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The Effect of Local and Global Interventions on Epidemic Spreading

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  • Jiarui Fan

    (School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China)

  • Haifeng Du

    (School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yang Wang

    (School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China)

  • Xiaochen He

    (School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Epidemic spreading causes severe challenges to the global public health system, and global and local interventions are considered an effective way to contain such spreading, including school closures (local), border control (global), etc. However, there is little study on comparing the efficiency of global and local interventions on epidemic spreading. Here, we develop a new model based on the Susceptible-Exposed-Infectious-Recovered (SEIR) model with an additional compartment called “quarantine status”. We simulate various kinds of outbreaks and interventions. Firstly, we predict, consistent with previous studies, interventions reduce epidemic spreading to 16% of its normal level. Moreover, we compare the effect of global and local interventions and find that local interventions are more effective than global ones. We then study the relationships between incubation period and interventions, finding that early implementation of rigorous intervention significantly reduced the scale of the epidemic. Strikingly, we suggest a Pareto optimal in the intervention when resources were limited. Finally, we show that combining global and local interventions is the most effective way to contain the pandemic spreading if initially infected individuals are concentrated in localized regions. Our work deepens our understandings of the role of interventions on the pandemic, and informs an actionable strategy to contain it.

Suggested Citation

  • Jiarui Fan & Haifeng Du & Yang Wang & Xiaochen He, 2021. "The Effect of Local and Global Interventions on Epidemic Spreading," IJERPH, MDPI, vol. 18(23), pages 1-13, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12627-:d:691696
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    References listed on IDEAS

    as
    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Weihsueh A. Chiu & Rebecca Fischer & Martial L. Ndeffo-Mbah, 2020. "State-level needs for social distancing and contact tracing to contain COVID-19 in the United States," Nature Human Behaviour, Nature, vol. 4(10), pages 1080-1090, October.
    3. Zhang, Xiaolei & Ma, Renjun & Wang, Lin, 2020. "Predicting turning point, duration and attack rate of COVID-19 outbreaks in major Western countries," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Per Block & Marion Hoffman & Isabel J. Raabe & Jennifer Beam Dowd & Charles Rahal & Ridhi Kashyap & Melinda C. Mills, 2020. "Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world," Nature Human Behaviour, Nature, vol. 4(6), pages 588-596, June.
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