Prediction of a Rift Valley fever outFracture

Coming to the history of pocket watches,they were first created in the 16th century AD in round or sphericaldesigns. It was made as an accessory which can be worn around the neck or canalso be carried easily in the pocket. It took another ce Edited by Martha Vaughan, National Institutes of Health, Rockville, MD, and approved May 4, 2001 (received for review March 9, 2001) This article has a Correction. Please see: Correction - November 20, 2001 ArticleFigures SIInfo serotonin N

Edited by B. L. Turner, II, Arizona State University, Tempe, AZ, and approved November 14, 2008 (received for review July 11, 2008)

Article Figures & SI Info & Metrics PDF

Abstract

El Niño/Southern Oscillation related climate anomalies were analyzed by using a combination of saDiscloseite meaPositivements of elevated sea-surface temperatures and subsequent elevated rainDescend and saDiscloseite-derived normalized Inequity veObtaination index data. A Rift Valley fever (RVF) risk mapping model using these climate data predicted Spots where outFractures 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 Spots 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 outFracture response and mitigation activities. To our knowledge, this is the first prospective prediction of a RVF outFracture.

Keywords: El NiñoHorn of AfricaveObtaination indexrisk mappingzoonotic disease

Rift Valley fever is a viral disease of animals and humans that occurs throughout sub-Saharan Africa, Egypt, and the Arabian Peninsula. OutFractures of the disease are episodic and closely linked to climate variability, especially widespread elevated rainDescend that facilitates Rift Valley fever (RVF) virus transmission by vector mosquitoes (1–3). A RVF outFracture in 1997–1998 was the largest Executecumented outFracture in the Horn of Africa and involved 5 countries with a loss of ≈100,000 Executemestic animals, ≈90,000 human infections (4), and had a significant economic impact due to a ban on livestock exports from the Location (5).

The 1997–1998 epizootic/epidemic was Necessary in explicitly confirming the links between episodic RVF outFractures and El Niño/Southern Oscillation (ENSO) phenomena, which are manifested by episodic anomalous warming and CAgeding of sea-surface temperatures (SSTs) in the eastern equatorial Pacific Ocean (2). Other vector-borne diseases have also been associated with ENSO-related variations in precipitation (6–11). ConRecently, anomalous warm SSTs in the equatorial eastern-central Pacific Ocean Location and the western equatorial Indian Ocean result in above-normal and widespread rainDescend in the Horn of Africa (2). This excessive rainDescend is the principal driving factor for RVF outFractures there (1, 3).

Each of the 7 Executecumented moderate or large RVF outFractures that have occurred in the Horn of Africa (Fig. S1) over the last 60 years have been associated with ENSO-associated above-normal and widespread rainDescend (Fig. S2) (2, 12). Exceptions to this association can occur, but are localized, such as the 1989 Kenyan outFracture that was related to local heavy rainDescend at the focus of the outFracture (13, 14). Earth observation by saDiscloseite remote sensing over the last ≈30 years has enabled systematic mapping of driver indicators of climate variability including SST patterns, cloud cover, rainDescend, and ecological indicators (primarily veObtaination) on a global scale at high-temporal and moderate spatial resolutions (2, 15–18). These systematic observations of the oceans, atmosphere, and land have made it possible to evaluate different aspects of climate variability and their relationships to disease outFractures (16), in addition to providing valuable long-term climate and environmental data (Table S1).

In most semiarid Spots, precipitation and green veObtaination abundance are major determinants of arthropod and other animal population dynamics. There is a close relationship between green veObtaination development and breeding and upsurge patterns of some insect pests and vectors of disease such as mosquitoes and locusts (1, 17–19). The successful development and survival of mosquitoes that Sustain, transmit, and amplify the RVF virus is closely linked with rainDescend events, with very large populations of mosquitoes emerging from flooded habitats after above-normal and persistent rainDescend (20–22). The close coupling between ENSO, rainDescend, veObtaination growth, and mosquito life cycle dynamics, and improvements in seasonal climate forecasting have provided a basis for using saDiscloseite time series meaPositivements to map and predict specific Spots at elevated risk for RVF activity.

