Infokiri

ECDC Respiratory Virus Modelling: RespiCompass Round 1 (winter 2024-2025 scenarios)

ECDC Respiratory Viruses and Legionella group; ECDC Modelling group

Viral Respiratory Diseases (National Focal Points and DNCC) network meeting, 16 October 2024

European Centre for Disease Prevention and Control

Agenda

1. Introduction and overview of ECDC Modelling activities (5 min)

2. National Focal Point survey results (5 min)

i. Summary of headline results

3. Country presentations (10 min)

i. Simon Couvreur (Sciensano, Belgium) ii. Katie O'Brien (Health Protection Surveillance Centre, Ireland) iii. Alberto Mateo Urdiales (Istituto Superiore di Sanità, Italy)

4. Discussion (10 min)

5. RespiCompass Round 1 (winter 2024-2025) results (20 min)

i. Objectives ii. Scenarios, parameters, assumptions iii. Results iv.Conclusions

6. Discussion (10 min)

2

1. Overview of ECDC Modelling activities

Overview of ECDC Modelling activities

Aims

• Leverage ERVISS data and mathematical modelling to:

1. Support interpretation of respiratory virus surveillance data

2. Develop early warning signals to support timely public health action

3. (Later) methods development standardise assessment of trends

• Increase awareness of modelling in Respiratory Virus network

- Engagement to understand modelling capacity, use and needs

- Collaborative development (internally, with stakeholders)

- Provide outputs beneficial to public health decision-making

4

• RespiCast (short term 1-4 week ahead forecasts)

ILI, ARI, SARI (+/- nowcasting)

• RespiCompass (mid-/long-term influenza + COVID-19 scenario projections)

Disease burden (timing and scale) Scenarios (transmission, vaccination coverage)

Stakeholder mapping

Overview of ECDC Modelling activities

Aims

• Leverage ERVISS data and mathematical modelling to:

1. Support interpretation of respiratory virus surveillance data

2. Develop early warning signals to support timely public health action

3. (Later) methods development standardise assessment of trends

• Increase awareness of modelling in Respiratory Virus network

- Engagement to understand modelling capacity, use and needs

- Collaborative development (internally, with stakeholders)

- Provide outputs beneficial to public health decision-making

5

• RespiCast (short term 1-4 week ahead forecasts)

ILI, ARI, SARI (+/- nowcasting)

• RespiCompass (mid-/long-term influenza + COVID-19 scenario projections)

Disease burden (timing and scale) Scenarios (transmission, vaccination coverage)

Stakeholder mapping

Addressed today

2. National Focal Point survey results

National Focal Point survey results

• Sent to NFPs for Viral Respiratory Disease

• 18/30 (60%) responses

• Austria, Belgium, Cyprus, Czechia, France, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Malta, Netherlands, Romania, Slovakia, Slovenia, Spain, Sweden

• Short summary of headline results, focussing on:

1. Modelling capacity

2. Likelihood of RespiCast or RespiCompass informing public health actions or decisions at the national level

3. Usefulness of RespiCast vs RespiCompass for decision-making at national level

4. Presence of a mechanism to integrate modelling into decision-making

7

National Focal Point survey results

1. Modelling capacity

How many staff do you have in your institute for mathematical modelling, irrespective of the disease area?

Conclusion

• Limited in-house modelling capacity in the majority of responding countries.

8

National Focal Point survey results

2. Likelihood of RespiCast or RespiCompass informing public health actions or decisions at the national level

Conclusion

• Responding countries primarily see outputs informing surveillance activities, but potentially for vaccination campaigns.

9

Healthcare capacity planning Inform surveillance activities

Planning vaccination campaigns Planning and procurement of medical countermeasures

National Focal Point survey results

3. Usefulness of RespiCast vs RespiCompass for decision-making at national level

Of the two proposed outputs, RespiCast and RespiCompass, which do you feel will be of most value for decision-making at your national institute?

Conclusion

• Majority of responding countries see both outputs as useful, but with a ~20% selecting RespiCast alone.

10

National Focal Point survey results

4. Presence of a mechanism to integrate modelling into decision-making

There is a clear and consistent mechanism for integrating modelling outputs (in-house, ECDC, or other) in public health decision-making at my national institute?

