et al., 2007). In addition to the significant burden of seasonal 
influenza, the persistent threat is that a pandemic causing strain 
may appear due to antigenic shift or reassortment. 
There are three types of influenza virus circulating in the world: 
A, B and C. Type A and B are the most commonly found in 
humans. Type A is further classified into subtypes (e.g., HIN1) 
based on the types of hemagglutinin and neuraminidase on the 
surface of the virus. 
In order to accommodate the ever changing circulating 
influenza strain, vaccine composition recommendations are 
updated twice a year. The recommendations are made by the 
World Health Organization (WHO) through its Global 
Influenza Surveillance Network (GISN). 
2. ENVIRONMENTAL DETERMINANTS 
2.1 Malaria and Dengue 
Many factors are known to contribute to malaria transmission, 
including meteorological and environmental conditions, 
socioeconomic status, military conflicts and natural disasters. 
Among these, meteorological and environmental factors are 
perhaps the most noticeable. For example, malaria transmission 
may increase with the arrival or the end of a rainy season, and 
living near forest or water bodies may pose greater risk of 
mosquito bites and getting malaria. The El Nifio-Southern 
Oscillation (ENSO) is a quasi-periodic climatic cycle that 
occurs every three to seven years across the tropical Pacific 
Ocean, and which causes excessive precipitation or droughts, 
has been shown to promote malaria transmission (Kovats 2003). 
Aside from precipitation, temperature and humidity are also 
important factors. Warmer temperatures hasten larval and 
vector development, and prolong mosquito life span and its 
consequent ability to transmit malaria. Warmer air retains more 
moisture and improves mosquito survivorship, so is higher 
humidity. 
Vegetation has also been linked to malaria as it indicates the 
vector’s breeding sites. For example, Anopheles dirus is a forest 
breeder, and An. maculates and An. sawadwongpori are rice 
field breeders. The Normalized Difference Vegetation Index 
(NDVI) (Tucker 1979) is one of the indices for vegetation 
condition. It is defined as the difference between the responses 
from the infrared and the red bands normalized by their mean. 
In modelling infectious diseases, NDVI is used most often to 
infer the precipitation which the area received before the 
satellite measurements were taken. The spatial distribution of 
NDVI can also be used to differentiate among urban, peri- 
urban, suburban and rural areas. Such information on the nature 
of the area is useful for malaria prevention and control. 
Like malaria, temperature, humidity and rainfall are the 
important environmental determinants for dengue transmission. 
However, their effects on dengue transmission may be less 
obvious than those for malaria transmission because dengue 
vectors can breed indoor. 
2.2 Influenza 
The spatiotemporal variation of influenza across latitudes 
suggests that climate and environmental factors may have roles 
in influenza transmission and pathogenesis. 
It is well known that influenza transmission in temperate 
climates is seasonal and peaks in the winter months. In the US, 
for example, influenza outbreaks often start as early as October, 
peak in February, and diminish by April or May; thus forming a 
distinct inter-annual oscillation pattern. In the tropics, there are 
significant influenza cases throughout the year, with one or two 
less distinct peak(s) whose timing varies geographically. It has 
been shown (e.g., Viboud et al., 2006) that influenza seasonal 
patterns vary with latitude, forming a traveling wave across the 
globe. Several studies that have explored the global migration 
pattern of influenza show varying travel pattern of influenza 
virus A migration out of the tropics and China; and migration 
between northern and southern hemispheres. Another study in 
Brazil showed that influenza starts in a low-population state 
near the equator during March-April, and travels southward 
towards temperate and more populous states (Alonso et al., 
2007). Temperature, humidity and rainfall are among the 
factors that have been frequently implicated in influenza 
transmission. 
3. REMOTE SENSING MEASUREMENTS 
Satellite measurements of precipitation for estimating disease 
risks are most often derived from the Tropical Rainfall 
Monitoring Mission (TRMM) (Kummerow 1998). TRMM is a 
collaboration between the US and Japan. Japan built the 
precipitation radar (PR) and launched the spacecraft in 1999. 
There are five instruments on board: PR, TRMM microwave 
imager (TMI), Visible and Infrared Scanner (VIIRS), Cloud and 
Earth Radiation Energy Sensor (CERES), and Lighting Image 
Scanner (LIS). The main sensor that measures precipitation is 
TMI. Because the spatial resolution of the TRMM measurement 
is low, sometimes NDVI measured from a medium resolution 
satellite is used to infer the recent rainfall at a higher spatial 
resolution. Such inference is most effective for arid regions 
where little rainfall is received and vegetation growth is 
sensitive to rainfall, but less effective for regions with plenty 
rainfall. Land surface temperature and NDVI are both provided 
by the moderate resolution imaging spectroradiometer 
(MODIS). This sensor has thirty-six bands spanning the visible 
to the near-infrared wavelengths. Both the Terra and Aqua 
observatories are equipped with MODIS. NDVI, however, can 
be computed from any satellite instruments with red and 
infrared channels. However, because of the differences in band 
definitions, instrument characteristics, satellite orbits and 
measuring conditions, NDVI from different sensors must first 
be cross calibrated before they can be compared. In addition to 
the datasets described above, quite a few other satellites also 
provide data for ground cover classification, identification of 
potential larval habitats, and modelling malaria risks. For 
example, the well-known Landsat and SPOT series of satellites 
and the Advanced Spaceborne Thermal Emission and 
Reflection Radiometer (ASTER) and the Advanced Land 
Imager (ALI) (USGS 2009) are some of the multispectral 
sensors used for health monitoring purposes. Microwave 
sensors like Radarsat and the Phased Array L-band Synthetic 
Aperture Radar (PALSAR) are used over areas that are 
obscured frequently by clouds (JAXA 2011). The geoscience 
laser altimeter system (GLAS) is a light detection and ranging 
(LIDAR) sensor that is useful for differentiating vegetation 
types (NASA 2011). For high spatial resolution imagery, 
commercial data from IKONOS, QuickBird, or WorldView can 
be used (GeoEye 2011; DigitalGlobe 2011). 
   
4. Mt 
Many fac 
conditions 
seasonal i 
conditions 
these disea 
evident foi 
is directly 
conditions. 
can be use 
biological 
types of 1 
meteoroloj 
remote sel 
also for : 
sensing-ba 
the impact 
In genera 
approache 
observatio 
In the sta 
with satell 
determine 
mean Squ 
Once the 
situations 
How the [ 
meteorolo 
modelled 
category 
network. 
network t 
Indonesia 
(ARIMA) 
discrete e 
among 23 
4.1 Mal 
Figure 1 
malaria c 
2006). " 
future in 
expected 
environ 
precipita! 
agreemer