1. Seasonality of influenza

2. SARINET May 2016, Seasonality, Global Influenza Program, WHO Analysis of seasonality To understand what is normal To anticipate influenza increase To vaccinate at the right time To group countries with similar patterns

3. SARINET May 2016, Seasonality, Global Influenza Program, WHO When to vaccinate? Systematic literature review of seasonality in the tropics and subtropics (NIVEL) CDC, PATH, NIVEL, WHO –Multiple data sources (FluNet, National Surveillance data) –Different seasons: 2002 - 2015 (excluded 2009-10) –Different inclusion / exclusion criteria –Different statistical approaches weekly prop. of positive cases over all positive cases for influenza within that year weekly prop. of samples testing positive for influenza Time series analysis Binomial model: log(p/(1-p)) = year + month –Different thresholds to define increased flu activity / year-round activity

4. SARINET May 2016, Seasonality, Global Influenza Program, WHO Different methods looking for seasonality combined PATH: Weekly proportion of positive cases over all positive cases for influenza within that year. NIVEL: FluNet: Eyeballing to identify months with high, low and no influenza activity in FluNet CDC: Weekly proportion of samples testing positive for influenza. Binomial model to predict the monthly flu activity as a factor of historical monthly and yearly activity –Also analysed whether peak flu % positivity occurred during similar months each year. WHOTime Series analysis. Missing values in time series replaced with either 0 or moving average (imputation). Time series plot (observed and imputed) to check if imputation makes sense. Autocorrelation function plot to display dependency between time points..

5. SARINET May 2016, Seasonality, Global Influenza Program, WHO Lao PDR No. of peaks Primary period of increased influenza activity Secondary period of increased influenza activity Seasons analysed Data source Lao PDR1Sep-Nov CDC NIVEL1Sep-Nov 2011-2013FluNet PATH1Sep-Nov 2010-2014FluNet WHO1Oct Nov 2010-2014FluNet Published1Aug-Dec 2008-2011ILI

6. SARINET May 2016, Seasonality, Global Influenza Program, WHO Influenza peaks Summary of seasonality analysis by CDC, NIVEL, PATH, WHO and published literature

7. SARINET May 2016, Seasonality, Global Influenza Program, WHO Start month of primary periodNo. of peaks Summary of seasonality analysis by CDC, NIVEL, PATH, WHO and published literature North Brazil (Mar- Apr); South Brazil (May- Jun) Exception - Jamaica

8. SARINET May 2016, Seasonality, Global Influenza Program, WHO Proposed influenza vaccination zone (Americas) Summary of seasonality analysis by CDC, NIVEL, PATH, WHO and published literature

9. SARINET May 2016, Seasonality, Global Influenza Program, WHO Influenza vaccination zones Summary of seasonality analysis by CDC, NIVEL, PATH, WHO and published literature

10. SARINET May 2016, Seasonality, Global Influenza Program, WHO Further refinement for the transmission zones Influenza transmission zones were created by World Health Organization (WHO) –present epidemiological and virological trends and monitor seasonal influenza activity Current zones are based on UN regions (with WHO adaptations). Since the 2009 H1N1 pandemic, enhanced surveillance has allowed for better characterization of influenza circulation over time. Systematic assessments elucidated the need to explore re-grouping countries in zones supported by similar epidemiological patterns of transmission.

11. SARINET May 2016, Seasonality, Global Influenza Program, WHO WHO Transmission Zones

12. SARINET May 2016, Seasonality, Global Influenza Program, WHO Objective Systematic assessments (biweekly) elucidated re-grouping need Re-examine current WHO transmission zones and assess the concordance of seasonality in countries within and in adjacent zones.

13. SARINET May 2016, Seasonality, Global Influenza Program, WHO Methods Country-level virological data were extracted from FluNet –From 2011 to 2015 (exclusion of 2009/2010 season) –Excluded countries with less than 100 crude virus detections/year –Examination of influenza type A and B collectively EPIPOI –Epidemiological Parameter Investigation from Population Observations Interface –Open source time series analysis software –Gives seasonal parameters (timing and magnitude of annual peaks in a time series A time-series hierarchical clustering analysis using average linkage Geographically contiguous clusters were generated based on the synchrony of seasonality.

