PLOS ONE promises fair, rigorous peer review, This procedure was repeated 1,000 times for different random choices of parameter combinations. 3 shows the resulting differences in mosquito abundance for two sites, Kabale and Kericho. East African highlands are one of the most populated regions in Africa. For the combined Burundi, Rwanda, and Uganda region, parasite rates in both high and low altitude regions are seen to generally increase until the late 1990s/early 2000s after which a marked decrease is displayed. We have extracted from the CRU data set the number of stations used for these four grid points over time, and there is no abrupt or monotonic change in these numbers across the end of the 1970s through to the 1980s. Because of its adaptive nature, SSA differs from the more classical decomposition of Fourier analysis and is in particular well suited to the analysis of nonstationary data, a property it shares with other more recent methods such as empirical mode decomposition (EMD) and wavelets (37). Consequently many of these semi-immune popu-lations experience severe outbreaks every few years. The results for deseasonalized data are almost identical to those for the unadjusted data for most of the samples (Table 2) showing that the seasonal cycle does not have a strong influence on our findings for the CRU datasets. represents the error. EO is supported by the Wellcome Trust as a Research Training Fellow (# 086166). 2 (see Methods) was either the one with an intercept and linear trend or the one with a linear trend only. The trend corresponds to the first eigenvalue in three of the sites, Gikongoro, Kabale, and Muhanga, and to the third eigenvalue, following the pair corresponding to seasonality, in Kericho (Fig. These findings also explain the results of Pascual et al. (2011) Temperature and Malaria Trends in Highland East Africa. We find that it is differences in data examined, not methods, that is responsible. We revisit this result using the same temperature data, now updated to the present from 1950 to 2002 for four high-altitude sites in East Africa where malaria has become a serious public health problem. According to the United Nations Population Fund, the region has the world's highest population growth rate. 15–17). Climate variability and malaria epidemics in the highlands of East Africa Simon I. Hay1, G. Dennis Shanks1, David I. Stern2, Robert W. Snow3, Sarah E. Randolph1 and David J. Rogers1 1Department of Zoology, University of Oxford, South Parks Road, Oxford, UK, OX1 3PS 2Department of Economics, Rensselaer Polytechnic Institute, Troy, NY 12180, USA … The role of climate change in the exacerbation of the disease has been controversial, and the specific influence of rising temperature (warming) has been highly debated following a previous study reporting no evidence to support a trend in temperature. The population densities in the highlands ranged between 158 persons/km2 in Ethiopia and 410 persons/km2 in Rwanda. Although the temperature time series (original and detrended) exhibit a RD of no more than 3%, the same measure for mosquito abundance grows to 30–40% in Kericho (Fig. SSA. For the unadjusted data there is no significant trend in the 1979-1995 period but there is a highly significant trend for 1979-2009. No, Is the Subject Area "Malarial parasites" applicable to this article? Thank you for your interest in spreading the word on PNAS. From 1986 to 1998, the tea estates of Kericho in western Kenya saw a rise in severe malaria cases from 16 to 120 per 1,000 per year 6 . This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Because only the total number of rainy days is provided by the CRU records, we generated daily presence/absence data with a prescribed autocorrelation structure by using a Markov model with two states (rain and no rain) adapted from Caswell (43) and with the frequency of rainy days varying monthly as given by the CRU data. [6] for the 1950-2002 period. Brief warm periods may facilitate malaria transmission and are therefore able to generate epidemic conditions in immunologically naive human populations living at high altitudes. [19] across which CRU TS 2.1 and 3.0 data were tested: 4°S, 4°N, 28°E, 38°E. The main vector An. However, the new CRU TS 2.1 and CRU TS 3.0 datasets shows a highly significant temperature increase in Kericho in both the 1966-1996 period examined by Chavez and Koenraadt [6], in the shorter 1970-1995 period examined by Hay et al. Yes Reported malaria incidence shows significant increases at these sites in the last decades of the 20th century (ref. Projection of the data onto a set of EOFs allows its reconstruction for selected components, such as those above the noise floor accounting for most of the significant signal. Our estimates of RD for the mosquitoes are slightly conservative, because we did not include other population effects that might amplify the signal even further, such as a positive effect of temperature on fecundity (e.g., ref. The trend components account for 10%, 20.5%, 14.7%, and 18% of the total variance, respectively, for Kericho (a), Gikongoro (c), Muhanga (d), and Kabale (b). Results for the Kericho hospital malaria inpatient series are shown in Table 4. Finally, regardless of its etiology, malaria in Kericho and many other areas of East Africa has decreased during periods of unambiguous warming. Theoretical studies suggest that drug resistance would evolve fastest either under high transmission intensity when encoded by a single gene or at both low and high transmission intensity when two or more genes are involved (33). For more information about PLOS Subject Areas, click A total of 999 randomizations were used for the computation of MC-SSA. MC-SSA estimates the parameters of the AR(1) model from the time series itself