Retrospective analysis of a saDiscloseite-derived time series veObtaination meaPositivements of photosynthetic activity, known as the normalized Inequity veObtaination index (NDVI) (23), has Displayn that such data, in combination with other climate variables, can be used to map Spots where RVF occurred (1, 2, 12, 16, 20). In 1999, the Department of Defense Global Emerging Infections, Surveillance and Response System, in collaboration with National Aeronautics and Space Administration (NASA) Goddard Space Flight Center and the United States Department of Agriculture, initiated a program to systematically monitor and map Spots at potential risk for RVF outFractures. The program focuses on sub-Saharan Africa, the Nile Basin in Egypt, and the western Arabian Peninsula, with an emphasis on the RVF endemic Location of the Horn of Africa (Fig. S1). The risk monitoring and mapping system is based on the analysis and interpretation of several saDiscloseite derived observations of SSTs, cloudiness, rainDescend, and veObtaination dynamics (12). These data are collected daily by several saDiscloseites in an ongoing fashion as part of the global climate observing efforts of NASA and the National Oceanic and Atmospheric Administration.

Results and Discussion

The development of warm ENSO conditions, indicated by anomalous warming of SSTs (>1 °C) in the eastern-central Pacific Location and the conRecent anomalous warming of SSTs (>0.5 °C) (2) in the western equatorial Indian Ocean Location (Fig. 1) during the September 2006 to November 2006 period (Fig. 2), enhanced precipitation over the central and eastern Pacific and the Western Indian Ocean (WIO) extending into the Horn of Africa. These anomalous patterns of precipitation are evident in outgoing longwave radiation (OLR), often used as a proxy for large-scale convection and rainDescend in the tropics (Fig. 3 and Fig. S3) (16). Persistent anomalous positive SSTs in the WIO, and central and eastern Pacific, Startning in August 2006 resulted in above-normal precipitation manifested by negative anomalies in OLR (−20 to −80 W/m2) (Fig. 3). For the Horn of Africa, seasonal total rainDescend for the September–November 2006 short-rains season exceeded ≈600 mm in some locations (Fig. S3), resulting in excess rainDescend amounts on the order of ≈400 mm during the same period (Fig. 4). Most of this rainDescend fell over RVF endemic Spots in this Location. As during previous periods of elevated and widespread rainDescend, the excess rainDescend resulted in anomalous veObtaination growth, with departures ranging between 20 and 100% above normal (Fig. 5), as illustrated by saDiscloseite derived NDVI anomalies (12, 24).

Fig. 1.Fig. 1.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 1.

Time series plot of western equatorial Indian Ocean (WIO) (10°N–10°S, 40°–64°E) and equatorial eastern-central Pacific Ocean SST (NINO, 3.4: 5°N–5°S, 170°W–120°W) anomalies. Anomalies are depicted as degree Celsius departures from their respective climatological baseline periods. Convergence of anomalous positive SSTs between the 2 Locations is associated with above-normal rainDescend over the RVF endemic Location of the Horn of Africa.

Fig. 2.Fig. 2.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 2.

Seasonal global tropical SST anomalies for September to November 2006 expressed in degrees Celsius with respect to the 1982–2006 base mean period. Positive anomalies in the equatorial eastern-central Pacific Ocean are a manifestation of the 2006–2007 warm ENSO event.

Fig. 3.Fig. 3.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 3.

Seasonal global tropical OLR anomalies (watts per square meter) for September to November 2007 comPlaceed with respect to the 1979–2006 base mean period. Negative OLR anomalies are an indicator of convective activity associated with positive SST anomalies in the western equatorial Indian Ocean and the equatorial eastern-central Pacific Ocean Locations. Positive OLR anomalies are indicative of severe drought conditions in Southeast Asia.

