<!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:Compatibility> <w:BreakWrappedTables/> <w:SnapToGridInCell/> <w:WrapTextWithPunct/> <w:UseAsianBreakRules/> </w:Compatibility> <w:BrowserLevel>MicrosoftInternetExplorer4</w:BrowserLevel> </w:WordDocument> </xml><![endif]--><!--[if !mso]><object classid="clsid:38481807-CA0E-42D2-BF39-B33AF135CC4D" id=ieooui></object> <style> st1\:*{behavior:url(#ieooui) } </style> <![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} </style> <![endif]--> <!--[if gte mso 9]><xml> <w:WordDocument> <w:View>Normal</w:View> <w:Zoom>0</w:Zoom> <w:Compatibility> <w:BreakWrappedTables/> <w:SnapToGridInCell/> <w:WrapTextWithPunct/> <w:UseAsianBreakRules/> </w:Compatibility> <w:BrowserLevel>MicrosoftInternetExplorer4</w:BrowserLevel> </w:WordDocument> </xml><![endif]--><!--[if !mso]><object classid="clsid:38481807-CA0E-42D2-BF39-B33AF135CC4D" id=ieooui></object> <style> st1\:*{behavior:url(#ieooui) } </style> <![endif]--><!--[if gte mso 10]> <style> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman";} </style> <![endif]--> Cholera is one of the deadliest diseases in Africa. Cholera outbreaks reappeared in the area of the African Rift in the late 70’s while strong signals of climate changes were noted (Lipp et al.,2002;  O’Reilly et al., 2003). It is suspected that climate change has triggered cholera epidemics through a change of lake condition, biodiversity host organisms. Fish diversity also shifted end of the 70’s at Lake Tanganyika (Plisnier et al, 2007). The African Rift is pointed as an area of propagation-source of Cholera (Bompangue et al., 2008a). A link between cholera, phytoplankton blooms and copepods zooplankton was demonstrated in Asia by Colwell et al. (1996). Great lakes such as Lake Tanganyika are highly suspected of playing a role as a reservoir of the cholera bacteria while human infection and movement propagate the disease inland.

Environmental and epidemiological data in the Rift Lake area were collected independently between 2002 and 2006 by CLIMFISH, a BELSPO project (MODIS satellite data) and by Bompangue et al. (2008) (epidemiologic data). Cross analyses unexpectedly highlighted simultaneous phytoplankton blooms and cholera outbreaks, a relation only shown in the ocean so far (Colwell et al.,1996). The comparable periodicities of blooms and cholera cases (Fig. 1) support the “lake reservoir hypothesis” and climate induced changes that we propose to test.

On this background the CHOLTIC project contributes to the bullet 3-objective of the SSD6-Belspo-call ‘Africa’ by the study of potential impact of climate changes  and ENSO  (El Nino Southern Oscillation)   on Lake Tanganyika ecosystem functioning and biodiversity in relation to the spread of a major infectious disease. The study could become a model for similar investigations within the Great Lakes area.

The CHOLTIC project supports the major Millennium Development Goals and Targets: Goal 4:  “Reduce Child mortality’ Target 1 Reduce by two thirds, between 1990 and 2015, the under- five mortality rate”. Goal 7 “Ensure environmental sustainability”, Target 3 “Halve, by 2015, the proportion of the population without sustainable access to safe drinking water”.

We aim to detail environmental conditions triggering cholera outbreaks at Lake Tanganyika in a interdisciplinary study:

(1) In situ monitoring: Previous data were acquired independently. For the CHOLTIC project, an integrated 3 years simultaneous field monitoring of meteorology, limnology, phytoplankton, zooplankton, fish abundance, fishermen and traders movements is necessary. Epidemiological statistics of cholera cases and fatalities added to a bacteriological monitoring (human, lake water and plankton) will be analysed in 3 stations. Standardized procedure including comparable step time will be used.

(2) Remote sensing. Previous CLIMLAKE and CLIMFISH projects showed that remote sensing is an efficient tool to get spatial and synoptic limnological information at Lake Tanganyika. We will produce time series of daily images of chlorophyll a (phytoplankton and indirectly zooplankton proxy), K490 (light attenuation coefficient) from MODIS-TERRA and AQUA and SST (lake surface temperature) from AVHRR satellites using validated procedures (Horion et al, 2010) for the period 2000-2014. Medium resolution satellite images will allow the environmental study of cholera epidemics.

(3).Eco-hydrological modelling will investigate links between climate, upwelling of nutrient rich water and occurrence of planktonic blooms at time of cholera outbreaks.
The interannual variation in local climate and planktonic blooms related to ENSO and IOD will be studied for predictability analysis. This should be useful for early warning detection in the whole region of Lake Tanganyika (including Lake Kivu). Numerical modelling will also be used to study the historical changes in lacustrine conditions in the period before cholera outbreaks (pre-mid 70’s) and post 70’s.

(4) Microbiology: microbiological confirmation will be performed in laboratories of INRB at Kinshasa.

(5) Genetic: characterisation and identification of cholera strains genotypes and mass spectra phenotypes will be carried out at AP-HM (Marseille). This will be helpful to understand the pathways of cholera outbreaks.

(6) Data analysis: The spatio-temporal relationships between environmental factors and health data will be explored by multivariate statistics integrating spatial and time series analysis results. A GIS (geographical information system) will contribute to the integration of interdisciplinary data. Correlation between climate, oceanic, limnological and epidemiologic data will be investigated as they could contribute to early warning of cholera outbreaks.  Teleconnection with ENSO and Indian Ocean conditions are known for hydrology (Bergonzini et al., 2004) and fisheries at Lake Tanganyika (Plisnier, 1997, 2004).

(7) Epidemiological modelling: A feasibility study will be conducted, so as to determine whether rather simple epidemiological models (Koelle and Pascual, 2004; Koelle et al., 2005, Keeling and Rohani, 2008) can be of use for representing past cholera outbreaks and, possibly, predict future ones.   For the first time, a interdisciplinary project involving specialists in each concerned topics is aiming to investigate the environmental factors favouring the reservoir-hosts as well as outbreak and spatial propagation of V. cholerae in a freshwater environment. The possible links with climate change and global indices could help in designing early warning methods.