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Recent Weather Extremes and Impacts on Agricultural Production and Vector-
borne Disease Outbreak Patterns VECTOR-BORNE DISEASES: EXPLORING THE ENVIRONMENTAL,
ECOLOGICAL, AND HEALTH CONNECTIONS INSTITUTE OF MEDICINE-FORUM ON MICROBIAL THREATS
WASHINGTON DC 16-17 SEPTEMBER 2014
Kenneth J. Linthicum and Assaf Anyamba
USDA-ARS Agricultural Research Service Center for Medical , Agricultural & Veterinary Entomology,
Gainesville, Florida and Goddard Space Flight Center, NASA
Greenbelt, Maryland
CO-AUTHORS
• Jennifer L. Small, Compton J. Tucker, Edwin W. Pak – Goddard space Flight Center, Biospheric Sciences laboratory,
NASA, Greenbelt, Maryland • Seth Britch
– Center for Medical, Agricultural & Veterinary Entomology, USDA-ARS, Gainesville, Florida
• Curt A. Reynolds, James Crutchfield – USDA-FAS, International Production & Assessment Division,
Washington DC
COLLABORATORS STEPHAN DE LA ROCQUE
EMPRES/FAO, ROME ITALY PIERRE FORMENTY
GLOBAL ALERT AND RESPONSE DEPARTMENT , WHO, GENEVA SWITZERLAND ROBERT BREIMAN, ALLAN HIGHTOWER
GLOBAL DISEASE DETECTION DIVISION, CHIEF, INTERNATIONAL EMERGING INFECTIONS PROGRAM, CDC – KENYA, NAIROBI, KENYA CDR JEAN-PAUL CHRETIEN, COL (RET) JOEL GAYDOS
ARMED FORCES HEALTH SURVEILLANCE CENTER, DIVISION FOR GLEIS OPERATIONS, SILVER SPRING MD MAJ DAVID SCHNABEL ; LTC JASON RICHARDSON
US ARMY MEDICAL RESEARCH UNIT – KENYA, NAIROBI, KENYA ROSEMARY SANG
KENYA MEDICAL RESEARCH INSTITUTE, NAIROBI, KENYA MAJ MARK BREIDENBAUGH, MAJ KARL HAAGSMA, MAJ THOMAS JANOUSEK, MAJ DON TIEG
757TH AIRLIFT SQUADRON, YOUNGSTOWN AIR RESERVE STATION, VIENNA, OHIO MARK LATHAM
MANATEE COUNTY MOSQUITO CONTROL, PALMETTO, FLORIDA HENRY B. LEWANDOWSKI
CHATHAM COUNTY MOSQUITO CONTROL, SAVANNAH, GEORGIA SALIH OSMAN MAGDI
FEDERAL MINISTRY OF HEALTH, KHARTOUM, SUDAN MOHAMED ALLY MOHAMED
MINISTRY OF HEALTH AND SOCIAL WELFARE, DAR ES SALAAM, TANZANIA PATRICK M. NGUKU
MINISTRY OF HEALTH, NAIROBI KENYA JEAN-MARC REYNES
INSTITUT PASTEUR DE MADAGASCAR, ANTANANARIVO MADAGASCAR ROBERT SWANEPOEL
NATIONAL INSTITUTE FOR COMMUNICABLE DISEASES, SANDRINGHAM SOUTH AFRICA CDR BRADLEY KILLENBECK
U.S. FLEET FORCES COMMAND, NORFOLK VIRGINIA LTC HEE CHOON LEE
WALTER REED ARMY MEDICAL CENTER, WASHINGTON DC COL (RET)TERRY KLEIN
US ARMY 18TH MEDCOM, SEOUL KOREA COL JULIE PAVILIN
ARMED FORCES RESEARCH INSTITUTE OF MEDICAL SCIENCES, US ARMY MEDICAL COMPONENT, BANGKOK THAILAND
TOPICS
1. IMPACT OF RECENT EXTREME WEATHER EVENTS
2. CLIMATE TELECONNECTIONS TO VECTOR BORNE DISEASES
3. NEAR FUTURE (2014-2015) POTENTIAL DISEASE RISKS
4. SUMMARY
1. RECENT WEATHER
EXTREMES AND IMPACTS ON AGRICULTURE AND VECTOR- BORNE DISEASE
From Anyamba et al. 2014 PLoS ONE
WEATHER EXTREMES 2010-2012
WNV
LOW COTTON/CORN PRODUCTION/FIRES/
AIR POLLUTION/ALLERGY
LOW WHEAT PRODUCTION
HIGH CORN, COTTON PRODUCTION
RVF, HIGH CORN PRODUCTION
MVE, KUNJIN, MALARIA
DENGUE & LOW
SORGHUM
NDVI AND LST DATA SETS
• We calculated NDVI and LST sums for each three-month growing season for each year from the start of the Terra MODIS record in March 2000 through December 2012:
∑=
=3
1mym,ys, NDVINDVI
• Here, m=1, 2, 3 represents the three months in growing season s and the year y. We then calculated the average sum for each season, taken over all years from 2000 to 2012
The seasonal anomalies measure the departure of the given season from the average season,
and are defined as:
The NDVI anomaly is expressed as a percentage of the average due to the non-intuitive nature of NDVI units.
