Analysis of seasonality To understand what is normal To anticipate influenza increase To vaccinate at the right time To group countries with similar patterns When to vaccinate Systematic literature review of seasonality in the tropics and subtropics NIVEL ID: 558993
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Slide1
Seasonality of influenza Slide2
Analysis of seasonalityTo understand what is normalTo anticipate influenza increase To vaccinate at the right timeTo group countries with similar patternsSlide3
When to vaccinate?Systematic literature review of seasonality in the tropics and subtropics (NIVEL)CDC, PATH, NIVEL, WHOMultiple data sources (
FluNet, National Surveillance data)Different seasons: 2002 - 2015 (excluded 2009-10)Different inclusion / exclusion criteriaDifferent statistical approachesweekly 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
activitySlide4
Different methods looking for seasonality combinedPATH: 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 FluNetCDC: 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
.
WHO
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.
.Slide5
Lao PDR
No. of peaks
Primary period of increased influenza activity
Secondary period of increased influenza activity
Seasons analysed
Data source
Lao PDR
1
Sep-Nov
CDC
NIVEL
1
Sep-Nov
2011-2013
FluNet
PATH
1
Sep-Nov
2010-2014
FluNet
WHO
1
Oct Nov
2010-2014
FluNet
Published
1
Aug-Dec
2008-2011
ILISlide6
Influenza peaksSummary of seasonality analysis by CDC, NIVEL, PATH, WHO and published literatureSlide7
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 - JamaicaSlide8
Proposed influenza vaccination zone(Americas)Summary of seasonality analysis by CDC, NIVEL, PATH, WHO and published literatureSlide9
Influenza vaccination zonesSummary of seasonality analysis by CDC, NIVEL, PATH, WHO and published literatureSlide10
Further refinement for the transmission zonesInfluenza 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. Slide11
WHO Transmission ZonesSlide12
ObjectiveSystematic assessments (biweekly) elucidated re-grouping needRe-examine current WHO transmission zones and assess the concordance of seasonality in countries within and in adjacent zones. Slide13
Methods Country-level virological data were extracted from FluNetFrom 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 collectivelyEPIPOIEpidemiological Parameter Investigation from Population Observations InterfaceOpen source time series analysis softwareGives 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. Slide14Slide15
All Influenza typesSlide16
South America ResultsSlide17
ResultsSouthern 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 countriesSouth America
[Mexico, Jamaica, Guatemala] clustered with countries in North AmericaSlide18
ConclusionsResults 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 dataSlide19
LimitationsNational representativenessMay mask subregional variability in seasonality patternsExtrapolation to neighbouring countries with inadequate dataSlide20
SummaryTime of vaccination is determined by seasonality analysis for a country / regionWHO will reorder the transmission zones National input will be asked before final adjustment
Slide21
AcknowledgementsNICs, WHO CCsEduardo Azziz-Baumgartner, Lizette Durand
Laura Newman, Niranjan BhatJohn PagetSiddhi Hirve, Lucia Soetens
, Thedi Ziegler, Wenqing Zhang and GIP colleagues
Saba Qasmieh
All that I might have forgottenSlide22
PATH
NIVEL
CDC
WHO
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
Inclusion
Lab
confirmed data
Lab
confirmed data
Lab
confirmed data.
Lab
confirmed data
Exclusion
2009
–
2010
Season
with
less
than 50 influenza cases
/ year
2009-2010
Season
with
<10
specimens per
week (
FluNet
)
Season
with
<50 flu
cases
or
<20
consecutive weeks of reported data (National surveillance data)
2009
–
2010
Less
than 10 samples tested each month
2009
–
2010
Year
with less than 100 influenza positive casesSlide23
PATH
NIVEL
CDC
WHO
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.Slide24
PATH
NIVEL
CDC
WHO
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.Slide25
PATH
NIVEL
CDC
WHO
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
Influenza was on average identified each month of the year