A Global Ethical Framework for Public Health Disasters

BackgroundPrivacy concerns by providers have been a barrier to disclosing patient information for public health purposes. This is the case even for mandated notifiable disease reporting. In the context of a pandemic it has been argued that the public good should supersede an individual's right to privacy. The precise nature of these provider privacy concerns, and whether they are diluted in the context of a pandemic are not known. Our objective was to understand the privacy barriers which could potentially influence family physicians' reporting of patient-level surveillance data to public health agencies during the Fall 2009 pandemic H1N1 influenza outbreak.MethodsThirty seven family doctors participated in a series of five focus groups between October 29-31 2009. They also completed a survey about the data they were willing to disclose to public health units. Descriptive statistics were used to summarize the amount of patient detail the participants were willing to disclose, factors that would facilitate data disclosure, and the consensus on those factors. The analysis of the qualitative data was based on grounded theory.ResultsThe family doctors were reluctant to disclose patient data to public health units. This was due to concerns about the extent to which public health agencies are dependable to protect health information (trusting beliefs), and the possibility of loss due to disclosing health information (risk beliefs). We identified six specific actions that public health units can take which would affect these beliefs, and potentially increase the willingness to disclose patient information for public health purposes.ConclusionsThe uncertainty surrounding a pandemic of a new strain of influenza has not changed the privacy concerns of physicians about disclosing patient data. It is important to address these concerns to ensure reliable reporting during future outbreaks.

Will Ebola change the game? Ten essential reforms before the next pandemic. The report of the Harvard-LSHTM Independent Panel on the Global Response to Ebola

BackgroundAs seen during past pandemic influenza outbreaks, pharmaceutical interventions (PHIs) with vaccines and antivirals are the most effective methods of mitigation. However, availability of PHIs is unlikely to be adequate during the early stages of a pandemic. Hence, for early mitigation and possible containment, non-pharmaceutical interventions (NPIs) offer a viable alternative. Also, NPIs may be the only available interventions for most underdeveloped countries. In this paper we present a comprehensive methodology for design of effective NPI strategies.MethodsWe develop a statistical ANOVA-based design approach that uses a detailed agent-based simulation as an underlying model. The design approach obtains the marginal effect of the characteristic parameters of NPIs, social behavior, and their interactions on various pandemic outcome measures including total number of contacts, infections, and deaths. We use the marginal effects to establish regression equations for the outcome measures, which are optimized to obtain NPI strategies. Efficacy of the NPI strategies designed using our methodology is demonstrated using simulated pandemic influenza outbreaks with different levels of virus transmissibility.ResultsOur methodology was able to design effective NPI strategies, which were able to contain outbreaks by reducing infection attack rates (IAR) to below 10% in low and medium virus transmissibility scenarios with 33% and 50% IAR, respectively. The level of reduction in the high transmissibility scenario (with 65% IAR) was also significant. As noted in the published literature, we also found school closure to be the single most effective intervention among all NPIs.ConclusionsIf harnessed effectively, NPIs offer a significant potential for mitigation of pandemic influenza outbreaks. The methodology presented here fills a gap in the literature, which, though replete with models on NPI strategy evaluation, lacks a treatise on optimal strategy design.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2458-14-1328) contains supplementary material, which is available to authorized users.

Pandemic influenza has been an important public health concern. During the 20th century, three major pandemics of influenza occurred in 1918, 1957, and 1968. The pandemic of 1918 caused 40 to 50 million deaths worldwide and more than 500,000 deaths in the United States. The 1957 pandemic, during a time with much less globalization than now, spread to the U.S. within 4 to 5 months of its origination in China, causing more than 70,000 deaths in the U.S., and the 1968 pandemic spread to the U.S. from Hong Kong within 2 to 3 months, causing 34,000 deaths. Pandemic influenza is considered to be a relatively high probability event, even inevitable by many experts. During a pandemic influenza outbreak, some key preparedness tasks cannot be accomplished by hospitals individually; regional resource allocation, patient redistribution, and use of alternative care sites all require collaboration among hospitals both in planning and in response. The research presented in this dissertation develops optimization models to be used by decision makers (e.g. hospital associations, emergency management agency, etc.) to determine how best to manage medical resources as well as suggest patient allocation among hospitals and alternative healthcare facilities. Both single-objective and multi-objective optimization models are developed to determine the patient allocation and resource allocation among healthcare facilities. The single-objective optimization models are developed to optimize the patient allocation in terms of minimizing the travel distance between patients and healthcare facilities while considering medical resource capacity constraints. During the pandemic, the surge demand most likely would exhaust all the medical resources, at which time the models can help predict the potential resource shortage so an appropriate contingency plan can be developed. If additional resource quantities become available, the models help to determine the best allocation of these resources among healthcare facilities. Various methods are proposed to conduct the sensitivity analysis to help decision makers determine the impact of different level of each type resource on the patient service. The multi-objective optimization model not only considers the objective of minimization of the total travel distance by patients to healthcare facilities, but also considers the minimization of maximum patient travel distance. A case study from Metro Louisville, Kentucky is presented to demonstrate how the models would aid in patient allocation and resource allocation during a pandemic influenza outbreak. A web-based application based on the optimization models developed in this dissertation is presented as an initial tool for decision makers.

