Analysis on Action Tracking Reports of COVID-19 Informs Control Strategies and Vaccine Delivery in Post-Pandemic Era

  1. Xiaofei Sun
  2. Tianjia Guan
  3. Tao Xue
  4. Chun Fan
  5. Meng Yang
  6. Yuxian Meng
  7. Tianwei Zhang
  8. Bahabaike Jiangtulu
  9. Fei Wu
  10. Jiwei Li

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Abstract

Understanding the spread of SARS-CoV-2 provides important insights for control policies such as social-distancing interventions and vaccine delivery in the post-pandemic era. In this work, we take the advantage of action tracking reports of confirmed COVID-19 patients, which contain the mobility trajectory of patients. We analyzed reports of patients from April 2020 to January 2021 in China, a country where the residents are well-prepared for the “new normal” world following COVID-19 spread. We developed natural language processing (NLP) tools to transform the unstructured text of action-tracking reports to a structured network of social contacts. An epidemiology model was built on top of the network. Our analysis provides important insights for the development of control policies. Under the “new normal” conditions, we find that restaurants, locations less protected by mask-wearing, have a greater risk than any other location categories, including locations where people are present at higher densities (e.g., flight). We find that discouraging railway transports is crucial to avoid another wave of breakout during the Chunyun season (a period of travel in China with extremely high traffic load around the Chinese New Year). By formalizing the challenge of finding the optimal vaccine delivery among various different population groups as an optimization problem, our analysis helps to maximize the efficiency of vaccine delivery under the general situation of vaccine supply shortage. We are able to reduce the numbers of infections and deaths by 7.4% and 10.5% respectively with vaccine supply for only 1% of the population. Furthermore, with 10% vaccination rate, the numbers of infections and deaths further decrease by 52.6% and 78.1% respectively. Our work will be helpful in the design of effective policies regarding interventions, reopening, contact tracing and vaccine delivery in the “new normal” world following COVID-19 spread.

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    SciScore for 10.1101/2021.04.08.21254953: (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 use BERT [29] as the model backbone.
    BERT
    suggested: (BERT, RRID:SCR_018008)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study has several limitations. First, the analyzed dataset has selection bias. Available action tracking reports are all from the time period after April 2021 when there were only sporadic breakouts in China. Reports during the first wave of outbreaks in January and February 2021 are not available, when there are substantially large numbers of confirmed cases, are not available. Therefore, the results of this study are limited with regard to both location and time. Second, because we were only able to collect reports for only a proportion of the confirmed patients, important aspects might have been missed: for example, since reports for international travelers were not available and were therefore excluded from the dataset. Thus, the observation that flight is a relatively safe means of transportation is limited to domestic flights and cannot be extended to international flights. Third, because all data were collected after April 2021 in China, the results and conclusions of this study are limited to the situation where the pandemic is generally well-contained, and mask-wearing and temperature testingare generally adopted. Therefore, the results cannot be readily generalized to situations with high rates of new daily cases. Despite these limitations, this work presents the first attempt to analyze the detailed action reports of confirmed cases with comprehensive tracking information, and captures important aspects of transmission of SARS-CoV-2, such as community location...

    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.

    About SciScore

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  2. Version published to 10.1101/2021.04.08.21254953 on medRxiv