SORMAS-Stats: A Novel Statistical Application for Real-time Estimation of Epidemiological Parameters of Communicable Diseases using Contact-tracing Data. (Preprint)

BACKGROUND The Surveillance Outbreak Response Management and Analysis System (SORMAS) contains a management module to support countries in epidemic response. It consists of documentation, linkage and follow-up of cases, contacts, and events. To allow SORMAS users to visualise, compute key surveillance indicators and estimate epidemiological parameters from such a network data in real time, we developed the SORMAS Statistics (SORMAS-Stats) application. OBJECTIVE The aim of this study is to describe the key visualisations, surveillance indicators and epidemiological parameters implemented in the SORMAS-Stats application, and illustrate the application of SORMAS-Stats to COVID-19 outbreak response. METHODS Based on literature review and user requests, we included the following visualisation and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number (Rt), dispersion parameter (k) and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting a lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptoms onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. We applied the Markov Chain Monte Carlo approach and estimated Rt using the incidence data and the observed SI data, computed from the transmission network data. RESULTS Using COVID-19 contact tracing data of confirmed cases reported between July 31, and October 29, 2021 in Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63570 nodes comprising 1.75% (1115/63570) events, 19.59% (12452/63570) case persons, and 78.66% (50003/63570) exposed persons, 1238 infector-infectee pairs, 3860 transmission chains with 24.69% (953/3860) having events as the index infector. The distribution with best fit to the observed SI data was lognormal distribution with mean 4.32 days (95% CI, 4.10–4.53 days). We estimated the dispersion parameter, k of 21.11 (95% CI, 7.57–34.66) and a reproductive number, R of 0.9 (95% CI, 0.58–0.60). The weekly estimated Rt values ranged from 0.80 to 1.61. CONCLUSIONS We provide an application for real-time estimation of epidemiological parameters, which are essential for informing outbreak response strategies. These estimates are commensurate with findings from previous studies. SORMAS-Stats application would greatly assist public health authorities in the regions using SORMAS or similar applications by providing extensive visualisations and computation of surveillance indicators.