Neil Ferguson MRC Centre for Outbreak Analysis and Modelling Imperial College London

1. Evidence supporting the use of non-pharmaceuticalinterventions in a pandemic Neil Ferguson MRC Centre for Outbreak Analysis and ModellingImperial College London

2. Timescale of spread • 2-4 months to peak at source, 1-3 months to spread to West (in absence of seasonality). • 1/3 of population might become ill, ~1 million new sick people per day at peak. • 15%+ absenteeism at peak. • 1st wave over ~3 months after 1st UK case. 2

3. What can we expect from NPIs? • Developed world – reduce attack rates until vaccine available. • Developing world – reduce attack rates: difficult, since measures don’t give permanent immunity . The most NPIs can do is eliminate the ‘overshoot’ inherent in an unmitigated epidemic.

4. Recent reviews • WHO working group (EID 2006) on National measures: • recommended hand hygiene, and that other measures considered based on circumstances. • but highlighted the very limited evidence base for the community impact of most non-pharm. measures (e.g. avoiding crowding, school closure, hand hygiene, masks, travel restrictions). • IOM report on reusability of face masks concluded more research critically needed to evaluate the effectiveness of face mask use. • IOM report on community mitigation: • “The evidence base is scant” for use of case isolation – “Neither modeling nor historical analyses provide support for these interventions”. • “The evidence suggests a role for community restrictions ... [but] does not allow for differentiating ... specific types of community restrictions”. • “... any discussion of using these interventions [should] consider not only their potential health benefits, but also their likely ethical, social, economic, and logistical costs.”

5. Closing the evidence gap • CDC NPIs studies on seasonal flu – 8 projects, now in 2nd year – looking at masks, hand hygiene etc. • Analysis of historical pandemic and seasonal flu data. • Surveys of public attitudes to NPIs and what measures people may take spontaneously (key issue – did people modify behaviour in past pandemics?). • Other studies.

6. Learning from the past: 1918 • Very different epidemic patterns seen in different US cities in fall 1918 (much more variation than UK). • Timing and nature of public health interventions varied between US cities. • Can public health interventions provide a plausible quantitative explanation of the variation between US cities? • What if? … measures hadn’t been imposed, or were imposed earlier… • 3 papers recently [Bootsma & Ferguson PNAS, Hatchett et al. PNAS, Markel et al., JAMA].

7. Trends in mortality • Both peak and total mortality weakly correlated with timing of epidemic and previous year’s mortality. • Peak mortality strongly correlated with ‘early’ interventions. • Peak mortality strongly correlated with presence of 2 autumn peaks, total mortality weakly so.

8. Results of 1918 analysis • Public health measures explain 1918 pattern well. • San Francisco, St Louis, Milwaukee and Kansas City had most effective policies (>30% drop in transmission). • But measures often started too late, always lifted too early. • Also evidence of spontaneous behaviour change.

9. A study of the household use of masks C Raina MacIntyre (1), Simon Cauchemez (2), Dominic Dwyer (3), Holly Seale (1), Mary Iskander (1), Pamela Cheung (1), Gary Browne (5), Michael Fasher (6), Robert Booy (1), Zhanhai Gao (1), NoemieOvdin (1), Neil Ferguson(2). Affiliation's: 1. Discipline of Pediatrics and Child Health, faculty of Medicine, University of Sydney and National Centre for Immunisation Research and Surveillance of Vaccine Preventable Diseases, Children’s Hospital at Westmead, 2. Imperial College, London, UK 3. Centre for Infectious Diseases and Microbiology, Westmead Hospital, Westmead, 5. Emergency Department, Children's Hospital at Westmead, Westmead, NSW, 6. Wenwest Division of General Practice

10. Study design • Recruitment: • Sydney, winter/spring of 2006 and 2007 • Families of children presenting to the emergency department and general practice with ILI: a temp of >37.8 and at least one respiratory symptom. • At least 2 well adults in household. • Intervention: • Random allocation of the 2 adults to one of three groups: surgical mask, P2 mask, and control groups. • 1-week follow up: • Incidence of ILI (phone call); • Adherence to mask use (phone call); • Nose throat viral swab obtained for PCR testing for influenza and other respiratory viruses (at recruitment for index case; visit for secondary cases).

11. Outcomes • Primary outcome – presence of ILI or respiratory virus infection within 1 week of enrollment: • Intention to treat analysis. • Secondary outcome – time lag between recruitment and infection: • Important to assess the impact of time-dependent variables such as adherence; • Important to demonstrate a temporal association between mask use and reduction in the risk of infection; • Multivariate Cox proportional-hazard survival analysis, with random effects for household clustering [Viboud et al, BMJ, 2004].

12. Intention to treat analysis No significant reduction in incidence due to mask use

13. Compliance Low - ~30% [no significant difference between mask groups]. % compliant Day of mask wearing

14. Survival analysis – time lag from recruitment to infection Assumption on incubation period: 1-day incubation period 2-day incubation period

15. Mask study summary • No significant impact of mask use on transmission of respiratory virus during winter seasonal outbreaks. Low compliance. • Compliant mask use was associated with a reduction in the hazard ratio of infection. • If compliance is higher in during a severe pandemic or an emerging disease outbreak, mask use might reduce the risk of transmission. • Study limitations: • Relatively small population size - underpowered to detect reductions in incidence smaller than 75%; inconclusive comparison of surgical and P2 masks. • Effect needs to be confirmed for the household context and investigated for other settings. More trials needed.

16. What’s next Angus Nicoll, ECDC – class dismissal/school closure Ben Schwartz, CDC – community mitigation in the US

17. Back up slides

18. Recruitment

19. Intention to treat