Cholera Dynamics and El Niño-Southern Oscillation Mercedes Pascual et al. Science 289, 1766 (2000); DOI: 10.1126/science.289.5485.1766 This copy is for your personal, non-commercial use only. If you wish to distribute this article to others, you can order high-quality copies for your colleagues, clients, or customers by clicking here. Permission to republish or repurpose articles or portions of articles can be obtained by following the guidelines here. The following resources related to this article are available online at www.sciencemag.org (this information is current as of June 10, 2013 ): Updated information and services, including high-resolution figures, can be found in the online on June 10, 2013 version of this article at: http://www.sciencemag.org/content/289/5485/1766.full.html This article cites 19 articles, 4 of which can be accessed free: http://www.sciencemag.org/content/289/5485/1766.full.html#ref-list-1 This article has been cited by 143 article(s) on the ISI Web of Science This article has been cited by 39 articles hosted by HighWire Press; see: http://www.sciencemag.org/content/289/5485/1766.full.html#related-urls www.sciencemag.org This article appears in the following subject collections: Medicine, Diseases http://www.sciencemag.org/cgi/collection/medicine Downloaded from Science (print ISSN 0036-8075; online ISSN 1095-9203) is published weekly, except the last week in December, by the American Association for the Advancement of Science, 1200 New York Avenue NW, Washington, DC 20005. Copyright 2000 by the American Association for the Advancement of Science; all rights reserved. The title Science is a registered trademark of AAAS. R EPORTS Although some uncertainties exist about these cli- International Earth Science Information Network at million live within currently malarious areas that are matic responses (23), the medium-high scenario ftp://ftp.ciesin.org/pub/data/Grid_Pop_World. This predicted to become unsuitable by 2050, a net in- ϩ commonly forms the basis of current attempts to record of the 1994 human population density per crease of 23 million, or 0.84% on the 1994 baseline predict the impact of climate change on human square kilometer was turned into a raster image at population data. For the high scenario, the corre- health. Outputs of the medium-high scenario are 1/12¡ spatial resolution and was subsequently used sponding figures are 389 million, 414 million, and a net decrease of 25 million or Ð0.92%, respectively the average of four separate GCM runs and are to estimate the total human population within the (Fig. 1C). given as differences between the modeled present malarious areas shown in Fig. 1, A through C, allow- Photogramm. Eng. and modeled future conditions; the high-scenario 21. Z. K. Ma and R. L. Redmond, ing for the different land areas corresponding to Remote Sensing 61 outputs are scaled versions of the medium-high , 435 (1995). pixels at different latitudes. Land pixels in the malaria Ann. Trop. Med. outputs (23). Following usual practice, the GCM 22. D. J. Rogers, S. I. Hay, M. J. Packer, map imagery were mapped onto their equivalent 6 Parasitol. 90 differences were added to the observed 30-year , 225 (1996). by 6 grid in the population density imagery, from climatic means (after cubic-spline interpolation to 23. M. Hulme and G. J. Jenkins, “Climate change scenar- which population totals were extracted and summed. the same spatial resolution), to generate the pre- ios for the UK: Scientific report” (Climatic Research dicted future climate surfaces that were used in This method estimated a total global population of Unit, Norwich, UK, 1998). the present analysis. 5611 million people in 1994, of which 2727 million 24. We thank the Department for International Develop- ment (grant R6626 to D.J.R.) and the Wellcome Trust 20. The “Gridded Population of the World” unsmoothed lived within the predicted malarious areas of Fig. 1A. (S.E.R.) for financial support and G. B. White and S. I. population density data file created by the Socioeco- Under the medium-high scenario, 357 million people Hay for helpful comments. nomic Data and Applications Center at Columbia Uni- live within areas that are currently malaria-free but versity (Palisades, NY) was obtained from the Center for are predicted to become malarious by 2050, and 334 31 March 2000; accepted 22 June 2000 forcing of disease transmission (9). To investi- Cholera Dynamics and El gate the role of ENSO in light of this alternative explanation, we consider a nonlinear time series Nin˜oÐSouthern Oscillation approach that allows us to compare specific alternative hypotheses for the underlying fac- Mercedes Pascual,1* Xavier Rodo«,2 Stephen P. Ellner,3 tors in cholera dynamics. Because the null Rita Colwell,4 Menno J. Bouma5 (non-ENSO) hypothesis is a nonlinear interac- tion between seasonality and cholera