Shamir N Mukhi

Agent-Based Simulation of Emergency Departments with Patient Diversion

We describe an agent based model of an emergency department and its utility for evaluating workflow and assessing patient diversion policies. The overall goal of the research is to develop tools to better understand and manage emergency departments. There are several modes in which agent based modeling tools may be of benefit. In a self contained manner the operation of an emergency department can be modeled. In this mode, policies such as staffing could be changed and the effect on parameters such as waiting times and throughput could be quantified. In an extended version, multiple emergency departments can be modeled and would allow for the evaluation of ambulance or patient redirection policies. In either case we also suggest an effective means of augmenting the simulation with empirical data collected using a proximity location and tracking system within an emergency department. Our agent based model allows for a simulation of a number of emergency departments and introduces a method of extracting real time patient data from emergency departments throughout a city allowing for the evaluation of patient diversion policies.

Agent-Based Modeling of the Spread of Influenza-Like Illness in an Emergency Department: A Simulation Study

The objective of this paper was to develop an agent based modeling framework in order to simulate the spread of influenza virus infection on a layout based on a representative hospital emergency department in Winnipeg, Canada. In doing so, the study complements mathematical modeling techniques for disease spread, as well as modeling applications focused on the spread of antibiotic-resistant nosocomial infections in hospitals. Twenty different emergency department scenarios were simulated, with further simulation of four infection control strategies. The agent based modeling approach represents systems modeling, in which the emergency department was modeled as a collection of agents (patients and healthcare workers) and their individual characteristics, behaviors, and interactions. The framework was coded in C + + using Qt4 libraries running under the Linux operating system. A simple ordinary least squares (OLS) regression was used to analyze the data, in which the percentage of patients that be came infected in one day within the simulation was the dependent variable. The results suggest that within the given instance con text, patient-oriented infection control policies (alternate treatment streams, masking symptomatic patients) tend to have a larger effect than policies that target healthcare workers. The agent-based modeling framework is a flexible tool that can be made to reflect any given environment; it is also a decision support tool for practitioners and policymakers to assess the relative impact of infection control strategies. The framework illuminates scenarios worthy of further investigation, as well as counterintuitive findings.

Determination of the relative economic impact of different molecular-based laboratory algorithms for respiratory viral pathogen detection, including Pandemic (H1N1), using a secure web based platform

BackgroundDuring period of crisis, laboratory planners may be faced with a need to make operational and clinical decisions in the face of limited information. To avoid this dilemma, our laboratory utilizes a secure web based platform, Data Integration for Alberta Laboratories (DIAL) to make near real-time decisions.This manuscript utilizes the data collected by DIAL as well as laboratory test cost modeling to identify the relative economic impact of four proposed scenarios of testing for Pandemic H1N1 (2009) and other respiratory viral pathogens.MethodsHistorical data was collected from the two waves of the pandemic using DIAL. Four proposed molecular testing scenarios were generated: A) Luminex respiratory virus panel (RVP) first with/without US centers for Disease Control Influenza A Matrix gene assay (CDC-M), B) CDC-M first with/without RVP, C) RVP only, and D) CDC-M only. Relative cost estimates of different testing algorithm were generated from a review of historical costs in the lab and were based on 2009 Canadian dollars.ResultsScenarios A and B had similar costs when the rate of influenza A was low (< 10%) with higher relative cost in Scenario A with increasing incidence. Scenario A provided more information about mixed respiratory virus infection as compared with Scenario B.ConclusionsNo one approach is applicable to all conditions. Testing costs will vary depending on the test volume, prevalence of influenza A strains, as well as other circulating viruses and a more costly algorithm involving a combination of different tests may be chosen to ensure that tests results are returned to the clinician in a quicker manner. Costing should not be the only consideration for determination of laboratory algorithms.

Bordetella pertussis in sporadic and outbreak settings in Alberta, Canada, July 2004 – December 2012

BackgroundProvLab Alberta provides all laboratory testing for Bordetella pertussis including sporadic cases and outbreak investigations through collaborations with provincial public health partners. We describe B. pertussis activity in Alberta from July 2004 to December 2012.MethodsLaboratory testing for pertussis was analyzed using interpreted laboratory data that was generated by DIAL, a secure web-based platform. Duplicate specimens from the same individual ≤90 days were excluded to generate a case-based dataset. Immunization status of confirmed pertussis cases from the provincial immunization repository was reviewed.ResultsOverall, 7.1% of suspected pertussis cases tested positive with a higher positivity rate in outbreak as compared to sporadic setting. Annual variations in sporadic pertussis cases were observed across the province with higher positivity rates in 2005, 2008, 2009 and 2012. A significantly higher positivity rate was observed in a northern region of Alberta. While the positivity rate in sporadic setting was highest in adolescents aged 10 to <15 years old (14.8%), population-based disease burden was highest in young children <5 years old. Of the 81.6% (n = 1,348) pertussis cases with immunization records, 48.3% were up-to-date with immunization. The pertussis cases that were up-to-date with their immunization were older (median age 12.9 years) as compared to those with incomplete (median age 9.7 years) or no pertussis immunization (median age 3.8 years).ConclusionsCyclic pattern of annual pertussis activity with geographic variation was observed in Alberta with no obvious case finding effect from outbreak investigations. The high positivity rates in adolescents suggested an underestimation of disease burden in this age group.

Effective animal health disease surveillance using a network-enabled approach.

