Timothy Lant

Credibility, salience, and legitimacy of boundary objects: water managers' assessment of a simulation model in an immersive decision theater

The connection between scientific knowledge and environmental policy is enhanced through boundary organizations and objects that are perceived to be credible, salient, and legitimate. In this study, water resource decision-makers evaluated the knowledge embedded in WaterSim, an interactive simulation model of water supply and demand presented in an immersive decision theater. Content analysis of individual responses demonstrated that stakeholders were fairly critical of the models validity, relevance, and bias. Differing perspectives reveal tradeoffs in achieving credible, salient, and legitimate boundary objects, along with the need for iterative processes that engage them in the co-production of knowledge and action. Copyright , Beech Tree Publishing.

WaterSim: a simulation model for urban water planning in Phoenix, Arizona, USA

WaterSim, a simulation model, was built and implemented to investigate how alternative climate conditions, rates of population growth, and policy choices interact to affect future water supply and demand conditions in Phoenix, AZ. WaterSim is a hierarchical model that represents supply from surface and groundwater sources and demand from residential, commercial, and agricultural user sectors, incorporating the rules that govern reservoirs, aquifer use, and land-use change. In this paper we: (1) report on the imperative for exploratory modeling in water-resource management, given the deep uncertainties of climate change, (2) describe the geographic context for the Phoenix case study, (3) outline the objectives and structure of WaterSim, (4) report on testing the model with sensitivity analyses and history matching, (5) demonstrate the application of the model through a series of simulation experiments, and (6) discuss the models use for scenario planning and climate adaptation. Simulation results show there are significant challenges to Phoenixs water sustainability from climate change and rapid growth. Policies to address these challenges require difficult tradeoffs among lifestyles, groundwater sustainability, the pace of growth, and what is considered to be an appropriate level of risk of climate-induced shortage.

Comparing Focus Group and Individual Responses on Sensitive Topics: A Study of Water Decision Makers in a Desert City

Focus groups have gained a reputation for facilitating data collection about sensitive topics. However, we know little about how focus group methods perform compared to individual response formats, particularly for sensitive topics. The goal of this study is to assess how well focus groups perform when compared to individual responses collected using open-ended self-administered questionnaires for sensitive policy-making topics among water decision makers in Phoenix, Arizona. The analysis compares focus group and self-administered questionnaire responses among fifty-five decision makers for three types of sensitive topics: competence, risk, and gatekeeping. The results indicate that respondents (1) gave similar responses in group and open-ended self-administered questionnaires when discussion topics were only moderately sensitive, (2) volunteered less information in focus groups than in open-ended self-administered questionnaires for very sensitive topics when there did not appear to be a compelling reason for respondents to risk being stigmatized by other group members, and (3) volunteered more information in focus groups than in open-ended self-administered questionnaires for very sensitive topics when there appeared to be an opportunity to exchange important information or solve a pressing problem. The authors conclude that multimethod research—including individual and group response formats—may be the best strategy for collecting data from decision makers about sensitive policy-related issues.

Towards real time epidemiology: data assimilation, modeling and anomaly detection of health surveillance data streams

An integrated quantitative approach to data assimilation, prediction and anomaly detection over real-time public health surveillance data streams is introduced. The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes’ theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.

Public health and medical preparedness for a nuclear detonation: the nuclear incident medical enterprise

AbstractResilience and the ability to mitigate the consequences of a nuclear incident are enhanced by (1) effective planning, preparation and training; (2) ongoing interaction, formal exercises, and evaluation among the sectors involved; (3) effective and timely response and communication; and (4) continuous improvements based on new science, technology, experience, and ideas. Public health and medical planning require a complex, multi-faceted systematic approach involving federal, state, local, tribal, and territorial governments; private sector organizations; academia; industry; international partners; and individual experts and volunteers. The approach developed by the U.S. Department of Health and Human Services Nuclear Incident Medical Enterprise (NIME) is the result of efforts from government and nongovernment experts. It is a “bottom-up” systematic approach built on the available and emerging science that considers physical infrastructure damage, the spectrum of injuries, a scarce resources setting, the need for decision making in the face of a rapidly evolving situation with limited information early on, timely communication, and the need for tools and just-in-time information for responders who will likely be unfamiliar with radiation medicine and uncertain and overwhelmed in the face of the large number of casualties and the presence of radioactivity. The components of NIME can be used to support planning for, response to, and recovery from the effects of a nuclear incident. Recognizing that it is a continuous work-in-progress, the current status of the public health and medical preparedness and response for a nuclear incident is provided.

