Health Applications of AIRS Data: Environmental Connection and Prediction

of Influenza and Dengue Fever

Heidar'Thor'Thrastarson1,2'1.'Joint'Ins4tute'For'Regional'Earth'System'Science'and'Engineering,''

University'of'California'Los'Angeles'

Joao'Teixeira2'2.'NASA'Jet'Propulsion'Laboratory,'California'Ins4tute'of'Technology'

Emily'Serman3,'Anna'Zeng4,'Anish'Parekh3,'Enoch'Yeo5'3.'University'of'Southern'California,'4.'Stanford'University,'5.'Harvey'Mudd'College'

''

NASA'Sounder'Science'Team'Mee4ng,'October,'2017'

Influenza Background •  Seasonal'influenza'epidemics'are'a'major'public'health'concern'

–  Millions'of'cases'of'severe'illness'worldwide'each'year'–  250,000'to'500,000'deaths'worldwide'each'year'–  Large'economic'toll'

•  In'temperate'regions'influenza'incidence'generally'has'pronounced'peaks'''''''''''''''''in'the'winter.'

–  But'specific'4ming,'magnitude'and'dura4on'of'individual'local'outbreaks'''''''''''''''''''''''''''''''''''''''''''in'any'given'year'are'variable'and'not'well'explained''

•  If'the'4ming'and'intensity'of'seasonal'influenza'outbreaks'can'be'forecast,'''''''''''''this'would'be'of'great'value'for'public'health'response'efforts.''

–  Could'guide'both'mi4ga4on'and'response'efforts'–  Planning'and'stockpiling'of'vaccines'and'drugs'–  Management'of'hospital'resources'–  Focusing'of'efforts'to'areas'with'more'urgent'need'

©'2017.'All'rights'reserved.'

Humidity & Influenza •  Recent'studies'have'highlighted'a'role'of'absolute'(or'specific)'humidity'condi4ons'

as'a'leading'explana4on'for'the'seasonal'behavior'of'influenza'outbreaks'^'Decreasing'absolute'humidity'associated'with'increased'influenza'ac4vity'

•  Lab'experiments:'^'Absolute'humidity'strongly'modulates'the'airborne'survival'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''and'transmission'of'the'influenza'virus.''

•  Climate'&'influenza'data'records:'^'Increased'winter4me'influenza^related'mortality'in'the'US'''''''''''''''''''''''''''''''''''''''''''''''''''''''''associated'with'anomalously'low'absolute'humidity'levels'

•  Humidity^driven'epidemiological'models'yielding'promising'results''•  The'reason'for'the'humidity–influenza'rela4onship'is'not'well'established''''''''''''''

but'a'few'mechanisms'are'proposed'^  Drying'of'mucous'membranes'^  Humidity'effects'on'droplet'sizes'and'travel'range'^  Increased'survival'4mes'for'the'virus''

Humidity & Influenza

-0.00025

-0.0002

-0.00015

-0.0001

-0.00005

0

0.00005

0.0001

0.00015

-40 -37 -34 -31 -28 -25 -22 -19 -16 -13 -10 -7 -4 -1 2 5 8 11 14 17 20

H2O

MM

R A

nom

aly

(kg/

kg)

Days from Onset

GFT Plus State Level: Days from Onset vs Humidity Anomaly (Smoothed)

Onset 150 Onset 200 Onset 250 Onset 300 -1

-0.8 -0.6 -0.4 -0.2

0 0.2 0.4 0.6 0.8

1 1.2

-40

-37

-34

-31

-28

-25

-22

-19

-16

-13

-10 -7

-4

-1 2 5 8 11

14

17

20

Tem

p A

nom

aly

(K)

Days from Onset

State Level: Onset vs Temperature Anomaly (Smoothed)

Onset 150 Onset 200 Onset 250 Onset 300

Credit:'Emily'Serman'

•  Shaman'et'al.'(2010)'used'30'years'of'flu^related'mortality'data'and'humidity'data'

•  Anomalously'low'humidity'preceding'the'onset'of'influenza'seasons'

•  We'have'used'AIRS'and'Google'Flu'Trends'data'at'city,'state'and'regional'scales'in'the'US'to'give'further'support'for'the'role'of'humidity'(and'temperature)'in'driving'influenza'seasonality'

Model of Influenza Outbreaks

•  We'have'developed'and'implemented'a'numerical'predic4on'system'that'is'driven'by'specific'humidity'to'predict'influenza'outbreaks.'

