Skip to main content
Springer Nature Link
Log in

Enhancing Public Health Surveillance by Measurement of Similarity Using Rough Sets and GIS

  • Conference paper
  • First Online:
Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1048))

  • 1206 Accesses

Abstract

Public healthcare plans are essential to regions present around the world and deriving optimal strategies to combat the negative effects is one of the tasks involved in the process. Measuring similarity between two regions based on a common attribute is a popular problem, and this paper aims to find a method for the same, the common attribute being a key statistic taken from public health surveillance records. The method uses the application of rough measurement concept to determine the similarity between different regions having disease-linked death rates, which in turn can effectively be used to derive contingency plans in the case of a relevant event or before the next one takes place. We also take advantage of GIS tools to help in the processing and visualization of spatial data, and this paper discusses the role of GIS in public health surveillance as well.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11(5), 341–356 (1982)

    Article  Google Scholar 

  2. Boulos, M.N.K.: Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom. Int. J. Health Geogr. 3(1) (2004)

    Google Scholar 

  3. Liao, W.: The rough method for spatial data subzone similarity measurement. J. Geogr. Inf. Syst. 4(01) (2012)

    Article  Google Scholar 

  4. Gunaratna, N., Liu, Y., Park, J.: Spatial autocorrelation. J. Recuperado el 2 (2013)

    Google Scholar 

  5. Scotch, M., et al.: Exploring the role of GIS during community health assessment problem solving: experiences of public health professionals. Int. J. Health Geogr. 5(1) (2006)

    Google Scholar 

  6. Sharmila Banu, K., Tripathy, B.K.: Data analytics in spatial epidemiology: a survey. J. Technol. 78(10), 159–165 (2016)

    Google Scholar 

  7. Quantum Geographic Information System (software). https://www.qgis.org/

  8. Centers for Disease Control and Prevention. https://www.cdc.gov/

  9. Lenarčič, A.: Rough sets, similarity and optimal approximations. Ph.D. thesis, McMaster University, June (2017)

    Google Scholar 

  10. Braun-Blanquet, J: Pflanzensoziologie. Springer (1928)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India

    Priyansh Jain, Harshal Varday, K. Sharmila Banu & B. K. Tripathy

Authors
  1. Priyansh Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Harshal Varday
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. Sharmila Banu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. B. K. Tripathy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Priyansh Jain .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jain, P., Varday, H., Sharmila Banu, K., Tripathy, B.K. (2020). Enhancing Public Health Surveillance by Measurement of Similarity Using Rough Sets and GIS. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_21

Download citation

Publish with us

Policies and ethics