Community knowledge, risk perception, and preparedness for the 2009 influenza A/H1N1 pandemic.

OBJECTIVE To examine public knowledge, perceptions, and preparedness for the 2009 influenza A/H1N1 pandemic. DESIGN We conducted a telephone survey of selected households in Arizona during the month of October 2009. RESULTS Among the 727 households interviewed, one-third (34%) were not aware that the terms swine flu and H1N1 refer to the same virus. Many believed that it is more difficult to contract 2009 H1N1 (27%) than seasonal influenza (14%). About three-quarters of respondents perceived the H1N1 situation as urgent (76%), but only about one-third of those surveyed believed a family member would get sick with H1N1 within a year (35%). Approximately half (53%) of those surveyed intended to get the H1N1 influenza vaccine. Family doctors, television news, and local public health officials were the most trusted sources for H1N1 information. CONCLUSIONS The survey highlighted a number of important misconceptions about H1N1 knowledge, treatment options and transmissibility. Increased efforts should be made to understand how messages are transmitted and received in the community during a pandemic to improve risk communication plans moving forward.

A pandemic influenza simulation model for preparedness planning

Pandemic influenza continues to be a national and international public health concern that has received significant attention recently with the recent swine flu outbreak worldwide. Many countries have developed and updated their preparedness plans for pandemic influenza. School closure has been recommended as one of the best ways to protect children and indeed all susceptible individuals in a community during a possible disease outbreak. In this paper, we present a geospatial and temporal disease spread model for pandemic influenza affecting multiple communities. School closure, one of the social distancing policies, is investigated in this paper with several questions such as: at what level should schools be closed, for how long should they be kept closed, and how should be the re-opening decisions made. These questions are considered in terms of minimizing: the total infection cases, total mortalities, and the impact on educational services to school children.

Estimating Ebola Treatment Needs, United States.

To the Editor: By December 31, 2014, the Ebola epidemic in West Africa had resulted in treatment of 10 Ebola case-patients in the United States; a maximum of 4 patients received treatment at any one time (1). Four of these 10 persons became clinically ill in the United States (2 infected outside the United States and 2 infected in the United States), and 6 were clinically ill persons medically evacuated from West Africa (Technical Appendix 1 Table 6). To plan for possible future cases in the United States, policy makers requested we produce a tool to estimate future numbers of Ebola case-patients needing treatment at any one time in the United States. Gomes et al. previously estimated the potential size of outbreaks in the United States and other countries for 2 different dates in September 2014 (2). Another study considered the overall risk for exportation of Ebola from West Africa but did not estimate the number of potential cases in the United States at any one time (3). We provide for practicing public health officials a spreadsheet-based tool, Beds for Ebola Disease (BED) (Technical Appendix 2) that can be used to estimate the number of Ebola patients expected to be treated simultaneously in the United States at any point in time. Users of BED can update estimates for changing conditions and improved quality of input data, such as incidence of disease. The BED tool extends the work of prior studies by dividing persons arriving from Liberia, Sierra Leone, and Guinea into the following 3 categories: 1) travelers who are not health care workers (HCWs), 2) HCWs, and 3) medical evacuees. This categorization helps public health officials assess the potential risk for Ebola virus infection in individual travelers and the subsequent need for post-arrival monitoring (4). We used the BED tool to calculate the estimated number of Ebola cases at any one time in the United States by multiplying the rate of new infections in the United States by length of stay (LOS) in hospital (Table). The rate of new infections is the sum of the rate of infected persons in the 3 listed categories who enter the United States from Liberia, Sierra Leone, or Guinea. For the first 2 categories of travelers, low and high estimates of Ebola-infected persons arriving in the United States are calculated by using low and high estimates of both the incidence of disease in the 3 countries and the number of arrivals per month (Table). Calculating the incidence among arriving HCWs required estimating the number of HCWs treating Ebola patients in West Africa (Technical Appendix 1, Tables 2–4). For medical evacuations of persons already ill from Ebola, we calculated low and high estimates using unpublished data of such evacuations through the end of December 2014. Table Calculated monthly rates of Ebola disease among persons arriving in the United States and additional secondary cases, 2014 Although only 1 Ebola case has caused additional cases in the United States (7), we included the possibility that each Ebola case-patient who traveled into the United States would cause either 0 secondary cases (low estimate) or 2 secondary cases (high estimate) (Table). Such transmission might occur before a clinically ill traveler is hospitalized or between a patient and HCWs treating the patient (7). To account for the possibility that infected travelers may arrive in clusters, we assumed that persons requiring treatment would be distributed according to a Poisson probability distribution. Using this distribution enables us to calculate, using the BED tool, 95% CIs around the average estimate of arriving case-patients. The treatment length used in both the low and high estimate calculations was 14.8 days, calculated as a weighted average of the LOS of hospitalized case-patients treated in West Africa through September 2014 (Technical Appendix 1 Table 5) (8). We conducted a sensitivity analysis using LOS and reduced case-fatality rate of patients treated in the United States (Technical Appendix 1 Table 6). For late 2014, the low estimate of the average number of beds needed to treat patients with Ebola at any point in time was 1 (95% CI 0–3). The high estimate was 7 (95% CI 2–13). In late 2014, the United States had to plan and prepare to treat additional Ebola case-patients. By mid-January 2015, the capacity of Ebola treatment centers in the United States (49 hospitals with 71 total beds [9]) was sufficient to care for our highest estimated number of Ebola patients. Policymakers already have used the BED model to evaluate responses to the risk for arrival of Ebola virus–infected travelers, and it can be used in future infectious disease outbreaks of international origin to plan for persons requiring treatment within the United States. Technical Appendix 1: Data inputs and assumptions; sensitivity analysis (length of stay and case-fatality rate); comparison with other published estimates; and limitations. Click here to view.(228K, pdf) Technical Appendix 2: Beds for Ebola Disease (BED) model. Click here to view.(161K, xlsx)

