Surveillance, Forecasting, & Nowcasting in an Influenza Pandemic Peter G Grove

1. Surveillance, Forecasting, & Nowcasting in an Influenza Pandemic Peter G Grove Health Protection Analytical Team Department of Health

2. Why Collect Data in a Pandemic? • Management of the response to the pandemic. • Research to help deal with later pandemics. • Public and Ministerial confidence.

3. Why Collect Data in a Pandemic? • Management of the response to the pandemic. • Research to help deal with later pandemics. • Public and Ministerial confidence.

4. Advised Planning Assumptions Up to 50% of the population ill (with serological rates up to 80-85%). Of which, from 10% up to 25% are expected to have complications, half of these bateriological. (With possibly as little a 35% overlap between the ‘at risk groups’ and those who actually get complications.) Peak illness rates of 10 - 12% (in new cases per week - of the population) in the peak fortnight. Absences rates for illness reach 15-20% in the peak weeks (at a 50% overall attack rate, assuming an average 7 working day absence for those without complications, 10 for those with, and some allowance for those at home caring for children.) Case hospitalisation demand rates in the range 0.55% to 4% with an average six day length of stay. - but, of which 25% would, if the capacity existed, require intensive care for 10 days. Case fatality rates in the range 0.4% to 2.5%.

5. Scientific Pandemic Influenza Advisory Committee (SPI): Subgroup on Modelling www.advisorybodies.doh.gov.uk/spi/minutes DH website:

6. Defence in Depth – Because we don’t know if any particular intervention will work, we need packages of interventions. Pre-pandemic vaccine Antivirals Antibiotics Social Measures. Pandemic Specific Vaccine

7. Main Decisions • Antiviral Policy • Current 25% stockpile • Building up to at least 50% • Possibly 75% (or even 125%) • School Closures

8. Countermeasures‘Household Prophylaxis’

9. Main Decisions • Antiviral Policy • Higher raw attack rate. • Reduction in transmission less than expected. • Higher dosing required • Fraud. • School Closures • If schools were closed, absenteeism due to those staying at home to look after children could rise to 17-18% throughout the period of school closure, giving a total absence level including illness of 30-35% at the peak

10. Antiviral Policy • At 25% level • Treatment of all those ill until effective attack rate is forecast to exceed 25% • Treatment of Children and at risk groups. • At 50-75% level • Household Prophylaxis • Treatment of all • Treatment of Children and at risk groups.

11. Decisions • If/When to change antiviral strategy? • Whom to prioritise? • When to open/close schools? • (Secondary Antiviral)

12. Figure: 50% raw attach rate comparing treatment and prophylaxis until antivirals run out Table: Attack rate and deaths at a 50% raw attack rate

13. Severity • Extreme Pandemic (CFR > ~ 3%) • Close schools • Cancel public events • Household quarantine • Travel restrictions • Mild Pandemic (CFR < 0.4, AR < 10%) • Revert to usual GP based approach.

14. Secondary care • Assistance to localities with specific problems.

15. From Data to Information -‘Real Time Modelling’

16. None of the decisions can be based on pure data. • Attack rate estimates depend on long term forecasts. • ‘Current’ and ‘total’ numbers of cases, deaths and case fatality rates depend on ‘nowcasts’.

17. Resilience and Alternative Views • Two approaches • Three teams • Health Protection Agency Centre for Infections. • Medical Research Council Centre for Outbreak Analysis • Health Protection Agency Centre for Emergency Preparedness.

18. Daily Nowcasts • Weekly long term forecasts. • HPA CfI results official view • Results compared weekly – discussed with senior officials. • Implications explained.

19. Data streams • Telephone system: • Daily, Current, includes ILI. • Calls. • Antivirals authorised (fraud) • Age • Complications

20. Databases • First 100’s of cases • Clinical database

21. GPs • RCGP • QFlu • Daily. • Complications • Antivirals for children under 3. • Hospitalisations.

22. Deaths from National Statistics. • Days or weeks delay. • High level of background. • Hopitalisations from standard NHS statistics.

23. FF100 First few 100’s of cases and contacts.

24. FF100 unique outputs needed for modelling • Early information on serial interval (and R0 ?) • Proportion of those infected with clinical symptoms. • Both need care tracing contacts and viral swabbing/serology.

25. Other useful outputs Early information on • disease course and • severity • initial case numbers for CCC/public.

26. Winter Willow Surveillance and Real Time Modelling

27. Winter Willow did not exercise real time modelling or surveillance systems directly • But…

28. Large number of simulated data sets, forecasts and reports had to be created.

29. Simulated data • Run model to construct epidemic (cases by age, hospitalisations, deaths.) • Add noise (ILI, delays in reporting) • Create simulated data set. • Run nowcasts and forecasts. • Use reporting system to present to decision makers.

30. Lessons to be learnt from process of creating reports and how they were used.

31. Winter Willow: forecasts at first UK case based on (simulated) WHO information.....

34. As epidemic develops....

35. Lessons • Surveillance and Real-time modelling are critical to management of a pandemic

36. Windows of opportunity for decision making as modelling evidence builds up. • Need to set suitable expectations. • Need to communicate uncertainty. • Distinction between ‘useful’ modelled outputs and ‘useless’ raw numbers.

37. Lessons • Need better process. • Need better presentation.

38. NOWCAST NATIONAL SUMMARY Note: Numbers in this section are modelled estimates Both current and past estimates may change from day to day as more information becomes available. Graph 1: Nowcast cumulative total of clinicaliicases of pandemic influenza broken down by age group[i] Graph 2: Nowcast cumulative total of excess deaths from pandemic influenza broken down by age group[ii] (if modelled data exists)

39. Questions?