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Multi-step polynomial regression method to model and forecast malaria incidence.

Chatterjee C, Sarkar RR - PLoS ONE (2009)

Bottom Line: We performed variable selection by simple correlation study, identification of the initial relationship between variables through non-linear curve fitting and used multi-step 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.

View Article: PubMed Central - PubMed

Affiliation: Centre for Cellular and Molecular Biology, CSIR, Hyderabad, India.

ABSTRACT
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 multi-step 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.

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Plot of fits of predictions based on the proposed model against the observed values for 5 clusters.(a) cluster I, (b) cluster II, (c) cluster III, (d) cluster IV, (e) cluster V (Straight line with white squares-observed P.vivax Deaths; Dashed line with black circles-predicted values of P.vivax Deaths from the model). Error bars are of 95% confidence intervals of the predicted response.
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pone-0004726-g006: Plot of fits of predictions based on the proposed model against the observed values for 5 clusters.(a) cluster I, (b) cluster II, (c) cluster III, (d) cluster IV, (e) cluster V (Straight line with white squares-observed P.vivax Deaths; Dashed line with black circles-predicted values of P.vivax Deaths from the model). Error bars are of 95% confidence intervals of the predicted response.

Mentions: Next, we model each cluster with respect to all the other climate variables. We state the final variables selected and the equation obtained for each cluster in Table 2. Fig. 6 shows the plot of fit as well as the predictions based on our model for each cluster. The residual plots of the models for each cluster are shown in Supporting Information (Section G of Text S1, Fig. S7).


Multi-step polynomial regression method to model and forecast malaria incidence.

Chatterjee C, Sarkar RR - PLoS ONE (2009)

Plot of fits of predictions based on the proposed model against the observed values for 5 clusters.(a) cluster I, (b) cluster II, (c) cluster III, (d) cluster IV, (e) cluster V (Straight line with white squares-observed P.vivax Deaths; Dashed line with black circles-predicted values of P.vivax Deaths from the model). Error bars are of 95% confidence intervals of the predicted response.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2648889&req=5

pone-0004726-g006: Plot of fits of predictions based on the proposed model against the observed values for 5 clusters.(a) cluster I, (b) cluster II, (c) cluster III, (d) cluster IV, (e) cluster V (Straight line with white squares-observed P.vivax Deaths; Dashed line with black circles-predicted values of P.vivax Deaths from the model). Error bars are of 95% confidence intervals of the predicted response.
Mentions: Next, we model each cluster with respect to all the other climate variables. We state the final variables selected and the equation obtained for each cluster in Table 2. Fig. 6 shows the plot of fit as well as the predictions based on our model for each cluster. The residual plots of the models for each cluster are shown in Supporting Information (Section G of Text S1, Fig. S7).

Bottom Line: We performed variable selection by simple correlation study, identification of the initial relationship between variables through non-linear curve fitting and used multi-step 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.

View Article: PubMed Central - PubMed

Affiliation: Centre for Cellular and Molecular Biology, CSIR, Hyderabad, India.

ABSTRACT
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 multi-step 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.

Show MeSH
Related in: MedlinePlus