The LST anomaly is expressed in absolute units of temperature (degrees
Celsius).
AGRICULTURAL PRODUCTION DATA We calculated average 2000-2011 production totals, annual production
anomalies, and finally annual percent anomalies (pct_anom) for each year y as:
REGION CROP CLASSIFICATION SOURCE US (TEXAS) NASS 2010 CROPLAND DATA LAYER:
HTTP://WWW.NASS.USDA.GOV/RESEARCH/CROPLAND/SARS1A.HTM
SOUTH AFRICA (FREE STATE/NORTH WEST)
2002 SADC LAND COVER DATABASE: (CULTIVATED DATA LAYER)
HTTP://GSDI.GEOPORTAL.CSIR.CO.ZA/PROJECTS
EAST AFRICA (SOMALIA/KENYA) SW RUSSIA (VOLGA DISTRICT) SE AUSTRALIA (NEW SOUTH
WALES) SW AUSTRALIA (WESTERN
AUSTRALIA)
GLOBCOVER 2009: HTTP://IONIA1.ESRIN.ESA.INT/
AGRICULTURAL REGIONS WITH EXTREME WEATHER EVENTS DURING 2010-2012
1. US (TEXAS), 2. SW RUSSIA (VOLGA DISTRICT), 3. EAST AFRICA (SOMALIA/KENYA), 4. SW AUSTRALIA (WESTERN AUSTRALIA), 5. SOUTH AFRICA (FREE STATE/NORTH WEST), AND 6. SE AUSTRALIA (NEW SOUTH WALES). AREAS SHOWN IN RED REPRESENT AREAS CLASSIFIED AS “AGRICULTURAL CROPLANDS” (FROM THE NASS 2010 CROPLAND DATA LAYER, 2002 SACD LAND COVER DATABASE, GLOBCOVER 2009 DATABASES) ** AREAS SHOWN IN RED REPRESENT AREAS CLASSIFIED AS “AGRICULTURAL CROPLANDS” (FROM THE NASS 2010 CROPLAND DATA LAYER, 2002 SACD LAND COVER DATABASE, GLOBCOVER 2009 DATABASES)
MODIS NDVI AND LST ANOMALIES DURING 2010-2012
TEXAS, US , JJA, 2011
SW RUSSIA (VOLGA DISTRICT) JJA 2010
EAST AFRICA (SOMALIA/ KENYA) DJF 2010/2011
SW AUSTRALIA (WESTERN AUSTRALIA) SON 2010
SOUTH AFRICA (FREE STATE/ NORTH WEST) DJF 2010/11
SE AUSTRALIA (NEW SOUTH WALES)
SON 2010
RAINFALL ANOMALIES: 2010-2012
Monthly time series of 2000-2012 MODIS NDVI (Black line) and LST (vertical red bars) anomalies for agricultural regions shown in previous slide
NDVI, LST TIME SERIES: 2000-2012 TEXAS, US , JJA, 2011
SW RUSSIA (VOLGA DISTRICT)
JJA 2010
EAST AFRICA (SOMALIA/ KENYA) DJF 2010/2011
SW AUSTRALIA (WESTERN AUSTRALIA) SON 2010
SOUTH AFRICA (FREE STATE/ NORTH WEST) DJF 2010/11
SE AUSTRALIA (NEW SOUTH WALES)
SON 2010
Crop production times series for selected regions in 2000-2011 expressed as annual totals (black lines)and as annual anomalies (red bars)
AGRICULTURAL PRODUCTION: 2000-2012 -50% COTTON
LARGEST WV OUTBRREAK
-14% WHEAT -51% BARLEY
-80% SORGHUM 1ST LARGE DENGUE
-30% WHEAT WORST AG PROD Y
+ CORN RVF OUTBREAK
+ 95% COTTON MVE
2012 FIRES
TEXAS, US , JJA, 2011
SW RUSSIA (VOLGA DISTRICT)
JJA 2010
EAST AFRICA (SOMALIA/ KENYA) DJF 2010/2011
SW AUSTRALIA (WESTERN AUSTRALIA) SON 2010
SOUTH AFRICA (FREE STATE/ NORTH WEST) DJF 2010/11
