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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TRMM

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2006 2007 2008 20090

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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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POS

Fitted

Validation

2006 2007 2008 20090

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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