Digital contact tracing and network theory to stop the spread of COVID-19 using big-data on human mobility geolocalization

  1. Matteo Serafino
  2. Higor S. Monteiro
  3. Shaojun Luo
  4. Saulo D. S. Reis
  5. Carles Igual
  6. Antonio S. Lima Neto
  7. Matías Travizano
  8. José S. Andrade
  9. Hernán A. Makse

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Abstract

The spread of COVID-19 caused by the SARS-CoV-2 virus has become a worldwide problem with devastating consequences. Here, we implement a comprehensive contact tracing and network analysis to find an optimized quarantine protocol to dismantle the chain of transmission of coronavirus with minimal disruptions to society. We track billions of anonymized GPS human mobility datapoints to monitor the evolution of the contact network of disease transmission before and after mass quarantines. As a consequence of the lockdowns, people’s mobility decreases by 53%, which results in a drastic disintegration of the transmission network by 90%. However, this disintegration did not halt the spreading of the disease. Our analysis indicates that superspreading k-core structures persist in the transmission network to prolong the pandemic. Once the k-cores are identified, an optimized strategy to break the chain of transmission is to quarantine a minimal number of ‘weak links’ with high betweenness centrality connecting the large k-cores.

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  1. Version published to 10.1371/journal.pcbi.1009865
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    SciScore for 10.1101/2020.08.12.20173476: (What is this?)

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    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

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  3. Version published to 10.1101/2020.08.12.20173476v1 on medRxiv