SE AUSTRALIA (NEW SOUTH WALES)
SON 2010
(A–D) Histograms of MODIS growing season land surface temperature (LST) distributions (red bars) compared against the long-term means (black lines) for selected regions of study. (E) Spatial pattern of LST anomalies for the US during June, July, August (JJA) 2012. The area shaded in olive green shows the dominant corn and soy growing region; the solid neon green line delineates the JJA 2012 seasonal 30°C isocline which encompasses the majority of the US agricultural region; and the dotted neon green line shows the JJA 2000–2011 long-term mean 30°C isocline. Persistent temperatures above 30°C destroy most crops and reduce yields
LST DISTRIBUTIONS AND DROUGHT EXTENT
GLOBAL DISTRIBUTION OF EPIDEMICS/EPIZOOTICS OF VECTOR-BORNE DISEASE OUTBREAKS: 2010–
2012
GLOBAL DISTRIBUTION OF EPIDEMICS/EPIZOOTICS OF VECTOR-BORNE
DISEASE OUTBREAKS: 2010–2012
•100 year drought •Veg activity -66% •Cotton -50% •Drought losses $10 billion in Texas •WNV highest on record in Texas •WNV in US highest since 1999 •Summer mean temps 30-330C
RECORD EVENTS AND CONSEQUENCES: 2012
•Rainfall 200% above normal •Cool temperatures •Flooding in Mississippi and Missouri basins ($5 billion in damage) •Ag losses in southern Canada = $1 billion
RECORD EVENTS AND CONSEQUENCES: 2011
Distribution of land surface temperatures (LST) and normalized difference vegetation index (NDVI)
HOT
COOL
COOL
LINCOLN, NEBRASKA
BLOEMFONTEIN, SOUTH AFRICA
PETERBOROUGH, AUSTRALIA
DRY/REDUCED VEGETATION
WETTER/GREENER
WETTER/GREENER
OTHER REGIONS: 2010-2011
WEATHER EXTREMES 2010-2012
WNV
LOW COTTON/CORN PRODUCTION/FIRES/
AIR POLLUTION/ALLERGY
LOW WHEAT PRODUCTION
HIGH CORN, COTTON PRODUCTION
RVF, HIGH CORN PRODUCTION
MVE, KUNJIN, MALARIA
DENGUE & LOW
SORGHUM
2. TELECONNECTIONS TO VECTOR-BORNE DISEASES
(dengue, chikungunya, Rift Valley fever, and malaria)
VECTOR-BORNE DISEASE – CLIMATE LINKS • Building evidence suggests links between El
Niño/Southern Oscillation (ENSO) driven climate anomalies and infectious diseases, particularly those transmitted by arthropods:
o Murray Valley encephalitis (Nicholls 1986)
o Bluetongue (Baylis et al. 1999)
o RVF (Linthicum et al. 1999)
o African Horse Sickness (Baylis et al 1999)
o Ross River virus (Woodruff et al. 2002)
o Dengue (Linthicum et al. 2007 IOM publication)
o Malaria (Bouma & Dye 1996)
o Chikungunya (Chretien et al. 2006)
EL NIÑO/ SOUTHERN OSCILLATION (ENSO)
• Influences the patterns of floods and drought on an interannual time scale.
