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Future Outlook

Increasing the Predictive Capability of Mapping Technologies for Healthcare in Disaster Management


Health Needs Assessment Map Flowchart

This shows the sequence to be followed in a post-disaster situation by relying on background data from various healthcare data sources to give an understanding of the disease trends existing in the affected region. This is followed-up by reviewing the features seen from using satellite imagery in making damage assessments and then correlating those with pre-disaster healthcare infrastructure data and background disease epidemiology information. The aim is to arrive at an overview of likely disease trends and level of healthcare infrastructure in place to support surviving populations. Following this a more detailed assessment of existing social infrastructure is made to help in determining the most appropriate site to locate a health post. The strength of this evaluation would depend on the availability of ground data. An evaluation of population distribution is then made and correlated with the post-disaster assessment to determine the size, location and if possible, age and gender distribution. All these are then used in developing a map that shows the best options for citing a health post, the size of the population that needs to be served in various locations and the disease risk of these population groups.

As a decision support tool, it will aid the planning of medical relief and also contribute to guiding the allocation of resources and personnel. It will serve as a template for monitoring the progress of healthcare interventions with time and would also be a good tool in looking at the best approach to take in instituting early recovery and long term healthcare rehabilitation.

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Figure 2: Health Needs Assessment Map Flowchart.

Health Impact Assessment

To aid in conducting a post-disaster rapid health impact assessment, a health risk score was developed. An initial preliminary disease assessment score to determine the risk of disease following a disaster was made. Factors considered included the relevant geographical, meteorological and environmental features that have been shown to have a direct bearing on the transmission of disease. These would then be evaluated and weighted statistically to give an estimation of the likelihood of disease occurrence in that population at that time.

For example in looking at malaria, the environmental factors that have been shown to have the strongest correlation to disease transmission are the following mentioned by Kazembe (2007):

  1. Nature of the climate
  2. Soil water holding capacity
  3. Normalized Difference Vegetation Index/ Enhanced Vegetation Index
  4. Precipitation

Other factors include humidity, temperature, altitude, temporary streams, level of urbanization etc.

For diarrhoeal diseases, they are usually influenced by the following factors described by Lama et al (2004):

  1. Tropical or sub-tropical climate
  2. Sea-surface temperature
  3. Phytoplankton bloom
  4. Sea surface height

The common diarrhoeal diseases are cholera, shigellosis (causing dysentery), typhoid and other gastroenteritides.

Another disease that could be a threat in post-disaster situations in susceptible areas is Dengue fever. Some of the satellite-observable factors for the disease are given by Rogers et al (2006)

  1. Urban population
  2. Land surface temperature

Closely related to that is Yellow fever a disease that shares the same vector, Aedes egypti, as Dengue fever. This mosquito specie is responsible, in both diseases, for transmission among humans. This disease however is distributed more in West Africa and South America while it is not seen in Asia for unexplained reasons. Dengue fever on the other hand is seen more in South-East Asia, South America and throughout the Pacific Islands. The geographical factors responsible for Yellow fever are thus:

  1. Greenness as observed using the NDVI or
  2. Humidity

Another interesting disease is the Japanese encephalitis which is transmitted by the Culex mosquito, and is carried by Ardeidae birds and pigs. Its transmission in humans usually follows an increase in Culex mosquito populations. It is common in flooded rice fields (WHO, 2008).v

For a number of these diseases, all that would be required may be just vector control and other methods of preventing vector-host transmission such as bed nets and water treatment. Not to be overlooked however is the role malnutrition could play in the spread of diseases by increasing susceptibility especially in children, the elderly and the immunosuppresed. In disasters where there has been destruction of crops and other agricultural produce, it is important to institute measures to prevent an outbreak of malnutrition which can further compound the spread of diseases such as measles and predispose to tuberculosis infection/reactivation especially in the immunosuppresed. However, with these noted, the environmental factors influencing measles transmission include the seasonal variations such as the 'dry spell', known to be responsible for increased transmission. Of course factors such as overpopulation and poor vaccination coverage also affect the spread.

It should be noted however that the relationship between these factors and disease transmission is not always linear. Also there is usually a time difference between the observation of spatial features and the manifestation of disease in a population, based on the incubation period of the disease. Thus the imagery that would be used in studying an ongoing outbreak of malaria would be that taken two weeks or more earlier, and that for studying Taeniasis-induced liver disease would be that taken about 10 years ago. This demonstrates the temporal consideration when using satellite imagery in disease modelling and prediction. The need always exist to take into account the natural history of the disease in plotting modelled patterns of disease transmission that rely on geospatial data.

