Graph Matching Based Decision Support Tools For Mitigating Spread Of Infectious Diseases Like H1N1

Diseases like H1N1 can be prevented from becoming a wide spread epidemic through timely detection and containment measures. Similarity of H1N1 symptoms to any common flu and its alarming rate of spread through animals and humans complicate the deployment of such strategies. We use dynamic implementation of graph matching methods to overcome these challenges. Specifically, we formulate a mixed integer programming model (MIP) that analyzes patient symptom data available at hospitals to generate patient graph match scores. Successful matches are then used to update counters that generate alerts to the Public Health Department when the counters surpass the threshold values. Since multiple factors like age, health status, etc., influence vulnerability of exposed population and severity of those already infected, a heuristic that dynamically updates patient graph match scores based on the values of these factors is developed. To better understand the gravity of the situation at hand and achieve timely containment, the rate of infection and size of infected population in a specific region needs to be estimated. To this effect, we propose an algorithm that clusters the hospitals in a region based on the population they serve. Hospitals grouped together affect counters that are local to the population they serve. Analysis of graph match scores and counter values specific to the cluster helps identify the region that needs containment attention and determine the size and severity of infection in that region. We demonstrate the application of our models via a case study on emergency department patients arriving at hospitals in Buffalo, NY

Using Dynamic Graph Matching and Gravity models for Early Detection of Bioterrorist Attack by Analysis of Hospital Patient Data

Timely detection of a bioterrorist attack is of profound significance for efficient emergency public health management. Various systems currently exist which are capable of detecting the biologic agents prior to (e.g. biosensors) and after exposure (syndromic surveillance) but suffer from limitations like high cost and false positives (Stoto et al., Williams). In this paper, we use novel dynamic graph matching and gravity models to formulate a more precise and efficient methodology for detection. The problem is complicated by the similarity of anthrax and small pox symptoms to common diseases like influenza, chickenpox, airborne characteristics of these agents (that increases the risk of infection spreading to proximal regions), and non uniform distribution of terrorism risk among areas belonging to the same region. Our methodology will analyze patient symptom data available at hospitals using dynamic graph matching algorithms. We propose a heuristic that dynamically updates the template graphs based on patient data before applying matching algorithms, a unique feature of this study. Successful matches will be used to update counters that generate alerts once the counters surpass the threshold values. We develop a heuristic that uses a gravity model to group hospitals in a region into clusters based on the population they serve. Hospitals grouped together as a cluster affect counters that are local to the population they serve and generate alarms to the Public Health Department when they surpass the set threshold values. In addition, we use the fact that some symptoms are unique to these agents to make our algorithms more robust. These models could be used to develop practical applications for agencies such as DHS due to its ability to increase not just the likelihood of detection of a bioterrorism attack but also to identify with greater precision the location(s) of the attack. With minor modification they could also be used to plan for other disasters/epidemics such as SARS, and bird flu