of infective bites that an individual may receive in 1 day). McDonald (1977) pioneered the use of vectorial capacity to study the transmission of malaria dynamics in Africa. Today we are left with a modified form of this (Garrett-Jones, 1964).
Using this model McDonald predicted that adulticides rather than larvicides would best reduce malaria transmission in Africa. ITNs were not an option at the time. However, models have flaws due to assumptions that must be made. McDonald’s model for instance, does not account for fluctuations in vector density caused by seasonality effects, survivorship, or age composition.
Ultimately models help our understanding of the factors that may affect a control program and predict what the outcome will be. Analysis of large data sets such as those generated in the Garki Project (Molineaux and Gramiccia, 1980) or the LSDI (Sharp et al., 2007a) will shed more light on the role of models in control. In order to be utilized, models must be incorporated into surveillance systems and not require a mathematical background.
Sustainable malaria control is often jeopardized by insufficient public health resources. By utilizing information in a geographic information system (GIS) it is feasible to rationally target limited resources. Although GIS has been advocated for NMCP (Sharma and Srivastava, 1997) there has been little use of its capabilities for entomological surveillance (Coleman et al., 2006).
GIS has been used most often in research to identify environmental factors responsible for vector and pathogen survival; it has been combined with a malaria