Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis

• Published in: PloS one Vol. 10; no. 6; p. e0128377
• Main Authors: Srimath-Tirumula-Peddinti, Ravi Chandra Pavan Kumar, Neelapu, Nageswara Rao Reddy, Sidagam, Naresh
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.06.2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Animals; Aquatic insects; Autoregressive models; Biochemistry; Bioinformatics; Climate; Climate Change; Climate models; Climate variability; Climatic analysis; Climatic data; Correlation analysis; Correlation coefficient; Correlation coefficients; Culicidae - physiology; Data processing; Disease prevention; Disease transmission; Epidemiology; Geographical distribution; Humans; Humidity; Incidence; India - epidemiology; Insect Vectors - physiology; Life cycle engineering; Life cycles; Linear Models; Malaria; Malaria - epidemiology; Malaria - parasitology; Malaria - transmission; Mapping; Modelling; Models, Biological; Mosquitoes; Multiple regression analysis; Parasites; Plasmodium falciparum; Population; Population (statistical); Precipitation; Predictions; Rainfall; Regression analysis; Relative humidity; Statistical analysis; Studies; Temperature; Variations; Vector-borne diseases; Vectors; India; China; Africa; Tanzania; Sudan; Zimbabwe; Ghana; Kenya; Ethiopia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0128377

Malarial incidence, severity, dynamics and distribution of malaria are strongly determined by climatic factors, i.e., temperature, precipitation, and relative humidity. The objectives of the current study were to analyse and model the relationships among climate, vector and malaria disease in district of Visakhapatnam, India to understand malaria transmission mechanism (MTM). Epidemiological, vector and climate data were analysed for the years 2005 to 2011 in Visakhapatnam to understand the magnitude, trends and seasonal patterns of the malarial disease. Statistical software MINITAB ver. 14 was used for performing correlation, linear and multiple regression analysis. Perennial malaria disease incidence and mosquito population was observed in the district of Visakhapatnam with peaks in seasons. All the climatic variables have a significant influence on disease incidence as well as on mosquito populations. Correlation coefficient analysis, seasonal index and seasonal analysis demonstrated significant relationships among climatic factors, mosquito population and malaria disease incidence in the district of Visakhapatnam, India. Multiple regression and ARIMA (I) models are best suited models for modeling and prediction of disease incidences and mosquito population. Predicted values of average temperature, mosquito population and malarial cases increased along with the year. Developed MTM algorithm observed a major MTM cycle following the June to August rains and occurring between June to September and minor MTM cycles following March to April rains and occurring between March to April in the district of Visakhapatnam. Fluctuations in climatic factors favored an increase in mosquito populations and thereby increasing the number of malarial cases. Rainfall, temperatures (20°C to 33°C) and humidity (66% to 81%) maintained a warmer, wetter climate for mosquito growth, parasite development and malaria transmission. Changes in climatic factors influence malaria directly by modifying the behaviour and geographical distribution of vectors and by changing the length of the life cycle of the parasite.