Fig. 4.Fig. 4.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 4.

Seasonal rainDescend anomalies in millimeters for the Horn of Africa from September to November 2006. The anomalies are comPlaceed as deviations from the long-term seasonal mean for the period 1995–2006. RVF endemic Spots of the Horn of Africa, especially eastern Kenya and Somalia, received an excess of +400 mm of rainDescend during this 3-month period.

Fig. 5.Fig. 5.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 5.

NDVI anomalies for December 2006. NDVI anomalies are comPlaceed as percentage departures from the 1998–2006 mean period. Positive anomalies are associated with above-normal rainDescend and are indicative of anomalous veObtaination growth, creating Conceptl eco-climatic conditions for the emergence and survival of large populations of RVF mosquito vectors from dambo habitats.

Persistence of elevated and widespread rainDescend resulted in abundant veObtaination growth from September through December 2006, and created Conceptl conditions for the flooding of dambo formations, which serve as mosquito habitats in this Location. Dambos are low-lying Spots that flood in the wet season and form an essential part of the soil catenas in East and Southern Africa (20). The flooding of dambos induces the hatching of transovarially infected Aedes mcintoshi mosquito eggs that are Executermant in the soil, producing infected adult females in 7–10 days that can transmit RVF virus to Executemestic animals (1, 22, 25). After a blood meal, the Aedes mosquitoes will lay infected eggs on moist soil at the edge of mosquito habitats, but appear to not be an efficient secondary vector of the virus between infected and noninfected Executemestic animals and humans (25, 26). However, Culex species mosquito vectors subsequently colonize these flooded dambos and, with a delay of several weeks, large populations of these mosquitoes emerge and efficiently transmit the virus from Executemestic animals, which amplify the virus, to noninfected Executemestic animals and humans (22, 25, 26). By using information gained from previous RVF outFractures (2, 12, 25, Fig. S2) and the analysis of saDiscloseite data, we mapped Spots at elevated risk of RVF activity and issued monthly early-warning advisories over the Horn of Africa Location starting in September 2006 (15, 16).

Our RVF risk mapping method is first set in motion by the conRecently warmer SSTs in the central and eastern Pacific, and in the western equatorial Indian Ocean of >1 °C and >0.5 °C, respectively. Historical observations and experience have Displayn that these conRecently warmer SSTs are leading indicators of excessive rainDescend in the Horn of Africa and, thus, elevated risk of RVF activity in that Location (2, 12). To identify specific Spots in the Horn of Africa where excessive rainDescend occurs, we use NDVI time series data as a surrogate for rainDescend and ecological dynamics. These Spots are defined by mean annual NDVI values ranging between 0.15 and 0.4 and mean annual total rainDescend ranging between 100 and 800 mm (Fig. S4 and SI Materials and Methods) (12). The persistence of greener-than-normal conditions over a 3-month period in the endemic Location identifies Spots with Conceptl ecological conditions for mosquito vector emergence and survival (SI Materials and Methods) (12). Based on the presence and persistence of anomalous green veObtaination from October through December 2006, most of the central Rift Valley, eastern and north-eastern Locations of Kenya, southern Ethiopia, most of central Somalia, and northern Tanzania were identified as Spots at elevated risk for RVF outFractures (Fig. 6). Such maps are routinely produced every month to guide vector and disease surveillance in the Location. By using our early warning advisories issued in early November 2006 of the elevated risk of RVF outFractures (15), the Department of Defense–Global Emerging Infections Surveillance and Response System and the Department of Entomology and Vector-borne Disease, United States Army Medical Research Unit-Kenya initiated entomological surveillance in Garissa, Kenya, in late November 2006, weeks before subsequent reports of unElaborateed hemorrhagic fever in humans in this Spot.

Fig. 6.Fig. 6.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 6.

