The SARS-CoV2 pandemic explained via asymptomatic infection and susceptibility heterogeneity. Buenos Aires first wave

  1. Manuela Bullo
  2. Santiago Poy-Piñeiro
  3. Pedro Cosatto-Ammann
  4. Hernan Seoane
  5. Emilio Picasso

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Abstract

Background

The rapid global spread of SARS-COV-2 forced governments to implement drastic interventions. The existence of a large but undetermined number of mild or non-symptomatic but infectious cases seems to be involved in the rapid spread, creating a high level of uncertainty due to the difficulty to measure them, and difficulty for epidemiologic modelling.

Methods

We developed a compartmental model with deterministic equations, that accounts for clinical status, mobility, r heterogenous susceptibility and non-pharmaceutical interventions. The model was calibrated using data from different regions and we used it to predict the dynamic in Buenos Aires Metropolitan Area (AMBA).

Results

The model adjusted well to different geographical regions. In AMBA the model predicted 21400 deaths at 300 days, with 27% of the population in the region immunized after the first wave, partly due to the high incidence of asymptomatic cases. The mobility restriction is approximately linear, with any restriction bringing a positive effect. The other interventions have a combined effect of 27% reduction in infection rates.

Conclusion

Our research underlines the role of asymptomatic cases in the epidemics’ dynamic and introduces the concept of susceptibility heterogeneity as a potential explanation for otherwise unexplained outbreak dynamics. The model also shows the big role of non-pharmaceutical interventions both in slowing down the epidemic dynamics and in reducing the eventual number of deaths. The model results are closely compatible with observed data.

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  1. ScreenIT

    SciScore for 10.1101/2021.04.26.21255801: (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
    We operationalize the intensity of this intervention by means of the mobility index produced by Google (xm), specifically the average of the transit and workplaces indexes as previously explained(16).
    Google
    suggested: (Google, RRID:SCR_017097)

    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:
    However, we think that these cases are out of the mainstream of the epidemic, and they pose no serious limitation to the model. On the other hand, the proposed model is highly flexible, allowing for both completely asymptomatic and mild symptomatic cases, that are thought to play a significant role in the SARS-CoV2 epidemic. Among the limitations of our research, we have to mention the data quality. Changes in real-time data due to corrections, poor data-quality and slow reporting may affect therefore the assumptions of our model. Under-reporting due to slow data processing, restrictive testing policies and lack of testing availability impacted on the cumulative number of cases acknowledged by official data sources. While we use Russell’s method to correct for this, we are introducing potential limitations of Russell’s method into our model. Also regarding data quality, imported cases also introduce uncertainty in the model, and data is not as granular as it is required to account for that. Another limitation is that in our model, 81% of the infections are asymptomatic. While as mentioned previously, some local data shows that for every PCR-diagnosed case there were 9 IgG SARS CoV-2 positive individuals that had not been diagnosed during the outbreak in a very poor neighborhood in the City of Buenos Aires (30) reaching 50-60% seroprevalence, studies conducted in Europe after local outbreaks show 15 to 20% seroprevalence (31). Further research is required to understand if this...

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.04.26.21255801v2 on medRxiv
  3. Version published to 10.1101/2021.04.26.21255801v1 on medRxiv