Ozgur M. Araz

Using Google Flu Trends data in forecasting influenza-like-illness related ED visits in Omaha, Nebraska.

INTRODUCTION Emergency department (ED) visits increase during the influenza seasons. It is essential to identify statistically significant correlates in order to develop an accurate forecasting model for ED visits. Forecasting influenza-like-illness (ILI)-related ED visits can significantly help in developing robust resource management strategies at the EDs. METHODS We first performed correlation analyses to understand temporal correlations between several predictors of ILI-related ED visits. We used the data available for Douglas County, the biggest county in Nebraska, for Omaha, the biggest city in the state, and for a major hospital in Omaha. The data set included total and positive influenza test results from the hospital (ie, Antigen rapid (Ag) and Respiratory Syncytial Virus Infection (RSV) tests); an Internet-based influenza surveillance system data, that is, Google Flu Trends, for both Nebraska and Omaha; total ED visits in Douglas County attributable to ILI; and ILI surveillance network data for Douglas County and Nebraska as the predictors and data for the hospitals ILI-related ED visits as the dependent variable. We used Seasonal Autoregressive Integrated Moving Average and Holt Winters methods with3 linear regression models to forecast ILI-related ED visits at the hospital and evaluated model performances by comparing the root means square errors (RMSEs). RESULTS Because of strong positive correlations with ILI-related ED visits between 2008 and 2012, we validated the use of Google Flu Trends data as a predictor in an ED influenza surveillance tool. Of the 5 forecasting models we have tested, linear regression models performed significantly better when Google Flu Trends data were included as a predictor. Regression models including Google Flu Trends data as a predictor variable have lower RMSE, and the lowest is achieved when all other variables are also included in the model in our forecasting experiments for the first 5 weeks of 2013 (with RMSE = 57.61). CONCLUSIONS Google Flu Trends data statistically improve the performance of predicting ILI-related ED visits in Douglas County, and this result can be generalized to other communities. Timely and accurate estimates of ED volume during the influenza season, as well as during pandemic outbreaks, can help hospitals plan their ED resources accordingly and lower their costs by optimizing supplies and staffing and can improve service quality by decreasing ED wait times and overcrowding.

Modeling Social Transmission Dynamics of Unhealthy Behaviors for Evaluating Prevention and Treatment Interventions on Childhood Obesity

Research evidence indicates that obesity has spread through social networks, but lever points for interventions based on overlapping networks are not well studied. The objective of our research was to construct and parameterize a system dynamics model of the social transmission of behaviors through adult and youth influence in order to explore hypotheses and identify plausible lever points for future childhood obesity intervention research. Our objectives were: (1) to assess the sensitivity of childhood overweight and obesity prevalence to peer and adult social transmission rates, and (2) to test the effect of combinations of prevention and treatment interventions on the prevalence of childhood overweight and obesity. To address the first objective, we conducted two-way sensitivity analyses of adult-to-child and child-to-child social transmission in relation to childhood overweight and obesity prevalence. For the second objective, alternative combinations of prevention and treatment interventions were tested by varying model parameters of social transmission and weight loss behavior rates. Our results indicated child overweight and obesity prevalence might be slightly more sensitive to the same relative change in the adult-to-child compared to the child-to-child social transmission rate. In our simulations, alternatives with treatment alone, compared to prevention alone, reduced the prevalence of childhood overweight and obesity more after 10 years (1.2–1.8% and 0.2–1.0% greater reduction when targeted at children and adults respectively). Also, as the impact of adult interventions on children was increased, the rank of six alternatives that included adults became better (i.e., resulting in lower 10 year childhood overweight and obesity prevalence) than alternatives that only involved children. The findings imply that social transmission dynamics should be considered when designing both prevention and treatment intervention approaches. Finally, targeting adults may be more efficient, and research should strengthen and expand adult-focused interventions that have a high residual impact on children.

Simulating school closure policies for cost effective pandemic decision making

BackgroundAround the globe, school closures were used sporadically to mitigate the 2009 H1N1 influenza pandemic. However, such closures can detrimentally impact economic and social life.MethodsHere, we couple a decision analytic approach with a mathematical model of influenza transmission to estimate the impact of school closures in terms of epidemiological and cost effectiveness. Our method assumes that the transmissibility and the severity of the disease are uncertain, and evaluates several closure and reopening strategies that cover a range of thresholds in school-aged prevalence (SAP) and closure durations.ResultsAssuming a willingness to pay per quality adjusted life-year (QALY) threshold equal to the US per capita GDP (

A pandemic influenza simulation model for preparedness planning

46,000), we found that the cost effectiveness of these strategies is highly dependent on the severity and on a willingness to pay per QALY. For severe pandemics, the preferred strategy couples the earliest closure trigger (0.5% SAP) with the longest duration closure (24 weeks) considered. For milder pandemics, the preferred strategies also involve the earliest closure trigger, but are shorter duration (12 weeks for low transmission rates and variable length for high transmission rates).ConclusionsThese findings highlight the importance of obtaining early estimates of pandemic severity and provide guidance to public health decision-makers for effectively tailoring school closures strategies in response to a newly emergent influenza pandemic.

