Raised temperatures over the Kericho tea estates: revisiting the climate in the East African highlands malaria debate
© Omumbo et al; licensee BioMed Central Ltd. 2011
Received: 6 November 2010
Accepted: 17 January 2011
Published: 17 January 2011
Whether or not observed increases in malaria incidence in the Kenyan Highlands during the last thirty years are associated with co-varying changes in local temperature, possibly connected to global changes in climate, has been debated for over a decade. Studies, using differing data sets and methodologies, produced conflicting results regarding the occurrence of temperature trends and their likelihood of being responsible, at least in part, for the increases in malaria incidence in the highlands of western Kenya. A time series of quality controlled daily temperature and rainfall data from Kericho, in the Kenyan Highlands, may help resolve the controversy. If significant temperature trends over the last three decades have occurred then climate should be included (along with other factors such as land use change and drug resistance) as a potential driver of the observed increases in malaria in the region.
Over 30 years (1 January 1979 to 31 December 2009) of quality controlled daily observations ( > 97% complete) of maximum, minimum and mean temperature were used in the analysis of trends at Kericho meteorological station, sited in a tea growing area of Kenya's western highlands. Inhomogeneities in all the time series were identified and corrected. Linear trends were identified via a least-squares regression analysis with statistical significance assessed using a two-tailed t-test. These 'gold standard' meteorological observations were compared with spatially interpolated temperature datasets that have been developed for regional or global applications. The relationship of local climate processes with larger climate variations, including tropical sea surface temperatures (SST), and El Niño-Southern Oscillation (ENSO) was also assessed.
An upward trend of ≈0.2°C/decade was observed in all three temperature variables (P < 0.01). Mean temperature variations in Kericho were associated with large-scale climate variations including tropical SST (r = 0.50; p < 0.01). Local rainfall was found to have inverse effects on minimum and maximum temperature. Three versions of a spatially interpolated temperature data set showed markedly different trends when compared with each other and with the Kericho station observations.
This study presents evidence of a warming trend in observed maximum, minimum and mean temperatures at Kericho during the period 1979 to 2009 using gold standard meteorological observations. Although local factors may be contributing to these trends, the findings are consistent with variability and trends that have occurred in correlated global climate processes. Climate should therefore not be dismissed as a potential driver of observed increases in malaria seen in the region during recent decades, however its relative importance compared to other factors needs further elaboration. Climate services, pertinent to the achievement of development targets such as the Millennium Development Goals and the analysis of infectious disease in the context of climate variability and change are being developed and should increase the availability of relevant quality controlled climate data for improving development decisions. The malaria community should seize this opportunity to make their needs heard.
Climate information needs for malaria
Malaria is a climate sensitive disease and climate information can be used to monitor and predict aspects of its spatial distribution [1, 2] seasonality  year-to-year variability  and longer term trends . Furthermore, climate information is increasingly recognized as necessary to enable accurate impact evaluations of malaria interventions [6, 7]. The biology of malaria transmission is markedly complex, involving interactions between multiple, constantly changing, extrinsic and intrinsic factors, many of which cannot be easily measured and are therefore challenging to model. Mathematical models of malaria transmission are highly sensitive to the non-linear response of both the vector and parasite to variations in temperature . Thus, the issue of temperature variability and change is often considered central to the discussion of whether malaria transmission is likely to increase if global temperatures rise [9–11].
Conflicting evidence from the Kenyan highlands
Here the focus is on the use of climate information to understand the possible impact of climatic trends on increases in malaria incidence in the East African Highlands over the last three decades (1980-2009). For over a decade, the highlands of malaria endemic countries have been considered areas of special concern for the impacts of climate change . Lindsay and Martens suggested in 1998 that, with all other factors remaining equal, global warming may result in the geographic spread of malaria transmission into previously malaria-free highland areas . Since then, the discussion around the evidence for this has raised a heated and highly polarized debate. Multiple peer reviewed publications [14–17], newspaper articles, editorials and blogs have been written and yet, to date, the debate continues to smolder.