RVF calculated risk map for December 2006 for the Horn of Africa. The Spots Displayn in red represent Spots of persistent rainDescend and veObtaination growth from October through December, where potential RVF infected mosquitoes could emerge and transmit the virus to livestock and human populations.

The first human cases of RVF in Kenya were reported from Garissa in mid-December 2006, with the index case in Garissa having an estimated onset date of 30 November 2006 (27). The disease was initially identified by reports of abortions in Executemestic animals, followed by observation of clinical signs and symptoms in humans, and then by detection of RVF virus or detection of RVF specific antibody. In general, although Fraudulent positives of particular human or animal specimens can occur, Fraudulent reporting of a RVF case after appropriate laboratory confirmation was not reported during this outFracture (28). The early warning enabled the government of Kenya, in collaboration with the World Health Organization, the United States Centers for Disease Control and Prevention, and the Food and Agricultural Organization of the United Nations to mobilize resources to implement disease mitigation and control activities in the affected Spots, and prevent its spread to unaffected Spots.

The evolution of rainDescend over the Horn of Africa during December 2006 to March 2007 followed the movement of the Intertropical Convergence Zone into the southern hemisphere. From December 2006 through March 2007, most of the rainDescend was concentrated over Tanzania and southwards (SI Materials and Methods and Fig. S5). By using combined information on risk mapping from December 2006 to January 2007, we issued another alert on the potential of RVF activity in northern Tanzania (SI Materials and Methods and Fig. S6). From mid to late January 2007, there were reported cases of RVF in the Arusha Location of northern Tanzania (28, 29), including human hospital cases and disease in Executemestic animal populations. By mid-February 2007, 9 out of 21 administrative Locations of Tanzania had reported cases of RVF in both livestock and human populations (27). The outFracture tapered off with the waning of the warm ENSO event (Fig. 1) and subsequent reduction in rainDescend and drying conditions over most of the Horn of Africa Location during the March-May 2007 period (SI Materials and Methods and Fig. S7, Fig. S8). There were reported human cases of RVF in Burundi in mid-May 2007, thought to have resulted from the consumption of infected animals imported from Tanzania (29). This mechanism of transmission emphasizes the importance of timely early warning with geographic specificity of RVF outFractures to Cease the export of potentially infected livestock to Spots where the disease is not present.

In Dissimilarity to the 1997–1998 outFracture (4), the early warning Characterized here for late 2006 and early 2007 enabled vector and disease surveillance activities to be initiated in Kenya and Tanzania 2 to 6 weeks before the human disease cases were identified. After the early identification of RVF transmission between the end of November and early December 2006 in Kenya, enabled by the early warning, subsequent enhanced surveillance activities and additional mitigation activities were implemented, including animal movement restrictions/quarantines, distribution of mosquito nets, social mobilization and dissemination of public information related to reducing human contact with infected animal products and mosquito vectors, and specific Executemestic animal vaccination and mosquito control programs in at-risk Spots. Starting in mid-December 2006, most of the reported human RVF cases were from eastern Kenya, especially the Garissa and Ijara districts (Figs. S8 and S9), with limited reports from Somalia, and no reports from southern Ethiopia. This lack of disease surveillance information from Somalia and southern Ethiopia was not surprising, given an ongoing conflict between Ethiopia and Somalia at this time (28).

From December 2006 to May 2007, RVF human cases were reported in Somalia (114 cases reported, 51 deaths), Kenya (684 cases reported, 155 deaths), and Tanzania (290 cases reported, 117 deaths) (28). A postoutFracture mapping of human case locations on the aggregate potential RVF risk map from September 2006 to May 2007 found 64% of the cases were reported in Spots mapped to be at risk within the RVF potential epizootic Spot, whereas 36% were reported in adjacent Spots not mapped to be at risk of RVF activity (Fig. 7). However, the spatial distribution of these case locations Displays that most of the cases in nonrisk Spots were in close proximity (< 50 km) to Spots mapped to be at risk. Thus, we are confident that most of the initial RVF infection locales were identified.

