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Penny Masuoka,
Terry A Klein,
Heung-Chul Kim,
David M Claborn,
Nicole Achee,
Richard Andre,
Judith Chamberlin,
Kevin Taylor,
Jennifer Small, Assaf Anyamba,
Michael Sardelis,
John Grieco
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ABSTRACT: Culex tritaeniorhynchus is the primary vector of Japanese encephalitis virus (JEV) throughout much of the tropical and temperate climates of Asia. Several recent papers have used ecological niche modeling programs, e.g., Maxent and GARP, to predict the distribution of disease vectors (e.g. Peterson and Shaw 2003, Moffett et al. 2007). In this on-going study, we used the Maxent program to model the distribution of Cx. tritaeniorhynchus in the Republic of Korea. Using mosquito collection data, temperature, precipitation, elevation, land cover, and SPOT normalized difference vegetation index (NDVI), models were created for each month for a period of five years. Output maps from the models matched several known ecological characteristics of this species' distribution. The output maps show the highest probabilities of mosquito occurrence in August and September, which correlates to the observed mosquito population density peaks. The model demonstrated low probabilities for forest covered mountains, which corresponds to findings in the literature that Cx. tritaeniorhynchus is infrequently found above 1,000 meters. The modeling effort demonstrated several limitations in the data set, including a low number of collection sites that did not cover the full range of environmental conditions within the study area. Additional collection sites would improve the models and allow for improved testing of the results. Future goals of this project include developing real-time predictions based on NDVI data and expanding the prediction to a larger geographical area.
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ABSTRACT: Recent clusters of outbreaks of mosquito-borne diseases (Rift Valley fever and chikungunya) in Africa and parts of the Indian Ocean islands illustrate how interannual climate variability influences the changing risk patterns of disease outbreaks. Although Rift Valley fever outbreaks have been known to follow periods of above-normal rainfall, the timing of the outbreak events has largely been unknown. Similarly, there is inadequate knowledge on climate drivers of chikungunya outbreaks. We analyze a variety of climate and satellite-derived vegetation measurements to explain the coupling between patterns of climate variability and disease outbreaks of Rift Valley fever and chikungunya.
We derived a teleconnections map by correlating long-term monthly global precipitation data with the NINO3.4 sea surface temperature (SST) anomaly index. This map identifies regional hot-spots where rainfall variability may have an influence on the ecology of vector borne disease. Among the regions are Eastern and Southern Africa where outbreaks of chikungunya and Rift Valley fever occurred 2004-2009. Chikungunya and Rift Valley fever case locations were mapped to corresponding climate data anomalies to understand associations between specific anomaly patterns in ecological and climate variables and disease outbreak patterns through space and time. From these maps we explored associations among Rift Valley fever disease occurrence locations and cumulative rainfall and vegetation index anomalies. We illustrated the time lag between the driving climate conditions and the timing of the first case of Rift Valley fever. Results showed that reported outbreaks of Rift Valley fever occurred after ∼3-4 months of sustained above-normal rainfall and associated green-up in vegetation, conditions ideal for Rift Valley fever mosquito vectors. For chikungunya we explored associations among surface air temperature, precipitation anomalies, and chikungunya outbreak locations. We found that chikungunya outbreaks occurred under conditions of anomalously high temperatures and drought over Eastern Africa. However, in Southeast Asia, chikungunya outbreaks were negatively correlated (p<0.05) with drought conditions, but positively correlated with warmer-than-normal temperatures and rainfall.
Extremes in climate conditions forced by the El Niño/Southern Oscillation (ENSO) lead to severe droughts or floods, ideal ecological conditions for disease vectors to emerge, and may result in epizootics and epidemics of Rift Valley fever and chikungunya. However, the immune status of livestock (Rift Valley fever) and human (chikungunya) populations is a factor that is largely unknown but very likely plays a role in the spatial-temporal patterns of these disease outbreaks. As the frequency and severity of extremes in climate increase, the potential for globalization of vectors and disease is likely to accelerate. Understanding the underlying patterns of global and regional climate variability and their impacts on ecological drivers of vector-borne diseases is critical in long-range planning of appropriate disease and disease-vector response, control, and mitigation strategies.
