Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March-December 2020

  1. Amna Tariq
  2. Juan M. Banda
  3. Pavel Skums
  4. Sushma Dahal
  5. Carlos Castillo-Garsow
  6. Baltazar Espinoza
  7. Noel G. Brizuela
  8. Roberto A. Saenz
  9. Alexander Kirpich
  10. Ruiyan Luo
  11. Anuj Srivastava
  12. Humberto Gutierrez
  13. Nestor Garcia Chan
  14. Ana I. Bento
  15. Maria-Eugenia Jimenez-Corona
  16. Gerardo Chowell

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Abstract

Mexico has experienced one of the highest COVID-19 mortality rates in the world. A delayed implementation of social distancing interventions in late March 2020 and a phased reopening of the country in June 2020 has facilitated sustained disease transmission in the region. In this study we systematically generate and compare 30-day ahead forecasts using previously validated growth models based on mortality trends from the Institute for Health Metrics and Evaluation for Mexico and Mexico City in near real-time. Moreover, we estimate reproduction numbers for SARS-CoV-2 based on the methods that rely on genomic data as well as case incidence data. Subsequently, functional data analysis techniques are utilized to analyze the shapes of COVID-19 growth rate curves at the state level to characterize the spatiotemporal transmission patterns of SARS-CoV-2. The early estimates of the reproduction number for Mexico were estimated between R t ~1.1–1.3 from the genomic and case incidence data. Moreover, the mean estimate of R t has fluctuated around ~1.0 from late July till end of September 2020. The spatial analysis characterizes the state-level dynamics of COVID-19 into four groups with distinct epidemic trajectories based on epidemic growth rates. Our results show that the sequential mortality forecasts from the GLM and Richards model predict a downward trend in the number of deaths for all thirteen forecast periods for Mexico and Mexico City. However, the sub-epidemic and IHME models perform better predicting a more realistic stable trajectory of COVID-19 mortality trends for the last three forecast periods (09/21-10/21, 09/28-10/27, 09/28-10/27) for Mexico and Mexico City. Our findings indicate that phenomenological models are useful tools for short-term epidemic forecasting albeit forecasts need to be interpreted with caution given the dynamic implementation and lifting of social distancing measures.

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  1. Version published to 10.1371/journal.pone.0254826
  2. Version published to 10.1101/2021.01.11.21249561v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.01.11.21249561: (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 [77].
    EpiEstim
    suggested: (EpiEstim, RRID:SCR_018538)
    These sequences were aligned to the reference genome taken from the literature [78] using MUSCLE [79] and trimmed to the same length of 29772 bp.
    MUSCLE
    suggested: (MUSCLE, RRID:SCR_011812)
    The phylodynamics analysis of that cluster have been carried out using BEAST v1.10.4 [81].
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    To perform clustering of 32 curves into smaller groups, we apply the dendrogram function in Matlab using the “ward” linkage as explained in ref [84].
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    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:
    This paper is not exempt from limitations. First, the IHME (current projections, mandated mask, and worst-case scenario) model utilized has been revised multiple times over the course of the pandemic and differs substantially in methodology, assumptions, range of predictions and quantities estimated. Second, the IHME has been irregular in publishing the downloadable estimates online for some periods. Third, we model the death estimates by date of reporting rather than by the date of death. Lastly, the unpredictable social component of the epidemic on ground is also a limiting factor for the study as we do not know the ground truth mortality pattern when the forecasts are generated. In conclusion, the reproduction number has been fluctuating around ∼1.0 since the end of July-end of September 2020, indicating sustained virus transmission in the region. Moreover, the country has seen much lower mobility reduction and mixed reactions towards the stay-at-home orders contributing towards the virus transmission in the country. Moreover, the spatial analysis indicates that states like Mexico, Michoacán, Morelos, Nuevo Leon, Baja California need to put in place stronger public health strategies to contain the rising patterns in growth rates. The GLM and sub-epidemic model applied to mortality data in Mexico provide reasonable estimates for short-term projections in near real-time. While the GLM and Richards models predict that the COVID-19 outbreak in Mexico and Mexico City may be on ...

    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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  4. Version published to 10.1101/2021.01.11.21249561v1 on medRxiv