Design of a modular DSS for public health decision-making in the context of a COVID-19 pandemic landscape.

The awareness of the occurrence of a new disease involves much uncertainty and the search for answers and also appropriate questions. In this paper we focus on the perspective of public health decision-makers. Typically, they would have a standard set of questions and supporting metrics that have been found in previous disease outbreaks to be useful in assessing the effectiveness of various 'solution' methods on the trajectory of the disease. There may be other relevant questions with which such public health domain experts may not be familiar and/or for which they are familiar but are not aware of methods for addressing such questions when there is limited data. Decision Support Systems (DSS) can be used to facilitate the exploration of established questions and some other relevant questions. Given an initial set of questions, the DSS designer should consider which sets of data analytic methods have the capabilities to adequately address. Some of these data analytic methods may also have the capability of addressing questions that could be of interest to the public health decision makers including researchers. In this paper we present a conceptual design for a relevant easy-to-construct DSS and an example of a multi-method DSS that is based on this conceptual design. Using publicly available data on the CoViD-19 pandemic, we illustrate benefits of the multi-method DSS in action.

Problem Formulation in Knowledge Discovery via Data Analytics (KDDA) for Environmental Risk Management.

With the growing popularity of data analytics and data science in the field of environmental risk management, a formalized Knowledge Discovery via Data Analytics (KDDA) process that incorporates all applicable analytical techniques for a specific environmental risk management problem is essential. In this emerging field, there is limited research dealing with the use of decision support to elicit environmental risk management (ERM) objectives and identify analytical goals from ERM decision makers. In this paper, we address problem formulation in the ERM understanding phase of the KDDA process. We build a DM³ ontology to capture ERM objectives and to inference analytical goals and associated analytical techniques. A framework to assist decision making in the problem formulation process is developed. It is shown how the ontology-based knowledge system can provide structured guidance to retrieve relevant knowledge during problem formulation. The importance of not only operationalizing the KDDA approach in a real-world environment but also evaluating the effectiveness of the proposed procedure is emphasized. We demonstrate how ontology inferencing may be used to discover analytical goals and techniques by conceptualizing Hazardous Air Pollutants (HAPs) exposure shifts based on a multilevel analysis of the level of urbanization (and related economic activity) and the degree of Socio-Economic Deprivation (SED) at the local neighborhood level. The HAPs case highlights not only the role of complexity in problem formulation but also the need for integrating data from multiple sources and the importance of employing appropriate KDDA modeling techniques. Challenges and opportunities for KDDA are summarized with an emphasis on environmental risk management and HAPs.