On the Gap between Epidemiological Surveillance and Preparedness

TL;DR

This paper discusses the need for a decision support system that integrates epidemiological data analytics with human expertise, utilizing computational intelligence to improve pandemic preparedness decision-making.

Contribution

It proposes a cognitive dynamic system framework combining machine reasoning and human expertise to bridge the gap in epidemiological surveillance and preparedness.

Findings

Highlights the importance of integrating data analytics into decision support systems.

Suggests probabilistic inference as a core technique for risk estimation.

Emphasizes the role of human-computer collaboration in pandemic preparedness.

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.