Orleans, Texas, among others) (Takken, 2006); 
alterations in the environment of water-borne diseases 
and pathogens (i.e., gastro-intestinal infections, Vibrios 
diseases including Cholera); alterations in the 
atmospheric boundary layer, and transmission of air- 
borne diseases (Meningococcal meningitis, respiratory 
ailments); alterations and regional changes in agricultural 
practices and food security (malnutrition, lack of fresh 
water). 
4. Climate/Environmental Variability and Remote 
Sensing 
Public health indicators and disease surveillance 
activities should be integrated with other in-situ 
observing systems such as Global Climate Observing 
System (GCOS), Global Ocean Observing System 
(GOOS), Global Terrestrial Observing System (GTOS), 
and Global Earth Observation System of Systems 
(GEOSS). Today, the use of satellites allows monitoring 
changes in environmental and climatic parameters at high 
resolution. The example and the detailed and integrated 
conceptual approach (CA) of Tele-epidemiology for the 
Rift Valley Fever (RVF) are given hereafter. 
II. DECISION MAKING and oTRANSCUBE 
CONCEPT’ 
1. Climate Variability and Decision Making 
BE 
  
Climate variability affects regional socio-economical 
cost/loss, reflecting the local balance/imbalance from 
temperature and soil moisture changes, use and abuse of 
fertilizers, pest and pathogens activity. Decision-making 
models used are thus to include 
1. Identification of “normal” impacts of disease (in lives 
and Euros). 
2. Definition of a “climate event” linked to a “health 
event" (epidemics, endemics, pandemics...). 
3. Definition of "increased impacts" and losses (in lives 
and Euros). 
4. Identification of effective methods to mitigate losses. 
5. Definition of costs (Euros) for implementation of the 
above and improve HIS. 
6. Quantification of the savings (in lives and Euros) if a 
"health event" does not occur? 
   
  
  
    
   
   
    
    
    
    
   
    
  
       
    
    
   
  
  
   
   
   
     
   
   
   
   
  
   
    
    
   
   
   
   
  
    
    
  
    
   
   
   
    
    
  
     
   
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
2. The *TransCube Concept 
Some epidemics are emerging or re-emerging all over the 
world. The integrated and trans-disciplinary ‘TransCube 
Concept’ presented for the first time at World Health 
Summit (Berlin, 2010) is being applied here and includes 
the Tele-epidemiology approach (see Figure to the left). 
It consists of three phases, namely the: 
1) Transition phase: Coping with new challenges from 
new and re-emerging diseases; 
2) Translation phase: Innovating beyond benches and 
bedsides by using high res. Technology (including optical 
and radar remote sensing); 
3) Transformation: Re-inventing public health politics, 
managerial and security issues, including new guidelines 
and terms of references (TORs) in a climate variability 
and change context, to be applied to effective early 
warning systems (EWS). 
Forecasting climate impacts on public health requires the 
development of scenario-based risks (ie., hazards + 
vulnerability) ^ assessments which must include 
consequences from demographic, social, political and 
economical disruptions. Integrated ^ mathematical 
modelling must be used (Martens, 2001) requiring all 
components of the chain of causation (each step being a 
link of the TransCube Approach). 
3. Tele-epidemiology 
The new conceptual approach of Tele-epidemiology has 
been put into action (Lafaye, 2006). It is to monitor and 
study the spread of human and animal infectious diseases 
which are closely tied to climate and environmental 
variability evaluated from space. By combining satellite- 
originated data on vegetation — (SPOT-image), 
meteorology (Meteosat, TRMM), oceanography 
(Topex/Poseidon; ENVISAT, JASON) with hydrology 
data (distribution of water bodies), with clinical data from 
humans and animals, entomological data, predictive 
mathematical models can be constructed. 
III. THE RIFT VALLEY FEVER (RVF) CASE 
The various components of the above approach have 
been thoroughly tested with the RVF in the Ferlo 
(Senegal). This successful approach has lead the 
Senegalese government to provide funding, and extend 
the approach in places where populations and cattle are 
exposed (Vignolles et al., 2009). 
The Ferlo region in Senegal, became prone to RVF in the 
late 1980s with the appearance of infected 
vector/mosquitoes from the Aedes vexans and Culex 
poicilipes species (Lacaux et al., 2007; Tourre et al., 
2008) near temporary ponds. RVF epizootic outbreaks in 
livestock cause spontaneous abortions and perinatal 
mortality. So far, human-related disease symptoms may 
include severe forms of encephalitis and hemorrhagic 
fevers. 
The ultimate goal has been to use specific Geographical 
Information System (GIS) tools (Tourre et al., 2007) and 
high resolution remote sensing images/data to detect the 
"beating" of the breeding ponds and evaluate areas at 
   
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