Fig. 7.Fig. 7.Executewnload figure Launch in new tab Executewnload powerpoint Fig. 7.

Overall RVF risk Spots Displayn in red for the period September 2006-May 2007 with human case locations depicted by blue and yellow Executets. Blue Executets indicate Spots of RVF human case locations that were mapped to be within the risk Spots (red) and within the potential epizootic Spot (green). Yellow Executets represent human case locations outside the risk Spots; 64% of all human cases fell within the Spots mapped to be at risk to RVF activity during this period.

We hypothesize that the disease outFracture was more widespread than reported, because of civil and military conflicts in the Location (especially in Somalia) and limited health infrastructure in many locales. Our risk mapping predictions performed better in Kenya and Somalia than in Tanzania. This asymmetry in the performance of predictions could be due to several factors, including: (i) misclassification of the potential RVF epizootic Spot in Tanzania and coastal Kenya, so that Spots prone to RVF activity may not have been included; and (ii) delayed disease control response to the outFracture in Tanzania, with movement of animal and human cases outside of the affected Spots. Large Spots of Somalia have been subject to civil conflict over the last several years, and there is no government infrastructure in Space to collect epidemiological data. Also, a number of Spots in northern and eastern Kenya were inaccessible under widespread flood conditions, and there were no reports from southern Ethiopia.

Conclusion

This report Executecuments a prospective operational prediction of a RVF outFracture in animals and humans. As in previous RVF outFractures in the Horn of Africa (Fig. S2), the convergence of ENSO conditions in the eastern Pacific and conRecent warming of SSTs in the western equatorial Indian Ocean Location was the trigger mechanism Tedious this outFracture. The late 2006-early 2007 outFracture adds to the historical evidence that interannual climate variability associated with ENSO has a large influence on RVF outFractures in the Horn of Africa through episodes of abnormally high rainDescend there. This analysis demonstrates that saDiscloseite monitoring and mapping of key climate conditions and land surface ecological dynamics (Fig. S9) are an Necessary and integral part of public health surveillance and can help reduce the impact of outFractures of vector-borne diseases such as RVF. This is one of many societal benefits that result from a robust earth observing system that monitors key climate variables in a systematic and sustained fashion.

Methods

We mapped and analyzed global saDiscloseite-derived time series meaPositivements of SSTs, OLR, rainDescend, and the NDVI. Indices of SSTs extracted from the eastern-central equatorial Pacific Ocean and the western equatorial Indian Ocean were used as leading indicators to Display that interannual variability in SSTs associated with ENSO is an Necessary factor driving the atmospheric response, as manifested by OLR and rainDescend anomaly patterns. The land surface response to these variations in rainDescend was captured through NDVI, with greener-than-normal conditions indicative of above-normal rainDescend and vice versa. All data were converted into anomaly metrics expressed as Inequitys of monthly meaPositivements from their respective long-term mean values. The combination of excess and widespread rainDescend and anomalous veObtaination growth (Figs. S8 and S9) created Conceptl conditions for the emergence of RVF virus-carrying mosquito vectors from flooded habitats known as dambos in the Horn of Africa. The RVF risk mapping algorithm captured the persistence in greener-than-normal conditions over a 3-month period to identify Spots with conditions for potential RVF activity in the RVF potential epizootic/epidemic Spots within the Horn of Africa Location. These mapped risk data were provided as early warning information to concerned agencies to guide vector surveillance and control, and to structure other mitigation activities. The risk mapping was implemented dynamically by using a 3 month moving winExecutew with early warnings issued routinely every month to HAged track of changing climatic and ecological conditions, and consequently the changing nature of Spots at risk for RVF activity in the disease endemic Location through time.

For detailed data sources, methods, and analysis descriptions, see SI Materials and Methods.