In this paper, heat transfer problems with sharp spatial gradients are analyzed using the Generalized Finite Element Method with global-local enrichment functions (GFEM gl). With this approach, scale-bridging enrichment functions are generated on the fly, providing specially-tailored enrichment functions for the problem to be analyzed with no a-priori knowledge of the exact solution. In this work, a decomposition of the linear system of equations is formulated for both steady-state and transient heat transfer problems, allowing for a much more computationally efficient analysis of the problems of interest. With this algorithm, only a small portion of the global system of equations, i.e., the hierarchically added enrichments, need to be re-computed for each loading configuration or time-step. Numerical studies confirm that the condensation scheme does not impact the solution quality, while allowing for more computationally efficient simulations when large problems are considered. We also extend the GFEM gl to allow for the use of hexahedral elements in the global domain, while still using tetrahedral elements in the local domain, to allow for automatic localized mesh refinement without the use of constrained approximations. Simulations are run with the use of linear and quadratic hexahedral and tetrahedral elements in the global domain. Convergence studies indicate that the use of a different partition of unity (PoU) in the global (hexahedral elements) and local (tetrahedral elements) domains does not adversely impact the solution quality.

Multi-objective optimization models for patient allocation during a pandemic influenza outbreak

BackgroundSince spring 2013, periodic emergence of avian influenza A(H7N9) virus in China has heightened the concern for a possible pandemic outbreak among humans, though it is believed that the virus is not yet human-to-human transmittable. Till June 2017, A(H7N9) has resulted in 1533 laboratory-confirmed cases of human infections causing 592 deaths. The aim of this paper is to present disease burden estimates (measured by infection attack rates (IAR) and number of deaths) in the event of a possible pandemic outbreak caused by human-to-human transmission capability acquired by A(H7N9) virus. Even though such a pandemic will likely spread worldwide, our focus in this paper is to estimate the impact on the United States alone.MethodThe method first uses a data clustering technique to divide 50 states in the U.S. into a small number of clusters. Thereafter, for a few selected states in each cluster, the method employs an agent-based (AB) model to simulate human A(H7N9) influenza pandemic outbreaks. The model uses demographic and epidemiological data. A few selected non-pharmaceutical intervention (NPI) measures are applied to mitigate the outbreaks. Disease burden for the U.S. is estimated by combining results from the clusters applying a method used in stratified sampling.ResultsTwo possible pandemic scenarios with R0 = 1.5 and 1.8 are examined. Infection attack rates with 95% C.I. (Confidence Interval) for R0 = 1.5 and 1.8 are estimated to be 18.78% (17.3–20.27) and 25.05% (23.11–26.99), respectively. The corresponding number of deaths (95% C.I.), per 100,000, are 7252.3 (6598.45–7907.33) and 9670.99 (8953.66–10,389.95).ConclusionsThe results reflect a possible worst-case scenario where the outbreak extends over all states of the U.S. and antivirals and vaccines are not administered. Our disease burden estimations are also likely to be somewhat high due to the fact that only dense urban regions covering approximately 3% of the geographic area and 81% of the population are used for simulating sample outbreaks. Outcomes from these simulations are extrapolated over the remaining 19% of the population spread sparsely over 97% of the area. Furthermore, the full extent of possible NPIs, if deployed, could also have lowered the disease burden estimates.

