Published on Mon Aug 10 2020

On the Gap between Epidemiological Surveillance and Preparedness

Svetlana Yanushkevich, Vlad Shmerko

A decision support system (DSS) with Computational Intelligence (CI) tools is required to bridge the gap between epidemiological model of evidence and expert group decision. We argue that such DSS shall be a cognitive dynamic system enabling the CI and human expert to work together.

0
0
0
Abstract

Contemporary Epidemiological Surveillance (ES) relies heavily on data analytics. These analytics are critical input for pandemics preparedness networks; however, this input is not integrated into a form suitable for decision makers or experts in preparedness. A decision support system (DSS) with Computational Intelligence (CI) tools is required to bridge the gap between epidemiological model of evidence and expert group decision. We argue that such DSS shall be a cognitive dynamic system enabling the CI and human expert to work together. The core of such DSS must be based on machine reasoning techniques such as probabilistic inference, and shall be capable of estimating risks, reliability and biases in decision making.

Mon Jun 01 2020
Machine Learning
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory
This document analyzes the role of data-driven methodologies in Covid-19. We provide a SWOT analysis and a roadmap that goes from the access to data sources to the final decision-making step. The focus is on the potential of well-known datadriven schemes.
0
0
0
Thu Jun 03 2021
Machine Learning
Adaptive Epidemic Forecasting and Community Risk Evaluation of COVID-19
The new model is based on a combination of public and private data. It can be used to predict the impact of a pandemic on a community.
0
0
0
Wed Jul 11 2012
Artificial Intelligence
Bayesian Biosurveillance of Disease Outbreaks
This paper reports an investigation of the use of causal Bayesian networks to model spatio-temporal patterns of a non-contagious disease. The number of parameters in such anetwork can become enormous, if not carefully managed.
0
0
0
Mon Apr 20 2009
Artificial Intelligence
Agent-Based Decision Support System to Prevent and Manage Risk Situations
The topic of risk prevention and emergency response has become a key social and political concern. One approach to address this challenge is to develop Decision Support Systems (DSS)
0
0
0
Sun Jun 07 2020
Artificial Intelligence
A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management
Researchers have developed statistical, data-driven,analytical, and algorithmic approaches for designing and improving emergency response systems. The problem is inherently difficult and entails spatio-temporal decision making under uncertainty.
0
0
0
Mon Apr 29 2019
Artificial Intelligence
Predictive Situation Awareness for Ebola Virus Disease using a Collective Intelligence Multi-Model Integration Platform: Bayes Cloud
Traditional and ad-hoc models frequently fail to provide proper predictive situation awareness (PSAW) Comprehensive PSAW for infectious disease can support decision making and help to hinder disease spread. System shall provide three main functions: (1) collaborative causal modeling, (2) causal model integration, and (3) causal model reasoning.
0
0
0