Epidemiology and transmission of COVID-19 in cases and close contacts in Georgia in the first four months of the epidemic
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Abstract
Background
Between February and June 2020, 917 COVID-19 cases and 14 COVID-19-related deaths were reported in Georgia. Early on, Georgia implemented non-pharmaceutical interventions (NPI) including extensive contact tracing and restrictions on movement.
Aim
To characterize the demographics of those tested and infected with COVID-19 in Georgia; to evaluate factors associated with transmission between cases and their contacts; and to determine how transmission varied due to NPI up to 24 June 2020.
Methods
We use data gathered by the Georgian National Center for Disease Control on all polymerase chain reaction tests conducted (among symptomatic patients, through routine testing and contact tracing); hospitalization data for confirmed cases, and contact tracing data. We calculated the number of contacts per index case, the secondary attack rate (% contacts infected), and effective R number (new cases per index case), and used logistic regression to estimate how age, gender, and contact type affected transmission.
Results
Most contacts and transmission events were between family members. Contacts <40 years were less likely to be infected, while infected individuals >50 were more likely to die than younger patients. Contact tracing identified 917 index cases with mean 3.1 contacts tested per case, primarily family members. The overall secondary attack rate was 28% (95% confidence interval [CI]: 26-29%) and effective R number was 0.87 (95%CI 0.81-0.93), peaking at 1.1 (95%CI 0.98-1.2) during the period with strongest restrictions.
Conclusion
Georgia effectively controlled the COVID-19 epidemic in its early stages, although evidence does not suggest transmission was reduced during the strict lockdown period.
Research in Context
Evidence before this study
We searched PubMed and MedRxiv for papers reporting research using contact tracing data to evaluate the characteristics of the COVID-19 epidemic in any country. A number of analyses were identified from Asia, including China, Taiwan, Maldives, Thailand, South Korea, and India, but none from other regions other than one previous analysis conducted in Europe, focusing on the first two months of the COVID-19 epidemic in Cyprus. Studies evaluated number of contacts and different contact types, secondary attack rate, and effective R number. However, none of these studies compared characteristics between different time periods or under varied levels of non-pharmaceutical interventions or restrictions on social mixing.
Added value of this study
In this study, we use contact tracing data from Georgia from all cases identified in the first four months of the epidemic, as well as testing and hospitalization data, to evaluate the number and type of contacts, effective R number (new cases per index case), and secondary attack rate (proportion of contacts infected) in this population, and whether these measures changed before, during, and after the lockdown period. We also evaluated how the chance of transmission varied by type of index case and contact. Our results indicate that number of contacts remained relatively low throughout the study period, so although the secondary attack rate was relatively high (28%) compared to that seen in studies in Asia (10-15%), the effective R number was less than one overall, peaking at 1.1 (0.98-1.2) during the strictest lockdown period, with easing of restrictions corresponding to a lower effective R of 0.87 (0.77-0.97). Most transmission occurred between family members with transmission very low between co-workers, friends, neighbours, and medical personnel, indicating that the restrictions on social mixing were effective at keeping the epidemic under control during this period.
Implications of all the available evidence
Our study presents the first analysis of the successful control of a COVID-19 epidemic in a European country, indicating that despite a high secondary attack rate, reduction in contacts outside the home, and a well-timed lockdown, were able to keep transmission under control.
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SciScore for 10.1101/2021.03.22.21254082: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Ethics: Ethical approval (exemption) was obtained for this study from the National Center for Disease Control (NCDC) institutional review board (IRB), under reference # 2020-027, 02 June 2020. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and limitations: This study used …
SciScore for 10.1101/2021.03.22.21254082: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement IRB: Ethics: Ethical approval (exemption) was obtained for this study from the National Center for Disease Control (NCDC) institutional review board (IRB), under reference # 2020-027, 02 June 2020. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Strengths and limitations: This study used national level public health data to evaluate how COVID-19 spread in the early stages of the epidemic in Georgia. A large number of tests were conducted and reported, providing a rich resource. However, some variables, such as specific symptoms (fever or cough), were underreported in the testing data, even among those tested for being symptomatic, with higher rates of missing data for those testing negative. This could lead to collider bias that distorts the association between variables, for example if a risk factor and outcome both have an impact on whether data are recorded 31. Furthermore, missing patient IDs in the contact tracing data may have led us to underestimate the transmission chains because some positive contacts could not be linked to others. In addition, only one reason for testing was given even though some patients might have fallen into multiple categories (e.g. an essential worker experiencing symptoms). Lastly, any hospitalization data was only available for the first 500 patients with disease outcomes, either discharge or death, and so we may have underrepresented more severe patients who were hospitalized but had not as yet one of these outcomes.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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