SCOPE-Spatial Data Sciences for COVID-19 Pandemic

On going

Spatial Data Sciences (SDS) can provide significant insights into explaining spatial patterns of infectious diseases, understanding and predicting spatio-temporal transmission dynamics, predicting and monitoring the impact of control interventions, which are driven by geographical factors, designing and evaluating optimal resources allocation strategies and cost-effectiveness analyses that incorporate a spatial component and analysing inequities in healthcare based on geography. SCOPE project aims at demonstrating that SDS can be successfully used to deliver a functional software prototype for spatial risk management during epidemic events delivering to health public administration new capabilities to prepare and respond in a short amount of time. This project will combine Machine Learning (ML), high performance computing (HPC) and information visualization (InfoViz) to manage big data.

The multidisciplinary team aim to answer the following five main questions:

- Where is the disease spreading to?

- How fast the disease is spreading?

- How can mobility data improve disease predictions in space?

- How to visualize maps of health variables and the associated uncertainty?

- What information can the high-resolution maps provide for faster and better decisions?

SCOPE will produce daily updates of health indicators maps and the uncertainty associated to the spatial predictions, retrieving the history of the COVID-19 pandemic. The potential for understanding the spatial patterns, especially during the mitigation and endemic phases, is huge bringing insights on the spatial dispersion of the disease over time.

Furthermore, also based on the historical maps, functional data analysis will be used to build spatio-temporal models that in turn, will be explored and analysed by public health experts to bring meaning and utility to their use and to define a subset of health indicators to use in spatial risk management of the disease.

We strongly believe that SCOPE will be a functional prototype for the processing of data available in Public Administration, in order to address risk management during epidemic events. Measures to contain the spread of the epidemic and prevent the disruption of health systems have major impacts for individuals and companies in social and economic terms, so SCOPE can bring enormous benefits.

Environmental Modelling
Start Date:
End Date:

Coordinator/Local PI


Proponent Institution



INSARJ: Instituto Nacional de Saúde DR. Ricardo Jorge
CEG-IST: Centro de Estudos de Gestão do Instituto Superior Técnico
ITI-Larsys: Interative Technologies Institute Larsys

Funding Programme

AI 4 COVID-19: Data Science and Artificial Intelligence in the Public Administration to strengthen the fight against COVID-19 and future pandemics - 2020

Total Funding
239 570,00 €
CERENA Funding
205 194,88 €

Funding Entities

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