Contact tracing of COVID-19 in Karnataka, India: Superspreading and determinants of infectiousness and symptomatic infection

  1. Mohak Gupta
  2. Giridara G. Parameswaran
  3. Manraj S. Sra
  4. Rishika Mohanta
  5. Devarsh Patel
  6. Amulya Gupta
  7. Bhavik Bansal
  8. Vardhmaan Jain
  9. Archisman Mazumder
  10. Mehak Arora
  11. Nishant Aggarwal
  12. Tarun Bhatnagar
  13. Jawaid Akhtar
  14. Pankaj Pandey
  15. Vasanthapuram Ravi
  16. Giridhara R. Babu

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Abstract

India has experienced the second largest outbreak of COVID-19 globally, yet there is a paucity of studies analysing contact tracing data in the region which can optimise public health interventions (PHI’s).

Methods

We analysed contact tracing data from Karnataka, India between 9 March and 21 July 2020. We estimated metrics of transmission including the reproduction number (R), overdispersion (k), secondary attack rate (SAR), and serial interval. R and k were jointly estimated using a Bayesian Markov Chain Monte Carlo approach. We studied determinants of risk of further transmission and risk of being symptomatic using Poisson regression models.

Findings

Up to 21 July 2020, we found 111 index cases that crossed the super-spreading threshold of ≥8 secondary cases. Among 956 confirmed traced cases, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases. Among 16715 contacts, overall SAR was 3.6% [95% CI, 3.4–3.9] and symptomatic cases were more infectious than asymptomatic cases (SAR 7.7% vs 2.0%; aRR 3.63 [3.04–4.34]). As compared to infectors aged 19–44 years, children were less infectious (aRR 0.21 [0.07–0.66] for 0–5 years and 0.47 [0.32–0.68] for 6–18 years). Infectors who were confirmed ≥4 days after symptom onset were associated with higher infectiousness (aRR 3.01 [2.11–4.31]). As compared to asymptomatic cases, symptomatic cases were 8.16 [3.29–20.24] times more likely to cause symptomatic infection in their secondary cases. Serial interval had a mean of 5.4 [4.4–6.4] days, and case fatality rate was 2.5% [2.4–2.7] which increased with age.

Conclusion

We found significant heterogeneity in the individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in the underlying number of contacts. To strengthen contact tracing in over-dispersed outbreaks, testing and tracing delays should be minimised and retrospective contact tracing should be implemented. Targeted measures to reduce potential superspreading events should be implemented. Interventions aimed at children might have a relatively small impact on reducing transmission owing to their low symptomaticity and infectivity. We propose that symptomatic cases could cause a snowballing effect on clinical severity and infectiousness across transmission generations; further studies are needed to confirm this finding.

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  1. Version published to 10.1371/journal.pone.0270789
  2. ScreenIT

    SciScore for 10.1101/2020.12.25.20248668: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data handling: Data collection was done in Microsoft Office Excel.
    Microsoft Office Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    Data cleaning and analysis was done in Stata 15.0 and Python v3.6.8.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Figures were prepared using matplotlib v3.3.2, seaborn 0.10.0, and GraphPad Prism 9.
    matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)
    Bayesian Markov Chain Monte Carlo sampling for estimating reproduction number and overdispersion from contact tracing data: We implemented a Bayesian Markov Chain Monte Carlo sampling method using the NUTS sampler in PyStan 2.18.0.0 with gamma prior estimates of R and k with mean 2.5 SD 2.0, and mean 0.45 SD 0.1 respectively based on previous studies.
    PyStan
    suggested: None

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    (10) Our study has certain limitations. Firstly, symptomatic status was based on data collected at the time of sample collection and hence some cases recorded as asymptomatic may have developed symptoms later. (46–48) This would overestimate the proportion of asymptomatic infections and also the relative transmissibility by asymptomatics since presymptomatic cases have been shown to be more infectious than asymptomatic carriers. (40,47,49) Second, any amount of case and/or contact under-ascertainment during surveillance and contact tracing carries the potential to bias our results. Although we have attempted to minimise this bias by analysing subgroups with high reliability of data (Table S2), some degree of bias can still be expected. Since the completeness of contact tracing is inherently limited by memory recall and logistics, R estimated from contact tracing data cannot capture the entirety of the outbreak and can thus be expected to underestimate the true R. Third, details of settings of transmission and timing of exposure of contact to index case were not available for the vast majority of cases which precluded any insightful analysis on the same. Finally, the dates of symptom onset in our study may be subject to recall bias. Additionally, our results should be interpreted in the context of the NPIs in place at the time of the study. From 25 March to 8 June, physical distancing was mandated in Karnataka with closures of schools and most public places, restrictions on la...

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 8. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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.

    About SciScore

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  3. Version published to 10.1101/2020.12.25.20248668v1 on medRxiv