Seasonal InfluenzaDependency on Environmental and Meteorological Parameters
Presented byJodi K. Haponski (GSSP Summer Program)
MentorsRadina P. Soebiyanto (USRA/NASA)Richard K. Kiang(NASA)
Background
Most common disease Annually Infects 5-15% of global
population Annually in the U.S:
▪ Up to 200,000 hospitalizations, at least 30,000 deaths
▪ Estimated economic burden is ~87.1 billionType Location Year Deaths
H1N1 Worldwide 1918-1919 50-100 million
H2N2 (ie. Asian) U.S. 1957-1958 70,000
H3N2 (ie. Hong Kong)
U.S. 1968-1969 34,000
SARS 37 Countries
2002 10% fatality
Motivation
Many new influenza strain first appear in tropical regions
Influenza spread varies with latitude Seasonal in
temperate climate
Year-round outbreaks in tropical climates
Influenza Process
Factors Relationship
Virus Survivorship
Temperature InverseHumidity InverseSolar irradiance
Inverse
Transmission Efficiency
Temperature InverseHumidity InverseVapor pressure
Inverse
Rainfall ProportionalENSO ProportionalAir travels and holidays
Proportional
Host susceptibility
Sunlight InverseNutrition Varies
My Studies
Study area: Hong Kong Sub-tropical climate▪ Comfortable temperatures in the
winter months▪ Temperature low of 6 C (~43 F)
▪ Summers are hot and humid with occasional showers and thunderstorms▪ Temperatures often exceed 31 C
(~88 F) DATA
All reported flu cases in Hong Kong Obtained weekly data from 2006-
2009
Satellite Derived Data Land Surface Temperature (LST)
from MODIS data set Precipitation from TRMM 3B42
GOALS Determine a
relationship between environmental factors and number of disease cases
Model and forecast with the above results
Methods for Modeling
1. Hilbert Huang Transform, using EEMD (Ensemble Empirical Mode Decomposition)
2. Stepwise Fit Includes only significant variables in model
3. Remove dependent variables in model
Original Data:
Nonlinear & Nonstationa
ry Time Series
HHT: Series of Decomposed Signals.
Uses empirical mode decomposition (EMD) to finitely decompose the original into a well-defined Hilbert transform.
Decomposed Signal:
• Linear and Stationary
1. EEMD
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200
400
1
TRMM
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200
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-100
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100
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-100
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-50
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-50
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6
-20
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2006 2007 2008 20090
50
100
8
Year
Original Signal
Deco
mpose
d S
ignals
2. Step-wise Fit: Best Signals
First let all decomposed environmental signals make up the model
Fit each signal with the flu signal (using univariate regression)
Remove any signal with a p-value greater than .05
ENV. VAR. ENV. SIGNAL CORR. COEF. P-VALUERAD 1 0.239801784 0.004048296EVAP 1 0.183394414 0.028914926
DPMEAN 1 0.18584175 0.02680726RHMAX 1 0.239835219 0.004043006
RHMEAN 1 0.208620596 0.012720276RHMIN 1 0.170545888 0.04243615TRMM 3 -0.215539279 0.009992214EVAP 3 0.166590239 0.047538091
RHMIN 3 -0.213320365 0.010804861TRMM 4 -0.280412355 0.000724752
SUN 4 0.270525076 0.001129463RAD 4 0.221017396 0.008212196
TRMM 5 -0.219176311 0.008776312RHMAX 5 0.39367631 1.25395E-06
RHMEAN 5 0.355709026 1.39754E-05RHMIN 5 0.42247252 1.63065E-07TRMM 6 0.351490427 1.79361E-05CLOUD 6 0.213488241 0.010741408
RAD 6 0.294883473 0.00036743RHMAX 6 -0.182948627 0.029313657
RHMEAN 6 0.183642636 0.028694914RHMIN 6 -0.178559371 0.033495689
WSPDMEAN 6 -0.219397274 0.008706846
2. Step-wise Fit: ‘In’
Variables
‘In’ Signals
ENV VAR ENV SIGNAL
TRMM 4
RAD 4
TRMM 5
RHMAX 5
RHMIN 5
CLOUD 6
WSPDMEAN 6
•Performed multivariate regression
•Signal is removed from model if it’s p-value is greater than .1
•Signal added back into the model if it’sp-value is less than .05
The correlation coefficients were computed between each signal decomposition
Two signals are labeled dependent if their correlation is greater than .5
Signals were eliminated based on p-values
Dependent Signals:
ENV VAR ENV SIGNAL
RHMAX 5
RHMIN 5
CLOUD 6
WSPDMEAN 6
Resulting Model:
First Figure:
Second Figure: TRMM, 5th signal
removed from model
2006 2007 2008 20090
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400
500
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700
POS
Fitted
Validation
2006 2007 2008 20090
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100
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300
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POS
FittedValidation
Independent Signals
ENV VAR ENV SIGNAL
TRMM 4
RAD 4
TRMM 5
RHMIN 5
WSPDMEAN 6
Conclusions
The EEMD method was able to give insight into the seasonal relationship between the influenza dynamics with the environmental factors
With only two years of training data, we were able to obtain relatively good prediction results