Estimation of the basic reproduction number for Streptococcus equi spp. equi outbreaks by meta‐analysis of strangles outbreak reports. (9th August 2022) - Houben, Rosa M. A. C.; van Maanen, Kees; Kemp‐Symonds, Jeremy G.; Waller, Andrew S.; Sloet van Oldruitenborgh‐Oosterbaan, Marianne M.; Heesterbeek, Hans
Abstract: Background: Streptococcus 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 ( R 0 ), are not well described. Objectives: Estimate R 0 for S. equi in equine populations from outbreak data. Study design: Systematic review and meta‐analysis of published and unpublished data. Methods: A 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 R 0 was computed by meta‐analysis. Results: Data 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 R 0 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 R 0 was estimated, was 2.7 (95% CI 2.1–3.3). Main limitations: Few 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. Conclusions: This estimate for R 0 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.