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Network DEA and Big Data with an Application to the Coronavirus Pandemic

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Data-Enabled Analytics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 312))

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Abstract

Network Data Envelopment Analysis (NDEA) has the potential to be usefully combined with Big Data sets. We first discuss the DEA technology coefficient matrix which incorporates certain Big Data characteristics including volume, velocity, and variety. In addition, we review potential problems that can arise in using DEA to estimate producer’s performance relative some true, but unobserved technology, and proposed aggregation methods to reduce the curse of dimensionality. The various form that NDEA models can take, including dynamic effects, spillovers between producers, joint production of desirable and undesirable outputs, and the reallocation of inputs, across time, to optimize production. An example of the use of NDEA is offered for the Covid Pandemic in the US. We find that an optimal reallocation of tests for Covid could have averted 10,800 deaths.

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Notes

  1. 1.

    We use the Haversine formula to calculate the great circle distance between two states j and k as Dist jk = a × cos−1{cosδ j cos δ k cos (λ j − λ k) +  sin δ j sin δ k} where a= radius of the earth in miles (2853.222 miles), δ j and δ k are the latitudes of j and k in radians and λ j and λ k are the longitudes of j and k in radians.

  2. 2.

    One could also impose joint weak input disposability between inputs (x t) and the two spillovers, b t − 1 and B t − 1 (see Fukuyama & Weber, 2017a, b). Joint weak input disposability would allow proportional expansions in inputs and spillovers.

  3. 3.

    States with 0 or negative values for 7 day moving average deaths were AK, HI, ID, ME, MT, ND, NH, SD, VT, WV, WY, and WA. States with negative values for seven day moving average tests were AL, KY, NC, and SC.

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Acknowledgments

We thank Shawna Grosskopf and Rolf Färe for helpful comments on an earlier draft of this paper.

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Authors and Affiliations

  1. Fukuoka University, Fukuoka, Japan

    Hirofumi Fukuyama

  2. Southeast Missouri State University, Cape Girardeau, MO, USA

    William L. Weber

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  1. Hirofumi Fukuyama
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Correspondence to Hirofumi Fukuyama .

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Editors and Affiliations

  1. Foisie Business School, Worcester Polytechnic Institute, Worcester, MA, USA

    Joe Zhu

  2. Buckingham Business School, University of Buckingham, Birmingham, UK

    Vincent Charles

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Fukuyama, H., Weber, W.L. (2021). Network DEA and Big Data with an Application to the Coronavirus Pandemic. In: Zhu, J., Charles, V. (eds) Data-Enabled Analytics. International Series in Operations Research & Management Science, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-75162-3_7

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