Lessons for preparedness and reasons for concern from the early COVID-19 epidemic in Iran

  1. Mahan Ghafari
  2. Bardia Hejazi
  3. Arman Karshenas
  4. Stefan Dascalu
  5. Alireza Kadvidar
  6. Mohammad A. Khosravi
  7. Maryam Abbasalipour
  8. Majid Heydari
  9. Sirous Zeinali
  10. Luca Ferretti
  11. Alice Ledda
  12. Aris Katzourakis

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Abstract

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  1. Version published to 10.1016/j.epidem.2021.100472
  2. Version published to 10.1101/2020.04.18.20070904v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.04.18.20070904: (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
    Thus, we use the remaining 19 whole-genome sequences to infer parameters of the epidemic using the phylogenetic software, BEAST [34].
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    We also construct the maximum likelihood tree of all sequences using PhyML [35].
    PhyML
    suggested: (PhyML, RRID:SCR_014629)

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

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite integrating multiple sources, our data have many limitations. We investigated air travel data only to countries with direct flights to Iran and discarded information from detected cases in countries such as Qatar and Canada since we were not able to independently verify the fraction of passengers on board the planes who travel from Iran to those countries. Also, given the lack of mobility data from Iran, we were unable to investigate possible international exportation of cases to Afghanistan, Iraq, Syria, Azerbaijan, Turkey, and other countries with a significant flow of ground transportation (i.e. trains, buses, and cars) from Iran. We did not have access to the province-level number of confirmed COVID-19 deaths which is a significant source of information to assess excess deaths in the winter and spring 2020. Also, there is likely extreme heterogeneity in the geographical spread of COVID-19 across the country due to various factors such as demographic structure of the provinces’ populations, the pattern of social contacts between age groups, and the quality of healthcare and effectiveness of NPIs in different districts. Our SEIR modelling analysis shows that in the most likely scenario by mid-July only 11% of the population have recovered from the disease which implies that a large fraction of the population is still vulnerable to contracting COVID-19. We also find strong indications that the outbreak was never brought under control since it emerged back in mid-Janu...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

    About SciScore

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  4. Version published to 10.1101/2020.04.18.20070904v1 on medRxiv