There are many benefits that derive from real-time knowledge of the health status of the national livestock population. Effective animal disease surveillance is a requirement for countries that trade in live animals and their products in order to comply with the World Organization for Animal Health (OIE) guidelines. Rapid identification of introduced and emerging disease allows rapid response and mitigation of the economic consequences. Connections between animal and human disease caused by a common pathogen can be recognized and control measures implemented, thereby protecting public health and maintaining public confidence in the food supply. Production-limiting diseases can be monitored, and control programmes be evaluated with benefits accruing from decreased economic losses associated with disease as well as reducing the welfare concerns associated with diseased animals. Establishing a surveillance programme across a wide area with diverse ecosystems and political administrations as Canada is a complex challenge. When funding became available from a government programme to enable early detection of a bio-terrorist attack on livestock, the Canadian Animal Health Surveillance Network (CAHSN) became officially established. An existing web-based information platform that supports intelligence exchange, surveillance and response for public health issues in Canada was adapted to link the network animal health laboratories. A minimum data set was developed that facilitated sharing of results between participating laboratories and jurisdictions as the first step in creating the capacity for national disease trend analysis. In each of the network laboratories, similar quality assurance and bio-containment systems have been funded and supported, and diagnostic staff have been trained and certified on a suite of diagnostic tests for foreign animal diseases. This ensures that national standards are maintained throughout all of the diagnostic laboratories. This paper describes the genesis of CAHSN, its current capability and governance, and potential for future development.

DIAL: A Platform for real-time Laboratory Surveillance

Laboratory information systems fulfill many of the requirements for individual result management within a public health laboratory. However, access to the systems by data users, timely data extraction, integration, and data analysis are difficult tasks. These difficulties are further complicated by often having multiple laboratory results for specific analytes or related analytes per specimen tested as part of complex laboratory algorithms requiring specialized expertise for result interpretation. We describe DIAL, (Data Integration for Alberta Laboratories), a platform allowing laboratory data to be extracted, interpreted, collated and analyzed in near real-time using secure web based technology, which is adapted from CNPHI’s Canadian Early Warning System (CEWS) technology. The development of DIAL represents a major technical advancement in the public health information management domain, building capacity for laboratory based surveillance.

Vehicular Traffic Modeling Governed by Cellular Phone Trajectories

This paper briefly outlines the combination of an Agent Based Modeling (ABM) framework for modeling the flow of traffic in an urban center, using a 3D gaming platform and incorporating real world data extracted from cell phone trajectories to guide agent movements. Results are compared against two other sets of real world data. The model validation shows considerable promise both for the simulation itself and the use of cellular location data to infer traffic patterns.

Innovative technology for web-based data management during an outbreak

Lack of automated and integrated data collection and management, and poor linkage of clinical, epidemiological and laboratory data during an outbreak can inhibit effective and timely outbreak investigation and response. This paper describes an innovative web-based technology, referred to as Web Data, developed for the rapid set-up and provision of interactive and adaptive data management during outbreak situations. We also describe the benefits and limitations of the Web Data technology identified through a questionnaire that was developed to evaluate the use of Web Data implementation and application during the 2009 H1N1 pandemic by Winnipeg Regional Health Authority and Provincial Laboratory for Public Health of Alberta. Some of the main benefits include: improved and secure data access, increased efficiency and reduced error, enhanced electronic collection and transfer of data, rapid creation and modification of the database, conversion of specimen-level to case-level data, and user-defined data extraction and query capabilities. Areas requiring improvement include: better understanding of privacy policies, increased capability for data sharing and linkages between jurisdictions to alleviate data entry duplication.

A Web-Based System for Mapping Laboratory Networks: Analysis of GLaDMap application

Public health emergencies such as H1N1 and SARS pandemics have demonstrated and validated the necessity of a strong and cohesive laboratory response system that is able to respond to threats in an efficient and timely manner. Individual laboratories, through connection with other laboratories or networks, are able to enhance their capacity for preparedness and response to emergencies. Efficient networks often establish standards and maintain best practices within member laboratories. The Global Laboratory Directory Mapping tool (GLaDMap) supports the efforts of laboratory networks to improve their connectivity by providing a simple and efficient tool to profile laboratories by geographic location, function or expertise. The purpose of this paper is to evaluate the effectiveness of the GLaDMap search tool and the completeness of the descriptive content of networks and laboratories that are currently contained within the GLaDMap database. We determined the extent of information volunteered and how the system is being used. Although the system aims to attract an array of users from around the globe, our analysis reveals minimal participation and information sharing and that the low profile participation rate limits the tool’s functionality. The Global Laboratory Directory platform has addressed barriers to participation by adding optional functionality such as restricted access to laboratory profiles to protect private information and by implementing additional functional applications complementary to GLaDMap.

A Confidence-based Aberration Interpretation Framework For Outbreak Conciliation

Health surveillance can be viewed as an ongoing systematic collection, analysis, and interpretation of data for use in planning, implementation, and evaluation of a given health system, in potentially multiple spheres (ex: animal, human, environment). As we move into a sophisticated technologically advanced era, there is a need for cost-effective and efficient health surveillance methods and systems that will rapidly identify potential bioterrorism attacks and infectious disease outbreaks. The main objective of such methods and systems would be to reduce the impact of an outbreak by enabling appropriate officials to detect it quickly and implement timely and appropriate interventions. Identifying an outbreak and/or potential bioterrorism attack days to weeks earlier than traditional surveillance methods would potentially result in a reduction in morbidity, mortality, and outbreak associated economic consequences. Proposed here is a novel framework that takes into account the relationships between aberration detection algorithms and produces an unbiased confidence measure for identification of start of an outbreak. Such a framework would enable a user and/or a system to interpret the anomaly detection results generated via multiple algorithms with some indication of confidence.