PLoS Neglected Tropical Diseases 01/2012; 6(1):e1465. · 4.69 Impact Factor
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Clara J Witt,
Allen L Richards,
Penny M Masuoka,
Desmond H Foley,
Anna L Buczak,
Lillian A Musila,
Jason H Richardson,
Michelle G Colacicco-Mayhugh,
Leopoldo M Rueda,
Terry A Klein, [......],
Elizabeth Kioko,
David C Abuom,
John P Grieco,
Erin E Richards,
Steven Tobias,
Matthew R Kasper,
Joel M Montgomery,
Dave Florin,
Jean-Paul Chretien,
Trudy L Philip
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ABSTRACT: The Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System Operations (AFHSC-GEIS) initiated a coordinated, multidisciplinary program to link data sets and information derived from eco-climatic remote sensing activities, ecologic niche modeling, arthropod vector, animal disease-host/reservoir, and human disease surveillance for febrile illnesses, into a predictive surveillance program that generates advisories and alerts on emerging infectious disease outbreaks. The program's ultimate goal is pro-active public health practice through pre-event preparedness, prevention and control, and response decision-making and prioritization. This multidisciplinary program is rooted in over 10 years experience in predictive surveillance for Rift Valley fever outbreaks in Eastern Africa. The AFHSC-GEIS Rift Valley fever project is based on the identification and use of disease-emergence critical detection points as reliable signals for increased outbreak risk. The AFHSC-GEIS predictive surveillance program has formalized the Rift Valley fever project into a structured template for extending predictive surveillance capability to other Department of Defense (DoD)-priority vector- and water-borne, and zoonotic diseases and geographic areas. These include leishmaniasis, malaria, and Crimea-Congo and other viral hemorrhagic fevers in Central Asia and Africa, dengue fever in Asia and the Americas, Japanese encephalitis (JE) and chikungunya fever in Asia, and rickettsial and other tick-borne infections in the U.S., Africa and Asia.
BMC Public Health 01/2011; 11 Suppl 2:S10. · 2.00 Impact Factor
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Penny Masuoka,
Terry A Klein,
Heung-Chul Kim,
David M Claborn,
Nicole Achee,
Richard Andre,
Judith Chamberlin,
Jennifer Small, Assaf Anyamba,
Dong-Kyu Lee,
Suk H Yi,
Michael Sardelis,
Young-Ran Ju,
John Grieco
[show abstract]
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ABSTRACT: Over 35,000 cases of Japanese encephalitis (JE) are reported worldwide each year. Culex tritaeniorhynchus is the primary vector of the JE virus, while wading birds are natural reservoirs and swine amplifying hosts. As part of a JE risk analysis, the ecological niche modeling programme, Maxent, was used to develop a predictive model for the distribution of Cx. tritaeniorhynchus in the Republic of Korea, using mosquito collection data, temperature, precipitation, elevation, land cover and the normalized difference vegetation index (NDVI). The resulting probability maps from the model were consistent with the known environmental limitations of the mosquito with low probabilities predicted for forest covered mountains. July minimum temperature and land cover were the most important variables in the model. Elevation, summer NDVI (July-September), precipitation in July, summer minimum temperature (May-August) and maximum temperature for fall and winter months also contributed to the model. Comparison of the Cx. tritaeniorhynchus model to the distribution of JE cases in the Republic of Korea from 2001 to 2009 showed that cases among a highly vaccinated Korean population were located in high-probability areas for Cx. tritaeniorhynchus. No recent JE cases were reported from the eastern coastline, where higher probabilities of mosquitoes were predicted, but where only small numbers of pigs are raised. The geographical distribution of reported JE cases corresponded closely with the predicted high-probability areas for Cx. tritaeniorhynchus, making the map a useful tool for health risk analysis that could be used for planning preventive public health measures.