Geographic prioritization of distributing pandemic influenza vaccines

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.

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

Pandemic influenza is an international public health concern. In light of the persistent threat of H5N1 avian influenza and the recent pandemic of A/H1N1swine influenza outbreak, public health agencies around the globe are continuously revising their preparedness plans. The A/H1N1 pandemic of 2009 demonstrated that influenza activity and severity might vary considerably among age groups and locations, and the distribution of an effective influenza vaccine may be significantly delayed and staggered. Thus, pandemic influenza vaccine distribution policies should be tailored to the demographic and spatial structures of communities. Here, we introduce a bi-criteria decision-making framework for vaccine distribution policies that is based on a geospatial and demographically-structured model of pandemic influenza transmission within and between counties of Arizona in the Unites States. Based on data from the 2009–2010 H1N1 pandemic, the policy predicted to reduce overall attack rate most effectively is prioritizing counties expected to experience the latest epidemic waves (a policy that may be politically untenable). However, when we consider reductions in both the attack rate and the waiting period for those seeking vaccines, the widely adopted pro rata policy (distributing according to population size) is also predicted to be an effective strategy.

A simulation model for policy decision analysis: A case of pandemic influenza on a university campus

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.

Simulation modeling for pandemic decision making: A case study with bi-criteria analysis on school closures

Pandemic influenza preparedness plans strongly focus on efficient mitigation strategies including social distancing, logistics and medical response. These strategies are formed by multiple decision makers before a pandemic outbreak and during the pandemic in local communities, states and nation-wide. In this paper, we model the spread of pandemic influenza in a local community, a university, and evaluate the mitigation policies. Since the development of an appropriate vaccine requires a significant amount of time and available antiviral quantities can only cover a relatively small proportion of the population, university decision makers will first focus on non-pharmaceutical interventions. These interventions include social distancing and isolation. The disease spread is modelled as differential equations-based compartmental model. The system is simulated for multiple non-pharmaceutical interventions such as social distancing including suspending university operations, evacuating dorms and isolation of infected individuals on campus. Although the model is built based on the preparedness plan of one of the biggest universities in the world, Arizona State University, it can easily be generalized for other colleges and universities. The policies and the decisions are tested by several simulation runs and evaluations of the mitigation strategies are presented in the paper.

Simulating pandemic influenza preparedness plans for a public university: a hierarchical system dynamics approach

Pandemic influenza continues to be a national and international public health concern, and has received significant attention worldwide with the A/H1N1 influenza outbreak in 2009. Many countries, including the United States, have developed preparedness plans for an influenza pandemic. Preparedness plans are falling under renewed scrutiny as decision-makers apply new findings and seek key leverage points for more effective preparedness and response. School closure has been recommended by the World Health Organization as one of the best ways to protect children and other susceptible individuals at the early stages of the pandemic. However, school closure is a difficult mitigation policy to implement from both strategic and operational points of view. Challenges include impacts on alternative education delivery services, such as student meals and after-school oversight, as well as direct and indirect economic outfalls. To help public health decision makers address these issues, we developed an epidemiological simulation tool for pandemic influenza which enables users to make decisions during a simulated pandemic. We then designed a school closure tabletop exercise using our simulation model as a decision-support tool for evaluating the effectiveness of school closure as a community mitigation strategy for pandemic influenza. We conducted two exercises in February 2009 for the Arizona Department of Health and Human Services including high-ranking health and education administrators from across the state. The purpose of these exercises was to test the states pandemic preparedness plans with respect to school closure timing and impact. The exercises required participants to make (hypothetical) strategic and operational decisions to mitigate the impacts of pandemic influenza at the state and local levels. Our simulation and decision analysis tool was used to assess the impact of key decisions in the exercises. This paper presents the technical details involved in the design and evaluation of this pandemic decision-support tool. Based on the decisions made in the exercises, we present a bi-criteria decision analysis framework to evaluate analytical results obtained from the simulation model. Our analyses show that sequential school closure and re-opening strategy with a specific decision rule gives the best compromised solution in terms of minimizing the total number of infections and providing minimal educational discontinuity.

A system dynamics model to simulate sustainable interventions on chronic disease outcomes in an urban community

Pandemic influenza preparedness plans strongly focus on efficient mitigation strategies including social distancing, logistics and medical response. These strategies are formed by multiple decisions-makers before pandemic outbreak and during the disaster by decision makers in local communities, states and nationwide. Depending on the community that will be affected by pandemic influenza, different strategies should be employed to decrease the severity of the disaster in multiple dimensions of social life. In this paper, a system dynamics methodology is applied to model the population behaviors and the effects of pandemic influenza on a public university community. The system is simulated for multiple non-pharmaceutical interventions with several policies that can be employed by local decision makers. System components are constructed from the pandemic influenza preparedness plan of one of the largest universities in the country. The policies and the decisions are tested by simulation runs and evaluations of the mitigation strategies are presented.