To-date research has focused on addressing four key questions regarding the relationship between malaria and temperature in the East African highlands: a) is malaria increasing or re-emerging? b) are temperatures increasing? c) if there is a warming trend in the highlands, is it related to global climate change? and d) if there is a warming trend, is there a causal relationship with trends in malaria incidence? Here the focus is on questions a, b and c; a quality controlled meteorological dataset for Kericho is made available for use in answering question d (see Additional file 1).
Is malaria increasing?
Detailed epidemiological data for Kericho and elsewhere in the highlands of Eastern Africa provide strong evidence of emergence and resurgence of malaria in the 1980s and 1990s [12, 17, 19, 20]. A supporting study by Malakooti and colleagues  shows that the majority of infections of patients attending the Kericho tea estate health facilities between 1990 and 1997 were acquired locally. However recent reinvigorated malaria control efforts may have arrested or reversed this trend in some areas [21–24].
Are temperatures increasing?
Central to the debate over the last decade has been whether or not a statistically significant upward trend in temperature in the highlands has occurred and whether such a rise could account, at least in part, for the observed increase in malaria. Initial analyses of the malaria data from the Kericho tea estate with contemporaneous monthly averaged meteorological data presented no evidence for any significant changes in climate during the period of study [18, 20].
A substantial constraint to the climate analyses of these and subsequent studies has been the very limited access to a sufficiently long time series of quality controlled daily observations of surface air temperature from meteorological stations in Kericho. Constraints to accessing such gold standard observations have meant that studies have relied heavily on limited time series of station data , have used data that are inadequately quality controlled  or have ignored local ground observations completely in favour of spatially interpolated datasets[27, 28]. These latter data are intended for regional or global-scale analyses and utilize only a fraction of the stations that are maintained by National Meteorological Services and thus employ substantial spatial interpolation in their construction often leading to insufficient information for local point scale analyses. These limitations are clearly stated by the developers of the datasets .
Are local climate processes related to global patterns?
A recent climate analysis for East Africa was conducted by Christy and co-authors examining air temperature trends at 60 stations across Kenya . After spatially interpolating the station-based data, the study reports finding a statistically significant upward trend in minimum temperature (Tn) in the Kenyan Highlands region. The magnitude of the area-average trend in Tn they identified was about +0.15°C per decade, based on an analysis covering the period 1979-2004. However, no statistically significant trend in maximum temperature (Tx) was found. Christy et al  suggest that Tx is a better measure of the climate trend signal, as Tn is more sensitive to local processes including changes in the land surface. The study, however, did not explicitly examine the relationship between temperature variations in highland areas with global scale climate variations.
Availability of quality controlled climate data
Accessing reliable, quality-controlled meteorological data has been a fundamental challenge in assessing temperature trends in East Africa's highlands, including their relationship to global processes. In particular, although available within national meteorological services, the cost associated with obtaining daily records, is a significant constraint to their use. Full access, quality control and proper use of these data requires the bridging of a number of technical, capacity and institutional gaps .
Although significant progress has been made to improve the quality of public domain, gridded meteorological data, their accuracy relies heavily on the quality and consistency of the observation input data used in their development. Where information from ground stations is sparse, the accuracy is necessarily limited. Given the limited access to station observations, many studies of temperature trends in Kericho have based their analyses on different versions of a gridded data set developed by the Climate Research Unit (CRU) of the University of East Anglia (UEA) [14, 19, 32]. The UEA CRU data are monthly mean values of surface climate variables for global land areas developed primarily for regional or global applications . The trend analysis results from these various studies (and versions of the dataset) to date have been inconclusive.
It is proposed that the lack of agreement regarding temperature trends over Kericho has been largely the result of the inappropriate use of spatially interpolated data over Africa obtained from UEA CRU. Here the quality of these data are examined and compared with analyses of trends using gold standard meteorological observations at Kericho. Three recent versions of the UEA CRU data are used, the most recent one incorporating an additional 4 years of observations; all are examined for temporal consistency. The local, observed climate variations at Kericho are then compared with larger climate variations, including tropical SST and El Niño-Southern Oscillation (ENSO).
