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Migration induced epidemics: dynamics of flux-based multipatch models
Title
Migration induced epidemics: dynamics of flux-based multipatch models.
Creator
Liebovitch, Larry S., Schwartz, Ira B.
Date Issued
2004
PURL
http://purl.flvc.org/FAU/165227
Subject Headings
Communicable diseases--Epidemiology--Mathematical models, Emerging infectious diseases, Epidemiologic Methods, Epidemiology, Biomathematics, Medicine-Mathematics, Dynamics-Mathematical models
Format
Document (PDF)
Dynamical response of multi-patch, flux-based models to the input of infected people: epidemic response to initiated events
Title
Dynamical response of multi-patch, flux-based models to the input of infected people: epidemic response to initiated events.
Creator
Liebovitch, Larry S., Schwartz, Ira B., Rho, Young-Ah
Date Issued
2008-07-21
PURL
http://purl.flvc.org/FAU/165229
Subject Headings
Communicable diseases--Epidemiology--Mathematical models, Epidemiologic Methods, Differential equations, Dynamics--Mathematical models, Spatial systems--Mathematical models, Population dynamics, Emerging infectious diseases
Format
Document (PDF)
Predictive Models for Ebola using Machine Learning Algorithms
Title
Predictive Models for Ebola using Machine Learning Algorithms.
Creator
Jain, Abhishek, Agarwal, Ankur, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Identifying and tracking individuals affected by this virus in densely populated areas is a unique and an urgent challenge in the public health sector. Currently, mapping the spread of the Ebola virus is done manually, however with the help of social contact networks we can model dynamic graphs and predictive diffusion models of Ebola virus based on the impact on either a specific person or a specific community. With the help of this model, we can make more precise forward predictions of the...
Show moreIdentifying and tracking individuals affected by this virus in densely populated areas is a unique and an urgent challenge in the public health sector. Currently, mapping the spread of the Ebola virus is done manually, however with the help of social contact networks we can model dynamic graphs and predictive diffusion models of Ebola virus based on the impact on either a specific person or a specific community. With the help of this model, we can make more precise forward predictions of the disease propagations and to identify possibly infected individuals which will help perform trace – back analysis to locate the possible source of infection for a social group. This model will visualize and identify the families and tightly connected social groups who have had contact with an Ebola patient and is a proactive approach to reduce the risk of exposure of Ebola spread within a community or geographic location.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00004919, http://purl.flvc.org/fau/fd/FA00004919
Subject Headings
Communicable diseases--Epidemiology., Public health surveillance., Ebola virus disease--Transmission., Machine learning., Computer algorithms., Virtual reality., Interactive multimedia., Computer graphics., History--Graphic methods., Historiography--Technological innovations.
Format
Document (PDF)