Transmission dynamics and control of COVID-19 in Chile, March-October, 2020

  1. Amna Tariq
  2. Eduardo A. Undurraga
  3. Carla Castillo Laborde
  4. Katia Vogt-Geisse
  5. Ruiyan Luo
  6. Richard Rothenberg
  7. Gerardo Chowell

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Abstract

Since the detection of the first case of COVID-19 in Chile on March 3 rd , 2020, a total of 513,188 cases, including ~14,302 deaths have been reported in Chile as of November 2 nd , 2020. Here, we estimate the reproduction number throughout the epidemic in Chile and study the effectiveness of control interventions especially the effectiveness of lockdowns by conducting short-term forecasts based on the early transmission dynamics of COVID-19. Chile’s incidence curve displays early sub-exponential growth dynamics with the deceleration of growth parameter, p , estimated at 0.8 (95% CI: 0.7, 0.8) and the reproduction number, R , estimated at 1.8 (95% CI: 1.6, 1.9). Our findings indicate that the control measures at the start of the epidemic significantly slowed down the spread of the virus. However, the relaxation of restrictions and spread of the virus in low-income neighborhoods in May led to a new surge of infections, followed by the reimposition of lockdowns in Greater Santiago and other municipalities. These measures have decelerated the virus spread with R estimated at ~0.96 (95% CI: 0.95, 0.98) as of November 2 nd , 2020. The early sub-exponential growth trend ( p ~0.8) of the COVID-19 epidemic transformed into a linear growth trend ( p ~0.5) as of July 7 th , 2020, after the reimposition of lockdowns. While the broad scale social distancing interventions have slowed the virus spread, the number of new COVID-19 cases continue to accrue, underscoring the need for persistent social distancing and active case detection and isolation efforts to maintain the epidemic under control.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.15.20103069: (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
    Analytical estimates of Rt were obtained within a Bayesian framework using EpiEstim R package in R language [68].
    EpiEstim
    suggested: (EpiEstim, RRID:SCR_018538)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study has some limitations. First, our study analyzes cases by the dates of reporting while it is ideal to analyze the cases by the dates of onset or after adjusting for reporting delays. On the other hand, a substantial fraction of the COVID-19 infections exhibits very mild or no symptoms at all, which may not be reflected by data [101, 102]. Moreover, the data are not stratified by local and imported cases; therefore, we assumed that all cases contribute equally to the transmission dynamics of COVID-19. Finally, the extent of selective underreporting, and its impact on these results is difficult to assess.

    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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  2. Version published to 10.1371/journal.pntd.0009070
  3. Version published to 10.1101/2020.05.15.20103069v3 on medRxiv
  4. Version published to 10.1101/2020.05.15.20103069v2 on medRxiv
  5. Version published to 10.1101/2020.05.15.20103069v1 on medRxiv