Ecology of Vibrio cholerae, epidemiology of cholera: What role for climate? Guillaume Constantin de Magny University of Maryland Institute for Advanced Computer Studies magny@umiacs.umd.edu

Outline Ecology of Vibrio cholerae Seasonality and inter-annual variability of cholera outbreaks Comparative study of recent cholera cases dynamics in three locations in Asia in regards to local environmental conditions. Conclusions

The Cholera Today Cholera: Acute intestinal infection causing copious, painless, watery diarrhoea, with vomiting. Contamination by ingestion of food or water contaminated with the bacterium Vibrio cholerae. Few hours to 5 days of incubation period. In 2008, 56 countries affected, for a total of 190,130 cases including 5,143 deaths, Case Fatality Rate of 2.7% (WHO). According to WHO, these numbers represent only 10% of the actual cases. It means closed to 2.0M of cases and over 50,000 deaths. This figure excludes the estimated 500–700 000 cases labeled as acute watery diarrhoea.

Ecology of Cholera Data on the association between plankton, either phytoplankton or zooplankton, and V. cholerae have been gathered over the last 20 years and strongly support the hypothesis of a commensal or symbiotic relationship between chitinous zooplankton and most particularly copepods and this bacterium. Because V. cholerae can be highly concentrated on zooplankton carapaces and in their gut, the risk of ingesting an infectious dose increases when untreated surface waters are used for consumption, leading to the well known human to human transmission. In this way, cholera shares some properties of a vector-borne disease.

How climate affects cholera? Physical & Chemical Characteristics of Water temperature sunlight rainfall pH dissolved oxygen salinity & nutrients Biological Characteristics algae bloom phytoplankton bloom Zooplankton bloom (enters into non-culturable state) V. Cholerae viable but non-culturable state in the water column & attached to particulates. Commensal or symbiotic relationships Fecal shedding returns V. cholerae to the water Transmission of V. cholerae to humans via ingested water containing colonized copepods or other vectors.

Cholera outbreaks frequency Seasonal cycle: - Single peak per year typically found Africa and in Latin America. - Two peaks per year in the surrounding regions of the Bay of Bengal, lands of cholera. Interannual cycle: - In endemic context, cholera appears to wax and wane from 3 to 8 years. Most of these studies were focusing on one geographical area at a time, using historic data. Today, what about cholera outbreak dynamics in three contrasted locations in Asia?

Comparative study of cholera outbreak dynamics in Asia. Cholera cases due to V. cholerae, confirmed by culture. Rainfall data: Global Precipitation Climatology Project Land Surface Temperature: NCEP global reanalysis. NEW DELHI 1999 2008 10 years KOLKATA 1996 2007 12 years MATLAB 1966 1998 33 years 1990 2007 18 years

Methods and Designs Wavelets analysis: Coherency: Phase Analysis: Because epidemiological and climatic time-series are typically noisy, complex and strongly non-stationary, Wavelet time series analysis, that decompose the variance of a time series into different frequencies at different localities in time, is particularly adapted. Wavelets analysis: Decomposing the variance of a time series into different frequencies at different localities in time. The Morlet wavelet function was used. Data treatments: 1. Square root transformation of epidemiological data only. 2. Normalization (mean=0, variance=1) all time series. Coherency: Coherency is similar to some classical correlation but for the oscillating components in a given frequency mode. Phase Analysis: When coherency is significant, the extraction of oscillatory component and phase calculation of the signal in a periodic band is used to quantify synchrony or delay.

New Delhi (1999-2008) Period Time series Wavelet power spectrum Spectral analysis 0.3 – 5 yrs 2 – 5 yrs 0.3 – 2 yrs

Matlab (1966-2007) Period Time series Wavelet power spectrum Spectral analysis 0.3 – 15 yrs 2 – 15 yrs Detection of seasonal cycle composed by 2 peaks, but not so regular along time. Surimposed of a 3 and 5 years cycle during 70 through mid-80s with a drift of the 4-5 year cycle to 6-7 year. 0.3 – 2 yrs

Cholera dynamics Seasonal pattern The bi-modal (6 and 12 months) cycle is observed in Bangladesh, but is not stationary along time. The 6 months cycle is detected but not significant in Kolkata and is absent in New Delhi. Interannual Variability: 3 year cycle for New Delhi, Kolkata (also, but not so clear). 3 and 4 year cycles for Matlab between 1970 and 1985 with a progressive increase from 4 to 6 years for the last two decades.

Coherency and Phases Analyses New Delhi vs. Kolkata (1999-2007) 0.3 - 3 yrs Phases 1 year New Delhi annual outbreak 1 month in advance on Kolkata

Asynchrony of cholera outbreaks Annual cycle 2 Months KOLKATA 1 Month MATLAB 1 Month NEW DELHI What about patterns in environmental parameters?

Coherency and Phases Analyses New Delhi Rainfall vs. Cholera Land Surface Temperature vs. Cholera

Cholera and Environment Annual cycle KOLKATA NEW DELHI MATLAB 1 Month 2 Months LST 1 month lag Rainfall Synchronous LST 2 months lag Rainfall Synchronous LST 3 months lag Rainfall 3 months lag

Conclusion Annual or bi-modal cycles but are not always stationary along time, as for example for very long time series, as shown for Matlab. Asynchrony of cholera outbreaks in India and Bangladesh, But dynamics are highly coherent with environmental parameters at different time lags. First approach will be to issue early warnings by using direct observations of LST to support preventive actions against cholera transmission. The next step will be to use climate seasonal forecast model outputs for rainfall and land surface temperature to setup a more developed early warning system in endemic settings. But what about cholera and climate in Africa…

Point of discussion How to integrate outputs of climate forecasting models at seasonal scale into a mechanistic SIRS model of cholera transmission? How to integrate social sciences in the cholera equations?

Collaborators Dr N.C. Sharma from Maharishi Valmiki Infectious Diseases Hospital, New Delhi, India Dr G.B. Nair, and Dr K. Rajendran from National Institute of Cholera and Enteric Diseases, Kolkata, India Dr M. Yunus from International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh Dr R.B. Sack from Johns Hopkins Bloomberg School of Public Health, Baltimore, USA Dr R. Murtugudde, Dr M. Sapiano, and Jim Beauchamp from Earth Science System Interdisciplinary Center, College Park, USA Dr R.R. Colwell, and Dr A. Huq from University of Maryland, College Park, USA

Thank you for your attention Guillaume Constantin de Magny University of Maryland Institute for Advanced Computer Studies College Park, MD USA magny@umiacs.umd.edu