by using a maximum-likelihood criterion (38). In all cases, these dominant and subdominant components are significantly different from the null hypothesis considered and lie outside the limits of the 95% confidence interval defined by the values expected for a red noise process with similar decorrelation time τ = −1/log r, where r is the lag-one autocorrelation value. (2002a) (Figure 1). was supported by a University of Michigan fellowship and Fundación Polar, Caracas, Venezuela. The Monte Carlo SSA test (MC-SSA, ref. Then, the disease became endemic. These components are all statistically significant, differing from those expected for red and white noise. Further values can be derived from the formulae in [12]. Discover a faster, simpler path to publishing in a high-quality journal. and malaria in the highlands of Ethiopia Alemayehu Midekisa1, Belay Beyene2, Abere Mihretie3, Estifanos Bayabil3 and Michael C. Wimberly1* ... elevation areas such as the highlands of East Africa The limitation of a coarse spatial resolution in a landscape of rapidly varying altitude has been especially underscored when no evidence for warming is found. 11. The reconstructed time series corresponding to the trend components is plotted in Fig. Contributed reagents/materials/analysis tools: PG AN EO RS GDS CK WT. This work forms part of the output of the Malaria Atlas Project (http://www.map.ox.ac.uk), principally funded by the Wellcome Trust, United Kingdom. The respective reconstructed components are shown in Fig. Because observed mosquito densities in the highlands are so low (23), their abundance is an important variable for malaria transmission in these regions. the population densities ranged between 158 persons/km2 in Ethiopia and 410 persons/km2 in … funestus disappeared. The test compares statistics of simulated red-noise time series with those of the climatic time series (20). [9] shows that again, adding post-1995 data results in a steeper trend. 's mean temperature series. Our results emphasize the importance of considering not just the statistical significance of climate trends but also their biological implications with dynamical models. These factors are likely behind the high rates of poverty among the populations. For red noise, we specifically consider here a first-order autoregressive process, AR(1), given by xt [4] for the period 1950 to 2002, finding positive and significant trend components. [8] applied two time series methods to data for the four locations analyzed by Hay et al. The only process that could depress mosquito population size at higher temperatures is increased desiccation of breeding cavities but, given the magnitude of the temperature signal increase (0.5°C over 20 years), we do not expect this process to have a major impact in vector population dynamics. L Given the different, but formally untested, arguments made against the Hay et al. The inference was made that, as there was no significant warming in these East African locations, global warming was unlikely to be responsible for the increases in malaria admissions seen at facilities in these locations. This measure is akin to a coefficient of variation in the sense of evaluating a deviation relative to the magnitude of the mean. Is the Subject Area "Malaria" applicable to this article? The overall increase in malaria cases is reduced to 23 but in the last four years only July 2009 saw that many cases and the average number of cases over the last four years has been just eight. A small increase in temperature could have led to the recent dramatic rise in malaria in the highlands of East Africa, say researchers. Discussion on various aspects of global warming has been intense but disjointed. L Renewed epidemic activity coincided with the emergence of chloro- quine-resistant Plasmodium falciparummalaria and may have been triggered by the failure of antimalarial drugs. The relative difference (RD) in the output of the model for the two temperature regimes shows that the mosquito dynamics significantly amplify the temperature increase. We also test for trends in the data from the Kericho meteorological station prepared by Omumbo et al. The ssa toolkit freeware software from www.atmos.ucla.edu/tcd/ssa was used for the analysis. Global-scale analyses are fraught with technical difficulties, including problems with separating out changes resulting from destruction of mosquito habitat, insecticide use, and antimalarial drug use from multidecadal trends in climate change. Based on the results of this analysis, two parameters (the slope of larval development and larval survival) were selected for their higher influence on RD and examined systematically for their effect on this quantity. where P is the probability that a larva remains in the larval stage as a function of temperature τ, F is the average daily fertility of a female mosquito, G is the probability that a larva develops into an adult as a function of temperature, and S is the daily survival rate of an adult mosquito as a function of temperature (18). This stage-structured model is a simplified version of a discrete-time system originally built to simulate the dynamics of Southern House mosquitoes under varying temperature and rainfall regimes in Hawaii (18). https://doi.org/10.1371/journal.pone.0024524.g002, https://doi.org/10.1371/journal.pone.0024524.g003. We also tested simpler AR models with independent and identically distributed random noise but found that these models failed to remove the autocorrelation of the residuals. We apply a new robust trend test to mean temperature time series data from three editions of the University of East Anglia's Climatic Research Unit database (CRU TS) for several relevant locations. Yes However, although the malaria cases observed at Kericho, Kenya rose during a period of resurgent epidemics (1994-2002) they have since returned to a low level. 