Malaria has existed in India since antiquity. Different periods of elimination and control policies have been adopted by the government for tackling the disease. Malaria parasite was discovered in India by Sir Ronald Ross who also developed the simplest mathematical model in early 1900. Malaria modelling has since come through many variations that incorporated various intrinsic and extrinsic/environmental factors to describe the disease progression in population. Collection of disease incidence and prevalence data, however, has been quite variable with both governmental and non-governmental agencies independently collecting data at different space and time scales. In this talk I will describe our work on modelling malaria prevalence using three different approaches. For monthly prevalence data, I will discuss (i) a regression-based statistical model based on a specific data-set, and (ii) a general mathematical model that fits the same data. For more coarse-grained temporal (yearly) data, I will show graphical analysis that uncovers some useful information from the mass of data tables. This presentation aims to highlight the suitability of multiple modelling methods for disease prevalence from variable quality data.