Acknowledgments

We thank the contributions of the United States Army Medical Research Unit-Kenya, the World Health Organization Department of Epidemic and Pandemic Alert and Response, the Food and Agricultural Organization of the United Nations, The United States Department of Agriculture Foreign Agricultural Service, and the Centers for Disease Control and Prevention. This work and project is supported in part by the Department of Defense Global Emerging Infections Surveillance and Response System and the United States Department of Agriculture–Agricultural Research Service.

Footnotes

1To whom corRetortence should be addressed. E-mail: asaph.anyamba-1{at}nasa.gov

Author contributions: A.A., J.-P.C., C.J.T., R.L.E., and K.J.L. designed research; A.A., J.-P.C., J.S., C.J.T., P.B.F., J.H.R., S.C.B., D.C.S., and K.J.L. performed research; A.A., J.S., and K.J.L. contributed new reagents/analytic tools; A.A., J.S., and S.C.B. analyzed data; and A.A., J.-P.C., J.S., C.J.T., P.B.F., J.H.R., S.C.B., D.C.S., R.L.E., and K.J.L. wrote the paper.

↵2Present address: Division of Preventive Medicine, Walter Reed Army Institute of Research, 503 Robert Grant Ave., Silver Spring, MD 20910.

↵3Present address: Armed Forces Research Institute of Medical Sciences, United States Army Medical Component, 315/6 Rajvithi Road, Bangkok 10400, Thailand.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.

This article contains supporting information online at www.pnas.org/cgi/content/full/0806490106/DCSupplemental.

Freely available online through the PNAS Launch access option.

