Skip to main content
SpringerLink
Log in

Constraint Programming for the Pandemic in Peru

  • Conference paper
  • First Online:
Applied Technologies (ICAT 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1388))

Included in the following conference series:

  • 840 Accesses

Abstract

Currently, the world requires techniques that match infected people and hospital beds together given various criteria such as the severity of infection, patient location, hospital capacity, etc. Deep Learning might seems to be a perfect fit for this: various configurations from a broad range of parameters that need to be reduced to a few solutions. But, this models require to be trained, hence the need for historical data on previous cases leading to a waste of time would in cleaning and consolidating a dataset and lengthy training sessions need to be performed with a variety of architectures.

Nevertheless, formulating this problem as a Constraint Satisfaction Problem (CSP), the aforementioned downsides will not be present while still optimal results, and without the need for any historical data. In this paper, a CSP model is used to search for the best distribution of COVID-19 patients with a severity of patients requiring hospitalization and patients requiring ICU beds, in hospitals in a part of Lima.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.who.int/news-room/commentaries/detail/estimating-mortality-from-covid-19.

  2. 2.

    COVID-19 Dashboard by John Hopkins University - https://bit.ly/3hRyIbg.

  3. 3.

    Open Data Peru - https://bit.ly/309k0GI.

  4. 4.

    Forbes Under-reporting of COVID-19 Deaths - https://bit.ly/3hSyhhd.

  5. 5.

    OR-tools - https://developers.google.com/optimization.

  6. 6.

    El Comercio - https://bit.ly/2Pd8bcs.

  7. 7.

    Cumulative covid-19 cases reported in the americas - https://bit.ly/39Tx9qV.

  8. 8.

    Villasís, G. - “El Comercio” - https://bit.ly/2CVeLlq.

  9. 9.

    MINSA - https://covid19.minsa.gob.pe/sala_situacional.asp.

  10. 10.

    MINSA - https://bit.ly/33aSMBv.

  11. 11.

    CNEPCE - https://bit.ly/3facgZm.

  12. 12.

    National Authority of Personal Data Protection - https://bit.ly/3ke9XY6.

  13. 13.

    https://covid19.orcebot.com/mapa-riesgo.

  14. 14.

    OR-tools - https://developers.google.com/optimization.

References

  1. Abdel-Basset, M., Mohamed, R., Elhoseny, M., Chakrabortty, R.K., Ryan, M.J.: A hybrid COVID-19 detection model using an improved marine predators algorithm and a ranking-based diversity reduction strategy. IEEE Access 8, 79521–79540 (2020)

    Article  Google Scholar 

  2. Adams, R., Ji, Y., Wang, X., Saria, S.: Learning models from data with measurement error: tackling underreporting. In: ICML (2019)

    Google Scholar 

  3. Aringhieri, R., Landa, P., Soriano, P., Tanfani, E., Testi, A.: A two level metaheuristic for the operating room scheduling and assignment problem. Comput. Oper. Res. 54, 21–34 (2015)

    Article  MathSciNet  Google Scholar 

  4. Ben Bachouch, R., Guinet, A., Hajri-Gabouj, S.: An integer linear model for hospital bed planning. Int. J. Prod. Econ. 140(2), 833–843 (2012)

    Article  Google Scholar 

  5. Biere, A., Heule, M., van Maaren, H., Walsh, T. (eds.): Handbook of Satisfiability. Frontiers in Artificial Intelligence and Applications, vol. 185. IOS Press, Amsterdam (2009)

    MATH  Google Scholar 

  6. Bistarelli, S., Faltings, B., Neagu, N.: Interchangeability with thresholds and degradation factors for soft CSPs. Ann. Math. Artif. Intell. 67(2), 123–163 (2013)

    Article  MathSciNet  Google Scholar 

  7. Brailsford, S.C., Vissers, J.: OR in healthcare: a European perspective. Eur. J. Oper. Res. 212(2), 223–234 (2011)

    Article  MathSciNet  Google Scholar 

  8. Cardoen, B., Demeulemeester, E., Beliën, J.: Operating room planning and scheduling: a literature review. Eur. J. Oper. Res. 201(3), 921–932 (2010)

    Article  Google Scholar 

  9. Che, M., Wang, L., Jiang, Z.: An approach to multidimensional medical data analysis based on the skyline operator. In: IEEM, pp. 1806–1810. IEEE (2018)

    Google Scholar 

  10. Chen, D., Deng, Y., Chen, Z., He, Z., Zhang, W.: A hybrid tree-based algorithm to solve asymmetric distributed constraint optimization problems. Auton. Agent. Multi-Agent Syst. 34(2), 1–42 (2020). https://doi.org/10.1007/s10458-020-09476-5