Conclusion

• Only 45% of respondents agree that a mechanism currently exists.

11

3. Country presentations

Country presentations

13

We were especially interested in those countries that reported any of the following:

1. Any in-house modelling capabilities or outsourced modelling activities

2. Existing participation in ECDC RespiCast and/or RespiCompass projects

3. Established mechanisms for integrating respiratory virus modelling outputs in public health decision-making

Invited country perspectives (Belgium, Ireland, Italy) to cover:

1. How is modelling applied to inform interpretation of respiratory virus surveillance data?

2. How are modelling outputs (in-house/ECDC/other) integrated in public health decision-making at your national institute?

3. What challenges are you currently facing, or do you foresee with applying modelling to inform interpretation of respiratory virus surveillance data? What would alleviate those challenges?

Simon Couvreur (Sciensano, Belgium)

• Belgium has several systems for respiratory virus surveillance

• Pathogen-specific laboratory surveillance

• Waste-Water surveillance

– Standardization (Pepper Mild Mottle Virus (PMMoV))

• ILI (Influenza Like Illness => GPs)

• SARI (Severe Acute Respiratory Infections => hospitals)

– Incidence estimation through IPW

– Exploring Nowcasting & Short-term forecasting (Respicast?)

• RespiCompass participation (by SIMID)

• SIMID is part of a modelling consortium coordinated by Sciensano

– Compartmental model for COVID-19

– Also including scenarios for other countries

• Short-term predictions of COVID-19 hospital occupancy (discont.)

Modelling for interpretation of surveillance data

• Public Health decision making in Belgium

• Contributions of modelling experts

• (Interpretation of) model-

derived epi parameters

• Scenario analyses

Modelling for public health decision-making

Risk assessment group

Assesses signals / epi situation & proposes measures

Structural representation of modelling experts

Risk management group

Translates advice into policy, coordinating federal & regional

Ad-hoc invitation of modelling experts

• Extending COVID-19 compartmental model to multi-pathogen models

• Adapting compartmental model to non-exhaustive (sentinel) surveillance

• Nowcasting: accounting for changes (improvements) in sentinel surveillance

• Data sharing with academic partners (legal barriers)

Challenges in modeling activities

Katie O'Brien (Health Protection Surveillance Centre, Ireland)

▪How is modelling applied to inform interpretation of respiratory virus surveillance data?

Models under development:

• For COVID-19, an SEIR model is under development.

• For influenza, RSV and COVID-19 use EpiEstim R package to model cases, estimate time-varying reproduction number and provide forecasts.

Ireland: Application of Modelling

Currently applied

▪ Monitoring growth rates of COVID-19/influenza/RSV via generalised additive models for cases and hospitalisations.

• For influenza, an SLIR (susceptible, latent, infectious, recovered model) using both sentinel GP ILI and hospitalisation data streams was utilised in winter 2023/24 to generate short term (2-3 weeks) forecasts, see figure.

▪How are modelling outputs (in- house/ECDC/other) integrated in public health decision-making at your national institute?

20

Ireland: Modelling and public health decision making

Modelling @HPSC

• In-house modelling outputs are shared with the HSPC respiratory virus unit, Ministry of Health and the national healthcare service to assist with decision making around winter planning.

Modelling @ECDC

• We have started to monitor ECDC RespiCast and will monitor RespiCompass.

• Outputs were not used last year for decision making, as we would need to monitor forecasts to assess their usefulness in the Irish setting.

▪What challenges are you currently facing, or do you foresee with applying modelling to inform interpretation of respiratory virus surveillance data?

▪What would alleviate those challenges?

Ireland: Challenges

General model interpretation challenges

• RespiCast outputs were not used last year for decision making, as more comprehensive surveillance data and modelling was available nationally.

• Training and time to communicate with the subject matter experts regarding model inputs (data and parameters) and model outputs (interpretation).

Challenges – Irish context

• We had planned to set up a Biostatistics and Modelling unit after the COVID-19 pandemic, but due to national recruitment freeze, this has not materialised. Currently 2-3 staff with support from academic experts.

• This limits output, ability to check models, plan for unforeseen events, etc.

