Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India

Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India Malaria being an endemic disease in the state of Chhattisgarh and ecologically dependent mosquito-borne disease, the study is intended to identify the ecological covariates of malaria risk in districts of the state and to build a suitable predictive model based on those predictors which could assist developing a weather based early warning system. This secondary data based analysis used one month lagged district level malaria positive cases as response variable and ecological covariates as independent variables which were tested with fixed effect panelled negative binomial regression models. Interactions among the covariates were explored using two way factorial interaction in the model. Although malaria risk in the state possesses perennial characteristics, higher parasitic incidence was observed during the rainy and winter seasons. The univariate analysis indicated that the malaria incidence risk was statistically significant associated with rainfall, maximum humidity, minimum temperature, wind speed, and forest cover (p < 0.05). The efficient predictive model include the forest cover [IRR-1.033 (1.024–1.042)], maximum humidity [IRR-1.016 (1.013–1.018)], and two-way factorial interactions between district specific averaged monthly minimum temperature and monthly minimum temperature, monthly minimum temperature was statistically significant [IRR-1.44 (1.231–1.695)] whereas the interaction term has a protective effect [IRR-0.982 (0.974–0.990)] against malaria infections. Forest cover, maximum humidity, minimum temperature and wind speed emerged as potential covariates to be used in predictive models for modelling the malaria risk in the state which could be efficiently used for early warning systems in the state. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Parasitic Diseases Springer Journals

Ecological covariates based predictive model of malaria risk in the state of Chhattisgarh, India

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Publisher
Springer India
Copyright
Copyright © 2017 by Indian Society for Parasitology
Subject
Medicine & Public Health; Infectious Diseases; Health Promotion and Disease Prevention; Medicine/Public Health, general
ISSN
0971-7196
eISSN
0975-0703
D.O.I.
10.1007/s12639-017-0885-7
Publisher site
See Article on Publisher Site

Abstract

Malaria being an endemic disease in the state of Chhattisgarh and ecologically dependent mosquito-borne disease, the study is intended to identify the ecological covariates of malaria risk in districts of the state and to build a suitable predictive model based on those predictors which could assist developing a weather based early warning system. This secondary data based analysis used one month lagged district level malaria positive cases as response variable and ecological covariates as independent variables which were tested with fixed effect panelled negative binomial regression models. Interactions among the covariates were explored using two way factorial interaction in the model. Although malaria risk in the state possesses perennial characteristics, higher parasitic incidence was observed during the rainy and winter seasons. The univariate analysis indicated that the malaria incidence risk was statistically significant associated with rainfall, maximum humidity, minimum temperature, wind speed, and forest cover (p < 0.05). The efficient predictive model include the forest cover [IRR-1.033 (1.024–1.042)], maximum humidity [IRR-1.016 (1.013–1.018)], and two-way factorial interactions between district specific averaged monthly minimum temperature and monthly minimum temperature, monthly minimum temperature was statistically significant [IRR-1.44 (1.231–1.695)] whereas the interaction term has a protective effect [IRR-0.982 (0.974–0.990)] against malaria infections. Forest cover, maximum humidity, minimum temperature and wind speed emerged as potential covariates to be used in predictive models for modelling the malaria risk in the state which could be efficiently used for early warning systems in the state.

Journal

Journal of Parasitic DiseasesSpringer Journals

Published: Feb 10, 2017

References

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