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Accepted for/Published in: JMIR Public Health and Surveillance

Date Submitted: Dec 27, 2021
Open Peer Review Period: Dec 26, 2021 - Feb 20, 2022
Date Accepted: Apr 26, 2022
Date Submitted to PubMed: Apr 29, 2022
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

A Novel Tool for Real-time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment

Silenou BC, Verset C, Kaburi BB, Leuci O, Ghozzi S, Duboudin C, Krause G

A Novel Tool for Real-time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment

JMIR Public Health Surveill 2022;8(5):e34438

DOI: 10.2196/34438

PMID: 35486812

PMCID: 9159465

A Novel Tool for Real-Time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment

  • Bernard C. Silenou; 
  • Carolin Verset; 
  • Basil B. Kaburi; 
  • Olivier Leuci; 
  • Stéphane Ghozzi; 
  • Cédric Duboudin; 
  • Gérard Krause

ABSTRACT

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 data, compute essential surveillance indicators, and estimate epidemiological parameters from such network data in real-time, we developed the SORMAS Statistics (SORMAS-Stats) application.

Objective:

This study aims to describe the essential visualisations, surveillance indicators, and epidemiological parameters implemented in the SORMAS-Stats application and illustrates the application of SORMAS-Stats in response to the COVID-19 outbreak.

Methods:

Based on findings from a rapid review and SORMAS user requests, we included the following visualisation and estimation of parameters in SORMAS-Stats: transmission network diagram, serial interval (SI), time-varying reproduction number R(t), dispersion parameter (k) and additional surveillance indicators presented in graphs and tables. We estimated SI by fitting lognormal, gamma, and Weibull distributions to the observed distribution of the number of days between symptom onset dates of infector-infectee pairs. We estimated k by fitting a negative binomial distribution to the observed number of infectees per infector. Furthermore, we applied the Markov Chain Monte Carlo approach and estimated R(t) using the incidence data and the observed SI 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 the Bourgogne-Franche-Comté region of France, we constructed a network diagram containing 63570 nodes. The network comprises 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 the best fit to the observed SI data was lognormal distribution with a mean of 4.30 days (95% CI, 4.09–4.51 days). We estimated the dispersion parameter k of 21.11 (95% CI, 7.57–34.66) and an effective reproduction number R of 0.9 (95% CI, 0.58–0.60). The weekly estimated R(t) values ranged from 0.80 to 1.61.

Conclusions:

We provide an application for real-time estimation of epidemiological parameters, essential for informing outbreak response strategies. The estimates are commensurate with findings from previous studies. SORMAS-Stats application would greatly assist public health authorities in the regions using SORMAS or similar tools by providing extensive visualisations and computation of surveillance indicators.


 Citation

Please cite as:

Silenou BC, Verset C, Kaburi BB, Leuci O, Ghozzi S, Duboudin C, Krause G

A Novel Tool for Real-time Estimation of Epidemiological Parameters of Communicable Diseases Using Contact-Tracing Data: Development and Deployment

JMIR Public Health Surveill 2022;8(5):e34438

DOI: 10.2196/34438

PMID: 35486812

PMCID: 9159465

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