A New Method of Exercising Pandemic Preparedness Through an Interactive Simulation and Visualization

As seen in the spring 2009 A/H1N1 influenza outbreak, influenza pandemics can have profound social, legal and economic effects. This experience has also made the importance of public health preparedness exercises more evident. Universities face unique challenges with respect to pandemic preparedness due to their dense student populations, location within the larger community and frequent student/faculty international travel. Depending on the social structure of the community, different mitigation strategies should be applied for decreasing the severity and transmissibility of the disease. To this end, Arizona State University has developed a simulation model and tabletop exercise that facilitates decision-maker interactions around emergency-response scenarios. This simulation gives policy makers the ability to see the real-time impact of their decisions. Therefore, tabletop exercises with computer simulations are developed to practice these decisions, which can possibly give opportunities for practicing better policy implementations. This paper introduces a new method of designing and performing table-top exercises for pandemic influenza via state-of-the-art technologies including system visualization and group decision making with a supporting simulation model. The presented exercise methodology can increase readiness for a pandemic through the support of computer and information technologies and the survey results that we include in this paper certainly support this result. The video scenarios and the computer simulation model make the exercise appear very compelling and real, which makes this presented method of exercising different than the other table-top exercises in the literature. Finally, designing a pandemic preparedness exercise with supporting technologies can help identifying the communication gaps between responsible authorities and advance the table-top exercising methodology.

Assessing the Capacity of the US Health Care System to Use Additional Mechanical Ventilators During a Large-Scale Public Health Emergency.

Objective A large-scale public health emergency, such as a severe influenza pandemic, can generate large numbers of critically ill patients in a short time. We modeled the number of mechanical ventilators that could be used in addition to the number of hospital-based ventilators currently in use. Methods We identified key components of the health care system needed to deliver ventilation therapy, quantified the maximum number of additional ventilators that each key component could support at various capacity levels (ie, conventional, contingency, and crisis), and determined the constraining key component at each capacity level. Results Our study results showed that US hospitals could absorb between 26,200 and 56,300 additional ventilators at the peak of a national influenza pandemic outbreak with robust pre-pandemic planning. Conclusions The current US health care system may have limited capacity to use additional mechanical ventilators during a large-scale public health emergency. Emergency planners need to understand their health care systems’ capability to absorb additional resources and expand care. This methodology could be adapted by emergency planners to determine stockpiling goals for critical resources or to identify alternatives to manage overwhelming critical care need. (Disaster Med Public Health Preparedness. 2015;9:634–641)