•  Standard'compartmental'epidemiological'model,'SIRS'(Suscep4ble^Infec4ous^Recovered^Suscep4ble)'type'

•  Two'coupled'first^order'Ordinary'Differen4al'Equa4ons'numerically'solved'for'the'number'of'suscep4ble'and'infected/infec4ous'people'in'a'given'popula4on.'

•  Rate'of'infec4ons'and'recoveries'parameterized'in'terms'of'average'length'of'immunity'and'mean'infec4ous'period'and'assumed'to'have'a'simple'dependence'on'input'specific'humidity.''

SIRS Model

SIRS-q: Humidity-Driven Influenza Model

Heidar Thor Thrastarson

August 3, 2015

1 SIRS Model

Model of influenza transmission dynamics. SIRS (Susceptible-Infectious-Recovered-Susceptible) typemodel. Similar to Shaman & Karspeck (2012). Two coupled first-order ODEs for S and I:

dS

dt=

N � I � S

L� �IS

N� ↵ (1)

dI

dt=

�IS

N� I

D+ ↵ (2)

where

� =R

0

D=

1

D[R

0min

+ (R0max

�R0min

)eaq] (3)

Some assumptions/limitations:

- Fractional persons

- Only a single influenza strain

- Partial and cross-immunities not represented

- Perfectly mixed - clustering and structured interactions of sub-populations not included

- Variations in host infectiousness not modeled

- Other potential drivers than humidity not included, e.g., school calendar

- No age stratification

Table 1: Variables/Parameters

N Population sizeS Susceptible personsI Infectious (= infected) personsL Average immunity duration [days]D Mean infectious period [days]↵ Rate of (travel-related) import of virus into model domain [(infections)/day]� Contact rate [(contacts)/day]R

0

(Daily) basic reproductive numbera (Negative) coe�cient in contact rate exponentialq Specific humidity [kg/kg]

1

SIRS-q: Humidity-Driven Influenza Model

Heidar Thor Thrastarson

August 3, 2015

1 SIRS Model

Model of influenza transmission dynamics. SIRS (Susceptible-Infectious-Recovered-Susceptible) typemodel. Similar to Shaman & Karspeck (2012). Two coupled first-order ODEs for S and I:

dS

dt=

N � I � S

L� �IS

N� ↵ (1)

dI

dt=

�IS

N� I

D+ ↵ (2)

where

� =R

0

D=

1

D[R

0min

+ (R0max

�R0min

)eaq] (3)

Some assumptions/limitations:

- Fractional persons

- Only a single influenza strain

- Partial and cross-immunities not represented

- Perfectly mixed - clustering and structured interactions of sub-populations not included

- Variations in host infectiousness not modeled

- Other potential drivers than humidity not included, e.g., school calendar

- No age stratification

Table 1: Variables/Parameters

N Population sizeS Susceptible personsI Infectious (= infected) personsL Average immunity duration [days]D Mean infectious period [days]↵ Rate of (travel-related) import of virus into model domain [(infections)/day]� Contact rate [(contacts)/day]R

0

(Daily) basic reproductive numbera (Negative) coe�cient in contact rate exponentialq Specific humidity [kg/kg]

1

SIRS-q: Humidity-Driven Influenza Model

Heidar Thor Thrastarson

August 3, 2015

1 SIRS Model

Model of influenza transmission dynamics. SIRS (Susceptible-Infectious-Recovered-Susceptible) typemodel. Similar to Shaman & Karspeck (2012). Two coupled first-order ODEs for S and I:

dS

dt=

N � I � S

L� �IS

N� ↵ (1)

dI

dt=

�IS

N� I

D+ ↵ (2)

where

� =R

0

D=

1

D[R

0min

+ (R0max

�R0min

)eaq] (3)

Some assumptions/limitations:

- Fractional persons

- Only a single influenza strain

- Partial and cross-immunities not represented

- Perfectly mixed - clustering and structured interactions of sub-populations not included