Alberto Mateo Urdiales (Istituto Superiore di Sanità, Italy)

www.iss.it/malattie-infettive

Classified as ECDC NORMAL

Modelling of RD at the ISS

• At the ISS, mathematical and statistical modelling of respiratory diseases (and others) is done within EPIQ, a joint lab ISS-Fondazione Bruno Kessler (FBK)

• The decision on what to do and on what data is decided together between the ISS, FBK and other stakeholders (e.g., MdS) when appropriate

• Mathematical modelling is usually centred around: a) identification of early warning alerts; b) pan/epidemic scenarios; c) estimation of key parameters; d) evaluation of interventions and e) prediction/forecasting

www.iss.it/malattie-infettive

Classified as ECDC NORMAL

Integrating modelling outputs into the decision-making process

• Modelling outputs that result from the collaboration between ISS and FBK, as well as other outputs (e.g., INFLUCAST, ECDC….) are reviewed regularly to plan surveillance activities, compare our data with what’s expected in Italy and elsewhere, and to complement our bulletins when informing relevant stakeholders (e.g., MdS, Regions…)

• Modelling outputs were, during the COVID-19 pandemic, and integral part of the weekly risk assessments that informed the decision-making process around NPI

• They are still a core component of pandemic preparedness plans and prevention strategies in general (e.g., identifying key groups and areas in measles)

www.iss.it/malattie-infettive

Classified as ECDC NORMAL

Main challenges in using mathematical modelling for decision-making

• Better data would help to create better models • Privacy barriers prevent linking data from ED, epi, micro and administrative

databases • Reporting delay and backlog impact on “real-time” assessment and forecasting’s

accuracy

• Improving our knowledge on some key aspects of human behaviour (e.g. behavioural changes in response to perceived risk, acceptance of restrictive measures, vaccine hesitancy, etc.) would increase accuracy and reliability of mathematical models.

• Uncertainty around scenarios/forecasting still poorly understood and difficult to communicate to non-scientific audiences

4. Discussion

5. RespiCompass Round 1 (winter 2024-2025) results

RespiCompass Round 1:

Influenza and COVID-19 burden scenarios for winter 2024-2025

ECDC Modelling and ECDC Biostatistics groups

Viral Respiratory Diseases (National Focal Points and DNCC) network meeting, 16 October 2024

Please note results presented are preliminary and subject to change, pending formal publication

Roadmap

• Objectives

• How does RespiCompass work

• RespiCompass results

• Summary remarks and next steps

Objectives of RespiCompass 2023/24

• Anticipate COVID-19 and influenza burden and the impact of vaccination on selected indicators under pre-defined scenarios

• Strengthen modelling capacity and institutional networks in the EU/EEA

31

Forecasts vs scenario modelling

? ? ?

? Observed data

RespiCast RespiCompass

RespiCompass work cycle

Question

Announce scenario round

Modelling

Multi-model analysis

Communication

of results

Evaluation

Scenario definitions

Scenario A Scenario C Scenario E

Scenario B Scenario D Scenario F

Uncertainty axis

U n

c e

rt a

in ty

a x is

+ Additional assumptions Model projections targets and horizon

RespiCompass Round 1 – Influenza

Optimistic vaccination Coverage in 65+yrs is

15% higher than in last season*

Pessimistic vaccination Coverage in 65+yrs is

15% lower than in last season*

No vaccination

counterfactual

Typical transmission potential Influenza transmission potential is similar to the last

three seasons, excluding COVID-19 pandemic

years.

Scenario A Scenario C Scenario E

Pessimistic transmission potential* Influenza transmission potential is 10% higher

relative to the last three seasons, excluding COVID-

19 pandemic years.

Scenario B Scenario D Scenario F

Vaccination axis

B io

lo g ic

a l a x is

RespiCompass Round 1 – Influenza

Optimistic vaccination Coverage in 65+yrs is

15% higher than in last season*

Pessimistic vaccination Coverage in 65+yrs is

15% lower than in last season*

No vaccination

counterfactual

Typical transmission potential Influenza transmission potential is similar to the last

three seasons, excluding COVID-19 pandemic

years.