Construction of monthly time series and homogeneity tests
Digitized time series of daily maximum (Tx) and minimum (Tn) temperature and accumulated Rainfall (R), constructed by the Kenya Meteorological Department (KMD) for their observing station at Kericho (33.35E, 0.36S), are the primary meteorological data used in the analysis. The available data cover the period 1 January 1979 to 31 December 2009 for Tx and Tn and 1 January 1980 to 31 December 2009 for R. Quality control of these data, performed at the Institute of Meteorological Training and Research (IMTR), Nairobi, included: updating of missing records, verification for new records, and range and consistency checking.
The first step in the analysis was to compute monthly mean values from the daily data for each of the three variables. Monthly means were not computed for months having more than one missing daily observation, resulting in monthly time series for Tx, Tn and P that were 97.1%, 99.2%, and 99.2% complete, respectively, for the analysis period. Following this step, the monthly time series of all three variables underwent a homogeneity test to identify any break-points (jumps) in the time series which can arise from non-climate factors such as changes in station location, instrumentation and instrument exposure. Spurious break-points can substantially influence the analysis of temporal trends in time series and their removal requires making adjustments to the original time series once they have been identified [33, 34]. Ideally, the dates of any changes in, for example, station location and instrumentation, would have been recorded in order to facilitate comparisons with the dates of any identified break-points. Here, when statistically significant break-points were identified without such documentation, it was assumed they did not represent true fluctuations in climate and were removed through adjustment of the original time series.
The open source software tool RHtestV3 (written in the R programming language and available at http://cccma.seos.uvic.ca/ETCCDMI/software.shtml) was used to check for break-points in the time series analyzed. This software tool was developed at the Climate Research Division at Environment Canada and is among the set of homogeneity testing methods recognized by the World Meteorological Organization for quality control of climatic time series . Essentially, the software tests the null hypothesis that there are no changes in the mean of a time series (which may, or may not, contain a secular trend) against the alternative hypothesis that such a shift in the mean does occur at a time k. There may be more than one break-point in the time series.
where t is time, N the total number of observations, μ the mean, α and β constants, and the shift at time k equal to μ2 - μ1 . The RHtestV3 software allows prescribing the confidence level for identifying break-points; here that was set to p < 0.01. Once the adjusted Tx and Tn time series were generated they were used to compute a monthly mean temperature (Tmean) as a third temperature variable where Tmean = (Tx+Tn)/2. The mean temperature was computed as the average of the daily minimum and maximum temperature specifically to enable comparison with previous studies. However readers should be aware that the calculation of the 'true' mean temperature is more complex; ideally requiring 24 hourly measurements . The quality controlled Tx Tn and Tmean monthly data for Kericho is made available (see Additional file 1) with agreement of the Kenya Meteorological Department.
Trend Analysis of Tx, Tn and Tmean
The adjusted, monthly time series of Tx, Tn and Tmean were examined to see if there have been any statistically significant temporal trends over the period 1977-2009. Linear trends were identified via a least-squares regression analysis with statistical significance assessed using a two-tailed t-test. Since trends need not be linear, the nonparametric Mann-Kendall (MK) test was also used to identify any statistically significant trends without any a priori assumption of their form . The MK test also reduces the effect of outliers on the trend analysis.
Kericho temperatures and their relation the global tropics
In the above expressions ρAi and ρBi are the ith month lag autocorrelation of variables A and B, with n set to 18 months (or the number of months when the autocorrelation remained positive if less than 18 months). N obs is the number of monthly values in the full time series and N dof is the number of degrees of freedom used to conduct the t-test.
Highland temperature variations based on gridded temperature analyses
The gridded data in this analysis are of 0.5 degree latitude/longitude resolution, derived by spatial interpolation of ground station observations . The data set includes the variables of maximum, minimum and mean temperature, rainfall, diurnal temperature range and vapor pressure. When direct observations of these variables are not available they are derived from other, observed variables. Time series from Versions CRU05, 2.1 and interim Version 3.0 of the UEA CRU data are plotted and investigated for consistency in variability and in trend in Tx and Tn relative to observations from Kericho.
