Severe acute respiratory syndrome (SARS) mathematical models and disease parameters: a systematic review.

SARS-CoV-1 was the first documented coronavirus to cause an acute epidemic in humans and remains a priority pathogen owing to the risk of re-emergence. Robust estimates of key epidemiological parameters are essential to guide outbreak responses and inform mathematical models. Existing systematic reviews have been limited in scope, warranting a comprehensive and up-to-date review. We conducted a systematic review (PROSPERO CRD42023393345) of studies of severe acute respiratory syndrome (SARS) transmission models and parameters characterising the transmission, evolution, natural history, severity, risk factors, and seroprevalence of SARS-CoV-1. Information was extracted using a custom database and quality assessment tool. We extracted data on 519 parameters, 243 risk factors, and 112 models from 289 papers. We found that SARS is characterised by high lethality (case-fatality ratio, 10·9%), transmissibility (R₀ range, 1·1-4·59), and superspreading events (approximately 91% of SARS-CoV-1 infections can be attributed to 20% of individuals who were most infectious). Infection risk was the highest among health-care workers and close contacts of infected individuals. Severe disease and death were associated with age and existing comorbidities. The natural history of SARS was poorly characterised, except for the incubation and mean onset-to-hospitalisation delays. The extracted data were compiled into our associated R package, epireview, which can be updated to incorporate novel findings, thus providing a key resource for informing response to future coronavirus outbreaks. By making data accessible through an updatable database, we support rapid, evidence-informed responses to potential re-emergence of SARS-CoV-1 or related coronaviruses.