Social-economic drivers overwhelm climate in underlying the COVID-19 early growth rate

  1. Zhenghua Liu

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Abstract

Identifying the drivers underlying the spatial occurrence and spreading rate of COVID-19 can provide valuable information for their preventions and controls. Here, we examine how socio-economic and climate drivers affect the early growth rates of COVID-19 in China and the other countries, the former of which have consistently stricter quarantine during early epidemic and thus are used to enquire the influences of human interventions on trainsimissions. We find that the early growth rates of COVID-19 are higher in China than the other countries, which is consistent with previous reports. The global spread is mainly driven by the socio-economic factors such as GDP per capita, human movement and population density rather than climate. Among socio-economic factors, GDP per capita is most important showing negative relationships in China, while positive in the other countries. However, the predicability of early growth rates by socio-economic and climate variables is at least 1.6 times higher in China’s provinces than the other countries, which is further supported by metapopulation network model. These findings collectively indicate that the stochasticity of transimission processes decrease upon strict quarantine measures such as travel restrictions.

Key findings

  • GDP per capita is most important in driving the spread of COVID-19, which shows negative relationships within China, while positive in the other countries.

  • Socio-economic and climate factors are key in driving the early growth rate of COVID-19, while the former is more important.

  • Socio-economic and climate features explain more variations of early growth rates in China due to the decreased stochasticity of transimission processes upon strict quarantine measures.

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      SciScore for 10.1101/2021.09.10.21263383: (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
      Model simulations were performed in Python 3.6.3 [49], while analyses were in R 3.6.1 [50].
      Python
      suggested: (IPython, RRID:SCR_001658)

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

      Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

      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 10.1101/2021.09.10.21263383v1 on medRxiv