Diana Prieto

A systematic review to identify areas of enhancements of pandemic simulation models for operational use at provincial and local levels

BackgroundIn recent years, computer simulation models have supported development of pandemic influenza preparedness policies. However, U.S. policymakers have raised several concerns about the practical use of these models. In this review paper, we examine the extent to which the current literature already addresses these concerns and identify means of enhancing the current models for higher operational use.MethodsWe surveyed PubMed and other sources for published research literature on simulation models for influenza pandemic preparedness. We identified 23 models published between 1990 and 2010 that consider single-region (e.g., country, province, city) outbreaks and multi-pronged mitigation strategies. We developed a plan for examination of the literature based on the concerns raised by the policymakers.ResultsWhile examining the concerns about the adequacy and validity of data, we found that though the epidemiological data supporting the models appears to be adequate, it should be validated through as many updates as possible during an outbreak. Demographical data must improve its interfaces for access, retrieval, and translation into model parameters. Regarding the concern about credibility and validity of modeling assumptions, we found that the models often simplify reality to reduce computational burden. Such simplifications may be permissible if they do not interfere with the performance assessment of the mitigation strategies. We also agreed with the concern that social behavior is inadequately represented in pandemic influenza models. Our review showed that the models consider only a few social-behavioral aspects including contact rates, withdrawal from work or school due to symptoms appearance or to care for sick relatives, and compliance to social distancing, vaccination, and antiviral prophylaxis. The concern about the degree of accessibility of the models is palpable, since we found three models that are currently accessible by the public while other models are seeking public accessibility. Policymakers would prefer models scalable to any population size that can be downloadable and operable in personal computers. But scaling models to larger populations would often require computational needs that cannot be handled with personal computers and laptops. As a limitation, we state that some existing models could not be included in our review due to their limited available documentation discussing the choice of relevant parameter values.ConclusionsTo adequately address the concerns of the policymakers, we need continuing model enhancements in critical areas including: updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, updating information for contact patterns, adaptation of recent methodologies for collecting human mobility data, and improvement of computational efficiency and accessibility.

An operational epidemiological model for calibrating agent-based simulations of pandemic influenza outbreaks

Uncertainty of pandemic influenza viruses continue to cause major preparedness challenges for public health policymakers. Decisions to mitigate influenza outbreaks often involve tradeoff between the social costs of interventions (e.g., school closure) and the cost of uncontrolled spread of the virus. To achieve a balance, policymakers must assess the impact of mitigation strategies once an outbreak begins and the virus characteristics are known. Agent-based (AB) simulation is a useful tool for building highly granular disease spread models incorporating the epidemiological features of the virus as well as the demographic and social behavioral attributes of tens of millions of affected people. Such disease spread models provide excellent basis on which various mitigation strategies can be tested, before they are adopted and implemented by the policymakers. However, to serve as a testbed for the mitigation strategies, the AB simulation models must be operational. A critical requirement for operational AB models is that they are amenable for quick and simple calibration. The calibration process works as follows: the AB model accepts information available from the field and uses those to update its parameters such that some of its outputs in turn replicate the field data. In this paper, we present our epidemiological model based calibration methodology that has a low computational complexity and is easy to interpret. Our model accepts a field estimate of the basic reproduction number, and then uses it to update (calibrate) the infection probabilities in a way that its effect combined with the effects of the given virus epidemiology, demographics, and social behavior results in an infection pattern yielding a similar value of the basic reproduction number. We evaluate the accuracy of the calibration methodology by applying it for an AB simulation model mimicking a regional outbreak in the US. The calibrated model is shown to yield infection patterns closely replicating the input estimates of the basic reproduction number. The calibration method is also tested to replicate an initial infection incidence trend for a H1N1 outbreak like that of 2009.

GPGPU parallelization of self-calibrating agent-based influenza outbreak simulation

Agent-based simulations of influenza spread are useful for decision making during public health emergencies. During such emergencies, decisions are required in cycles of less than day, and agent-based models should be adapted to support such decisions. The most important considerations for model adaptation are fast calibration of the model, low computational complexity as the population size is scaled up, and dependability of the results with low replication quantity. In previous work, we presented a self-calibrating model for agent-based influenza simulations. We now investigate whether general-purpose GPU computation is effective at accelerating the processing of this model to support health policy decision-making for pandemic and seasonal strains of the virus. The results of this paper indicate that a speedup of 94.3x is obtained with GPU algorithms for simulation sizes of 50 million people. Our GPU implementation scales linearly in the number of people which makes it a good choice for real-time decision support.