Assessment of Kericho meteorological observation station data
Results of trend analysis and significance testing of in Tx, Tn and Tmean*
Linear trend °C/decade
Confidence Level of Trend
p < 0.01
p < 0.01
p < 0.01
0.10 - 0.37
0.11 - 0.31
0.14 - 0.29
Temporal correlations between climate variables*
Tropical SST, Tropical Land Temperature
Tropical Land Temperature, Kericho Tmean
Tropical SST, Kericho Tmean
Tropical Land Temperature, Kericho Tn
Tropical Land Temperature, Kericho Tx
Tropical SST, Kericho Tn
Tropical SST, Kericho Tx
Assessment of the UEA CRU data
Initial analyses of the Kericho tea estate malaria data with contemporaneous local monthly averaged meteorological observations recorded at the centre of the estates by the Tea Research Foundation, found no evidence of any significant changes in climate during the period of epidemiological transition [18, 42]. The authors concluded that drug resistance, rather than climate change, was the likely key driver of the increasing trend. Furthermore, Fourier analysis of an extended version of the same data sets was used to infer that the observed inter-epidemic period of malaria incidence at Kericho was best explained as function of intrinsic population dynamics rather than cyclical changes in temperature or rainfall[19, 43]. Including the Southern Oscillation Index (the atmospheric component of ENSO) in the analysis did not change the result. Subsequent studies by the same group of authors, reinforced by the use of data extracted from the UEA CRU gridded data set (see above) provided additional support for this perspective[14, 19, 20, 27, 28] and led these authors to conclude that 'analyses of malaria time-series at sites such as Kericho have shown that malaria incidence has increased in the absence of co-varying changes in climate.'. It is proposed that the extensive use of the UEA CRU data set to examine temperature trends in Kericho and surrounding areas, despite the data providers indication of the unsuitability of the data for this purpose [29, 44] has been central to controversy with regard to evidence for warming or otherwise in the East African highlands. Some authors have misconstrued these results to be based on high quality meteorological records and as a result have concluded that climate cannot be considered an important factor in changes in the frequency or intensity of malaria epidemics in the Kenyan highlands. For example, in a 2008 review article on malaria and global warming in this journal, Reiter , referred to the Kericho analysis and, citing Hay et al , wrote "Moreover, a set of well-maintained meteorological records shows no significant change in temperature over recent decades." Pascual and colleagues  reported that they had utilized an updated version of the UEA CRU dataset (interim v3.0) to perform an analysis of trends in temperature at the same grid point locations as in the 2002 Hay et al study  and concluded that there were, in fact, statistically significant positive trends at four locations around Kericho. In a more recent publication, Alonso et al  drove a malaria model with a mean daily temperature data series created by dovetailing the records from two meteorological stations within the Kericho tea estates, together with adjustments for altitude based on mean temperature data from a number of stations in Kenya spanning a broader altitude range. Predicted malaria cases were found to exhibit a highly nonlinear response to warming, with a significant increase from the 1970s to the 1990s, although typical epidemic sizes are below those observed. The authors proposed that a more detailed analysis using daily minimum and maximum temperature was warranted. Although unable to access a single time series of meteorological data, their result (0.3°C warming over 30 years) is only just outside of the confidence interval (0.14 - 0.29) for mean temperature obtained in the current analysis as presented in Table 1.
A detailed comparison of the UEA CRU data set used in these studies (Figure 9) reinforces the perspective of the data providers regarding the lack of suitability of this data set for local analysis . This study highlights the importance of knowing the origins of the climate data, the need for quality control as well as an understanding of the mechanisms by which local climate variability and change relate to larger climatic processes, before inferences are made. Such an approach has been critical in the evaluation of Kericho temperature trends presented here. Using gold standard meteorological information, the current study has shown clear evidence of an increasing warming trend in Tn, Tx and Tmean over Kericho between 1979-2009. These results are partially supported by the recent study by Christy and colleagues; however the present study differs in that a significant positive trend was also found in Tx. Furthermore the highly significant (p < 0.01) positive trends (0.21°C, 0.24°C and 0.21°C per decade for Tmean, Tx and Tn respectively) presented here were found after careful correction of the time series, a process which will have reduced rather than exaggerated any previously reported trends in the Kericho data set.
