Roles of Remote Sensing for Influenza Roles of Remote Sensing for Influenza Risk Prediction and Early Warning Risk Prediction and Early Warning Richard Kiang, Radina Soebiyanto, Farida Adimi Richard Kiang, Radina Soebiyanto, Farida Adimi NASA Goddard Space Flight Center NASA Goddard Space Flight Center GEO Health & Environment Community of Practice Workshop GEO Health & Environment Community of Practice Workshop Centre National d’Études Spatiales (CNES) Centre National d’Études Spatiales (CNES) Paris, France, 27-29 July 2010 Paris, France, 27-29 July 2010
Epidemic-prone acute respiratory diseases have no Epidemic-prone acute respiratory diseases have no borders, and can be spread rapidly around the world. borders, and can be spread rapidly around the world. Global, coordinated surveillance & control efforts are Global, coordinated surveillance & control efforts are essential. essential. 2003 SARS Spread to 37 countries in weeks 2004 H5N1 Avian Influenza Spread to 62 countries since 2004. There are still frequent outbreaks in Indonesia, Egypt, and some Southeast Asian countries. 2009 H1N1 Pandemic Spread to 48 countries in a month despite heightened public awareness and substantial preventive and control efforts
hemaglutinin neuraminidase Cilia being invaded by flu virus Source: National Geographic Source: CDC
Genetic & Antigenic Variation Genetic & Antigenic Variation Among Influenza Viruses Among Influenza Viruses Seasonal epidemic Antigenic drift new strains continue to appear mutations in HA & NA Pandemics Antigenic shift e.g., 1918 H1N1 Spanish Flu , 1957 novel genes through H2N2 Asian Flu , 1968 H3N2 Hong reassortment Kong Flu Pandemic potentials Animals to humans H5N1 Avian Flu , 2009 H1N1 “Swine jumping across species Flu” pandemic
H5N1 AI — THE PROBLEM H5N1 AI — THE PROBLEM � First appeared in Hong Kong in 1996- 1997, HPAI has spread to approximately 60 countries. More than 250 million poultry were lost. � Worldwide the mortality rate is 53%. � Co-infection of human and avian influenza in humans may produce deadly strains of viruses through genetic reassortment. � On average one major pandemic occurred in each century. 90 years have passed since the 1918 pandemic (0.675M deaths in the US, and 21-50M deaths worldwide). richard.kiang@nasa.gov
DISTRIBUTION OF H5N1 HUMAN CASES DISTRIBUTION OF H5N1 HUMAN CASES Source: WHO. Cases from 2003 to June 19, 2008. richard.kiang@nasa.gov
Highly Pathogenic AI Cases Since January 2010 Highly Pathogenic AI Cases Since January 2010 FAO EMPRES FAO EMPRES
H5N1 TRANSMISSION PATHWAYS H5N1 TRANSMISSION PATHWAYS POULTRY TRADE BIRD TRADE MIGRATORY BIRDS LPAI HPAI poultry, products, feed, spill over spill back waste, personnel, equipment wild birds domestic birds ducks & geese POULTRY Sectors 1&2 Sectors 3&4 ? pandemic human flu HUMANS strain virus reassortment richard.kiang@nasa.gov
Analysis of Global Spread of H5N1 through Analysis of Global Spread of H5N1 through Phylogenetic Evidence, Poultry & Bird Trades, Phylogenetic Evidence, Poultry & Bird Trades, And Bird Migration Data And Bird Migration Data Europe US 87% thru mig. birds Most likely thru poultry to surrounding countries first, then thru migratory birds to US mainland Africa Asia 25% thru poultry 43% thru poultry 38% thru mig. birds 14% thru mig. birds Source: Kilpatrick et al., PNAS 2006. richard.kiang@nasa.gov
Objective 2 Objective 1 Objective 3 Objective 4
OBJECTIVES OBJECTIVES � Perform empirical AI outbreak risk analyses based on outbreak history, environmental parameters, and socio-economic factors. � Identify spatiotemporal risk for AI outbreaks based on wetland distributions, prevalence of bird species, flyways of migratory birds, surface characteristics, and socioeconomic factors. � Model the spread of AI virus from large commercial poultry farms to small and backyard farms under typical environmental and socioeconomic conditions. � Model weekly influenza-like illness cases based on observed and forecast meteorological parameters for regions in the US and other tropical countries. richard.kiang@nasa.gov
What environmental and socio-economical factors may contribute to highly pathogenic AI outbreaks?