Systematic Identification of Coordination Gaps in Pediatric Care

In the United States, the status of coordination among pediatric care services is not well understood. Through the use of quality improvement (QI) techniques, coordination gaps were systematically identified in the interagency network of pediatric services in Kalamazoo MI. Gaps were found in transportation resources, follow-up procedures, awareness of services, interagency communication, insurance limitations, population behaviors, and resource utilization. This preliminary study reveals the need for (1) protocols for intraand inter-agency communication, (2) mechanisms for easy and fast retrieval of pediatric resources, and (3) health information exchange.

Literature review of data-based models for identification of factors associated with racial disparities in breast cancer mortality

Abstract In the United States, early detection methods have contributed to the reduction of overall breast cancer mortality but this pattern has not been observed uniformly across all racial groups. A vast body of research literature shows a set of health care, socio-economic, biological, physical, and behavioural factors influencing the mortality disparity. In this paper, we review the modelling frameworks, statistical tests, and databases used in understanding influential factors, and we discuss the factors documented in the modelling literature. Our findings suggest that disparities research relies on conventional modelling and statistical tools for quantitative analysis, and there exist opportunities to implement data-based modelling frameworks for (1) exploring mechanisms triggering disparities, (2) increasing the collection of behavioural data, and (3) monitoring factors associated with the mortality disparity across time.

A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery

BackgroundBreast-conservation surgery with radiotherapy is a treatment highly recommended by the guidelines from the National Comprehensive Cancer Network. However, several variables influence the final receipt of radiotherapy and it might not be administered to breast cancer patients. Our objective is to propose a systematic framework to identify the clinical and non-clinical variables that influence the receipt of unexpected radiotherapy treatment by means of Bayesian networks and a proposed heuristic approach.MethodsWe used cancer registry data of Detroit, San Francisco-Oakland, and Atlanta from years 2007–2012 downloaded from the Surveillance, Epidemiology, and End Results Program. The samples had patients diagnosed with in situ and early invasive cancer with 14 clinical and non-clinical variables. Bayesian networks were fitted to the data of each region and systematically analyzed through the proposed Zoom-in heuristic. A comparative analysis with logistic regressions is also presented.ResultsFor Detroit, patients under stage 0, grade undetermined, histology lobular carcinoma in situ, and age between 26−50 were found more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients with age between 51−75, and grade II were found to be more likely to receive breast-conservation surgery with radiotherapy. For San Francisco-Oakland, patients under stage 0, grade undetermined, and age >75 are more likely to receive BCS. For stages I, IIA, and IIB patients with age >75 are more likely to receive breast-conservation surgery without radiotherapy. For Atlanta, patients under stage 0, grade undetermined, year 2011, and primary site C509 are more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients in year 2011, and grade III are more likely to receive breast-conservation surgery without radiotherapy.ConclusionFor in situ breast cancer and early invasive breast cancer, the results are in accordance with the guidelines and very well demonstrates the usefulness of the Zoom-in heuristic in systematically characterizing a group receiving a treatment. We found a subset of the population from Detroit with ductal carcinoma in situ for which breast-conservation surgery without radiotherapy was received, but potential reasons for this treatment are still unknown.

Hybrid multi-threaded simulation of agent-based pandemic modeling using multiple GPUs

Epidemiology computation models are crucial for the assessment and control of public health crises. Agent-based simulations of pandemic influenza forecast the infectious disease spreading in order to help public health policy makers during emergencies. In such emergencies, decisions are required for public health preparedness in cycles of less than a day, and the agent-based model should be adaptable and tractable for quick and simple calibration with low computational overhead. GPU accelerated computing involves the use of graphics processing units (GPUs) in combination with the CPU to perform heterogeneous computing by offloading compute-intensive portions of the program to the GPU while the remaining program runs on the CPU. In this paper, we demonstrate the utilization of the hardware environment and software tools and discuss strategies for porting agent-based simulations to multiple GPUs. We further compare the performance of simulations using two or four GPUs with the sequential execution on the CPU, in terms of time and speedup. The multi-GPU implementations exhibit great performance and support populations with up to 100 million individuals.

Parallel Programming Approaches for an Agent-based Simulation of Concurrent Pandemic and Seasonal Influenza Outbreaks

Abstract In this paper we propose parallelized versions of an agent-based simulation for concurrent pandemic and seasonal influenza outbreaks. The objective of the implementations is to significantly reduce the replication time and allow faster evaluation of mitigation strategies during an ongoing emergency. The simulation was initially parallelized using the g++ OpenMP library. We simulated the outbreak in a population of 1,000,000 individuals to evaluate algorithm performance and results. In addition to the OpenMP parallelization, a proposed CUDA implementation is also presented.