Authors
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Suresh KP, Ashwini M, Azhahianambi P, Majumdar SS, Hegde NR -More
Category
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Systematic review
Journal»Frontiers in veterinary science
Year
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2026
BACKGROUND AND OBJECTIVES: Both natural and anthropogenic factors impact the emergence, sustenance, and spread of zoonotic diseases and Transboundary Animal Diseases (TADs). Surveillance of such diseases depends on establishing baseline prevalence data, which necessitates a scientifically rigorous plan for data collection. This study aimed to establish a robust plan for estimating the prevalence of 10 zoonotic diseases (brucellosis, tuberculosis, Q-fever, scrub typhus, Japanese encephalitis, Crimean-Congo haemorrhagic fever, cysticercosis, cryptosporidiosis, listeriosis, salmonellosis) and four TADs (swine influenza, porcine reproductive and respiratory disease, lumpy skin disease, African swine fever) in India.
METHODS: Based on a systematic review and meta-analysis conducted in accordance with PRISMA Guidelines, using random effects model with heterogeneity quantified by the I2 statistics estimate of preliminary prevalence rates, and incorporating predetermined sensitivity and specificity values for the assays to be employed, a two-stage stratified random surveillance plan was generated with 95% confidence level, using an in-house developed epi-calculator, with the village as the stratification variable for the targeted animal species in different states of India.
RESULTS: The pooled prevalence estimates of the 14 diseases ranged from 4 to 30% (95% confidence intervals reported in the corresponding meta-analysis estimates). Based on these pooled prevalence estimates, a village-level surveillance plan comprising 34,975 samples across 2,018 villages was generated for India.
CONCLUSION: This is the first scientifically rigorous meta-analysis-based sampling plan under one platform for many diseases. This could facilitate a robust prevalence study, which could serve as an effective tool for monitoring zoonotic diseases and TADs that are a threat to India.
Epistemonikos ID: 7eee8e6c1d23ecb008c62ff11e50a42cf01626e9
First added on: Oct 31, 2026