surveillance and prediction of seasonal influenza and early

NASA Goddard Space Flight Center ▪ Greenbelt, Maryland USA

GEO TASK HE‐09‐02ESURVEILLANCE AND PREDICTION OF SEASONALINFLUENZA AND EARLY DETECTION OFPANDEMIC INFLUENZA

Presented byRadina P. Soebiyanto (UMBC/NASA)On behalf ofRichard K. Kiang (NASA)


GEO Task HE‐09‐02e

Task Description

Area Health

Overarching Task Monitoring and Prediction Systems for Health (HE‐09‐02)

Sub Task Surveillance and Prediction of Seasonal Influenza and Early Detection of Pandemic Influenza

Related CoP Health and Environment

Leads

USANASA Richard K. Kiang (PoC) [email protected]

NASA John Haynes [email protected]

France CNES Murielle Lafaye [email protected]

Japan JAXA Tamotsu Igarashi [email protected]


Background/Motivation

• Seasonal influenza annual epidemics:

– Infects 5 to 15% of world population, 500,000 deaths

– Economic burden in the US ~87.1 billion

• Spatio‐temporal pattern of epidemics vary with latitude

– Temperate regions: distinct annual oscillation with winter peak

– Tropics: less distinct seasonality, and often peak more than once a year

• Suggests role of environmental and climatic factors

Factors Relationship

Virus Survivorship

Temperature InverseHumidity InverseSolar irradiance Inverse

Transmission Efficiency

Temperature InverseHumidity InverseVapor pressure InverseRainfall ProportionalENSO ProportionalAir travels and holidays Proportional

Host susceptibility

Sunlight InverseNutrition Varies

Factors Implicated in Influenza from literature


Task Definition

• Expand the availability, use and application of environmental information for influenza decision‐making

• Assess current activities, needs and priorities in the use of Earth observations for the surveillance, modeling and prediction of seasonal influenza and the early detection of pandemic influenza

• Focus on the sharing of not only environmental data, but also influenza monitoring to identify observation priorities, gaps in knowledge and overlaps in current activities (while preserving overlaps that would lend to comparison and cross‐validation of techniques)


Task Activities and Outputs

Earth Observation (EO)• Satellite data• Ground measures• Climate forecast

Short‐ & long‐term influenza forecast capabilities

Influenza types and subtypes sensitivity to environmental factors

Database of historical and forecasted EO indicators for influenza

Outputs

Calibrate and Validate best‐fit models:Neural Network, Time series regression (ARIMA), Biological‐based model

Determine the best EO indicators for influenza in each country

Modeling

Influenza epidemiological data• Laboratory confirmed• ILI (Influenza‐Like Illnesses) rate

Data Processing

Capacity building• Technology transfer• Workshop and training

Influenza data sharing agreement• Preliminary data processing

Collaborative Partnership

• WHO GISN FluNet• WHO Euro Flu• PAHO Communicable Diseases• Euro CDC

Int’l Public Health Agencies

Publicly Available Data


Resources

• Utilizing modeling and analysis work funded by NASA

Project title Avian Influenza Risk Prediction & Pandemic Influenza Early Warning

Modeling Global Influenza Risks Using NASA Data

Coordinator Richard Kiang Richard KiangPartners • US Naval Medical Research Unit

2 (NAMRU‐2) , Cambodia• USDA – APHIS• Wetlands International

Indonesia Programme• Cobbs Indonesia

CDC Influenza Division

Period 3 years 2 years (2011‐2012)


CDC International Influenza Program

Activities and Supports in 2009 Overseas Staff & Representatives

Task External Partners


Task Advisors

• Dr. Marc‐Alain Widdowson, US CDC Influenza Division• Met previously for NASA projects, and will be invited as

advisors for GEO Task– Dr. Anthony Mounts, WHO GISN FluNet & FluID– Dr. Joshua Mott, WHO EURO Flu– Dr. Ottavio P de Oliva, PAHO Communicable Diseases– Dr. Jan Semenza, ECDC Future Threats and Determinants


Preliminary Capabilities• Calibrated models and short‐term forecast for influenza cases based on

environmental factors derived from satellite measurements and ground observations– Neural Network (NN)– Time series regression: AutoRegressive Integrated Moving Average (ARIMA)

• Test case: Hong Kong, New York City, Maricopa County (Arizona, USA)HONG KONG

MARICOPA COUNTYNEW YORK CITY


Capabilities Under Development

• Influenza subtypes sensitivity to environmental / meteorological factors• Example: Hong Kong

H3N2

• Biological‐based models (i.e. compartmental SEIR model)• One season ahead prediction using climate forecast


Current Activities

• Collect and process influenza epidemiological data from current partners and publicly available database– WHO FluNet: 25 countries – Europe (5), Asia (9), Africa (3), Americas (8)

• Earth Observation data processing – Precipitation: Daily TRMM and Other Rainfall Estimate (3B42 V6)– Surface temperature: MODIS (MOD11C1)

• Ground measurement (as needed)– Humidity, hours with bright sunshine, wind speed, etc

Bangladesh Denmark Guatemala Panama Sri LankaBelgium Dominican Republic Honduras Philippines SwitzerlandCambodia El Salvador Israel Senegal ThailandCosta Rica French Guiana Japan Singapore UruguayCote d'Ivore Ghana Latvia Slovenia Vietnam


Planned Activities

Description2011 2012 2013

1 2 3 4 1 2 3 4 1 2 3 4

Multilateral collaborative effort discussion

Invite external advisor

Modeling and forecasting influenza for currently available data

Capacity Building with multilateral collaborative partner (as needed)

• Database of earth observation products identified as predictors

• Workshops on utilizing remote sensing and other earth observation data for public health use, and on modeling & forecasting techniques

Influenza predictive models

surveillance and prediction of seasonal influenza and early

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