- Variations in host infectiousness not modeled

- Other potential drivers than humidity not included, e.g., school calendar

- No age stratification

Table 1: Variables/Parameters

N Population sizeS Susceptible personsI Infectious (= infected) personsL Average immunity duration [days]D Mean infectious period [days]↵ Rate of (travel-related) import of virus into model domain [(infections)/day]� Contact rate [(contacts)/day]R

0

(Daily) basic reproductive numbera (Negative) coe�cient in contact rate exponentialq Specific humidity [kg/kg]

1

Specific'humidity'

Quasi-Operational Forecasting System

Humidity'Observa4ons'

Humidity'Forecasts'

Influenza'Observa4ons'

SIRS'Model'

Influenza'Forecasts'

•  Daily'upda4ng'most'recent'values'for'near^surface'H2O'mixing'ra4o,'AIRS'level'3'data'(v6)''

•  NCEP'forecasts'for'near^surface'humidity'

•  Influenza'data'assimilated'

'•  Center'for'Disease'

Control'(CDC):'^ 'Regional,'weekly'surveillance'records'for'the'propor4on'of'doctor’s'visits'for'influenza^like'illness'(ILI)'^ 'Combined'with'lab'virology'results'for'the'percentage'of'influenza'posi4ve'samples'

•  Google'Flu'Trends:''^'Near'real^4me'es4mates'of'influenza'infec4on'rates'based'on'online^search'queries'

•  The'output'is'the'number'of'infected'and'suscep4ble'people'in'a'popula4on'(city/state/region)'

Retrospective Simulations !  Pseudo-forecasts performed for 21 US cities (in hindsight with

observed humidity) for 2005-2015 seasons

!  5-day and 10-day influenza forecasts (with AIRS humidity) are often quite accurate (example below for LA, 2013/14 winter)

Thrastarson & Teixeira, 2017

Quasi-Operational Prediction System applied to US cities

•  The'system'has'been'running'quasi^opera4onally'for'several'US'ci4es'

•  AIRS'near^surface'humidity'is'key'component'of'a'quasi^opera4onal'(produced'daily)'influenza'predic4on'system'

'•  Most'recent'values'of'AIRS'near^

surface'specific'humidity'as'well'as'NCEP'humidity'predic4ons'regularly'incorporated'into'the'model'

•  ‘Observa4onal’'data'for'influenza'incidence'from'CDC/Google'assimilated'to'make'analysis'and're^ini4alize'model'

•  Ensembles'of'forecasts'run'with'different'model'parameter'values'drawn'from'distribu4ons'reflec4ng'limited'constraints'

Example'results'for'the'2016^2017'season'

•  Timing'and'rela4ve'behavior'of'influenza'outbreaks'generally'captured'fairly'well,'while'gejng'absolute'numbers'of'affected'people'is'more'challenging'

AIRS and Influenza in South Africa !  Near-surface humidity plays critical role in influenza epidemics

!  AIRS near-surface humidity correlates well with influenza cases

!  Mathematical relations depend on regional climate (provinces)

!  These relations can be used to monitor and predict influenza

4500

3500

2500

15000.004 0.013

Casesp

er100

,000

AverageHumidity[kg/kg]

GautengR2 =0.84705

y =3E+07x2 - 814548x+6944.6

WesternCapeR2 =0.70196

y =1.0196x-1.599

Kwazulu-NatalR2 =0.90757

y =4E+07x2 - 1E+06x+9018

0.004 0.013 0.004 0.013

CorrelationofFluCasesandHumidity

Leastsquaresregressionmodelscomparingaveragenear-surfacehumiditywithaverageflucountsfromtheGoogleFluTrendsdatabase.

