Graph Matching Based Decision Support Tools For Mitigating Spread Of Infectious Diseases Like H1N1
Identifieur interne : 001E83 ( Main/Exploration ); précédent : 001E82; suivant : 001E84Graph Matching Based Decision Support Tools For Mitigating Spread Of Infectious Diseases Like H1N1
Auteurs : Jomon Aliyas Paul ; Kedar SambhoosSource :
- Journal of Homeland Security and Emergency Management [ 1547-7355 ] ; 2012.
English descriptors
- Teeft :
- Additional decision, Alarming rate, Algorithm, Bioterrorist attacks, Case study, Chronic conditions, Chronic diseases, Cluster, Consecutive days, Constraint, Containment, Containment measures, Continuous scanning, Counter values, Data graph, Data graph edge, Data graph node, Data point, Data points, Data streams, Decision support tools, Demand cluster, Demand clusters, Different rates, Dynamic graph, Dynamic score, Early detection, Efficient detection, Emergency management, Exact approach, Extant literature, Extra information, False alarms, Graph, Graphical techniques, Gravity model, Gravity models, Group hospitals, Grouping, Grouping algorithm, Gruyter, Health officials, Health status, Heuristic, High risk groups, Higher probability, Homeland security, Hospital grouping, Hospital grouping approach, Hospital patient data, Hospital patient symptom data, Hospital utilization, Ieee transactions, Incubation period, Inexact, Inexact graph, Infection, Infectious disease spread, Infectious diseases, Influenza, Influenza outbreak, Jhsem, Jomon aliyas paul, Larger number, Machine intelligence, Many clusters, Medical care, Multiple days, Multiple factors, Necessary resources, Node, Normal week data, Operational framework, Optimal allocation, Outbreak, Pandemic, Pandemic influenza, Particular cluster, Patient cases, Patient condition, Patient data, Patient flows, Patient graph, Patient information, Patient symptom data, Patient symptom graph, Pattern analysis, Pattern recognition, Possible outbreak, Pregnant women, Process flow, Public health officials, Real population, Risk group, Sambhoos, Same cluster, Same number, Same time, Severity, Silhouette index, Similarity score, State level, Susceptible population, Symptom, Symptom template, Syndromic surveillance, Template, Template edge, Template graph, Template graph node, Threshold value, Threshold values, Time window, Timely containment, Timely detection, Total graph, World health organization, Worldwide spread.
Abstract
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.
Url:
DOI: 10.1515/1547-7355.1978
Affiliations:
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<term>Containment</term>
<term>Containment measures</term>
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<term>Same number</term>
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<front><div type="abstract" xml:lang="en">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.</div>
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