• Extremes have an impact on
the emergence, propagation and survival of disease vectors/pathogens
• Results in episodic patterns of disease outbreaks as they dance in tune with climate variability
Red: Hotter/Warmer, Blue: Cold/Cooler than Normal
SST: Sea Surface Temperature
OLR: Outgoing Longwave Radiation
ENSO TELECONNECTIONS
• Differential impacts at specific regional locations around the world
• ENSO +| Floods and excess rainfall in EEA, E. E. Pacific, Southern Brazil/Argentina, Southern-tire US
• ENSO +| Drought and >+ temperatures (Southern Africa, SE Asia, NE Brazil, C Africa
• ENSO – [Largely reverse conditions) Green/Blue: +/Wetter Yellow/Red: -/Drier
through Global Precipitation
From Anyamba et al. 2012 PLOS Neglected Tropical Diseases
DENGUE SUMMARY • Hot -dry periods precede elevated DHF • Elevated DHF is likely the result of short Extrinsic Incubation
period in mosquito
Year
Cases
0
50
100
150
200
250
300
1973 / 1974 / 1975 / 1976 / 1977 / 1978 / 1979 / 1980 / 1981 / 1982 / 1983 / 1984 / 1985 / 1986 / 1987 / 1988 / 1989 / 1990 / 1991 / 1992 / 1993 / 1994 / 1995 / 1996 / 1997 / 1998 /
Figure Three: Seasonal Distribution of Serologically Confirmed Dengue Cases at Bangkok Children's Hospital from 1973-1998.
HOT-DRY
97-98 89-90 87 84 80
From Anyamba et al. 2012 PLOS Neglected Tropical Diseases
RECENT CHIKUNGUNYA OUTBREAKS IN RELATION TO HUMAN POPULATION DENSITY
IMPACT OF TEMPERATURE ON CHIKUNGUNYA OUTBREAKS (2004-2010)
RURAL /SYLVAN
URBAN- HOT
URBAN /RURAL
-HOT
RURAL - HOT
IMPACT OF DROUGHT ON CHIKUNGUNYA OUTBREAKS (2004-2010)
URBAN- DROUGHT
RURAL /SYLVAN
- WET
URBAN /RURAL
DROUGHT OR WET
RURAL – WET
GEOGRAPHIC DISTRIBUTION OF RECENT OUTBREAK CLUSTERS OF CHIKUNGUNYA (2004-2006) AND RIFT VALLEY FEVER (2006 – 2009) OVER AFRICA AND THE INDIAN OCEAN
ISLANDS
Rainfal l anomalies for Eastern Afr ica showing the large-scale regional drought during the period October – December 2005. Anomalies are calculated with reference to the 1995-2000 base mean period. Epicenters of chikungunya outbreaks during this period are shown by black dots
RAINFALL ANOMALIES: OCT-DEC 2005
Cumula t i ve ra in fa l l anomaly and vege ta t ion index anomal ies pa t terns p reced ing outbreaks of Ri f t Val le y fever in (a ) East Af r ica : Sep tember 2006 - December 2006 (c , ) Sudan: June 2007 - Sep tember 2007, (e , ) Sou thern Af r ica: October 2007 - January 2008. Each ou tb reak was p receded by pers is tent and above normal ra in fo r a per iod of ~ 2 - 4 months, wi th excess ra in fa l l on the o rder o f +200mm over the 2 -4 month t ime per iod. Th is resu l ted in anomalous green-up of vege ta t ion wh ich c rea ted idea l eco log ica l cond i t ions for the product ion o f Aedes and Culex mosqui to vectors tha t t ransmi t the Ri f t Va l ley fever v i rus to domest ic an imals and humans. These anomal ies in vege ta t ion a re fo r (b ) East A f r ica : Oc tober 2006 - January 2007 (d) Sudan: Ju ly 2007 - September 2007, ( f ) Southern Af r ica: October 2007 - January 2008. Areas of Ri f t Va l ley fever ou tb reaks a re marked by the ye l low do ts
RAINFALL AND NDVI ANOMALIES: 2006-2008
RVF RELATIONSHIP TO WET CONDITIONS AND CHIKUNGUNYA RELATIONSHIP TO DRY
CONDITIONS
MALARIA IN REPUBLIC OF KOREA
HOT AND DRY
P. Vivax Malaria in ROK from 1979 – 2011 and Cumulative OLR Data
From Linthicum et al. 2014 Military Medicine
NORTH KOREA (DPRK)
SOUTH KOREA (ROK)
Temperature anomalies from 1950 - 2011
3. NEAR FUTURE (2014-2015) POTENTIAL DISEASE RISKS
Above normal seasonal SSTs have developed in the equatorial eastern Pacific Ocean (~ 20C) and also in the equatorial Indian
Ocean (~ 10C) during June-August 2014
Outgoing longwave Radiation (OLR) anomalies for June – August 2014 that are used to infer tropical precipitation currently show very dry conditions (brown to red colors) developing over western Pacific and central Indian Ocean, persisting over the Caribbean region and enhanced precipitation over southern Africa and eastern Pacific. Other areas of enhanced precipitation
include Sudan, Mexico, southern Brazil, central Canada and northern/mid-west U.S.
