESULTS: The aSIR model showed an excellent fit for the number of reported COVID-19 cases, and the 1-day forecast mean absolute prediction error was <2.6% across all states. However, the 7-day forecast mean absolute prediction error approached 16.2% and strongly overestimated the number of cases when the Rt was rapidly decreasing. The maximal Rt displayed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We found that the aSIR model can rapidly adapt to an increase in the number of tests and an associated increase in the reported cases of infection. Our results also suggest that intensive testing may be an effective method of reducing Rt. CONCLUSIONS: The aSIR model provides a simple and accurate computational tool for continuous Rt estimation and evaluation of the efficacy of mitigation measures.