Poultry Outbreaks, Human Cases, Wet Markets, Poultry Outbreaks, Human Cases, Wet Markets, And Distribution Centers And Distribution Centers January – February 2006 January – February 2006 Based on Media & Publicly Available Information Based on Media & Publicly Available Information richard.kiang@nasa.gov
Histograms of Distance from Neighborhoods Histograms of Distance from Neighborhoods With/without Outbreaks to Other Locations With/without Outbreaks to Other Locations Log (N+1) Log (N+1) meters
What areas around wetlands may have higher risks for AI outbreaks?
NAMRU-2 Bird Surveillance Sites on Java
NAMRU-2 Bird Surveillance Study NAMRU-2 Bird Surveillance Study � The role of migratory birds in the spread of H5N1 remains under considerable debates. � In Indonesia, migratory pathways are only known for shorebirds (East Asian-Australasian flyway) and migratory ducks and geese (East Asian & Central Asian flyways). � 4067 birds comprising of 98 species and 23 genera were collected in 2006-2007. � Most common birds: striated heron, common sandpiper, and domestic chicken. 3% 6% 14%
(continued) (continued) � RNA was extracted from swabs; RT-PCR was conducted for H5N1 genes; antibodies was detected using hemagglutination inhibition and other tests. � Species with the highest seropostive rates in each category are Muschovy duck (captive), striated heron (non-migratory) and Pacific golden plover (migratory). � 16% of the captive birds (duck, swan, pigeon, etc.) showed H5N1 antibody. � Infected captive birds can be asymptomatic. � In Indonesia, the role of migratory birds in H5N1 transmission is limited.
ASTER False-Color, Google Earth ASTER False-Color, Google Earth And Land Use Maps Around Indramayu And Land Use Maps Around Indramayu
Supervised Classification Supervised Classification
Buffer zones can be established to limit the spread Buffer zones can be established to limit the spread of H5N1 around wetlands and the nearby farmlands of H5N1 around wetlands and the nearby farmlands EU’s & UK’s Practice: 3 km protection zone 10 km surveillance zone larger restricted zone
How do AI viruses spread on and off farms, within and across poultry sectors, and into the environment?
Densely Populated Sector I Poultry Production Area Google Earth image
Detection of H5N1 Infection on a Poultry Farm Detection of H5N1 Infection on a Poultry Farm � Highly pathogenic AI infection on a poultry farm cannot be detected immediately. � Some infected poultry may not look very sick. � In a poultry house with 20,000 chickens, an infection of <1% may not be detected in a walkthrough. � Using a SEIR model, it can be shown that it may take 4-5 days to detect an outbreak. � Before an infection is detected, viruses continue to spread on farm and off farm, through service personnel, equipment, materials, and the poultry that have been shipped out. richard.kiang@nasa.gov
On-Farm and Off-Farm Spread of H5N1 On-Farm and Off-Farm Spread of H5N1 richard.kiang@nasa.gov
Within and Across-Sector Spread of H5N1 Within and Across-Sector Spread of H5N1 richard.kiang@nasa.gov
How does seasonality vary geographically? How is influenza transmission influenced by the environment? How can this be used for forecasting and pandemic early warning?
Empirical Evidences of Environmental Influences Empirical Evidences of Environmental Influences On Influenza Transmissions On Influenza Transmissions � Latitudinal variability in influenza transmission pattern � Experimental findings on the effect of meteorological factors in influenza transmission, virus survivorship and host susceptibility Viboud et al. (2006). PLoS Med 3(4):e89
Hong Kong Hong Kong Land surface temperature Evaporation Air temperature Pressure Solar irradiance Rainfall Relative humidity Sunshine hours Dew point Windspeed
Hong Kong Hong Kong Time series for environmental parameters and weekly seasonal influenza cases Prediction Training
Maricopa County, Arizona Maricopa County, Arizona Pressure Land surface temperature Windspeed Air temperature Solar irradiance Rainfall Relative humidity Dew point
Maricopa Maricopa County, Arizona County, Arizona Time series for environmental parameters and weekly seasonal influenza cases Training Prediction
New York City New York City Time series for environmental parameters and weekly seasonal influenza cases Training Prediction
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