While it is possible that local factors may be contributing to the upward trend in Tx at Kericho (as suggested for Tn in the multi-station analysis of Christy et al ), temperature variations at Kericho in this study were found to be consistent with those in the global tropics (Figures 4 and 5, and Table 2). This indicates that both local and large-scale climate variations are likely at work at Kericho and gives additional validity to the finding of an increasing trend in temperatures at this site.
Climate is one of many potential drivers of malaria that are measured outside of the health sector. What makes climate measurements unique is the fact that they are recorded according to globally recognized standards at defined, regular time intervals and can be systematically analyzed at the local and global scale allowing comparison across geographical sites and over extended time periods. The fundamental characteristics of climate including its climatology, seasonality, diurnal rhythm and potential predictability at multiple timescales (weather, seasonal, decadal and climate change), make it ideal as an additional layer of information for the health sector for application in malaria vulnerability assessment, surveillance and forecasting. It is argued here that there is potential benefit of the additional information provided by climate data in understanding the epidemiological characteristics of malaria within the changing global environment. A prerequisite is relevant quality controlled climate and epidemiological data.
This study demonstrates that a lack of access to quality controlled meteorological data has substantially undermined the quality of the climate and malaria analyses undertaken to-date. The positive trends in all three climate variables (Tmean, Tx and Tn) suggest that climatic factors in Kericho should not be dismissed as a potentially significant driver of variability and trends in malaria incidence on the erroneous assumption that warming has not taken place. The significance of the warming trend observed in this study ( > 0.2°C per decade) to changes in malaria transmission potential has yet to be assessed but indications from other studies suggest that in this region, even a modest change in temperature can have a significant effect on transmission [11, 45]. The missing links in addressing knowledge gaps of the role of local climate processes in disease transmission are high quality data coupled with the skilled interpretation of epidemiologists working in collaboration with climate scientists.
New opportunities exist for improved management of climate-related health risks including malaria. To make use of these opportunities the health community must establish collaborative partnerships with climate/environment research and service communities, work to overcome policy and institutional barriers and identify opportunities for the effective use of climate information in health policy and decision-making. Likewise the meteorological and climate community need to take steps to provide effective, policy relevant climate information and services for the health sector. The Global Framework for Climate Services, the key outcome of the World Climate Conference III, in September 2009  along with several African initiatives including the Climate for Development in Africa (ClimDev-Africa) Programme [47, 48] are seeking to address this call for readily accessible meteorological data to support development initiatives. Services should be tailored to appropriate demand and the health community should therefore make their needs heard .
This paper is funded in part by a grant/cooperative agreement from the National Oceanic and Atmospheric Administration, NA10OAR4310210; and in part by a grant from Google.org. The views expressed herein are those of the authors alone and may not reflect the views of either NOAA, or any of its sub-agencies, nor Google.org. We also thank Derek Willis of IRI/CRED/Columbia University for his valuable comments on the manuscript.
