On the Gap between Epidemiological Surveillance and Preparedness
This work addresses the challenge of improving pandemic preparedness for decision-makers and experts, but it appears incremental as it builds on existing computational intelligence and machine reasoning techniques without introducing a fundamentally new approach.
The paper tackles the problem of integrating epidemiological surveillance data into preparedness networks by proposing a decision support system (DSS) that combines computational intelligence with human expertise, aiming to bridge the gap between evidence and expert decision-making.
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.