Geospatial health 11/2010; 5(1):45-57. · 3.00 Impact Factor
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Assaf Anyamba,
Kenneth J Linthicum,
Jennifer Small,
Seth C Britch,
Edwin Pak,
Stephane de La Rocque,
Pierre Formenty,
Allen W Hightower,
Robert F Breiman,
Jean-Paul Chretien, [......],
David Schnabel,
Rosemary Sang,
Karl Haagsma,
Mark Latham,
Henry B Lewandowski,
Salih Osman Magdi,
Mohamed Ally Mohamed,
Patrick M Nguku,
Jean-Marc Reynes,
Robert Swanepoel
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ABSTRACT: Historical outbreaks of Rift Valley fever (RVF) since the early 1950s have been associated with cyclical patterns of the El Niño/Southern Oscillation (ENSO) phenomenon, which results in elevated and widespread rainfall over the RVF endemic areas of Africa. Using satellite measurements of global and regional elevated sea surface temperatures, elevated rainfall, and satellite derived-normalized difference vegetation index data, we predicted with lead times of 2-4 months areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa, Sudan, and Southern Africa at different time periods from September 2006 to March 2008. Predictions were confirmed by entomological field investigations of virus activity and by reported cases of RVF in human and livestock populations. This represents the first series of prospective predictions of RVF outbreaks and provides a baseline for improved early warning, control, response planning, and mitigation into the future.
The American journal of tropical medicine and hygiene 08/2010; 83(2 Suppl):43-51. · 2.59 Impact Factor
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ABSTRACT: Larval mosquito habitats of potential malaria vectors and related species of Anopheles from three provinces (Gyeonggi, Gyeongsangbuk, Chungcheongbuk Provinces) of the Republic of Korea were surveyed in 2007. This study aimed to determine the species composition, seasonal occurrence and distributions of Anopheles mosquitoes. Satellite derived normalized difference vegetation index data (NDVI) was also used to study the seasonal abundance patterns of Anopheles mosquitoes.
Mosquito larvae from various habitats were collected using a standard larval dipper or a white plastic larval tray, placed in plastic bags, and were preserved in 100% ethyl alcohol for species identification by PCR and DNA sequencing. The habitats in the monthly larval surveys included artificial containers, ground depressions, irrigation ditches, drainage ditches, ground pools, ponds, rice paddies, stream margins, inlets and pools, swamps, and uncultivated fields. All field-collected specimens were identified to species, and relationships among habitats and locations based on species composition were determined using cluster statistical analysis.
In about 10,000 specimens collected, eight species of Anopheles belonging to three groups were identified: Hyrcanus Group - Anopheles sinensis, Anopheles kleini, Anopheles belenrae, Anopheles pullus, Anopheles lesteri, Anopheles sineroides; Barbirostris Group - Anopheles koreicus; and Lindesayi Group - Anopheles lindesayi japonicus. Only An. sinensis was collected from all habitats groups, while An. kleini, An. pullus and An. sineroides were sampled from all, except artificial containers. The highest number of Anopheles larvae was found in the rice paddies (34.8%), followed by irrigation ditches (23.4%), ponds (17.0%), and stream margins, inlets and pools (12.0%). Anopheles sinensis was the dominant species, followed by An. kleini, An. pullus and An. sineroides. The monthly abundance data of the Anopheles species from three locations (Munsan, Jinbo and Hayang) were compared against NDVI and NDVI anomalies.
The species composition of Anopheles larvae varied in different habitats at various locations. Anopheles populations fluctuated with the seasonal dynamics of vegetation for 2007. Multi-year data of mosquito collections are required to provide a better characterization of the abundance of these insects from year to year, which can potentially provide predictive capability of their population density based on remotely sensed ecological measurements.
Malaria Journal 02/2010; · 3.19 Impact Factor
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ABSTRACT: Abstract
Background
Larval mosquito habitats of potential malaria vectors and related species of Anopheles from three provinces (Gyeonggi, Gyeongsangbuk, Chungcheongbuk Provinces) of the Republic of Korea were surveyed in 2007. This study aimed to determine the species composition, seasonal occurrence and distributions of Anopheles mosquitoes. Satellite derived normalized difference vegetation index data (NDVI) was also used to study the seasonal abundance patterns of Anopheles mosquitoes.