dynamics, Analysis of a monthly 18-year cholera time series from Bangladesh shows that the use of standard linear time series models the temporal variability of cholera exhibits an interannual component at the would strongly bias the comparison in favor of on June 10, 2013 dominant frequency of El Nin˜oÐSouthern Oscillation (ENSO). Results from the ENSO alternative. nonlinear time series analysis support a role for both ENSO and previous disease Lacking information that could be used to levels in the dynamics of cholera. Cholera patterns are linked to the previously specify a valid mechanistic model for the described changes in the atmospheric circulation of south Asia and, consistent ENSO effect, we use time series models that with these changes, to regional temperature anomalies. are both nonlinear and nonparametric and are effective at modeling high-dimensional rela- Cholera remains a major public health problem bacterium that causes the disease, is now known tionships. The dynamics of a variable of in- in many areas of the world, including Bang- to inhabit brackish waters and estuarine systems terest, Nt, a measure of cholera levels, are ladesh and India. A climate influence on cholera (2) and thus might be sensitive to climate pat- modeled with a nonlinear equation of the www.sciencemag.org has long been debated (1), and it has been terns. Here we examine the associations be- form suggested that ENSO, a major source of inter- tween cholera and ENSO and between cholera annual climate variability, drives the interannual and climate at interannual time scales, using an ϭ ͩ Nt ϩ Tp f Nt, Nt Ϫ ␶, Nt Ϫ 2␶, ... Nt Ϫ ͑d Ϫ 1͒␶, variation of the disease (2, 3). For example, 18-year record from Bangladesh where the dis- cholera reappeared in Peru with the El Nin˜o ease is endemic. A nonlinear time series ap- 2␲ 2␲ ͪ ϩ event of 1991–92 and seems to fluctuate season- proach allows us to consider different hypothe- sin t, cos t, E Ϫ ␶ e (1) 12 12 t f t ally in Bangladesh with sea surface temperature ses for the roles of environmental driving vari- Downloaded from (SST) in the Bay of Bengal (2, 4). Recent ables and the inherent disease dynamics in pro- where Tp is a prediction time, f is a nonlinear studies of time series for diarrhoeal diseases in ducing the interannual variability of cholera. function, and Et is the environmental forcing Peruvian children have shown an increase in The disease data consist of a monthly time under consideration (10, 11). The sin and cos cases associated with warmer temperatures and series for cholera incidence between January functions implement a seasonal clock and et the 1997–98 El Nin˜o (5, 6). Vibrio cholerae, the 1980 and March 1998 in Dhaka, Bangladesh represents the IID random noise variables. The ␶ ␶ (Fig. 1A). Over the same time span, the month- parameters , f , and d denote, respectively, 1Center of Marine Biotechnology, University of Mary- ly SST anomaly in a region of the equatorial two different time lags and the number of time land Biotechnology Institute, 701 East Pratt Street, Pacific provides an index for ENSO (Fig. 1B). delay variables. Time delay coordinates are Suite 236, Columbus Center, Baltimore, MD 21202, The cholera time series displays the well- used in the model as surrogates for unobserved USA, and Biology Department, Woods Hole Oceano- graphic Institution, Woods Hole, MA 02543, USA. known seasonal variation of the disease—typ- variables influencing the endogenous dynamics 2Climate Research Group, PCBÐUniversity of Barce- ically described as bimodal, with a small peak of the disease, such as the fraction of suscepti- lona, and Department of Ecology, University of Bar- in the spring and a larger one in the fall or early ble individuals in the population (12, 13). The celona, 08028 Barcelona, Catalunya, Spain. 3Depart- winter—but also shows a multiyear modulation functional form of f is not specified in a rigid ment of Ecology and Evolutionary Biology, Cornell of the seasonal cycles. The interannual variabil- form. Instead, the shape of f is determined by University, Ithaca, NY 14853, USA. 4Center of Marine Biotechnology, University of Maryland Biotechnology ity of cholera cases has a dominant frequency of the data, using an objective model selection Institute, Baltimore, MD 21202, USA, and Department 1/3.7 years, as shown by singular spectrum criterion: generalized cross-validation (GCV) of Cell and Molecular Biology, University of Maryland, analysis (7, 8) (Fig. 2). The same dominant (14). We used the GCV criterion to compare 5 College Park, College Park, MD 20742, USA. Depart- frequency is found for the ENSO time series, models with and without seasonality and with ment of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, University which suggests that climate variability acts as a and without the environmental covariate Et of London, London WC1E 7HT, UK.