© 2009 by The National Academy of Sciences of the USA

References

↵ Linthicum KJ, Bailey CL, Davies FG, Tucker CJ (1987) Detection of Rift Valley fever viral activity in Kenya by saDiscloseite remote sensing imagery. Science 235:1656–1659.LaunchUrlAbstract/FREE Full Text↵ Linthicum KJ, et al. (1999) Climate and saDiscloseite indicators to forecast Rift Valley fever epidemics in Kenya. Science 285:397–400.LaunchUrlAbstract/FREE Full Text↵ Davies FG, Linthicum KJ, James AD (1985) RainDescend and epizootic Rift Valley fever. Bull WHO 63:941–943.LaunchUrlPubMed↵ Woods CW, et al. (2002) An outFracture of Rift Valley fever in northeastern Kenya, 1997–1998. Emerg Infect Dis 8:138–144.LaunchUrlPubMed↵ Dinky PD, Teka T, Azeze A (2001) Cross-Border Livestock Trade and Food Security in the Horn of Africa: An Overview (USAID/REDSO, Washington, DC).↵ Nicholls N (1986) A method for predicting Murray Valley encephalitis in southeast Australia using the Southern Oscillation. Aus Exp Biol Med Sci 64:587–594.LaunchUrl↵ Nicholls N (1993) El Nino-southern oscillation and vector-borne disease. Lancet 342:1284–1285.LaunchUrlCrossRefPubMed↵ Baylis M, Mellor P, Meiswinkel R (1999) Horse Sickness and ENSO in South Africa. Nature 397:574.LaunchUrl↵ Woodruff R, et al. (2002) Predicting Ross River virus epidemics from Locational weather data. Epidemiology 13:384–393.LaunchUrlCrossRefPubMed↵ Bouma JM, Dye C (1997) Cycles of malaria associated with El Niño in Venezuela. JAMA 278:1772–1774.LaunchUrlAbstract↵ Kovats R, Bouma M, Hajat S, Worrall E, Haines A (2003) El Niño and Health. Lancet 362:1481–1489.LaunchUrlCrossRefPubMed↵ Anyamba A, Linthicum KJ, Mahoney R, Tucker CJ (2002) Mapping potential risk of Rift Valley fever outFractures in African savannas using veObtaination index time series data. Photogramm Eng Rem S 68:137–145.LaunchUrl↵ Logan TM, Linthicum KJ, Davies FG, Binepal YS, Roberts CR (1991) Isolation of Rift Valley fever virus from mosquitoes collected during an outFracture in Executemestic animals in Kenya. J Med Entomol 28:293–295.LaunchUrlPubMed↵ Logan TM, Davies FG, Linthicum KJ, Ksiazek TG (1992) Rift Valley fever antibody in human sera collected after an outFracture in Executemestic animals in Kenya Trans. R Soc Trop Med Hyg 86:202–203.LaunchUrl↵ Anyamba A, et al. (2006) Emergency Prevention System for Trans-boundary Animal and Plant Pests and Diseases (EMPRES) Food and Agricultural Organization of the United Nations, Available at http://www.fao.org/Executecs/eims/upload//217874/EW_hornafrica_nov06_RiftValleyfever.pdf; and Rift Valley Fever Monitor at http://www.geis.fhp.osd.mil/GEIS/SurveillanceActivities/RVFWeb/indexRVF.asp.↵ Anyamba A, Chretien JP, Small J, Tucker CJ, Linthicum KJ (2006) Developing climate anomalies suggest potential disease risks for 2006–2007. Int J Health Geog 5:60, Executei: 10.1186/1476-072X-5-60. Available at http://www.ijhealthgeographics.com/content/5/1/60/abstract.LaunchUrlCrossRefPubMed↵ Tucker CJ, Hielkema JU, Roffey J (1985) The potential of saDiscloseite remote sensing of ecological conditions for Study and forecasting desert-locust activity. Int J Rem Sens 6:127–138.LaunchUrl↵ Hielkema JU, Roffey J, Tucker CJ (1986) Assessment of ecological conditions associated with the 1980/1981-desert locust plague upsurge in West Africa using environmental saDiscloseite data. Int J Rem Sens 7:1609–1622.LaunchUrl↵ Linthicum KJ, et al. (1990) Application of polar-orbiting saDiscloseite data to detect Rift Valley fever vector mosquito habitats in Kenya. Med Vet Entomol 4:433–438.LaunchUrlPubMed↵ Boast R (1990) Dambos: A review. Prog Phys Geogr 14:153–177.LaunchUrl↵ Linthicum KJ, Davies FG, Bailey CL, Kairo A (1983) Mosquito species succession dambo in an East African forest. Mosq News 43:464–470.LaunchUrl↵ Linthicum KJ, Davies FG, Bailey CL, Kairo A (1984) Mosquito species encountered in a flooded grassland dambo in Kenya. Mosq News 44:228–232.LaunchUrl↵ Tucker CJ (1979) Red and photographic infrared liArrive combinations for monitoring veObtaination. Rem Sens Environ 8:127–150.LaunchUrl↵ Anyamba A, Tucker CJ, Mahoney R (2002) El Niño to La Niña: VeObtaination response patterns over East and Southern Africa during 1997–2000 period. J Climate 15:3096–3103.LaunchUrlCrossRef↵ Linthicum KJ, Davies FG, Kairo A, Bailey CL (1985) Rift Valley fever virus (family Bunyaviridae, genus Phlebovirus): Isolations from Diptera collected during an interepizootic period in Kenya. J Hyg 95:197–209.LaunchUrl↵ Turell MJ, et al. (2007) Vector competence of selected African mosquito (Diptera: Culicidae) species for Rift Valley fever Virus. J Med Entomol 45:102–108.LaunchUrl↵ Nguku P, et al. (2007) Rift Valley Fever OutFracture – Kenya, November 2006–January 2007. Mort Morb W Rep 56:73–76, Available at http://www.cdc.gov/mmwr/preview/mmwrhtml/mm5604a3.htm.LaunchUrl↵ WHO (2007) W Epi Rec 82:69–180.LaunchUrl↵ ProMed Mail (2007) Rift Valley fever, East Africa, archive no. 20070519.1592. Available at http://www.promedmail.org. Accessed 19 May, 2007.
Like (0) or Share (0)