Methods
Mosquito larvae from various habitats were collected using a standard larval dipper or a white plastic larval tray, placed in plastic bags, and were preserved in 100% ethyl alcohol for species identification by PCR and DNA sequencing. The habitats in the monthly larval surveys included artificial containers, ground depressions, irrigation ditches, drainage ditches, ground pools, ponds, rice paddies, stream margins, inlets and pools, swamps, and uncultivated fields. All field-collected specimens were identified to species, and relationships among habitats and locations based on species composition were determined using cluster statistical analysis.
Results
In about 10,000 specimens collected, eight species of Anopheles belonging to three groups were identified: Hyrcanus Group - Anopheles sinensis , Anopheles kleini , Anopheles belenrae , Anopheles pullus , Anopheles lesteri , Anopheles sineroides ; Barbirostris Group - Anopheles koreicus ; and Lindesayi Group - Anopheles lindesayi japonicus . Only An. sinensis was collected from all habitats groups, while An. kleini, An. pullus and An. sineroides were sampled from all, except artificial containers. The highest number of Anopheles larvae was found in the rice paddies (34.8%), followed by irrigation ditches (23.4%), ponds (17.0%), and stream margins, inlets and pools (12.0%). Anopheles sinensis was the dominant species, followed by An. kleini, An. pullus and An. sineroides . The monthly abundance data of the Anopheles species from three locations (Munsan, Jinbo and Hayang) were compared against NDVI and NDVI anomalies.
Conclusion
The species composition of Anopheles larvae varied in different habitats at various locations. Anopheles populations fluctuated with the seasonal dynamics of vegetation for 2007. Multi-year data of mosquito collections are required to provide a better characterization of the abundance of these insects from year to year, which can potentially provide predictive capability of their population density based on remotely sensed ecological measurements.
Malaria Journal. 01/2010;
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ABSTRACT: Japanese encephalitis virus (JEV), the causative agent of Japanese encephalitis (JE), is endemic to the Republic of Korea (ROK) where unvaccinated United States (U.S.) military Service members, civilians and family members are stationed. The primary vector of the JEV in the ROK is Culex tritaeniorhynchus. The ecological relationship between Culex spp. and rice fields has been studied extensively; rice fields have been shown to increase the prevalence of Cx. tritaeniorhynchus. This research was conducted to determine if the quantification of rice field land cover surrounding U.S. military installations in the ROK should be used as a parameter in a larger risk model that predicts the abundance of Cx. tritaeniorhynchus populations. Mosquito data from the U.S. Forces Korea (USFK) mosquito surveillance program were used in this project. The average number of female Cx. tritaeniorhynchus collected per trap night for the months of August and September, 2002-2008, was calculated. Rice fields were manually digitized inside 1.5 km buffer zones surrounding U.S. military installations on high-resolution satellite images, and the proportion of rice fields was calculated for each buffer zone.
Mosquito data collected from seventeen sample sites were analyzed for an association with the proportion of rice field land cover. Results demonstrated that the linear relationship between the proportion of rice fields and mosquito abundance was statistically significant (R2 = 0.62, r = .79, F = 22.72, p < 0.001).
The analysis presented shows a statistically significant linear relationship between the two parameters, proportion of rice field land cover and log10 of the average number of Cx. tritaeniorhynchus collected per trap night. The findings confirm that agricultural land cover should be included in future studies to develop JE risk prediction models for non-indigenous personnel living at military installations in the ROK.
International Journal of Health Geographics 01/2010; 9:32. · 2.62 Impact Factor
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ABSTRACT: Abstract
Background
Japanese encephalitis virus (JEV), the causative agent of Japanese encephalitis (JE), is endemic to the Republic of Korea (ROK) where unvaccinated United States (U.S.) military Service members, civilians and family members are stationed. The primary vector of the JEV in the ROK is Culex tritaeniorhynchus . The ecological relationship between Culex spp. and rice fields has been studied extensively; rice fields have been shown to increase the prevalence of Cx. tritaeniorhynchus . This research was conducted to determine if the quantification of rice field land cover surrounding U.S. military installations in the ROK should be used as a parameter in a larger risk model that predicts the abundance of Cx. tritaeniorhynchus populations.