14. SARINET May 2016, Seasonality, Global Influenza Program, WHO

15. All Influenza types

16. SARINET May 2016, Seasonality, Global Influenza Program, WHO South America Results

17. SARINET May 2016, Seasonality, Global Influenza Program, WHO Results Southern Asia/South-East Asia –[People’s Republic of Lao, Nepal, India, Bhutan], [Sri Lanka, Singapore, Malaysia], [Thailand, Philippines, Cambodia, Viet Nam] Eastern Asia –[China, Japan, Republic of Korea, Mongolia] Western Asia –[Egypt, Pakistan, Iran (Islamic Republic of)] –Georgia currently in Western Asia clustered closely with Eastern Europe countries South America –[Mexico, Jamaica, Guatemala] clustered with countries in North America

18. SARINET May 2016, Seasonality, Global Influenza Program, WHO Conclusions Results were in concordance with recently analyzed vaccination zones in tropical countries. Analysis based on countries with available data –Should be re-evaluated as more countries report on influenza activity. Data-driven recommendations should supplement climate data

19. SARINET May 2016, Seasonality, Global Influenza Program, WHO Limitations National representativeness May mask subregional variability in seasonality patterns Extrapolation to neighbouring countries with inadequate data

20. SARINET May 2016, Seasonality, Global Influenza Program, WHO Summary Time of vaccination is determined by seasonality analysis for a country / region WHO will reorder the transmission zones National input will be asked before final adjustment

21. SARINET May 2016, Seasonality, Global Influenza Program, WHO Acknowledgements NICs, WHO CCs Eduardo Azziz-Baumgartner, Lizette Durand Laura Newman, Niranjan Bhat John Paget Siddhi Hirve, Lucia Soetens, Thedi Ziegler, Wenqing Zhang and GIP colleagues Saba Qasmieh All that I might have forgotten

22. SARINET May 2016, Seasonality, Global Influenza Program, WHO PATHNIVELCDCWHO Data source / Seasons analysed FluNet: 2010 – 2015, 131 countries FluNet: 2010 – 2014, 131 countries. National surveillance data: 2000 – 2014, 18 countries FluNet, PAHO, Nat surveillance data: 2002 – 2014, 16 countries of Central and South America FluNet. 2010 – 2014, 131 countries InclusionLab confirmed data Lab confirmed data.Lab confirmed data Exclusion1)2009 – 2010 2)Season with less than 50 influenza cases / year 1)2009-2010 2)Season with <10 specimens per week (FluNet) 3)Season with <50 flu cases or <20 consecutive weeks of reported data (National surveillance data) 1)2009 – 2010 2)Less than 10 samples tested each month 1)2009 – 2010 2)Year with less than 100 influenza positive cases

23. SARINET May 2016, Seasonality, Global Influenza Program, WHO PATHNIVELCDCWHO Approach used Weekly proportion of positive cases over all positive cases for influenza within that year. FluNet: Eyeballing to identify months with high, low and no influenza activity in FluNet. National surveillance data: Data were pooled on a monthly basis and a case proportion (i.e. a monthly proportion of samples testing positive for influenza) was calculated Weekly proportion of samples testing positive for influenza. Binomial model to predict the monthly flu activity as a factor of historical monthly and yearly activity – Also analysed whether peak flu % positivity occurred during similar months each year. Time Series analysis. Missing values in time series replaced with either 0 or moving average (imputation). Time series plot (observed and imputed) to check if imputation makes sense. Autocorrelation function plot to display dependency between time points.

24. SARINET May 2016, Seasonality, Global Influenza Program, WHO PATHNIVELCDCWHO Criteria to define peak / period of increased influenza activity Month with 10% or more of total yearly cases of influenza for two or more years. Second set of increased flu activity separated by 2 or more months of non-peak activity. FluNet: Eye-balling to define months with high levels of activity. National surveillance data: Peak defined as week with highest no. of cases. If highest no. in 2 or more weeks, peak defined as the central week of the 3-wk or 5-wk period with the highest no. of cases. Then counted the no. of times the peak occurred in each month of the year. The monthly proportion of samples testing positive for flu was used to identify months which had high levels of flu activity. Predicted flu activity exceeded the annual median proportion of positive cases for at least 2 consecutive months. Start and end of epidemic defined as the first month when activity exceeded and remained below the annual median proportion. Time -series analysis to define peak. Decomposed the time series into seasonal, trend and residual components.

25. SARINET May 2016, Seasonality, Global Influenza Program, WHO PATHNIVELCDCWHO Criteria to define year- round activity Eight or more months of increased flu activity, or 3 or more peaks of influenza activity each separated by at least 2 months Influenza was on average identified each month of the year