Data from: Estimating transmission dynamics and serial interval of the first wave of COVID-19 infections under different control measures: A statistical analysis in Tunisia from February 29 to May 5, 2020

Background: Describing transmission dynamics of the outbreak and impact of intervention measures are critical to planning responses to future outbreaks and providing timely information to guide policy makers decision. We estimate serial interval (SI) and temporal reproduction number (Rt) of SARS-CoV-2 in Tunisia. Methods: We collected data of investigations and contact tracing between March 1, 2020 and May 5, 2020 as well as illness onset data during the period February 29-May 5, 2020 from National Observatory of New and Emerging Diseases of Tunisia. Maximum likelihood (ML) approach is used to estimate dynamics of Rt. Results: 491 of infector-infectee pairs were involved, with 14.46% reported pre-symptomatic transmission. SI follows Gamma distribution with mean 5.30 days [95% CI 4.66-5.95] and standard deviation 0.26 [95% CI 0.23-0.30]. Also, we estimated large changes in Rt in response to the combined lockdown interventions. The Rt moves from 3.18 [95% CI 2.73-3.69] to 1.77 [95% CI 1.49-2.08] with curfew prevention measure, and under the epidemic threshold (0.89 [95% CI 0.84-0.94]) by national lockdown measure. Conclusions: Overall, our findings highlight contribution of interventions to interrupt transmission of SARS-CoV-2 in Tunisia. Data is obtained from contact tracing between March 1 and May 5, 2020 from the National Observatory of New and Emerging Diseases of Tunis, Tunisia. It was screened to clearly identified transmission events, which are a known pairs of index and secondary cases and the dates of symptom onset for both cases. This metadata includes two datasets: the first one consists of contact tracing including information for pairs, dates of symptoms onset for both infector and infectee as well as the ID for them, the second one consists of a daily time series including information for dates and number of incident cases.