Mosquito data from the U.S. Forces Korea (USFK) mosquito surveillance program were used in this project. The average number of female Cx. tritaeniorhynchus collected per trap night for the months of August and September, 2002-2008, was calculated. Rice fields were manually digitized inside 1.5 km buffer zones surrounding U.S. military installations on high-resolution satellite images, and the proportion of rice fields was calculated for each buffer zone.
Results
Mosquito data collected from seventeen sample sites were analyzed for an association with the proportion of rice field land cover. Results demonstrated that the linear relationship between the proportion of rice fields and mosquito abundance was statistically significant (R<sup>2 </sup>= 0.62, r = .79, F = 22.72, p < 0.001).
Conclusions
The analysis presented shows a statistically significant linear relationship between the two parameters, proportion of rice field land cover and log<sub>10 </sub>of the average number of Cx. tritaeniorhynchus collected per trap night. The findings confirm that agricultural land cover should be included in future studies to develop JE risk prediction models for non-indigenous personnel living at military installations in the ROK.
International Journal of Health Geographics. 01/2010;
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ABSTRACT: El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of satellite measurements of elevated sea-surface temperatures and subsequent elevated rainfall and satellite-derived normalized difference vegetation index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted areas where outbreaks of RVF in humans and animals were expected and occurred in the Horn of Africa from December 2006 to May 2007. The predictions were subsequently confirmed by entomological and epidemiological field investigations of virus activity in the areas identified as at risk. Accurate spatial and temporal predictions of disease activity, as it occurred first in southern Somalia and then through much of Kenya before affecting northern Tanzania, provided a 2 to 6 week period of warning for the Horn of Africa that facilitated disease outbreak response and mitigation activities. To our knowledge, this is the first prospective prediction of a RVF outbreak.
Proceedings of the National Academy of Sciences 02/2009; 106(3):955-9. · 9.68 Impact Factor
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Seth C Britch,
Kenneth J Linthicum, Assaf Anyamba,
Compton J Tucker,
Edwin W Pak,
Francis A Maloney,
Kristin Cobb,
Erin Stanwix,
Jeri Humphries,
Alexandra Spring,
Benedict Pagac,
Melissa Miller
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ABSTRACT: The United States faces many existing and emerging mosquito-borne disease threats, such as West Nile virus and Rift Valley fever. An important component of strategic prevention and control plans for these and other mosquito-borne diseases is forecasting the distribution, timing, and abundance of mosquito vector populations. Populations of many medically important mosquito species are closely tied to climate, and historical climate-population associations may be used to predict future population dynamics. Using 2003-2005 U.S. Army Center for Health Promotion and Preventive Medicine mosquito surveillance data, we looked at populations of several known mosquito vectors of West Nile virus, as well as possible mosquito vectors of Rift Valley fever virus, at continental U.S. military installations. We compared population changes with concurrent patterns for a satellite-derived index of climate (normalized difference vegetation index) and observed instances of population changes appearing to be direct responses to climate. These preliminary findings are important first steps in developing an automated, climate-driven, early warning system to flag regions of the United States at elevated risk of mosquito-borne disease transmission.
Military medicine 08/2008; 173(7):677-83. · 0.92 Impact Factor
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ABSTRACT: We used geographic information system (GIS) and long-term mosquito surveillance data from Lake, Pasco, Manatee, and Sarasota Counties, FL, to look at patterns of invasion by Aedes albopictus and concurrent changes in resident Ae. aegypti. We investigated environmental factors associated with population changes in these species with the use of satellite climate data. Aedes aegypti densities attenuated rapidly following the arrival of Ae. albopictus in most counties, yet both species persisted in equilibrium in Manatee County. We discuss the relative importance of rainfall, habitat, and proximity to urban areas in the population dynamics of these species in sympatry.
