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Data science tool for Epidemic FOrecastinG

Project description

Forecasting the course of an epidemic

Outbreaks of infectious diseases may lead to a sudden increase of cases within a particular region and cause an epidemic. Being able to predict the geographic spread of disease and the number of infected individuals is central for public health interventions and broader clinical and societal preparedness. EU-funded DEFOG developed a software-based solution for integrating surveillance data, practitioners’ data and pharmacy data in real time as a better way to understand and predict the dynamics of epidemic progression. Infectieradar.be was developed as an additional source of data and a citizen science tool. It collects syndromic surveillance data and can give earlier warnings of increase in infection for respiratory pathogens like RSV, influenza and SARS-CoV-2.

Objective

Several organisations active in the health care market make decisions based on an estimation of the number of individuals infected with an infectious disease. These organisations benefit from a timely and trust-worthy prediction of the number of infected individuals. To date, infectious disease predictions tools are inadequate because they typically include limited and domain-specific data and because the translation to impact for specific stakeholders in the healthcare market is lacking. DEFOG proposes a data science solution as we will integrate classical surveillance data, pharmacy sales data, out-of-hours general practitioners data and social contact data in a novel real-time forecasting tool that will yield better and more rapid warning signals of the number of infected cases. DEFOG will produce software and know-how under intellectual property of the principal investigator which will be exploited through licence agreements with and consultancy services for various stakeholders. DEFOG will build on recent advances in mathematical modelling for infectious diseases as part of the original ERC-TransMID project. The team offers a vast amount of expertise in mathematical and infectious disease modelling, computational processing, business development and has an extensive collaborative network with research centres, regulatory agencies and companies, of which 4 entities already expressed interest in exploring the use and/or sharing data for of the proposed innovative real-time disease forecasting tool.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

ERC-POC - Proof of Concept Grant

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Call for proposal

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(opens in new window) ERC-2018-PoC

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Host institution

UNIVERSITEIT HASSELT
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 131 563,00
Address
MARTELARENLAAN 42
3500 Hasselt
Belgium

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Region
Vlaams Gewest Prov. Limburg (BE) Arr. Hasselt
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 131 563,00

Beneficiaries (1)

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