We propose a novel data-driven framework for assessing the a-priori epidemic risk of a geographical area and for identifying high-risk areas within a country. Our risk index is evaluated as a function of three different components: the hazard of the disease, the exposure of the area and the vulnerability of its inhabitants. As an application, we discuss the case of COVID-19 outbreak in Italy. We characterize each of the twenty Italian regions by using available historical data on air pollution, human mobility, winter temperature, housing concentration, health care density, population size and age. We find that the epidemic risk is higher in some of the Northern regions with respect to Central and Southern Italy. The corresponding risk index shows correlations with the available official data on the number of infected individuals, patients in intensive care and deceased patients, and can help explaining why regions such as Lombardia, Emilia-Romagna, Piemonte and Veneto have suffered much more than the rest of the country. Although the COVID-19 outbreak started in both North (Lombardia) and Central Italy (Lazio) almost at the same time, when the first cases were officially certified at the beginning of 2020, the disease has spread faster and with heavier consequences in regions with higher epidemic risk. Our framework can be extended and tested on other epidemic data, such as those on seasonal flu, and applied to other countries. We also present a policy model connected with our methodology, which might help policy-makers to take informed decisions.

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Purpose This paper addresses the complex problem of multi-stakeholder decisions in urban freight transport policy-making from a public authority perspective, by proposing a procedure based on a modelling approach to support stakeholder involvement in the decisionmaking process. The paper analyses the existing methods that can be used for participatory decision-support, with the intent of contextualizing and introducing the innovative modelling approach. Methods The modelling approach consists of a well-thought integration of discrete choice models (DCM) with agent-based models (ABM) as an effective way to account for stakeholders' opinions in the policy-making process, while mimicking their interaction to find a shared policy package. The integrated modelling approach is able to combine the advantages of the two methods while overcoming their respective weaknesses. Since it is well grounded on sound microeconomic theory, it provides a detailed (static) stakeholders' behavioural knowledge, but it is also capable of reproducing agents' (dynamic) interaction during the decision-making process. The integration allows performing an ex-ante behavioural analysis, with the aim of testing the potential acceptability of the solutions proposed. The methodology is applied in a real case study to prove its feasibility and usefulness for participatory decision-making. Results The integrated modelling approach can be used for participatory decision-support and it can be casted in the overall UFT policy-making process. The results of the behavioural analysis, in terms of ranking of potentially accepted policies, linked with the technical evaluations from transport network modelling tools, provide a sound basis for active participation and deliberation with stakeholders and policy-makers. The aim is to guide an effective participation process aimed at consensus building among stakeholders, by proposing them a subset of policies that, as a result of a preliminary analysis, are likely to be accepted while performing well in terms of technical results. Conclusions This approach, integrating DCM and ABM, represents a promising way to tackle the complexity of multistakeholder involvement in UFT policy-making and to support an efficient and effective decision-making process. It produces an added value for UFT policy-making and it can be framed in the overall context of transport planning. In fact, together with technical and economic analyses, the stakeholder behavioural analysis proposed contributes to the ex-ante policy assessment needed to support decision-makers in taking well-thought decisions.

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