- Craig MH, Snow RW, Le Sueur D: A climate based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today. 1999, 15: 105-111. 10.1016/S0169-4758(99)01396-4.View ArticlePubMedGoogle Scholar
- Hay SI, Snow RW, Rogers DL: Predicting malaria seasons in Kenya using multitemporal meteorological satellite sensor data. Trans R Soc Trop Med Hyg. 1998, 92: 12-20. 10.1016/S0035-9203(98)90936-1.View ArticlePubMedGoogle Scholar
- Grover-Kopec E, Kawano M, W Klaver RW, Blumenthal B, Ceccato P, Connor SJ: An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malar J. 2005, 4: 6-10.1186/1475-2875-4-6.PubMed CentralView ArticlePubMedGoogle Scholar
- Thomson MC, Mason SJ, Phindela T, Connor SJ: Use of rainfall and Sea Surface Temperature monitoring for Malaria Early Warning in Botswana. Am J Trop Med Hyg. 2005, 73: 214-221.PubMedGoogle Scholar
- Mouchet J, Manguin S: Global warming and malaria expansion. Annales de la Société Entomologique de France. 1999, 35: 549-555.Google Scholar
- WHO: World Malaria Report 2009. 2009, Geneva, Switzerland: World Health OrganizationGoogle Scholar
- Steketee RW, Campbell CC: Impact of national malaria control scale-up programmes in Africa: magnitude and attribution of effects. Malar J. 2010, 9: 299-10.1186/1475-2875-9-299.PubMed CentralView ArticlePubMedGoogle Scholar
- McKenzie FE: Why model malaria?. Parasitol Today. 2000, 16: 511-516. 10.1016/S0169-4758(00)01789-0.View ArticlePubMedGoogle Scholar
- Martens P, Kovat RS, Nijhof S, de Vries P, Livermore MTJ, Bradley DJ, Cox J, McMichael AJ: Climate change and future populations at risk of malaria. Global Environmental Change. 1999, 9: S89-S107. 10.1016/S0959-3780(99)00020-5.View ArticleGoogle Scholar
- Lafferty KD: The ecology of climate change and infectious diseases. Ecology. 2009, 90: 888-900. 10.1890/08-0079.1.View ArticlePubMedGoogle Scholar
- Paaijmans KP, Read AF, Thomas MB: Understanding the link between malaria risk and climate. Proc Natl Acad Sci USA. 2009, 106: 13844-13849. 10.1073/pnas.0903423106.PubMed CentralView ArticlePubMedGoogle Scholar
- Cox J, Craig M, Le Sueur D, Sharp B: Mapping malaria risk in the highlands of Africa. MARA/HIMAL Technical Report. 1999Google Scholar
- Lindsay SW, Martens WJM: Malaria in the African highlands: past, present and future. Bull World Health Organ. 1998, 76: 33-45.PubMed CentralPubMedGoogle Scholar
- Hay SI, Rogers DJ, Randolph SE, Stern DI, Cox J, Shanks GD, Snow RW: Hot topic or hot air? Climate change and malaria resurgence in East African highlands. Trends Parasitol. 2002, 18: 530-534. 10.1016/S1471-4922(02)02374-7.PubMed CentralView ArticlePubMedGoogle Scholar
- Pascual M, Cazelles B, Bouma MJ, Chaves LF, Koelle K: Shifting patterns: malaria dynamics and rainfall variability in an African highland. Proc R Soc B. 2008, 275: 123-132. 10.1098/rspb.2007.1068.PubMed CentralView ArticlePubMedGoogle Scholar
- Reiter P: Global warming and malaria: knowing the horse before hitching the cart. Malar J. 2008, 7: S1-10.1186/1475-2875-7-S1-S3.View ArticleGoogle Scholar
- Rogers DJ, Randolph SE: The global spread of malaria in a future, warmer world. Science. 2000, 289: 1763-1966. 10.1126/science.289.5478.391b.View ArticlePubMedGoogle Scholar
- Malakooti MA, Biomndo K, Shanks GD: Re-emergence of epidemic malaria in the highlands of western Kenya. Emerg Infect Dis. 1998, 4: 671-676. 10.3201/eid0404.980422.PubMed CentralView ArticlePubMedGoogle Scholar
- Hay SI, Cox J, Rogers DJ, Randolph SE, Stern DI, Shanks GD, Myers MF, Snow RW: Climate change and the resurgence of malaria in the East African highlands. Nature. 2002, 415: 905-909. 10.1038/415905a.PubMed CentralView ArticlePubMedGoogle Scholar
- Shanks GD, Hay SI, Stern DI, Biomndo K, Snow RW: Meteorologic influences on Plasmodium falciparum malaria in the highland tea estates of Kericho, Western Kenya. Emerg Infect Dis. 2002, 8: 1404-1408.PubMed CentralView ArticlePubMedGoogle Scholar
- Okiro EA, Alegana VA, Noor AM, Snow RW: Changing malaria intervention coverage, transmission and hospitalization in Kenya. Malar J. 2010, 9: 285-10.1186/1475-2875-9-285.PubMed CentralView ArticlePubMedGoogle Scholar
- Githeko AK, Ndegwa W: Predicting malaria epidemics in the Kenyan highlands using climate data: a tool for decision makers. Global change & Human Health, Kluwer Academic Publishers. 2001, 2 (1): 54-63.View ArticleGoogle Scholar
- Hay SI: The inter-sectoral response to the 2002 malaria outbreak in the highlands of western Kenya. Report of an individual consultancy to the UNICEF, Kenya Country Office (KCO) Special Service Agreement 059 (SSA/KENA/2002/00001290-0) 01 -16 August 2002.