    Article  Google Scholar 

  11. Demeester, P., Souffriau, W., Causmaecker, P.D., Berghe, G.V.: A hybrid tabu search algorithm for automatically assigning patients to beds. Artif. Intell. Med. 48(1), 61–70 (2010)

    Article  Google Scholar 

  12. Fu, Z., Wu, Y., Zhang, H., Hu, Y., Zhao, D., Yan, R.: Be aware of the hot zone: A warning system of hazard area prediction to intervene novel coronavirus COVID-19 outbreak. In: SIGIR. ACM (2020)

    Google Scholar 

  13. Gavanelli, M.: An algorithm for multi-criteria optimization in CSPs. In: ECAI (2002)

    Google Scholar 

  14. Hu, S., et al.: Weakly supervised deep learning for COVID-19 infection detection and classification from CT images. IEEE Access 8, 118869–118883 (2020)

    Article  Google Scholar 

  15. Mahmud, T., Rahman, M.A., Fattah, S.A.: CovXNet: a multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest x-ray images with transferable multi-receptive feature optimization. Comp. Bio. and Med. 122, 103869 (2020)

    Article  Google Scholar 

  16. Marynissen, J., Demeulemeester, E.: Literature review on multi-appointment scheduling problems in hospitals. Eur. J. Oper. Res. 272(2), 407–419 (2019)

    Article  MathSciNet  Google Scholar 

  17. Nasrabadi, A.M., Najafi, M., Zolfagharinia, H.: Considering short-term and long-term uncertainties in location and capacity planning of public healthcare facilities. Eur. J. Oper. Res. 281(1), 152–173 (2020)

    Article  MathSciNet  Google Scholar 

  18. Rossi, F., van Beek, P., Walsh, T. (eds.): Handbook of Constraint Programming. Foundations of Artificial Intelligence, vol. 2. Elsevier, Amsterdam (2006)

    MATH  Google Scholar 

  19. Schiendorfer, A., Knapp, A., Anders, G., Reif, W.: MiniBrass: soft constraints for MiniZinc. Constraints Int. J. 23(4), 403–450 (2018)

    Article  MathSciNet  Google Scholar 

  20. Turhan, A.M., Bilgen, B.: Mixed integer programming based heuristics for the patient admission scheduling problem. Comput. Oper. Res. 80, 38–49 (2017)

    Article  MathSciNet  Google Scholar 

  21. Ugarte, W., Boizumault, P., Loudni, S., Crémilleux, B., Lepailleur, A.: Soft constraints for pattern mining. J. Intell. Inf. Syst. 44(2), 193–221 (2013). https://doi.org/10.1007/s10844-013-0281-4

    Article  MATH  Google Scholar 

  22. Ugarte, W., Loudni, S., Boizumault, P., Crémilleux, B., Termier, A.: Compressing and querying skypattern cubes. In: Wotawa, F., Friedrich, G., Pill, I., Koitz-Hristov, R., Ali, M. (eds.) IEA/AIE 2019. LNCS (LNAI), vol. 11606, pp. 406–421. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22999-3_36

    Chapter  Google Scholar 

  23. Vermeulen, I.B., Bohte, S.M., Elkhuizen, S.G., Lameris, H., Bakker, P.J.M., Poutré, H.L.: Adaptive resource allocation for efficient patient scheduling. Artif. Intell. Med. 46(1), 67–80 (2009)

    Article  Google Scholar 

  24. Waheed, A., Goyal, M., Gupta, D., Khanna, A., Al-Turjman, F., Pinheiro, P.R.: CovidGAN: data augmentation using auxiliary classifier GAN for improved covid-19 detection. IEEE Access 8, 91916–91923 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad Peruana de Ciencias Aplicadas (UPC), Lima, Peru

    Willy Ugarte

Authors
  1. Willy Ugarte
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Willy Ugarte .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Miguel Botto-Tobar

  2. Universidad de las Fuerzas Armadas (ESPE), Quito, Ecuador

    Sergio Montes León

  3. Escuela Politécnica Nacional, Quito, Ecuador

    Oscar Camacho

  4. Escuela Politécnica Nacional, Quito, Ecuador

    Danilo Chávez

  5. Universidad Técnica Particular de Loja, Loja, Ecuador

    Pablo Torres-Carrión

  6. Universidad Técnica del Norte, Ibarra, Ecuador

    Marcelo Zambrano Vizuete

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ugarte, W. (2021). Constraint Programming for the Pandemic in Peru. In: Botto-Tobar, M., Montes León, S., Camacho, O., Chávez, D., Torres-Carrión, P., Zambrano Vizuete, M. (eds) Applied Technologies. ICAT 2020. Communications in Computer and Information Science, vol 1388. Springer, Cham. https://doi.org/10.1007/978-3-030-71503-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-71503-8_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-71502-1

  • Online ISBN: 978-3-030-71503-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation