The Full Picture

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able free of charge. The products are provided to the user commu-

nity via the IRI Data Library web site.

Users can remotely:

• Visualize a colour composite where the vegetation appears in

green, the bare soils in brown and the water in blue

• Compute long-term series of vegetation indices and compare them

with historical data.

Here, we select a location in North Kenya to display the evolution

of the Normalized Difference Vegetation Index (NDVI). This index

provides information on the vegetation status. The time-series for

years 2005, 2006 and 2007 indicates that November to December

2006 and January 2007 had vegetation index values greater than

those in 2005. The development of above normal vegetation in that

region during November to December 2006 was associated with an

outbreak of Rift Valley Fever.

Similarly, the National Malaria Control Program (NMCP) from the

Ministry of Health based in Asmara, Eritrea, monitors the develop-

ment of vegetation in the northern part of Eritrea along the Red Sea

Coast. Between December 2006 and April 2007, the NMCP noted

an increase in vegetation. Despite good environmental conditions,

the number of Malaria cases in the region remained under control.

However, the development of vegetation also created the perfect

conditions for the development of Desert Locusts. The same infor-

mation provided by IRI was used by the Food and Agriculture

Organization (FAO) of the United Nations and the Ministry of

Agriculture in Asmara to direct survey teams to the area.

Subsequently Desert Locusts were found, and an alert was issued by

FAO to the Ministries of Agriculture in countries potentially affected

by the locusts and to the donor community via the FAO Locust

Watch website.

While monitoring environmental conditions using

remotely sensed data plays an important role in assess-

ing the risk of epidemics, it is important to stress that

socio-economic, demographic and immunological

factors also play an important role in the vulnerability

of communities to the diseases. Early Warning Systems

that integrate all these factors must be implemented to

help health services to manage diseases more effectively.

Integration of remote sensing to

produce early warning systems

The concept of an early warning system for the predic-

tion of epidemics predates satellite technology by many

decades. In fact an early warning system in response to

the massive malaria epidemics that occurred periodi-

cally in pre-independence India was routinely operated

in the Punjab from the early 1920s until the early 1950s.

S. R. Christophers observed that between 1868 and 1908

severe and explosive ‘fever’ epidemics of two-to-three

month duration (August-October) were common in the

region.

In particular he noted that the worst of the

epidemics, which had a periodicity of seven-to-eight

years, coincided with high grain prices and famine.

Christophers saw this ‘human factor’ as an ‘essential

requirement’ which undermined the population and

resulted in high death rates as a result of the epidemics.

Despite this example, much of the interest in early

warning systems for Malaria epidemics was lost during

the Global Malaria Control/Eradication Era. It was not

until the 1990s that renewed interest was stimulated by

a number of epidemics reported from the East African

highlands and a regional epidemic in Southern Africa. At

its launch in 1998 the Roll Back Malaria partnership

recommended the development of a framework to estab-

lish Malaria Early Warning Systems (MEWS) in

sub-Saharan Africa. The MEWS framework sets out a

series of activities which together form the basis of an

integrated monitoring process to identify changes in

epidemic potential and increased risk of transmission.

In the MEWS framework, the first step involves consid-

eration of the dynamic factors which make populations

more vulnerable to severe epidemic outcomes. Drought,

inadequate food security and nutritional/economic status,

increasing levels of drug or insecticide resistance, reduc-

tion in health service provision or a high burden of other

diseases such as HIV/AIDS, compromise any immunity and

increase vulnerability to epidemics. While these factors are

unlikely to give an indication of when an epidemic might

occur, they do provide warning of the severity that can be

expected if one does occur and is not prevented.

The second MEWS monitoring process considers the

forthcoming season’s climate. Will it be a drier, normal,

or wetter season? A number of years of drought may

disrupt populations, lower immunity and make popu-

lations more susceptible when higher, or even normal

rainfall levels occur. The use of climate forecasts to

predict Malaria epidemics has been demonstrated in

Botswana, where climate forecast products predict the

occurrence of Malaria epidemics months in advance.

NDVI analysis

NDVI interface with time series extraction of any particular location of

interest – here, a location in North Kenya. The time-series for years 2005,

2006 and 2007 indicates that November to December 2006 and January

2007 had NDVI values greater than those in 2005. The development of

vegetation in that region was correlated with an outbreak of Rift Valley Fever

a) NDVI from most recent 12-month period

b) Multi-year NDVI comparison

Description

a) 16-day Normalized Difference Vegetation Index (NDVI) for the selected region during the most recent 12

month period.

b) Same as (a) for the current calendar year (thick black line), as indicated by the axis labels. NDVI estimates

from previous years are also shown (blue: -1 yr from present; magenta -2 yr from present).

Data Sources

Normalized Difference Vegetation Index

Data: 16-day NDVI at a resolution of 250m

Data Source: United States Geological Survey, Land Processes Distributed Active Archive Center, Moderate

Resolution Imaging Spectroradiometer (USGS LandDAAC MODIS)

Source: IRI Data Library Map Room

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