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Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan

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Listed:
  • Huang, Chuanli
  • Wang, Min
  • Rafaqat, Warda
  • Shabbir, Salman
  • Lian, Liping
  • Zhang, Jun
  • Lo, Siuming
  • Song, Weiguo

Abstract

We aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern.

Suggested Citation

  • Huang, Chuanli & Wang, Min & Rafaqat, Warda & Shabbir, Salman & Lian, Liping & Zhang, Jun & Lo, Siuming & Song, Weiguo, 2022. "Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    • Handle: RePEc:eee:soceps:v:80:y:2022:i:c:s0038012121000835
    DOI: 10.1016/j.seps.2021.101091
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    References listed on IDEAS

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    4. Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Jannsen, Nils & Kooths, Stefan & Mösle, Saskia, 2020. "German Economy Summer 2020 - German economy faces sluggish recovery," Kiel Institute Economic Outlook 68, Kiel Institute for the World Economy (IfW Kiel).
    5. Ocampo, Lanndon & Yamagishi, Kafferine, 2020. "Modeling the lockdown relaxation protocols of the Philippine government in response to the COVID-19 pandemic: An intuitionistic fuzzy DEMATEL analysis," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
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