GeoHealth: A Surveillance and

Response System Resource for

Vector Borne Disease in the

Americas John B. Malone1, Rebecca Christofferson1 Prixia del Mar Nieto1, Jeffrey Luvall2,

SJ Park3, Mara Bavia4, Elivelton Fonseca5, Raul Guimaraes5

1 Pathobiological Sciences, LSU School of Veterinary Medicine; 2 NASA Marshall Space Flight Center, Huntsville AL, 3 Electrical Engineering and Computer Science, LSU

4 Federal University of Bahia, Salvador, Brazil 5 Sao Paulo State University, Presidente Prudente, Brazil

GeoHealth Epidemiology + GIS Landscape Epidemiology – Eco Epidemiology -Ecological Niche Modeling

- Diseases have habitats much like a species - Ecological niche have unique environmental preferences and limits of tolerance that determine spatial distribution and abundance - One species to a niche Source: The NASA Earth Observatory Newsroom

John B Malone, Prixia Nieto – Louisiana State University, USA

Penelope Vounatsou , Juerg Utzinger – Swiss TPH, Switzerland

Mara Bavia – Universidade Federal da Bahia, Brazil

Thomas Kristensen – University of Copenhagen, Denmark

Mapping and Modeling Neglected Tropical

Diseases and Poverty in Brazil, Bolivia and

Colombia

ISPRS-wg8 , Santa Fe

September12, 2011

Objectives

Data Portal – A municipality level data base accessible by FTP was developed in Colombia, Bolivia and Brazil for 6 NTD (Chagas disease, Leishmaniasis, Schistosomiasis, Leprosy, Lymphatic Filariasis and Soil-Transmitted Helminths), with climatic, environmental, socioeconomical and demographic data.

Risk Modeling – Maximum Entropy, Bayesian and GIS methodologies were used to map and model environmental and socioeconomic risk of the 6 NTD at municipality scales

Course Development – A 4-day short course was developed for use by PAHO on data portal access and geospatial analysis using ArcGIS 9.3.1, Maximum Entropy (Maxent) and Bayesian Stat modeling. Different methods as validation.

1-Data Portal -All data clipped to the country boundary. -WGS84 projection. -1 km spatial resolution. -ASCII format for Maxent or Bayesian modeling. This example shows the data available for Colombia and all SA

Develop a geo-referenced database

1.1 NTDs data

1.2 Socioeconomical and demographics data

1.3 Environmental and climatic data

1.1 NTD’s data / Health data

Obtained from several different resources ( 2005-2009) and include:

Bolivia: Ministerio de Salud y Deportes/ Sistema Nacional de información en Salud

Brazil: Ministerio da Saude, SINAN

Colombia: Ministerio de la Protección Social/ Instituto Nacional de salud

1,2 Socioeconomical Data Obtained from the following resources:

Bolivia: *Instituto Nacional de Estadística/ Estadística sociales / Demográficas

Brazil: *Instituto Brasileiro de Geografia e Estatística IBGE

Colombia: *Departamento Administrativo Nacional de Estadística DANE *Instituto Geografico Agustin Codazzi. *Agencia Presidencial para la Acción Social. United Nations Development Programme (UNDP).

HDI UNDI is a composite statistic from data on life expectancy, education

and per-capita GNI (as an indicator of standard of living) used to rank

countries by level of "human development" and separate by categories.

1,2 Socioeconomical Data *Displacement ( just in COL) 1-Infant Mortality

2-Life expectancy

3-Unsatisfied basic needs (UBN)

4-Human development index (HDI)

1.3 Environmental data

World Clime Developed by: Museum of Vertebrate Zoology, University of California, Berkeley, (CIAT), Rainforest CRC.

A set of global climate grids that were generated through Interpolation of average monthly climate data

from weather stations on a 1km2 resolution grid. 19 derived bioclimatic variables

-Monthly precipitation and mean temp

-Minimum temp

-Maximum temp

*Annual trends (e.g., mean annual temperature, annual precipitation)

*Seasonality (e.g., annual range in temperature and precipitation) and

*Extreme or limiting environmental factors (eG temperature of the coldest and warmest month,

and precipitation of the wet and dry quarters).

Satellite Imagery MODIS and LandSat images/data NDVI, EVI, LST (day and night)

and elevation.

GIS Databases Worldclim

Ü Rhodnius prolixusEnvironmental Model

9.3

.1

Triatoma dimidiataEnvironmental Model

8.7

.1 Ü

2-Risk Models - Potential Distribution Models

Chagas Vector Distribution

Output of the variables suitable for Chagas vector presence - Environmental niche model

Chagas Environmental Niche Model

Environmental model of CL in Bolivia

Argentina

Chile

Ecological zones of Bolivia

Amazon Forested areas, transitional areas,

Inter-Andinean countries

Hot, deserted,

semi-arid lowlands

3-Course at Washington D.C PAHO headquarters

GeoHealth: A geospatial surveillance

and response system resource

for vector borne diseases in the Americas

Grant No. 80 NSSC 18K0517, February 2018-January 2020

John B. Malone1, Rebecca Christofferson1 , Jeffrey Luvall2, SJ Park3, Mara Bavia4, Elivelton Fonseca5, Raul Guimaraes5

