Automated or partially-automated contact tracing tools are being deployed by many countries to contain SARS-CoV-2; however, the evidence base for their use is not well-established. Methods We undertook a rapid systematic review of automated or partially-automated contact tracing, registered with PROSPERO (CRD42020179822). We searched PubMed, EMBASE, OVID Global Health, EBSCO COVID Portal, Cochrane Library, medRxiv, bioRxiv, arXiv and Google Advanced for articles relevant to COVID-19, SARS,

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... influenza or Ebola from 1/1/2000-14/4/2020. Two authors reviewed all full-text manuscripts. One reviewer extracted data using a pre-piloted form; a second independently verified extracted data. Primary outcomes were the number or proportion of contacts (and/or subsequent cases) identified; secondary outcomes were indicators of outbreak control, app/tool uptake, resource use, cost-effectiveness and lessons learnt. The Effective Public Health Practice Project tool or CHEERS checklist were used in quality assessment. Findings 4,033 citations were identified and 15 were included. No empirical evidence of automated contact tracing's effectiveness (regarding contacts identified or transmission reduction) was identified. Four of seven included modelling studies suggested that controlling COVID-19 requires high population uptake of automated contact-tracing apps (estimates from 56% to 95%), typically alongside other control measures. Studies of partially-automated contact tracing generally reported more complete contact identification and follow-up, and greater intervention timeliness (0.5-5 hours faster), than previous systems. No meta-analyses were possible. Interpretation Automated contact tracing has potential to reduce transmission with sufficient population uptake and usage. However, there is an urgent need for well-designed prospective evaluations as no studies provided empirical evidence of its effectiveness.