Data Preparation for West Nile Virus Agent-Based Modelling: Protocol for Processing Bird Population Estimates and Incorporating ArcMap in AnyLogic.

Background: West Nile Virus (WNV) was first isolated in 1937. Since the 1950s, many outbreaks have occurred in various countries. The first appearance of infected birds in Manitoba, Canada was in 2002.

A mHealth application for chronic wound care: findings of a user trial.

This paper reports on the findings of a user trial of a mHealth application for pressure ulcer (bedsore) documentation. Pressure ulcers are a leading iatrogenic cause of death in developed countries and significantly impact quality of life for those affected. Pressure ulcers will be an increasing public health concern as the population ages.

An agent based model for simulating the spread of sexually transmitted infections.

This work uses agent-based modelling (ABM) to simulate sexually transmitted infection (STIs) spread within a population of 1000 agents over a 10-year period, as a preliminary investigation of the suitability of ABM methodology to simulate STI spread. The work contrasts compartmentalized mathematical models that fail to account for individual agents, and ABMs commonly applied to simulate the spread of respiratory infections. The model was developed in C++ using the Boost 1.

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.

Load Balancing at Emergency Departments using 'Crowdinforming'.

Background: Emergency Department (ED) overcrowding is an important healthcare issue facing increasing public and regulatory scrutiny in Canada and around the world. Many approaches to alleviate excessive waiting times and lengths of stay have been studied. In theory, optimal ED patient flow may be assisted via balancing patient loads between EDs (in essence spreading patients more evenly throughout this system).

Improving Agent Based Models and Validation through Data Fusion.

This work is contextualized in research in modeling and simulation of infection spread within a community or population, with the objective to provide a public health and policy tool in assessing the dynamics of infection spread and the qualitative impacts of public health interventions. This work uses the integration of real data sources into an Agent Based Model (ABM) to simulate respiratory infection spread within a small municipality. Novelty is derived in that the data sources are not necessarily obvious within ABM infection spread models.

Agent based modeling of "crowdinforming" as a means of load balancing at emergency departments.

This work extends ongoing development of a framework for modeling the spread of contact-transmission infectious diseases. The framework is built upon Agent Based Modeling (ABM), with emphasis on urban scale modelling integrated with institutional models of hospital emergency departments. The method presented here includes ABM modeling an outbreak of influenza-like illness (ILI) with concomitant surges at hospital emergency departments, and illustrates the preliminary modeling of 'crowdinforming' as an intervention.

Models of emergency departments for reducing patient waiting times.

In this paper, we apply both agent-based models and queuing models to investigate patient access and patient flow through emergency departments. The objective of this work is to gain insights into the comparative contributions and limitations of these complementary techniques, in their ability to contribute empirical input into healthcare policy and practice guidelines. The models were developed independently, with a view to compare their suitability to emergency department simulation.