Epidemic intelligence to minimize COVID-19's public health, societal and economical impact.
This stream of work is focused on applying state-of-the-art mathematical models and statistical datasets to understand the patterns underlying COVID-19. Providing reliable estimates of key factors associated with COVID-19 help us better understand the dynamics of the virus and respond more effectively.
Under this stream of work, we are using mathematical models to investigate the impact of intervention measures - such as travel bans or school closures - introduced to control the spread and severity of the pandemic.
This work package is interested in how the pandemic is influencing people’s day-to-day lives. We are monitoring awareness of and attitudes to COVID-19 over time, assessing the effectiveness of the information provided by public health and other authorities and gathering information on how the pandemic is changing people’s behaviour. For example in relation to their contact with others. These insights will be vital in informing regional, national and international responses to the pandemic.
This programme of work examines the impact of COVID-19 on the economy. We look at the cost-effectiveness of the measures introduced to control the virus and estimate the burden on healthcare facilities in real-time.
‘CoMix’ is a groundbreaking study that follows households across Europe in real-time over the course of the COVID-19 pandemic. The survey asks people about their awareness, attitudes and behaviours in response to COVID-19 and measures how these change over time.
Infectieradar (BE), Infectieradar (NL) and Influweb (IT) are participatory surveillance platforms which monitor the spread of infectious diseases by collecting health information from members of the public. Now, as part of the EpiPose project, these platforms are expanding to capture data on COVID19. This work is being led by Daniela Paolotti and the team at ISI Foundation in collaboration with the University of Hasselt, RIVM and the University of Antwerp.
The project received funding (€ 4 548 391,25) from the European Union’s Research and Innovation Action under the H2020 work programme (grant agreement number ID: 101003688)