Pandemic influenza represents a serious threat not only to the population of the United States, but also to its economy. In this study, we analyze the total economic consequences of potential influenza outbreaks in the United States for four cases based on the distinctions between disease severity and the presence/absence of vaccinations. The analysis is based on data and parameters on influenza obtained from the Centers for Disease Control and the general literature. A state-of-the-art economic impact modeling approach, computable general equilibrium, is applied to analyze a wide range of potential impacts stemming from the outbreaks. This study examines the economic impacts from changes in medical expenditures and workforce participation, and also takes into consideration different types of avoidance behavior and resilience actions not previously fully studied. Our results indicate that, in the absence of avoidance and resilience effects, a pandemic influenza outbreak could result in a loss in U.S. GDP of $25.4 billion, but that vaccination could reduce the losses to $19.9 billion. When behavioral and resilience factors are taken into account, a pandemic influenza outbreak could result in GDP losses of $45.3 billion without vaccination and $34.4 billion with vaccination. These results indicate the importance of including a broader set of causal factors to achieve more accurate estimates of the total economic impacts of not just pandemic influenza but biothreats in general. The results also highlight a number of actionable items that government policymakers and public health officials can use to help reduce potential economic losses from the outbreaks.

Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area.

Intervention strategies for an influenza pandemic taking into account secondary bacterial infections

This study enters the ‘kitchens of science’ or the ‘backrooms’ of international development agencies to examine the methods used to develop global COVID-19 response frameworks for education. These global frameworks need to be scrutinized as they are used to guide the development of national COVID-19 response frameworks for education. Drawing upon the theory of change, the study examines how the interventions embedded in these global frameworks are produced. The results show methodological flaws and epistemic violence in the production process of these global frameworks. It is suggested that epistemic accountability and epistemic reflexivity are necessary to resolve these methodological and epistemic vices.

Pandemic influenza is considered catastrophic to global health, with severe economic and social effects. Consequently, a strategy for the rapid deployment of essential medical supplies used for the prevention of influenza transmission and to alleviate public panic caused by the expected shortage of such supplies needs to be developed. Therefore, we employ integrated information concepts to develop a simulated influenza medical material supply system to facilitate a rapid response to such a crisis. Various scenarios are analyzed to estimate the appropriate inventory policy needed under different pandemic influenza outbreaks, and to establish a mechanism to evaluate the necessary stockpiles of medications and other requirements in the different phases of the pandemic. This study constructed a web-based decision support system framework prototype that displayed transparent data related to medical stockpiles in each district and integrated expert opinion about the best distribution of these supplies in the influenza pandemic scenarios. A data collection system was also designed to gather information through a daily VPN transmitted into one central repository for reporting and distribution purposes. This study provides timely and transparent medical supplies distribution information that can help decision makers to make the appropriate decisions under different pandemic influenza outbreaks, and also attempts to establish a mechanism of evaluating the stockpiles and requirements in the different phases of the pandemic.

Aims:This paper aims to summarise the growing literature concerning an imminent future influenza pandemic, from the primary care perspective.Methods:Sources of literature were scanned and relevant material short-listed for further study from: (1) WHO and CDC websites; (2) PUBMED; and (3) papers mentioned in references of full-text papers.Results:Outbreaks of avian influenza in Asia and elsewhere indicate that the world may be moving towards a pandemic influenza outbreak. The WHO Global InfluenzaPreparedness Plan 2005 unifies the world with the vision of tackling the next pandemic influenza outbreak as a global effort that includes healthcare provider and patient alike.Conclusions:We need to update ourselves and keep our staff and patients informed to make infection control measures part of our daily activities. In areas where there are contacts with animal reservoirs of influenza A, patients need to be reminded that they need to protect themselves from being infected.

Two-scale approach to predict multi-site cracking potential in 3-D structures using the generalized finite element method

Operating under harsh conditions and exposed to fluctuating loads for extended periods, wind turbines experience a heightened vulnerability in their key components. Early fault detection is crucial to enhance the reliability of wind turbines, minimize downtime, and optimize power generation efficiency. Although deep learning techniques have been widely applied to fault diagnosis tasks, yielding remarkable performance, practical implementations frequently confront the obstacle of acquiring a substantial quantity of labeled data to train an effective deep learning model. Consequently, this paper introduces an unsupervised global and local domain adaptation network (GLDAN) for fault diagnosis across wind turbines, enabling the model to efficiently transfer acquired knowledge to the target domain in the absence of labeled data. This feature renders it an appropriate solution for situations with limited labeled data availability. Employing adversarial training, GLDAN aligns global domain distributions, diminishing the overall discrepancy between source and target domains, and local domain distributions within a single fault category for both domains, capturing more intricate and specific fault features. The proposed approach is corroborated using actual wind farm data, and comprehensive experimental results demonstrate that GLDAN surpasses deep global domain adaptation methods in cross-wind turbine fault diagnosis, underlining its practical value in the field.