An evaluation of these parameters and how they predispose to disease spread will be made and subsequently the risk would be scored and used in assessing the overall disease risk faced by the area. Subsequent to this another risk assessment is made to decide on where best to locate health centres. The higher this second risk assessment s in an area, the greater the likelihood that setting up health centres at that location would fail.

No. Factor Score: Criteria Data Sources
1 Population 1: Less than a third displaced

2: Between a third and two-thirds displaced

3: Greater than two-thirds displaced
Damage/Change Assessment and Population Grid Data
2 Accessibility 1: Motorable roads and intact voice telecommunications at location

2: Roads and/or telecommunications access within 5km of location

3: Inaccessible by roads and/or telecommunications
Damage/Change Assessment and Ground Data (where available)
3 Potable Water 1: Access to clean and potable water source

2: Access to treatable water source

3: Limited or no access to any water source
Damage/Change Assessment, Land Cover, Ground Data
4 Electricity 1: Electricity supply intact

2: Intermittent electricity supply

3: No power source available
Ground Data, DMSP-OLS (0.5km High, 2.75km Low Resolution)
5 Population Shift 1: Area is being resettled post disaster

2: More people leaving than returning

3: Area not being resettled at all
High Resolution Imagery, Ground Data

Table 2: Rapid HIA risk score.

The risk assessment may be graded as shown below with suggested interventions.

≤ 5 = Low risk: Resuscitate existing health centres or set up a basic mobile unit to cater for the population.

6-10 = Medium risk: Evaluate needs further and decide on location, nature and extent of healthcare relief efforts.

11-15 = High risk: Conduct triage and consider possible evacuation of affected location.

The information gained from this assessment would thence influence the location, nature and extent of healthcare intervention to be made. The aim is to use satellite imagery in deriving as much of this information as possible and use that in developing a model that would be used in generating an interactive map. These maps can then be used as decision support in guiding recovery efforts.


In seeking to enhance the value of all stages of the disaster management cycle, it has become desirable to rely on satellite solutions to provide a backbone for most of the activities. Their role in disaster mitigation and preparedness is also a crucial factor in ensuring that hazardous events do not end up becoming disasters. This is best accomplished by training and retaining strong local capacity on ground in every country, especially in the disaster-prone areas. This way the availability of geographic core data and skilled personnel to facilitate relief and recovery efforts would help to speed up the role of satellite solutions in disaster management. The practise of healthcare as a core part of disaster management stands to benefit from the use of the additional capabilities provided by satellite imagery.


Special thanks to Dr Einar Bjorgo of the UNITAR Operational Satellite Applications Programme and Prof Walter Peeters of the International Space University for their kind support in conducting research towards this paper.


Dr. Simon O. M. Adebola
16, Avenue Du Jura
Residence Parc de Ferney Ville
01210, Ferney Voltaire
Tel: + 41 767 263 008


  1. Kazembe L., 2007. Spatial modelling and risk factors of malaria incidence in northern Malawi. Acta Tropica Volume 102, Issue 2, May 2007, Pages 126-137.
  2. Lama J.R. et al,. 2004. Environmental Temperature, Cholera, and Acute Diarrhoea in Adults in Lima, Peru. J HEALTH POPUL NUTR 2004 Dec 22(4):399-403.
  3. Rainer, F., 1999. Environmental Health Impact assessment, Evaluation of a ten-step model. Epidemiology 1999; 10 (5), p. 618-625.
  4. Rogers D.J. et al. 2006. Global distribution of Yellow fever and Dengue. In Global mapping of infectious diseases Global Mapping of Infectious Diseases: Methods, Examples and Emerging Applications. By Simon I. Hay, Inc NetLibrary, Alastair Graham, David J. Rogers, Published by Academic Press, 2006 pp 182-209.
  5. Ruijten, M., 2007. The Dutch experience with Health Impact Assessment of disasters. The European Journal of Public Health, 17 (1), p 5-6.
  6. Russell, D. & Saunders, P., 2007. The UK experience with Health Impact Assessment of disasters. The European Journal of Public Health, 17(1), p 4-5.
  7. WHO Regional Office for Europe/ European Centre for Health Policy, 1999. Gothenburg Consensus paper: Health Impact assessment, Main concepts and Suggested approach. Brussels, Belgium December 1999. ECHP: Brussels.
  8. WHO, 2008. Communicable Disease Risk Assessment and Intervention. Cyclone Nargis, Myanmar. [online] Available at /1/cluster%20approach%20page/Myanmar/Health/Myanmar CycloneNargis090508.pdf. [Accessed on 19 Feb 2009].

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