1. Framework for Infectious Disease Analysis: A comprehensive and integrative multi-modeling approach to disease prediction and management.
- Author
-
Erraguntla, Madhav, Zapletal, Josef, and Lawley, Mark
- Subjects
- *
ALGORITHMS , *COMMUNICABLE diseases , *CONCEPTUAL structures , *CONFIDENCE intervals , *DECISION support systems , *EPIDEMICS , *INFORMATION storage & retrieval systems , *MEDICAL databases , *MACHINE learning , *MATHEMATICAL models , *NATURAL language processing , *PUBLIC health surveillance , *REGRESSION analysis , *RESEARCH funding , *RISK assessment , *DATA mining , *DISEASE management , *THEORY , *SOCIAL media , *DESCRIPTIVE statistics , *DATA analytics - Abstract
The impact of infectious disease on human populations is a function of many factors including environmental conditions, vector dynamics, transmission mechanics, social and cultural behaviors, and public policy. A comprehensive framework for disease management must fully connect the complete disease lifecycle, including emergence from reservoir populations, zoonotic vector transmission, and impact on human societies. The Framework for Infectious Disease Analysis is a software environment and conceptual architecture for data integration, situational awareness, visualization, prediction, and intervention assessment. Framework for Infectious Disease Analysis automatically collects biosurveillance data using natural language processing, integrates structured and unstructured data from multiple sources, applies advanced machine learning, and uses multi-modeling for analyzing disease dynamics and testing interventions in complex, heterogeneous populations. In the illustrative case studies, natural language processing from social media, news feeds, and websites was used for information extraction, biosurveillance, and situation awareness. Classification machine learning algorithms (support vector machines, random forests, and boosting) were used for disease predictions. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF