1. Neil Ferguson Dept. of Infectious Disease Epidemiology Faculty of Medicine Imperial College WG 7: Strategies to Contain Outbreaks and Prevent Spread © All material copyright Neil Ferguson 2005. No reproduction without prior written permission

2. Is containment of a respiratory pathogen feasible? Never been achieved previously. Are we attempting the impossible Or has it…. GB: 1918-19

3. What are our objectives? Containment: what does it take? What do we need to know? Transmission dynamics & modeling. How transmissible is influenza? How fast does it spread? Is seasonality important? Data needs – household & cohort studies. Data needs – school/workplace transmission. Data needs – ‘community’ transmission. Data needs – antivirals & vaccines. Data needs – population/movement data. ‘Social distance’ measures – did they have an effect in past pandemics? Topics to cover!

4. ‘Containment’ = eradication or dramatic slowing of spread. Probably only feasible at source (SE asia?). For other countries, objectives are more varied:  Minimizing mortality/moribidity.  Maximizing societal resilience.  Minimizing economic impact.  ‘Keeping it out’ (e.g. GB).  Slowing spread until the cavalry arrive. Tensions can exist between objectives. Need clearer policy guidance. What are our objectives?

5. Containment – what does it take (in theory)? The spread of an infectious pathogen is characterised the basic reproduction number, R 0 – the average number of secondary cases generated by a single case in an entirely susceptible population. Control policies optimally reduce transmission so that R 0 <1 – since at that level an epidemic cannot sustain itself. Hence control policies need to eliminate a fraction 1-1/ R 0 of transmission – i.e. 33% for R 0 =1.5, 50% for R 0 =2, 75% for R 0 =4. This can be achieved by:  Reducing contact (quarantine, increasing social distance).  Reducing susceptibility (vaccination, antiviral prophylaxis).  Reducing infectiousness (antiviral treatment). Key issues are who is targeted, how much effort is needed, and how fast do we need to act.

6. What do we need to know? Aetiology (avian- or ILI-like) How transmissible the virus is. Major contexts of transmission (home, school, workplace, random contacts). Risk groups for severe disease. The effect of specific control measures on transmission rates. Quality and timeliness of surveillance (ascertainment). Logistic constraints on control measures.

7. Epidemiological analyses aim to gain quantitative insight into: Disease transmission route(s). Heterogeneities and risk factors for disease spread. The effectiveness of disease-control/risk-reduction policies. Mathematical epidemic models precisely represent knowledge and assumptions about disease transmission. Statistical methods allow key parameters to be estimated from epidemiological data, hypotheses to be tested and robust predictions (with confidence bounds) to be made. Integrating the two increases power of analysis, but is more difficult. Transmission dynamics and modelling

8. Transmissibility: R 0 Mills et al 2004 – R 0 =2-3 But this assumes serial interval of 3+ days (on the basis of little data). Probably closer to 2 days – gives reduced estimates <2.

9. How fast will it spread? Depends on R 0 and average interval between ‘rounds’ of infection. Data on generation time very limited. Historical estimates are more assumptions than data-based. Incubation: mean~1.5 days (Moser et al) Infectiousness profile (Cauchemez 2004 etc): mean ~2.4 days.

10. Seasonality Known to be important for inter-pandemic flu in temperate countries. Degree of variation in transmission unclear though. Could seasonality slow pandemic spread? Research priority.

11. Data needs :household transmission Longini et al. compiled household data set from family cohort study data.. Cauchemez (Statist. Med. 23:3469-87, 2004) analysed newer French data Data allows household transmission rates to be approximately estimated, but more data needed (a new ‘Tecumseh’?). 2 people 4 people 5 people 3 people Number of people infected Proportion Proportion infected Matching household data fixes ratio of within- to between- household spread.

12. Data needs: school/workplace transmission Limited data – children known to be most at risk of inter-pandemic flu, but no robust quantification of transmission in school. Can approximately quantify non-household transmission, but difficult to partition. Age specific attack rate data is only partly informative. Some work underway attempting to quantify drop in transmission during school holidays. Better data on this critical to obtain reliable estimates of the effect of school closure.

13. Data needs: age-specific attack rates Limited data, and not all agrees. (Stuart-Harris 1970) ILI attack rate (attendance at GP)

14. Data needs: effect of antivirals/usage options Rough estimates (Hayden, Longini & Halloran) can be derived from clinical trial data:  Uninfected individual on prophylaxis has 30% drop in susceptibility.  Infected person has 60% drop in infectiousness.  Additionally, a treated infected person has 65% reduction in chance of becoming a ‘case’.  However, these estimates are for H3/H1 – not H5. Policy options:  treatment of risk groups/essential personnel.  treatment of all cases.  + prophylaxis of households, schools, workplaces.  + blanket spatially targeted usage (everyone within x km).

15. Data needs: effect of vaccines Effectiveness of vaccines on current flu strains reasonably well quantified (both in terms of clinical effectiveness and protection against infection, reduction in infectiousness). No data on pandemic vaccine effectiveness, however.

16. Data needs: detailed population data Need both population data (density, age, household). Landscan (Oakridge Natl. Lab.)

17. Data needs: population movements To model global or continental spread, need data on travel (frequency/distance) – both local (travel to school/work), and long- range (flights). Need systematic exercise to collate this (sensitive) data. Population behaviour during pandemic?

18. Research issues relating to social distance measures Almost certain that it can have a significant effect. Probably happened in previous pandemics (spontaneously?). Hence if we use estimates of R 0 derived from 1 st wave attack rates, possible that these already include the effect of school closure etc. How do we avoid such ‘double-counting’? Also, how do the rates of other types of contact change when school or workplaces are closed? This makes any prediction of the impact of school/workplace closure highly speculative. Quantifying effect medium-long range movement restrictions (area quarantine) less difficult.

19. International movement restrictions Recent analysis indicates travel restrictions would have to be >99% effective to gain any significant delay in spread from source country to unaffected country.

20. Key data needs/modelling assumptions Need to know where transmission occurs to develop targeted, efficient interventions.  Currently just have rough estimates partitioning transmission between households and ‘other’.  Models therefore have to make informed guesses about school, workplace & ‘community’ transmission.  How valid are these for new pandemic strains? Population behaviour during a pandemic??? Basic natural history parameters (infectiousness through time). Disease severity (morbidity/mortality). How good is surveillance (depends on severity of disease)?

21. Ongoing UK research Can restrictions on international travel delay the spread of a pandemic? How fast will a pandemic strain spread within a country? What will the healthcare burden? How can antivirals best be used to:  reduce mortality/morbidity?  protect key personnel/reduce social disruption?  slow spread/reduce attack rates? What use do social distance measures (school closure, movement restrictions) have in:  slowing spread?  reducing attack rates? Logistical contraints/economic costs. Need a flexible strategy – to be able to respond to a pandemic with 0.01%, 0.1%, 1% or even 10% population mortality.