Gauteng Kwazulu-Natal Western Cape

Credit:'Anish'Parekh'

Dengue Fever and Environment Dependence •  Dengue'Fever'is'the'most'common'

mosquito^borne'virus'in'the'world''•  Carried'by'Aedes'aegyp4'mosquitos'

(same'as'Zika,'Chikungunya'and'Yellow'Fever)'–'strongly'affected'by'environmental'condi4ons'

•  Temperature'affects'mosquito'development'and'reproduc4on,'frequency'of'feeding,'virus'incuba4on'period'and'geographical'range'of'the'vector'(tropics'and'sub^tropics)'

•  Precipita4on'provides'breeding'sites'and's4mulates'egg'hatching,'but'can'also'hurt'habitats'through'flooding'and'humidity'has'also'been'iden4fied'as'a'substan4al'factor'affec4ng'favorable'condi4ons'for'the'vector'

•  Typically'expect'effects'of'temperature'and'humidity'to'take'6^8'weeks'

AIRS – Dengue •  We'are'exploring'the'applica4on'of'AIRS'climate'data'to'the'predic4on'of'dengue'fever'incidence.'

•  An'ul4mate'goal'is'to'create'and'implement'an'improved'predic4on'model'for'Dengue.'

•  We'have'done'a'focused'study'on'Dengue'fever'in'Mexico,'from'2003^2015'

•  Mexico'has'significant'Dengue'incidence'in'varied'climate'condi4ons,'with'weekly'Google'Dengue'Trends'data'available'at'state'level'as'an'es4mate'of'disease'ac4vity'

•  AIRS'variables:'surface'air'temperature,'specific'humidity,'rela4ve'humidity'

•  Examined'trends,'palerns'and'4me'lags,'regression'models'of'varying'complexity'

Den

gue

case

s (n

orm

aliz

ed)

AIRS and Dengue Fever in Mexico

These relations are being tested to monitor and predict Dengue incidence during its decay phase

Dengue cases correlate particularly well with AIRS humidity during decay phase H

umid

ity (g

/kg)

Time (week)

Red – Dengue cases

Blue - humidity

Dengue and Climate in Mexico Comparisons'of'year^averaged'Dengue'ac4vity'(Google'Dengue'Trends,'top)'and'AIRS'near^surface'temperature'and'humidity'(bolom)'show'climate^related'regional'differences''

Tropical'

Arid'

Dengue'Ac4vity'(GDT,'year^averaged)'

Atmospheric'Variables'(AIRS,'year^averaged)'

Abs.'Hum

idity

'Re

l.'Hu

midity

'

Different'states'

More%tropical%regions%

More%arid%%%%regions%

More'extended'and'elevated'dengue'seasons'

Dengue'peaks'more'sharply'and'only'once'

In'correla4on'with'extended'and'elevated'temperatures'and'humidity'

In'correla4on'with'steeper'rises'and'falls'in'temperature'and'humidity''

Shorter'4me'lags'in'the'climate^dengue'rela4onship'

Longer'4me'lags'in'the'climate^dengue'rela4onship'''

K'

%'

''

Credit:'Anna'Zeng'

Temp'

g/kg'

Summary

•  Results'using'AIRS'and'influenza'data'support'role'of'humidity'and'temperature'in'driving'the'seasonality'of'influenza'outbreaks'

•  We'have'developed'a'humidity'driven'influenza'model'opera4ng'on'a'city,'state'or'regional'scale'

•  AIRS'near^surface'humidity'is'key'component'of'the'quasi^opera4onal'(produced'daily)'influenza'predic4on'system'

'•  For'Dengue'Fever,'we'have'done'a'focused'study'in'Mexico,'exploring'

trends,'palerns'and'4me'lags'for'Dengue'and'environmental'variables,'using'regression'models'of'varying'complexity.''

•  Promising'correla4ons'and'climate^related'regional'differences'have'been'iden4fied.'

Future Work '•  Further'valida4on'of'the'predic4on'system'

•  Obtaining'and'incorpora4ng'more'specific'influenza'incidence'data'from'medical'networks'and'authori4es'and'internet'sources.''

•  Developing'confidence'and'uncertainty'measures'(including'effects'of'AIRS'humidity'data'uncertainty)'

•  Beler'constraining'of'parameters'and'methods'that'account'for'their'variability'and'uncertainty'

'•  Implemen4ng'and'assessing'longer'term'seasonal'predic4ons'(using'climatology,'maybe'

longer'term'humidity'predic4ons'in'the'future)'

•  Exploring'different'types'of'models,'virus'subtypes,'popula4on'age'structure,'geographical'spread'

•  More'engagement'with'poten4al'end'users'