DOI: 10.6084/m9.figshare.31120746.v1
title: A framework for collaborative identification of geographical information for map-based dashboards to support pandemic response policy-making

description: This article presents an innovative collaborative value modeling process to engage policy-makers and other stakeholders in Portugal in discussing relevant geographic information for COVID-19 response. The process determines what geographic information should be included in map-based dashboards to effectively support public health authorities’ policy decisions and interventions in responding to the pandemic. It combined a Web-Delphi process with 56 health stakeholders and experts alongside virtual workshops with a core group of eight key players who discussed and refined the Delphi results. Seventy-seven geographic information elements related to incidence, community transmission, vaccination, transmissibility, testing, mortality and lethality, and resource allocation emerged as relevant to inform the pandemic response. The process provided structured and sound information – as seen by a large number of health professionals, experts, and policy-makers – on relevant geographic elements for dashboards to support pandemic-designed policies. These elements constitute a coherent body of spatial knowledge that advances geographic information science by informing the design of dashboards to support spatial decision-making. The proposed collaborative modeling process constitutes a framework that contributes to bridge the gap between generating geographic information and making pandemic-related decisions, in the context of the COVID-19 pandemic in Portugal, but can be used in other map-based dashboard contexts. We propose a novel participatory format – combining Web-Delphi with workshops – to promote a collective learning process among health stakeholders and policy-makers in the ideation and validation of geographic information elements to include in dashboards and inform pandemic-related policy-making.We show that 77 information elements (IEs), across seven areas of concern (Incidence, Community Transmission, Vaccination, Transmissibility, Testing, Mortality and Lethality, and Resource Allocation) are relevant to include in fit-for-purpose map-based dashboards to inform pandemic-related decisions.The set of IEs constitute a coherent body of spatial knowledge that advances Geographic Information Science by informing the development of dashboards designed to support spatial decision-making in all phases of pandemic evolution.Following our analysis, dashboards can entail flexible visualization options so that local, regional, or national policy-makers can access relevant information.The collaborative process advances the development of spatial decision support systems by establishing a shared judgmental knowledge base for identifying geographical IEs critical to map-based dashboards that support pandemic-related decision-making. We propose a novel participatory format – combining Web-Delphi with workshops – to promote a collective learning process among health stakeholders and policy-makers in the ideation and validation of geographic information elements to include in dashboards and inform pandemic-related policy-making. We show that 77 information elements (IEs), across seven areas of concern (Incidence, Community Transmission, Vaccination, Transmissibility, Testing, Mortality and Lethality, and Resource Allocation) are relevant to include in fit-for-purpose map-based dashboards to inform pandemic-related decisions. The set of IEs constitute a coherent body of spatial knowledge that advances Geographic Information Science by informing the development of dashboards designed to support spatial decision-making in all phases of pandemic evolution. Following our analysis, dashboards can entail flexible visualization options so that local, regional, or national policy-makers can access relevant information. The collaborative process advances the development of spatial decision support systems by establishing a shared judgmental knowledge base for identifying geographical IEs critical to map-based dashboards that support pandemic-related decision-making.