2). Sensitivity analysis shows that the above amplification is not restricted to the mosquito parameters of these particular runs but applies to a large region of parameter space. Funding: This work was supported by the Wellcome Trust. Fig. The CRU TS 2.1 and TS 3.0 data are similar to the data we used previously for the period 1970-1995 but there are differences. The dominant eigenvalues for Gikongoro (c), Kabale (b), and Muhanga (d) and the subdominant eigenvalue for Kericho (a) lie outside this interval and are significantly different from noise. represents a first-order AR(1) component, the sum terms represent the seasonal AR components [SAR(p)] for p ≥ 1, β corresponds to the linear trend in time, and ε A comprehensive sensitivity analysis of the mosquito model has been presented elsewhere (18). Compared to the data we used previously, the CRU TS 3.0 data also show uniformly greater temperature increases and more significant t-statistics, especially for Kericho, though the changes are smaller than in the CRU TS 2.1 data. Further support was provided by a Centennial Fellowship by the James S. McDonnell Foundation in Global and Complex Systems and by joint funding from the National Science Foundation–National Institutes of Health (Ecology of Infectious Diseases Grant EF 0430 120) and the National Oceanic and Atmospheric Administration (Oceans and Health Grant NA 040 AR 460019) (to M.P.). https://doi.org/10.1371/journal.pone.0024524.g004. (41): wrote the paper. The application of SSA in combination with this red-noise test is known as “Monte Carlo SSA” (38). We focus on these two parameters, because they exhibited the strongest effects on RD in the random exploration of parameter space and the associated sensitivity test (see Methods). 1) were extracted from the Climate Research Unit (CRU, Norwich, U.K.) global grid of 0.5° resolution (data set CRU TS 2.1) (19). 's [4] results; (E) the new dataset (CRU TS 3.0) for 1970:1-1995:12 to test if there are any differences in moving from CRU TS 2.1 to CRU TS 3.0; (F) the CRU TS 3.0 series for 1966:1-1995:12 in order to test if adding earlier years influenced the results; (G) the CRU TS 3.0 for 1966:1-2006:6 to test whether the trend in temperature persists or accelerates after 1995; (H) Omumbo et al. 18). A mosquito population model shows that the population dynamics of the vector can dramatically amplify even small changes in the climatic signal. Again, the deseasonalized results are very similar. Whether the incidence of malaria will be (or has been) affected by the warming climate is poorly resolved. The evidence that climate change is the most significant factor in recent malaria resurgences in the highlands of East Africa is at best equivocal, at worst unfounded. Yes We wish to thank Charles Godfray, Dave L. Smith, and several anonymous referees for their comments on the manuscript. In collaboration with the Kenyan meteorological service, Omumbo et al. independent identically distributed normal errors. (7) argued that the use of a global climate data set was inappropriate given its coarse resolution (0.5 × 0.5°) and the large altitudinal variation within these regions. The question arises whether the increase of malaria incidence in the East African highlands since the end of the 1970s is already a manifestation of climate change. (11). In the 1966 to 1995 period we find a 0.76 K temperature increase at Kericho, which is significant at the 1% level. SH is funded by a Senior Research Fellowship from the Wellcome Trust (# 079091) which also supports CK and PG. We find significant trends in the data extracted from newer editions of the database but not in the older version for periods ending in 1996. b is a parameter derived by Bunzel and Vogelsang [12] and:(3)where RSS1 is the sum of squared residuals from (1), and RSS4 is the sum of squared residuals from the following regression:(4). https://doi.org/10.1371/journal.pone.0024524.g001. This has been extensively debated (5–7, 11–14). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For the CRU TS 1.0 data used by Hay et al. Despite this caveat, it is relevant to ask whether there is evidence of warming in these records, and if so, whether the observed magnitude of change is of potential biological significance. Although based on an opportunistic assembly of available community parasite rate surveys, and despite substantial within- and between- year variation not explained by the smoothed trends, these simple summary plots point to a consistent and substantial decline in P. falciparum prevalence since 2002 or earlier across both low and high altitude regions of East Africa. We also thank the Kenyan Meteorological Department for providing the daily rainfall data at a local station in Kericho and especially Peter Mirara for his kind assistance in identifying available data. L.F.C., and X.R. Specifically, the development probability G from larvae to adults is nonlinear with a threshold at low temperature values (below 15°C) that are too cold for development to proceed. Spatial Ecology and Epidemiology Group, Department of Zoology, University of Oxford, Oxford, United Kingdom, Affiliation Our results differ from the previous findings of Hay et al. Temperature is known to influence the mosquito life cycle and in particular the development rate of larvae and adult survival (e.g., refs. Countries for which P. falciparum parasite rate data were analyzed are labeled. NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, Kenya Medical Research Institute – University of Oxford - Wellcome Trust Collaborative Programme, Kenyatta National Hospital Grounds, Nairobi, Kenya, Affiliation
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