- Nyarango PM, Gebremeskel T, Mebrahtu G, Mufunda J, Abdulmumini U, Ogbamariam A, Kosia A, Gebremichael A, Gunawardena D, Ghebrat Y, Okbaldet Y: A steep decline of malaria morbidity and mortality trends in Eritrea between 2000 and 2004: the effect of combination of control methods. Malar J. 2006, 5: 33-10.1186/1475-2875-5-33.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhou G, Minakawa N, Githeko AK, Yan G: Association between climate variability and malaria epidemics in the East African highlands. Proc Natl Acad Sci USA. 2004, 101: 2375-2380. 10.1073/pnas.0308714100.PubMed CentralView ArticlePubMedGoogle Scholar
- Wandiga SO, Opondo M, Olago D, Githeko A, Githui F, Marshall M, Downs T, Opere A, Yanda PZ, Kangalawe R, Kabumbuli R, Kirumira E, Kathuri J, Apindi E, Olaka L, Ogallo L, Mugambi P, Sigalla R, Nanyunja R, Baguma T, Achola P: Vulnerability to climate induced highland malaria in East Africa. AIACC Working Paper No 25. Washington DC: Assessments of Impacts and Adaptations to Climate Change (AIACC) in Multiple Regions and Sectors; 2006.Google Scholar
- Rogers DJ, Randolph SE, Snow RW, Hay SI: Satellite imagery in the study and forecast of malaria. Nature. 2002, 415: 710-715. 10.1038/415710a.PubMed CentralView ArticlePubMedGoogle Scholar
- Shanks GD, Hay SI, Omumbo JA, Snow RW: Malaria in Kenya's western highlands. Emerg Infect Dis. 2005, 11: 1425-l1432.PubMed CentralView ArticlePubMedGoogle Scholar
- New M, Hulme M, Jones P: Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate. Journal of Climate. 2000, 13: 2217-2238. 10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2.View ArticleGoogle Scholar
- Christy JR, Norris WB, McNider RT: Surface Temperature Variations in East Africa and Possible Causes. Journal of Climate. 2009, 22: 3342-3356. 10.1175/2008JCLI2726.1.View ArticleGoogle Scholar
- IRI: A Gap Analysis for the Implementation of the Global Climate Observing System Programme in Africa. Palisades, NY: International Research Institute for Climte and Society. 2006, 1-52.Google Scholar
- Pascual M, Ahumada JA, Chaves LF, Rodo X, Bouma M: Malaria resurgence in the East African Highlands: Temperature trends revisited. Proc Natl Acad Sci USA. 2006, 103: 5829-5834. 10.1073/pnas.0508929103.PubMed CentralView ArticlePubMedGoogle Scholar
- Peterson TC, Easterling DR, Karl TR, Groisman P, Nicholls N, Plummer N, Torok S, Auer I, Boehm R, Gullett D, Vincent L, Heino R, Tuomenvirta H, Mestre O, Szentimrey T, Salinger J, Førland EJ, Hanssen-Bauer I, Alexandersson H, Jones P, Parker D: Homogeneity adjustments of in situ atmospheric climate data. International Journal Climatology. 1998, 18: 1493-1517. 10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T.View ArticleGoogle Scholar
- Reeves J, Chen J, Wang XL, Lund R, Lu Q: A review and comparison of changepoint detection techniques for climate data. J Applied Meteorology Climatology. 2007, 46: 900-915. 10.1175/JAM2493.1.View ArticleGoogle Scholar
- WMO: Climate Data and Monitoring. World Meteorological Organization. 2009Google Scholar