1 Pathobiological Sciences, LSU School of Veterinary Medicine; 2 NASA Marshall Space Flight Center, Huntsville AL, 3 Electrical Engineering and Computer Science, LSU

4 Federal University of Bahia, Salvador, Brazil 5 Sao Paulo State University, Presidente Prudente, Brazil

Objectives

1.Construct a geospatial health resource data portal (GeoHealth) compatible with the interoperable, open access standards of the Global Earth Observation System of Systems (GEOSS) 2.Map and model the distribution and spatial variation in epidemiological risk of visceral leishmaniasis in Brazil using GeoHealth data portal resource data. VL 3.Develop geospatial models for mapping arbovirus transmission and emergence prediction in the Americas using GeoHealth data portal resource data. AV 4.Provide a platform for processing massive big data resources to discover ‘hidden’ associations of disease for ecological niche modeling as an alternative to classical hypothesis-driven statistical analysis. 5.Implementa a plan for dissemination and training programs to promote geospatial mapping and modeling for vector borne diseases as envisioned in GEOSS.

Site selection VL

Bahia State Source: SESAB/SUVISA/DIVEP, 2017

Bahia municipalities classification level of VL transmission. 2013-16

No cases notified

Sporadic < 2,4 casos (175)

Moderate: ≥ 2,4 e < 4,4 (26)

Intense ≥ 4,4 (16)

2000a2016

•6.165cases(anualmean385)

•Inicidence:1.3to11.8(100,000inhabitants)

•Meanlethality6.7% Source: SESAB/SUVISA/DIVEP, 2017

Feira de Santana VL

NDVI and visceral leishmaniasis cases, seropositives dogs, sand fly (Lutzomyia longipalpis) in Feira de Santana, Bahia, Brazil .

Sao Paulo Site selection

Aedes mosquito-borne diseases

Extrinsic Incubation Period (EIP). This process is known to be influenced by both intrinsic factors (such as viral strain and/or mosquito population) and extrinsic factors (such as temperature and humidity)

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Growing Degree Day-Water Budget Model

Annual potential

generations

Temperature required for development

Time from engorged to first emerge

Work in the vector lab

The newest and more advanced imagery and data (more detailed and real time ) The following image compares full-disk images captured by two NOAA satellites --GOES-16

and GOES-13 --at same time on same day: 1:07 p.m. EST on January 15, 2017.

Regional Scale Data : Climate Data:Worldclim -Precipitation, Tmax, Tmin, Potential Evapotranspiration monthly Bioclim (1km2) = 50 year long term normal climate data.NCEP/NCAR CDAS Re Analysis (50km2); daily, complete data eg. specific humidity Earth Observing Satellite data: MODIS8day-16day NDVI, LST, other products (1km2) SRTM Shuttle Radar Topography Mission Global Precipitation Measurement Mission (GPM) –3 hour/daily GOES 16 –Land Surface Temperature –3 hour/daily ECOSTRESS –5day composite data; day-night pairs SMOS ESA Soil Moisture Ocean Salinity (50km2); Resample to 1km by MODIS . Feature data: DIVA Political boundaries, World Wildlife Fund Ecosystems Hydrology –Rivers, streams, lakes; Watersheds Landscan –global population data; Census tract population data .

1-GeoHealth Data Portal Content

Community Scale (15-30 m) Landsat 8; Landsat Legacy data; ASTER (including Topography ESA Sentinel L2 and L1 (cloud-free); Harmonized Landsat/Sentinel-2 (HLS) v. 1.2 Land Use, Soil Type Habitat-Household Scale (<1m) Worldview 2, Worldview 3; LANCE AMSR2 Near Real Time land imagery data in Worldview.

GeoHealth Data Portal Content

• Establish an InterAmerican working group to implement a geospatial surveillance and response systems resource for vector borne diseases in the Americas (GeoHealth) as part of the AmeriGEOSS initiative of GEO community .

• Internationalize the research and education missions of LSU through sustainable InterAmerican faculty-student partnerships in the GEO Health and Environment Community of Practice.

Workshop Objectives

GEO Community Launched in response to calls for actions by :

World summit on sustainable development, earth summits and by the G8.

What is GEO? Is a global network connecting government academic and research institutions, data providers, businesses, engineers, scientists and experts to create innovative solutions to global challenges.

Set Up in 2009. Now with more than 150 members (governments and institutions). The GEO health and environmental community is focusing in integrate environment and health to improve decision making at regional, country and district level. *Annual meetings*

The GEO community is creating

The Global Earth Observation System of Systems (GEOSS) To integrate observing systems and share data by connecting existing data / images using

common standards.

There are more than 400 million open data resources in GEOSS from more than 150 national

and regional providers.

NASA , ESA, WMO ,Digital Globe, UNEP, WHO, World Bank and many others.

The AmeriGEOSS initiative seeks to promote collaboration and coordination among the

GEO members in the American continent to unify actions and information in a

coordinated and comprehensive and sustainable way for the benefit of the region.

Agriculture Biodiversity ecosystems disasters water

We Invite You to Join With us in the GeoHealth Community Of Practice Workplan to Develop a Surveillance and

Response Resource System Resource for Vector Borne Diseases in the Americas.

Please leave your card or contact information and area of interest with the leads and contributors : Dr. Jeff Luvall or Dr. John B. Malone at vmalon@lsu.edu we will contact you on next steps to join the GeoHealth group

Invitation