Bluetongue risk map for vaccination and surveillance strategies in India

Chanda, Mohammed Mudassar; Purse, Bethan V. ORCID: https://orcid.org/0000-0001-5140-2710; Sedda, Luigi; Benz, David; Prasad, Minakshi; Reddy, Yella Narasimha; Yarabolu, Krishnamohan Reddy; Byregowda, S.M.; Carpenter, Simon; Prasad, Gaya; Rogers, David John. 2024 Bluetongue risk map for vaccination and surveillance strategies in India [in special issue: Current vaccine strategies and novel vaccine development for bluetongue virus and other orbiviruses] Pathogens, 13 (7), 590. 18, pp. https://doi.org/10.3390/pathogens13070590

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Abstract/Summary

Bluetongue virus (BTV, Sedoreoviridae: Orbivirus) causes an economically important disease, namely, bluetongue (BT), in domestic and wild ruminants worldwide. BTV is endemic to South India and has occurred with varying severity every year since the virus was first reported in 1963. BT can cause high morbidity and mortality to sheep flocks in this region, resulting in serious economic losses to subsistence farmers, with impacts on food security. The epidemiology of BTV in South India is complex, characterized by an unusually wide diversity of susceptible ruminant hosts, multiple vector species biting midges (Culicoides spp., Diptera: Ceratopogonidae), which have been implicated in the transmission of BTV and numerous co-circulating virus serotypes and strains. BT presence data (1997–2011) for South India were obtained from multiple sources to develop a presence/absence model for the disease. A non-linear discriminant analysis (NLDA) was carried out using temporal Fourier transformed variables that were remotely sensed as potential predictors of BT distribution. Predictive performance was then characterized using a range of different accuracy statistics (sensitivity, specificity, and Kappa). The top ten variables selected to explain BT distribution were primarily thermal metrics (land surface temperature, i.e., LST, and middle infrared, i.e., MIR) and a measure of plant photosynthetic activity (the Normalized Difference Vegetation Index, i.e., NDVI). A model that used pseudo-absence points, with three presence and absence clusters each, outperformed the model that used only the recorded absence points and showed high correspondence with past BTV outbreaks. The resulting risk maps may be suitable for informing disease managers concerned with vaccination, prevention, and control of BT in high-risk areas and for planning future state-wide vector and virus surveillance activities.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.3390/pathogens13070590
UKCEH and CEH Sections/Science Areas:	Biodiversity (Science Area 2017-)
ISSN:	2076-0817
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	Culicoides, arbovirus, Culicoides imicola, remote sensing, risk mapping
NORA Subject Terms:	Ecology and Environment Zoology Biology and Microbiology Data and Information
Date made live:	23 Jul 2024 11:03 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537745

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