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Vector dynamics predicts transmission dynamics: a simple, realistic and sensible approach for measuring malaria endemicity

Published online by Cambridge University Press:  09 December 2019

Nitika Pradhan
Affiliation:
KIIT School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India Regional Medical Research Centre, Bhubaneswar, Odisha, India
Rojalini Tarai
Affiliation:
Regional Medical Research Centre, Bhubaneswar, Odisha, India
Rupenangshu K. Hazra*
Affiliation:
Regional Medical Research Centre, Bhubaneswar, Odisha, India
*
Author for correspondence: Rupenangshu K. Hazra, Email: [email protected]

Abstract

Entomological indicators such as vector density, distribution, biology and bionomics and their vectorial attributes are important parameters for measuring the pattern and intensity of malaria transmission. Although published articles provide evidence for the existence of associations between entomological indices and malaria transmission dynamics, none of them is able to establish a strong correlation. In order to address this issue, the present study aims to assess how malaria transmission is influenced and can be predicted by local major vector dynamics. We carried out an entomological assessment of major Anopheline vector abundance, habit/habitat, resting and feeding behavior, infectivity rates, and other entomological parameters. Results suggest that malaria transmission was correlated with a vector control intervention and non-intervention scenario in a high endemic region of Kalahandi district of Odisha, India. Amongst all indices, infective anthropophagic vectors established a strong positive correlation with malaria morbidity in comparison to infective or anthropophagic vector species during both the study periods. Though other entomological parameters influenced the transmission intensity, little quantifiable association was detected among study sites. This study provides strong baseline evidence of an association between entomological indices and malaria transmission dynamics, which could be used as an early warning system for outbreak prediction.

Type
Research Paper
Copyright
Copyright © Cambridge University Press 2019

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