- Wang XL: Penalized maximal F-test for detecting undocumented mean-shifts without trend-change. J Atmospheric Oceanographic Technology. 2008, 25 (3): 368-384. 10.1175/2007JTECHA982.1.View ArticleGoogle Scholar
- Karl TR, Williams CN, Young PJ, Wendland WM: A model to estimate the time of observation bias associated with monthly mean maximum, minimum and mean temperatures for the United States. J Climate Appl Meteor. 1986, 25: 161-179. 10.1175/1520-0450(1986)025<0161:NHSATV>2.0.CO;2.View ArticleGoogle Scholar
- Sneyers R: On the statistical analysis of series of observations. World Meteorological Organization. 1990, 192-Google Scholar
- Smith TM, Reynolds RW, Peterson TC, Lawrimore J: Improvements to NOAA's historical merged land-ocean surface temperature analysis (1880-2006). J Climate. 2008, 21: 2283-2296. 10.1175/2007JCLI2100.1.View ArticleGoogle Scholar
- Brohan P, Kennedy JJ, Harris I, Tett SFB, Jones PD: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. Journal of Geophysical Research. 2006, 111 (D12106): 1-12.Google Scholar
- Livezey RE, Chen WY: Statistical field significance and its determination by Monte Carlo techniques. Monthly Weather Review. 1983, 111: 46-59. 10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2.View ArticleGoogle Scholar
- Shanks GD, Biomndo K, Hay SI, Snow RW: Changing patterns of clinical malaria since 1965 among a tea estate population located in the Kenyan highlands. Trans R Soc Trop Med Hyg. 2000, 94: 253-255. 10.1016/S0035-9203(00)90310-9.PubMed CentralView ArticlePubMedGoogle Scholar
- Hay SI, Myers MF, Burke DS, Vaughn DW, Endy T, Ananda N, Shanks GD, Snow RW, Rogers DJ: Etiology of interepidemic periods of mosquito-borne disease. Proc Natl Acad Sci USA. 2000, 97: 9335-9339. 10.1073/pnas.97.16.9335.PubMed CentralView ArticlePubMedGoogle Scholar
- Patz JA, Hulme M, Rosenzweig C, Mitchell TD, Goldberg RA, Githeko AK, Lele S, McMichael AJ, Le Sueur D: Climate change (Communication arising): Regional warming and malaria resurgence. Nature. 2002, 420: 627-628. 10.1038/420627a.View ArticlePubMedGoogle Scholar
- Alonso D, Bouma MJ, Pascual M: Epidemic malaria and warmer temperatures in recent decades in an East African highland. Proc R Soc B. 2010Google Scholar
- Anon: Conference Statement: Summary of the Expert Segment. Procedia Environmental Sciences. 2010, 1: 4-26. 10.1016/j.proenv.2010.09.003.View ArticleGoogle Scholar
- OSS: The Great Green Wall Initiative for the Sahara and the Sahel: OSS. CEN-SAD. Tunis: Sahara and Sahel Observatory. 2008, 1-44.Google Scholar
- GCOS: Climate Information for Development Needs an action plan for Africa: report and implementation strategy. 2006, 1-99.Google Scholar
- Connor SJ, Omumbo J, Green C, DaSilva J, Mantilla G, Delacollette C, Hales S, Rogers D, Thomson M: Health and Climate - Needs. Procedia Environmental Sciences. 2010, 1: 27-36. 10.1016/j.proenv.2010.09.004.View ArticleGoogle Scholar
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