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Wiley, Equine Veterinary Journal, 3(55), p. 506-514, 2022

DOI: 10.1111/evj.13865

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Estimation of the basic reproduction number for Streptococcus equi spp. equi outbreaks by meta‐analysis of strangles outbreak reports

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

AbstractBackgroundStreptococcus equi spp. equi (S. equi), the cause of strangles in horses, is considered a highly contagious pathogen affecting equines and the equine industry worldwide. Fundamental epidemiological characteristics of outbreaks, such as the basic reproduction number (R0), are not well described.ObjectivesEstimate R0 for S. equi in equine populations from outbreak data.Study designSystematic review and meta‐analysis of published and unpublished data.MethodsA literature search for outbreak reports was carried out. Depending on data available in the reports, the early epidemic growth rate or final attack rate (AR) approach was used to estimate the basic reproduction number for that outbreak. Other recorded outbreak characteristics were the type of housing (group vs. individual). An overall estimate for R0 was computed by meta‐analysis.ResultsData from eight outbreaks were extracted from peer‐reviewed publications. Data from two additional, non‐published outbreaks was also included in the meta‐analysis. A conservative estimate for R0 was 2.2 (95% confidence interval [CI] 1.9–2.5). A less conservative estimate, including outbreaks with a 100% AR for which a lower limit R0 was estimated, was 2.7 (95% CI 2.1–3.3).Main limitationsFew papers describing longitudinal incidence data were found so most estimates were based on the outbreaks' final size. Several outbreaks had a 100% attack rate and could therefore only be included as a lower limit estimate in the meta‐analysis. The reported result therefore may be an underestimation.ConclusionsThis estimate for R0 for S. equi informs parameters for future mathematical modelling, quantifies desired preventive vaccine coverage and helps evaluate the effect of prevention strategies through future modelling studies.