MONTH 2006-2007 2014 - 2015 WIO NINO3.4 WIO NINO3.4
APRIL -0.05 -0.19 +0.10 -0.24 MAY +0.21 +0.06 +0.37 +0.46 JUNE -0.01 +0.20 +0.10 +0.46 JULY -0.13 +0.13 -0.09 +0.18 AUGUST +0.17 +0.40 -0.01 +0.20
Comparison of the evolution of Western Indian Ocean (WIO) and eastern Pacific Ocean region 3.4 (NINO3.4 SST) anomaly indices in degrees centigrade between
2006-2007 El Niño event and the currently developing 2014-2015 event. Chance of has decreased from 80% (early summer to 60% (current: fall)
ENSO TELECONNECTIONS
• Differential impacts at specific regional locations around the world
• ENSO +| Floods and excess rainfall in EEA, E. E. Pacific, Southern Brazil/Argentina, Southern-tire US
• ENSO +| Drought and >+ temperatures (Southern Africa, SE Asia, NE Brazil, C Africa
• ENSO – [Largely reverse conditions)
Green/Blue: +/Wetter Yellow/Red: -/Drier
through Global Precipitation
Correlation between Pacific Ocean (NINO 3.4) [El Niño] SST and Rainfall Anomalies
Indonesia, Malaysia, Thailand and most of the Southeast Asia Islands: Increased risk for dengue and chikungunya transmission caused by drought conditions which (1) increase water storage around houses leading to elevated Aedes aegypti mosquito populations and (2) elevate ambient air temperatures which will reduce the extrinsic incubation period for the virus in vector Aedes aegypti and Aedes albopictus mosquitoes increasing vector capacity; respiratory illnesses due to haze from uncontrolled burning of tropical forests when extreme drought occurs. 2. Coastal Peru, Venezuela, Colombia: Malaria due to elevated Anopheles vector populations which will
develop when various types of immature habitats are flooded after heavy rainfall. 3. Bangladesh, coastal India, Sri Lanka: Elevated risk for cholera and malaria outbreaks. 4. East Africa (Kenya, Tanzania, Somalia, Uganda and Ethiopia): Increased risk for RVF and malaria resulting from elevated mosquito vector populations, and cholera caused by flooding due to heavy rainfall in dry land areas. We advise early surveillance of vector populations and disease control planning. Much will depend on how the El Niño plays out and the evolution of rainfall conditions in the September – November 2014 period. 5. South West USA (New Mexico, Arizona, Colorado, Utah, Texas, California): Hantavirus pulmonary syndrome and plague due to elevated rodent populations caused by heavy rainfall. Impact on new Ae. aegypti populations in central California and Ae. albopictus in southern California uncertain (has survived w/o rain) . 6. Southern and Southeast USA, particularly along the Gulf Coast: Elevated rainfall conditions may increase Aedes albopictus and Aedes aegypti populations, potentially increasing the likelihood of local transmission of dengue and chikungunya virus following introduction from endemic regions in the Caribbean and/or Central/South America. 7. Northeast Brazil: Increased risk for dengue and respiratory illnesses due to drought conditions and large scale forest fires. Additionally, increased risk of chikungunya introduction from the Caribbean and/or Central/South America into Brazil.
GLOBAL ASSESSMENT AS S U M IN G E L N IÑ O OC C U R S IN FALL 2014
4. SUMMARY • Extremes in either direction (+/-) of
precipitation/temperature have significant implications for disease vectors and pathogen emergence and spread
• Magnitude of ENSO influence on precipitation/temperature cannot be currently predicted — rely on average history and patterns.
• Timing of event and emergence disease can be exploited to undertake vector control and preparedness measures.
• Need to invest in early ground surveillance and the use of rapid field diagnostic capabilities for vector identification and virus isolation
HTTP://WWW.ARS.USDA.GOV/BUSINESS/DOCS.HTM?DOCID=23464
