Background: In 2009 and the early part of 2010, the northern hemisphere had to cope with the first waves of the new influenza A (H1N1) pandemic. Despite high-profile vaccination campaigns in many countries, delays in administration of vaccination programs were common, and high vaccination coverage levels were not achieved. This experience suggests the need to explore the epidemiological and economic effectiveness of additional, reactive strategies for combating pandemic influenza. Methods: We use a stochastic model of pandemic influenza to investigate realistic strategies that can be used in reaction to developing outbreaks. The model is calibrated to documented illness attack rates and basic reproductive number (R₀) estimates, and constructed to represent a typical mid-sized North American city. Results: Our model predicts an average illness attack rate of 34.1% in the absence of intervention, with total costs associated with morbidity and mortality of US$81 million for such a city. Attack rates and economic costs can be reduced to 5.4% and US$37 million, respectively, when low-coverage reactive vaccination and limited antiviral use are combined with practical, minimally disruptive social distancing strategies, including short-term, as-needed closure of individual schools, even when vaccine supply-chain-related delays occur. Results improve with increasing vaccination coverage and higher vaccine efficacy. Conclusions: Such combination strategies can be substantially more effective than vaccination alone from epidemiological and economic standpoints, and warrant strong consideration by public health authorities when reacting to future outbreaks of pandemic influenza.

Introduction: Over 19% of deceased organ donors are labeled increased risk for disease transmission (IRD) for viral blood‐borne disease transmission. Many potential organ recipients need to decide between accepting an IRD organ offer and waiting for a non–IRD organ.Methods: Using machine learning and simulation, we built transplant and waitlist survival models and compared the survival for patients accepting IRD organ offers or waiting for non–IRD organs for the heart, liver, and lung. The simulation consisted of generating 20 000 different scenarios of a recipient either receiving an IRD organ or waiting and receiving a non–IRD organ.Results: In the simulations, the 5‐year survival probabilities of heart, liver, and lung recipients who accepted IRD organ offers increased on average by 10.2%, 12.7%, and 7.2%, respectively, compared with receiving a non–IRD organ after average wait times (190, 228, and 223 days, respectively). When the estimated waitlist time was at least 5 days for the liver, and 1 day for the heart and lung, 50% or more of the simulations resulted in a higher chance of 5‐year survival when the patient received an IRD organ versus when the patient remained on the waitlist. We also developed a simple equation to estimate the benefits, in terms of 5‐year survival probabilities, of receiving an IRD organ versus waiting for a non–IRD organ, for a particular set of recipient/donor characteristics.Conclusion: For all three organs, the majority of patients are predicted to have higher 5‐year survival accepting an IRD organ offer compared with waiting for a non–IRD organ.

Multivariate CUSUM charts formed over spatial clusters have been used over the last several years to detect emerging disease clusters in spatiotemporal biosurveillance. The control limits for the CUSUM charts are typically calibrated by trial-and-error simulation, but this task can be time-consuming and challenging when the monitoring area is large. This article introduces an analytical method that approximates the control limits and average run length when spatial correlation is not strong. In addition, the practical range of the scan radius in which the approximation method works well is investigated. Also studied is how the outbreak radius and spatial correlation impact the scheme’s outbreak detection performance with respect to two metrics: detection delay and identification accuracy. Experimental results show that the approximation method performs well, making the design of the multivariate CUSUM chart convenient; and higher spatial correlation does not always yield faster detection but often facilitates accurate identification of outbreak clusters.

The COVID-19 pandemic provides an opportunity to explore the impact of government mandates on movement restrictions and non-pharmaceutical interventions on a novel infection, and we investigate these strategies in early-stage outbreak dynamics. The rate of disease spread in South Africa varied over time as individuals changed behavior in response to the ongoing pandemic and to changing government policies. Using a system of ordinary differential equations, we model the outbreak in the province of Gauteng, assuming that several parameters vary over time. Analyzing data from the time period before vaccination gives the approximate dates of parameter changes, and those dates are linked to government policies. Unknown parameters are then estimated from available case data and used to assess the impact of each policy. Looking forward in time, possible scenarios give projections involving the implementation of two different vaccines at varying times. Our results quantify the impact of different government policies and demonstrate how vaccinations can alter infection spread.