Efficient and rapidly SCAlable EU-wide evidence-driven Pandemic response plans through dynamic Epidemic data assimilation

Projektinformationen

ESCAPE

ID Finanzhilfevereinbarung: 101095619

DOI

10.3030/101095619

EK-Unterschriftsdatum 6 Oktober 2022

Startdatum 1 Januar 2023

Enddatum 31 Dezember 2026

Finanziert unter

• Health

Gesamtkosten

€ 2 420 930,00

EU-Beitrag

€ 2 420 929,75

Koordiniert durch

UNIVERSITEIT HASSELT
Belgium

Periodic Reporting for period 1 - ESCAPE (Efficient and rapidly SCAlable EU-wide evidence-driven Pandemic response plans through dynamic Epidemic data assimilation)

Berichtszeitraum: 2023-01-01 bis 2024-06-30

Zusammenfassung vom Kontext und den Gesamtzielen des Projekts

ESCAPE aims to enhance early pandemic response by improving efficiency and scalability through evidence-based guidelines, standardized protocols, and digital tools. It unites experts in various fields to address past shortcomings and strengthen interactions between scientists, policymakers, and the public.
ESCAPE aims to:
● SO1: provide a science-based blueprint for faster and better decisions to manage a pathogen X pandemic
● SO2: increase the availability, sharing and sustainability of high-quality data streams needed to understand pathogen X, i.e. to increase data readiness
● SO3: advance epidemic analytics and models to understand and project the transmission dynamics of pathogen X
● SO4: improve and share digital tools to monitor and plan an efficient response to pathogen X
● SO5: identify determinants of success (and failure) in managing pathogen X based on retroactive analysis of SARS-CoV-2
● SO6: contribute to building and sustaining the collaborative intelligence required to defeat an epidemic by fostering an intelligent community which is better equipped to respond to pathogen X

Arbeit, die ab Beginn des Projekts bis zum Ende des durch den Bericht erfassten Berichtszeitraums geleistet wurde, und die wichtigsten bis dahin erzielten Ergebnisse

WP2: We are working on the identification of pathogens with pandemic potential and review of their epidemiology (in terms of transmissibility, severity, routes of transmission, serial interval etc.).
WP3: A survey on the data needs and availability was created and shared with both modelers within and outside the consortium. A scoping review protocol was written to identify cohort studies and establish principles for setting up pandemic preparedness cohorts. We identified the first data needed to inform synthetic population design. Next to that, the first meeting of the data stewards network for pandemic preparedness was organized and translated into a first report.
WP4: Progress was made on models for transmission dynamics, tools to plan effective response for mitigation and control, and statistical methodology to identify key epidemiological parameters. At the time of the report, the main focus is on modeling approaches and key findings in relation to archetype infections in collaboration with WP2.
WP5: We defined indicators of success and failure across different pandemic stages, with the input of scientists and experts in different disciplines. Based on these findings extensive data collection and documentation is performed.
WP6: The stakeholder analysis involving policy and decision-makers, the scientific community, media, and the general public is ready, but updated regularly. Internal and external workshops to consult stakeholder groups and showcase results were organized. The process of writing a guidance document for the media has started. Next to that, there is continuous communication of project outputs on the website and social media.

Fortschritte, die über den aktuellen Stand der Technik hinausgehen und voraussichtliche potenzielle Auswirkungen (einschließlich der bis dato erzielten sozioökonomischen Auswirkungen und weiter gefassten gesellschaftlichen Auswirkungen des Projekts)

WP2: We are providing public health authorities with the knowledge, confidence, and tools they need to respond more effectively to potential pandemics of pathogen X.
WP3: We’ve reviewed pandemic plans across Europe and assessed the data needs of the modeling community. We’ve compared characteristics of various cohorts for pandemic preparedness and designed a first algorithm to help create synthetic populations. We also organized the first meeting of the Data Stewards network.
WP4: These new methods and tools, along with existing ones, will be made available as digital resources for the scientific community. We’ve also explored methods to compare the COVID-19 responses across European countries during the first wave.
WP5: Our analysis across 61 countries showed that right-wing government ideology negatively impacted vaccination rates, while cultural factors were influential. A survey in 14 EU countries revealed a trade-off between excess mortality, economic growth, and the population’s wellbeing and quality of life.
WP6: We integrated stakeholder perspectives into our research, are developing a media guidance document with effective communication strategies, and aiming to set new standards for clear and effective dissemination of information.