Journal of the American Mosquito Control Association 04/2008; 24(1):115-20. · 0.91 Impact Factor
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Biosurveillance and Biosecurity, International Workshop, BioSecure 2008, Raleigh, NC, USA, December 2, 2008. Proceedings; 01/2008
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Jean-Paul Chretien, Assaf Anyamba,
Sheryl A Bedno,
Robert F Breiman,
Rosemary Sang,
Kibet Sergon,
Ann M Powers,
Clayton O Onyango,
Jennifer Small,
Compton J Tucker,
Kenneth J Linthicum
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ABSTRACT: Epidemics of chikungunya fever, an Aedes spp.-borne viral disease, affected hundreds of thousands of people in western Indian Ocean islands and India during 2005-2006. The initial outbreaks occurred in coastal Kenya (Lamu, then Mombasa) in 2004. We investigated eco-climatic conditions associated with chikungunya fever emergence along coastal Kenya using epidemiologic investigations and satellite data. Unusually dry, warm conditions preceded the outbreaks, including the driest since 1998 for some of the coastal regions. Infrequent replenishment of domestic water stores and elevated temperatures may have facilitated Chikungunya virus transmission. These results suggest that drought-affected populations may be at heightened risk for chikungunya fever, and underscore the need for safe water storage during drought relief operations.
The American journal of tropical medicine and hygiene 04/2007; 76(3):405-7. · 2.59 Impact Factor
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Seth C. Britch,
Kenneth J. Linthicum,
Barry R. Miller,
John T. Roehrig,
Pierre E. Rollin,
Chester G. Moore,
Christy A. Tedrow, Assaf Anyamba,
Judy Akina,
Kristine E. Bennett, [......],
Jeffrey Root,
Jack C. Rhyan,
Daniel A. Strickman,
Steven J. Sweeney,
Sherrilyn H. Wainwright,
Todd Weaver,
William C. Wilson,
Franco Basile,
Scott N. Miller,
Li Zou
Emerging Infectious Diseases. 01/2007; 13.
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Emerging infectious diseases 04/2006; 12(3):518-20. · 6.17 Impact Factor
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ABSTRACT: El Niño/Southern Oscillation (ENSO) related climate anomalies have been shown to have an impact on infectious disease outbreaks. The Climate Prediction Center of the National Oceanic and Atmospheric Administration (NOAA/CPC) has recently issued an unscheduled El Niño advisory, indicating that warmer than normal sea surface temperatures across the equatorial eastern Pacific may have pronounced impacts on global tropical precipitation patterns extending into the northern hemisphere particularly over North America. Building evidence of the links between ENSO driven climate anomalies and infectious diseases, particularly those transmitted by insects, can allow us to provide improved long range forecasts of an epidemic or epizootic. We describe developing climate anomalies that suggest potential disease risks using satellite generated data.
Sea surface temperatures (SSTs) in the equatorial east Pacific ocean have anomalously increased significantly during July - October 2006 indicating the typical development of El Niño conditions. The persistence of these conditions will lead to extremes in global-scale climate anomalies as has been observed during similar conditions in the past. Positive Outgoing Longwave Radiation (OLR) anomalies, indicative of severe drought conditions, have been observed across all of Indonesia, Malaysia and most of the Philippines, which are usually the first areas to experience ENSO-related impacts. This dryness can be expected to continue, on average, for the remainder of 2006 continuing into the early part of 2007. During the period November 2006 - January 2007 climate forecasts indicate that there is a high probability for above normal rainfall in the central and eastern equatorial Pacific Islands, the Korean Peninsula, the U.S. Gulf Coast and Florida, northern South America and equatorial east Africa. Taking into consideration current observations and climate forecast information, indications are that the following regions are at increased risk for disease outbreaks: Indonesia, Malaysia, Thailand and most of the southeast Asia Islands for increased dengue fever transmission and increased respiratory illness; Coastal Peru, Ecuador, Venezuela, and Colombia for increased risk of malaria; Bangladesh and coastal India for elevated risk of cholera; East Africa for increased risk of a Rift Valley fever outbreak and elevated malaria; southwest USA for increased risk for hantavirus pulmonary syndrome and plague; southern California for increased West Nile virus transmission; and northeast Brazil for increased dengue fever and respiratory illness.
The current development of El Niño conditions has significant implications for global public health. Extremes in climate events with above normal rainfall and flooding in some regions and extended drought periods in other regions will occur. Forecasting disease is critical for timely and efficient planning of operational control programs. In this paper we describe developing global climate anomalies that suggest potential disease risks that will give decision makers additional tools to make rational judgments concerning implementation of disease prevention and mitigation strategies.