Scenario A Scenario C Scenario E

Pessimistic transmission potential* Influenza transmission potential is 10% higher

relative to the last three seasons, excluding COVID-

19 pandemic years.

Scenario B Scenario D Scenario F

VE against symptomatic infection: 40% Vaccination in other age groups as in 23/24 No NPIs

Vaccination axis

B io

lo g ic

a l a x is

Burden assessed:

Country-specific, weekly projections of ILI+ in 65+ age group until June

2025

ILI+ = ILI x positivity for influenza

Sentinel positivity for all countries apart from MT, IS, HR, RO, LV, FI

RespiCompass Round 1 – COVID-19

Optimistic vaccination Coverage in 60+yrs is

15% higher than 23/24 season

Pessimistic vaccination Coverage in 60+yrs is

15% lower than 23/24 season

No vaccination

counterfactual

Optimistic waning Vaccine-induced immunity against infection drops

within 6 months to 50%

Immunity against severe outcomes: no waning

Scenario A Scenario C Scenario E

Pessimistic waning Vaccine-induced immunity against infection drops

within 6 months to 30%

Immunity against severe outcomes: 6

months median time to transition to 60% of the

initial immunity

Scenario B Scenario D Scenario F

Vaccination axis

B io

lo g ic

a l a x is

RespiCompass Round 1 – COVID-19

Optimistic vaccination Coverage in 60+yrs is

15% higher than 23/24 season

Pessimistic vaccination Coverage in 60+yrs is

15% lower than 23/24 season

No vaccination

counterfactual

Optimistic waning Vaccine-induced immunity against infection drops

within 6 months to 50%

Immunity against severe outcomes: no waning

Scenario A Scenario C Scenario E

Pessimistic waning Vaccine-induced immunity against infection drops

within 6 months to 30%

Immunity against severe outcomes: 6

months median time to transition to 60% of the

initial immunity

Scenario B Scenario D Scenario F

Vaccination axis

B io

lo g ic

a l a x is

VE against infection: 50% VE against hospitalisation: 75% Vaccination in other groups as in 23/24 No NPIs, no game-changer variant

Burden assessed:

Country-specific, weekly projections of COVID-19

hospitalisations in 65+ age group until June 2025

Creating an ensemble estimate

Influenza

Question 1 What is the expected influenza

burden for the 2024/25 season in the EU/EEA?

Answer ILI+ burden in the 65+ is

projected to be higher in the upcoming season relative to

the previous season.

• ILI+ burden in the 65+ age group is projected

to be 136% (95% UI: 98% to 192%) in the

upcoming season relative to last year’s

season.

Projected ILI+ burden in 65+ relative to last season in

the EU/EEA

ILI+ burden in the 65+ is projected to be higher in the upcoming season relative to the previous season.

• ILI+ burden in the 65+ age group is projected

to be 136% (95% UI: 98% to 192%) in the

upcoming season relative to last year’s

season.

• Results are robust across vaccine uptake

scenarios: 154% (95% UI: 99% to 226%)

and 118% (95% UI: 77% to 175%) for low

versus high uptake, respectively.

Projected ILI+ burden in 65+ age group relative to last season in

the EU/EEA

ILI+ burden in the 65+ is projected to be higher in the upcoming season relative to the previous season.

• ILI+ burden in the 65+ age group is

projected to be 136% (95% UI: 98% to

192%) in the upcoming season relative to

last year’s season.

• Results are robust across vaccine uptake

scenarios: 154% (95% UI: 99 to 226%) and

118% (95% UI: 77% to 75%) for low versus

high uptake, respectively.

• Likely explained by calibration against an

–in average- higher ILI+ burden.

Past ILI+ burden in 65+ relative to the 2023/24 season

What might cause a much worse season?

• Bad match of the vaccine

• Dominant subtype with higher transmission

potential* and/or higher severity

?

ILI+ burden in the 65+ is projected to be higher in the upcoming season relative to the previous season.

Question 2 What is the impact of vaccination

versus no vaccination on the 2024/25 influenza burden in the

EU/EEA?

Answer Vaccination is expected to avert between 23% and 46% of ILI+ burden in 65+ age group. The impact for a country strongly

depends on its vaccine uptake.

• Autumn 2024 influenza vaccination

averts 29% (95% UI: 23% to 46%) ILI+ in

the 65+ age group in the EU/EEA.

Expected averted ILI+ burden in the 65+ age group attributable to

vaccination in the EU/EEA

RespiCompass insight: Influenza vaccine impact

Expected averted ILI+ burden in the 65+ population attributable to

vaccination in the EU/EEA • Autumn 2024 influenza vaccination

averts 29% (95% UI: 23% to 46%) ILI+ in

the 65+ age group in the EU/EEA.

• Results differ for vaccine uptake

scenarios: 24% (95% UI: 17% to 44%)

and 33% (95% UI: 25% to 49%) for low

versus high uptake, respectively.

RespiCompass insight: Influenza vaccine impact

• Influenza vaccination coverage is highly

heterogeneous in the EU, ranging from

5.6% to 78% uptake in 65+ age group.

• The impact of vaccines depends strongly

on a country’s vaccine uptake: every

10% vaccine coverage is correlated with

6.2% reduction in ILI+ burden in 65+.

Averted ILI+ burden in 65+ versus vaccination coverage

RespiCompass insight: Influenza vaccine impact

• Influenza vaccination coverage is highly

heterogeneous in the EU, ranging from

5.6% to 78% uptake in 65+ age group.

• The impact of vaccines depends strongly

on a country’s vaccine uptake: every

10% vaccine coverage is correlated with

6.2% reduction in ILI+ burden in 65+.

Averted ILI+ burden in 65+ versus vaccination coverage

RespiCompass insight: Influenza vaccine impact

What might reduce the impact from vaccines?

• Mismatch of the vaccine

• A late-season peak

COVID-19

RespiCompass Round 1 – COVID-19

Optimistic vaccination Coverage in 60+yrs is

15% higher than 23/24 season

Pessimistic vaccination Coverage in 60+yrs is

15% lower than 23/24 season

No vaccination

counterfactual

Optimistic waning Vaccine-induced immunity against infection drops

within 6 months to 50%

Immunity against severe outcomes: no waning

Scenario A Scenario C Scenario E

Pessimistic waning Vaccine-induced immunity against infection drops

within 6 months to 30%

Immunity against severe outcomes: 6

months median time to transition to 60% of the

initial immunity

Scenario B Scenario D Scenario F

Vaccination axis

B io

lo g ic

a l a x is

VE against infection: 50% VE against hospitalisation: 75% Vaccination in other groups as in 23/24 No NPIs, no game-changer variant

Burden assessed:

Country-specific, weekly projections of COVID-19

hospitalisations in 65+ age group until June 2025

Question 3 What can we expect of the

EU/EEA COVID-19 burden in 2024/25?

Answer Marked lack of agreement across

models leading to wide uncertainty intervals for the burden of COVID-19

hospitalisations in 65+ age group. This is due to lack of seasonal dynamics,

immunity accumulated over the summer, and other unknowns.

Question 4 What is the impact of vaccination

versus no vaccination on the 2024/25 COVID-19 burden in the

EU/EEA?

Answer Vaccination is expected to avert

between 15% and 21% of COVID-19 hospitalisations in the 65+ age group. The impact for a country strongly depends on its

vaccine uptake.

• Autumn 2024 COVID-19 vaccination averts

16% (95% UI: 15% to 21%) hospitalisations

in the 65+ age group. EU/EEA region,

2024/25.

• Results are robust across scenarios of

immunity waning and final vaccine uptake

RespiCompass insight: COVID-19 vaccine impact

Averted COVID-19 hospitalisations in the 65+

population attributable to vaccination

• Seasonal COVID-19 vaccination coverage

ranging from 0.01% to 63% uptake in 60+

age group.

• Every 10% increase in coverage is

correlated with 6.8% reduction in hospital

burden.

Averted COVID-19 hospitalisations in 65+ versus vaccination

coverage

RespiCompass insight: COVID-19 vaccine impact

What might reduce the impact from vaccines?

• New variant with strong immune escape

• A late-season peak

Executive summary

• 2024/25 season burden:

• Influenza burden (ILI+ in 65+ age group) will likely be larger than in the previous season

• COVID-19 burden (hospitalisations in 65+ age group) remains highly uncertain

• Impact of vaccines on selected disease indicators:

• 10% increase in uptake is associated with a 6.2% reduction in ILI+ in 65+ age group

• 10% increase in uptake is associated with a 6.8% reduction in COVID-19 hospitalisations in 65+ age group

Developments for 2025

• Retrospective evaluation of model projections, communication and impact of RespiCompass Round 1

• Close involvement of diseases experts and RespiCompass end-users in design of modelling questions and scenarios

• Health-economic assessment of scenario round results

Acknowledgements

57

We are extremely grateful to:

• EU/EEA member states who collect and provide data

• Hub participants

• Contractor (consortium led by ISI foundation, Italy)

• US colleagues who provided open-source code

• All collaborating teams for their active participation in these hubs.

6. Discussion

Discussion – support questions

RespiCompass Round 1 (winter 2024-2025)

1. Do you find the burden projections useful?

2. What limitations do you see in the approach presented (e.g. scenario selection, burden assessed, communication)?

3. How could the work be improved to maximise impact?

NFP survey

1. How do you see modelling informing your surveillance activities and/or vaccination strategy?

2. What challenges are you currently facing, or do you foresee, with integrating modelling outputs in decision-making?

3. What would alleviate those challenges?

59

Thank you

Questions: [email protected]

Saatja: ECDC respiratory viruses <[email protected]>

Saadetud: 23.10.2024 13:25

Adressaat: ECDC respiratory viruses <[email protected]>;

Költringer Fiona <[email protected]>; monika.redlberger

<[email protected]>; Claire Brugerolles

<[email protected]>; Nathalie Bossuyt

<[email protected]>; Ivelina Trifonova

<[email protected]>; korsun <[email protected]>; maja.ilic

<[email protected]>; goranka.petrovic <[email protected]>; Stalo

Zani <[email protected]>; Christopher Haralambous

<[email protected]>; m.mendris <[email protected]>; Despo

Constantinou <[email protected]>; Tonia Adamidi

<[email protected]>; Helena Jirincova <[email protected]>; Jan

Kynčl <[email protected]>; Ramona Trebbien <[email protected]>; Tyra Grove

Krause <[email protected]>; Julia Geller <[email protected]>; Olga

Sadikova <[email protected]>; Hanna Nohynek

<[email protected]>; Niina Ikonen <[email protected]>; Anna MAISA

<[email protected]>; sibylle.bernard-stoecklin

<[email protected]>; Isabelle Parent du

Chatelet <[email protected]>; budas <[email protected]>;

haasw <[email protected]>; k.papadima <[email protected]>; a.andreopoulou

<[email protected]>; molnar.zsuzsanna

<[email protected]>; <[email protected]>; Guðrún Erna

Baldvinsdóttir <[email protected]>; Kamilla Sigríður Jósefsdóttir -

Landl <[email protected]>; Lisa Domegan <[email protected]>; Eve

Robinson <[email protected]>; Joan.ODonnell <[email protected]>;

Puzelli Simona <[email protected]>; Mateo Urdiales Alberto

<[email protected]>; Walser-Domjan Esther <Esther.Walser-

[email protected]>; greta.gargasiene <[email protected]>;

svajune.muralyte <[email protected]>; Joël Mossong

<[email protected]>; Gérard Scheiden <[email protected]>;

Melillo Tanya at Health Regulation <[email protected]>; Melillo Jackie

M at Health Regulation <[email protected]>; Rianne van Gageldonk-

Lafeber <[email protected]>; Adam Meijer

<[email protected]>; Trine Hessevik Paulsen

<[email protected]>; Hungnes, Olav <[email protected]>;

istankiewicz <[email protected]>; cristina.barbara

<[email protected]>; rodica.popescu

<[email protected]>; <[email protected]>;

edita.staronova <[email protected]>; jan.mikas

<[email protected]>; maja.socan <[email protected]>; eva.grilc

<[email protected]>; Susana Monge Corella <[email protected]>; Francisco

Pozo Sanchez <[email protected]>; lena.dillner

<[email protected]>; anneli.carlander

<[email protected]>

Koopia: NC_CCB_Austria <[email protected]>; sigrid.kiermayr

<[email protected]>; koen.blot

<[email protected]>; <[email protected]>; Zhivka Getsova

<[email protected]>; Iva Christova <[email protected]>; kcapak

<[email protected]>; bernard.kaic <[email protected]>; Elisavet

Constantinou <[email protected]>; <[email protected]>; Hana

Orlíková <[email protected]>; Kamilla Grønborg Laut <[email protected]>;

Stine Ulendorf Jacobsen <[email protected]>; Kärt Sõber

<[email protected]>; TA Info <[email protected]>; Hanna Sepp

<[email protected]>; Otto Helve <[email protected]>; Savolainen-

Kopra Carita <[email protected]>; Paula GARCIA-LOBATO

<[email protected]>; Anne-Catherine VISO <anne-

[email protected]>; Lea Rathmachers

<[email protected]>; <[email protected]>; Rexroth, Ute <[email protected]>;

AnderHeidenMa <[email protected]>; Theodora Kalomama

<[email protected]>; Dimitrios Paraskevis

<[email protected]>; Christakis Chatzichristodoulou

<[email protected]>; <[email protected]>;

phc.office <[email protected]>; Rezsőfi Judit

<[email protected]>; Guðrún Aspelund - Landl

<[email protected]>; lois.oconnor <[email protected]>;

Aine Grace <[email protected]>; Patricia Garvey

<[email protected]>; Louise Cullen <[email protected]>; Lisa

Domegan <[email protected]>; Aine Grace <[email protected]>; Francesco

Maraglino <[email protected]>; Antra Bormane

<[email protected]>; Dehler Silvia, Dr. med.

<[email protected]>; Jurgita Pakalniškienė

<[email protected]>; greta.gargasiene

<[email protected]>; Jurgita Pakalniškienė

<[email protected]>; Jean-Claude Schmit <jean-

[email protected]>; Gauci Charmaine at Health Regulation

<[email protected]>; Hester de Melker <[email protected]>;

Susan van den Hof <[email protected]>; Macdonald, Emily Ann

<[email protected]>; Heidi Lange <[email protected]>; Zacharczuk

Katarzyna <[email protected]>; Mariana Ferreira

<[email protected]>; Pedro Licinio Pinto Leite

<[email protected]>; <[email protected]>; André Peralta

Santos <[email protected]>; Adriana Pistol

<[email protected]>; ecdc <[email protected]>; jan.mikas

<[email protected]>; <[email protected]>; Marta.vitek

<[email protected]>; Simón Soria. Fernando <[email protected]>;

<[email protected]>;

<[email protected]>; Agneta Falk Filipsson

<[email protected]>; Sara Bengtsson

<[email protected]>; birgitta.lesko

<[email protected]>; anette.richardson

<[email protected]>; ECDC Info

<[email protected]>; ECDC Info <[email protected]>

Teema: ECDC Respiratory Virus Modelling: RespiCompass Round 1 (Winter

2024-2025 scenarios)

Tähelepanu! Tegemist on väljastpoolt asutust saabunud kirjaga. Tundmatu

saatja korral palume linke ja faile mitte avada.

To: National Focal Points, DNCC for Viral Respiratory Diseases

Cc: National Coordinators

Dear National Focal Points for Viral Respiratory Diseases,

Thank you to all those that participated and kindly presented at last

weeks meeting: ECDC Respiratory Virus Modelling: RespiCompass Round 1

(Winter 2024-2025 scenarios). Your feedback is incredibly valuable for

developing modelling outputs that support EU/EEA member states.

Slides are attached for your reference. Please note that RespiCompass

Round 1 results presented remain preliminary, pending formal publication.

A reminder that we welcome your additional reflections (see slide 59) via

e-mail on the information presented.

Kind regards,

ECDC Respiratory Viruses and Legionella group; ECDC Modelling group

European Centre for Disease Prevention and Control (ECDC)

Gustav III:s boulevard 40, 169 73 Solna, Sweden

Phone &#43;46 (0)8 58 60 10 00

Follow ECDC on:

Confidentiality NoticeIf you are not the intended recipient of this

message, you are hereby kindly requested, to, consecutively, refrain from

disclosing its content to any third party, delete it and inform its

sender of the erroneous transmittal.