Malaria is one of the most severe problems faced by the world even today. Understanding the causative factors such as age,
sex, social factors, environmental variability etc. as well as underlying transmission dynamics of the disease is important for
epidemiological research on malaria and its eradication. Thus, development of suitable modeling approach and
methodology, based on the available data on the incidence of the disease and other related factors is of utmost importance.
In this study, we developed a simple non-linear regression methodology in modeling and forecasting malaria incidence in
Chennai city, India, and predicted future disease incidence with high confidence level. We considered three types of data to
develop the regression methodology: a longer time series data of Slide Positivity Rates (SPR) of malaria; a smaller time series
data (deaths due to Plasmodium vivax) of one year; and spatial data (zonal distribution of P. vivax deaths) for the city along
with the climatic factors, population and previous incidence of the disease. We performed variable selection by simple
correlation study, identification of the initial relationship between variables through non-linear curve fitting and used multistep
methods for induction of variables in the non-linear regression analysis along with applied Gauss-Markov models, and
ANOVA for testing the prediction, validity and constructing the confidence intervals. The results execute the applicability of
our method for different types of data, the autoregressive nature of forecasting, and show high prediction power for both
SPR and P. vivax deaths, where the one-lag SPR values plays an influential role and proves useful for better prediction.
Different climatic factors are identified as playing crucial role on shaping the disease curve. Further, disease incidence at
zonal level and the effect of causative factors on different zonal clusters indicate the pattern of malaria prevalence in the
city. The study also demonstrates that with excellent models of climatic forecasts readily available, using this method one
can predict the disease incidence at long forecasting horizons, with high degree of efficiency and based on such technique
a useful early warning system can be developed region wise or nation wise for disease prevention and control activities.