International Journal of Health Geographics 02/2006; 5:60. · 2.62 Impact Factor
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ABSTRACT: Abstract
Background
El Niño/Southern Oscillation (ENSO) related climate anomalies have been shown to have an impact on infectious disease outbreaks. The Climate Prediction Center of the National Oceanic and Atmospheric Administration (NOAA/CPC) has recently issued an unscheduled El Niño advisory, indicating that warmer than normal sea surface temperatures across the equatorial eastern Pacific may have pronounced impacts on global tropical precipitation patterns extending into the northern hemisphere particularly over North America. Building evidence of the links between ENSO driven climate anomalies and infectious diseases, particularly those transmitted by insects, can allow us to provide improved long range forecasts of an epidemic or epizootic. We describe developing climate anomalies that suggest potential disease risks using satellite generated data.
Results
Sea surface temperatures (SSTs) in the equatorial east Pacific ocean have anomalously increased significantly during July – October 2006 indicating the typical development of El Niño conditions. The persistence of these conditions will lead to extremes in global-scale climate anomalies as has been observed during similar conditions in the past. Positive Outgoing Longwave Radiation (OLR) anomalies, indicative of severe drought conditions, have been observed across all of Indonesia, Malaysia and most of the Philippines, which are usually the first areas to experience ENSO-related impacts. This dryness can be expected to continue, on average, for the remainder of 2006 continuing into the early part of 2007. During the period November 2006 – January 2007 climate forecasts indicate that there is a high probability for above normal rainfall in the central and eastern equatorial Pacific Islands, the Korean Peninsula, the U.S. Gulf Coast and Florida, northern South America and equatorial east Africa. Taking into consideration current observations and climate forecast information, indications are that the following regions are at increased risk for disease outbreaks: Indonesia, Malaysia, Thailand and most of the southeast Asia Islands for increased dengue fever transmission and increased respiratory illness; Coastal Peru, Ecuador, Venezuela, and Colombia for increased risk of malaria; Bangladesh and coastal India for elevated risk of cholera; East Africa for increased risk of a Rift Valley fever outbreak and elevated malaria; southwest USA for increased risk for hantavirus pulmonary syndrome and plague; southern California for increased West Nile virus transmission; and northeast Brazil for increased dengue fever and respiratory illness.
Conclusion
The current development of El Niño conditions has significant implications for global public health. Extremes in climate events with above normal rainfall and flooding in some regions and extended drought periods in other regions will occur. Forecasting disease is critical for timely and efficient planning of operational control programs. In this paper we describe developing global climate anomalies that suggest potential disease risks that will give decision makers additional tools to make rational judgments concerning implementation of disease prevention and mitigation strategies.
International Journal of Health Geographics. 01/2006;
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ABSTRACT: All known Rift Valley fever virus outbreaks in East Africa from 1950 to May 1998, and probably earlier, followed periods of
abnormally high rainfall. Analysis of this record and Pacific and Indian Ocean sea surface temperature anomalies, coupled
with satellite normalized difference vegetation index data, shows that prediction of Rift Valley fever outbreaks may be made
up to 5 months in advance of outbreaks in East Africa. Concurrent near–real-time monitoring with satellite normalized difference
vegetation data may identify actual affected areas.
Science 07/1999; 285(5426):397-400. · 31.20 Impact Factor
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ABSTRACT: Contrary to assertions of widespread irreversible desertification in the African Sahel, a recent increase in seasonal greenness over large areas of the Sahel has been observed, which has been interpreted as a recovery from the great Sahelian droughts. This research investigates temporal and spatial patterns of vegetation greenness and rainfall variability in the African Sahel and their interrelationships based on analyses of Normalized Difference Vegetation Index (NDVI) time series for the period 1982–2003 and gridded satellite rainfall estimates. While rainfall emerges as the dominant causative factor for the increase in vegetation greenness, there is evidence of another causative factor, hypothetically a human-induced change superimposed on the climate trend.
Global Environmental Change.
