Skip to main content
SpringerLink
Log in

Malaria Control: Epidemic Progression Calculation Based on Individual Mobility Data

  • Conference paper
  • First Online:
Book cover Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 306))

  • 373 Accesses

Abstract

Malaria remains an endemic disease in many developing countries around the world. For several decades, various malaria control strategies have been developed and implemented in many countries. Many fields like medicine, biology, physics, mathematics, and computer sciences are used to set up these strategies. Mathematical models have proven to be very useful in understanding malaria progression and dynamics. Mainly, these models are based on differential equations and take into account the clinical and biological characteristics of patients and vectors. They made it possible to study the parts of the population which pass from a state of exposure to the disease to different possible states as a result. These mathematical models in general consider global estimates of the evolution of the epidemic based on unspecified percentages of the population. Individuals are considered fairly evenly. However, the health status of each individual as well as their own daily activities can have a particular influence on the epidemic evolution. The objective of this paper is to propose a model based on a daily evolution of the individual’s condition, which depends on their mobility and the characteristics of the territory they visit. Thus, mobility data of a single person moving from one area to another is used to calculate disease progression and predict outcomes. We implement our solution and demonstrate its effectiveness through empirical experiments. The results show how promising the model is in providing possible information on the failure of disease control in the Kédougou region.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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.

    www-bd.lip6.fr/wiki/site/recherche/projets/m4e/start.

References

  1. Armbrust, M., et al.: Spark SQL: relational data processing in spark. In: SIGMOD International Conference on Management of Data, pp. 1383–1394. ACM (2015)

    Google Scholar 

  2. Arthur, S.: Malaria incubation period (2017). http://malaria.emedtv.com/malaria/malaria-incubation-period.html. Accessed 29 Mar 2017

  3. Barrett, C.L., Bisset, K.R., Eubank, S.G., Feng, X., Marathe, M.V.: Episimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks. In: SC 2008: Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, pp. 1–12 (2008)

    Google Scholar 

  4. Bisset, K.R., Chen, J., Feng, X., Kumar, V.A., Marathe, M.V.: Epifast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems. In: Proceedings of the 23rd International Conference on Supercomputing. p. 430–439. ICS 2009, Association for Computing Machinery, New York, NY, USA (2009). https://doi.org/10.1145/1542275.1542336

  5. Bratman, M.: Intention, Plans, and Practical Reason. Harvard University Press, Cambridge, MA (1987)

    Google Scholar 

  6. Chen, Y..-D.., Chen, H.., King, C..-C..: Social network analysis for contact tracing. In: Infectious Disease Informatics and Biosurveillance. ISIS, vol. 27, pp. 339–358. Springer, Boston, MA (2011). https://doi.org/10.1007/978-1-4419-6892-0_15

    Chapter  Google Scholar 

  7. Chitnis, N., Cushing, J., Hyman, J.: Bifurcation analysis of a mathematical model for malaria transmission. SIAM J. Appl. Math. 67(1), 24–45 (2006). https://doi.org/10.1137/050638941

    Article  MathSciNet  MATH  Google Scholar 

  8. Chitnis, N., Hyman, J.M., Cushing, J.M.: Determining important parameters in the spread of malaria through the sensitivity analysis of a mathematical model. Bull. Math. Biol. 70(5), 1272–1296 (2008). https://doi.org/10.1007/s11538-008-9299-0

    Article  MathSciNet  MATH  Google Scholar 

  9. Cook, V.J., et al.: Transmission network analysis in tuberculosis contact investigations. J. Infect. Dis. 196(10), 1517–1527 (2007). https://doi.org/10.1086/523109

    Article  Google Scholar 

  10. Dharmawardena, P., et al.: Response of imported malaria patients to antimalarial medicines in Sri Lanka following malaria elimination. PLOS ONE 12(11), 1–14 (2017). https://doi.org/10.1371/journal.pone.0188613

    Article  Google Scholar 

  11. Diekmann, O., Heesterbeek, J.: Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation. Wiley Series in Mathematical and Computational Biology, Wiley (2000). https://books.google.sn/books?id=5VjSaAf35pMC

  12. Dimitrov, N.B., Meyers, L.A.: Mathematical Approaches to Infectious Disease Prediction and Control, Chapter 1, pp. 1–25. INFORMS (2010). https://doi.org/10.1287/educ.1100.0075

  13. For Disease Control, C., Prevention (2015). https://www.cdc.gov/malaria/about/disease.html. Accessed 29 May 2017

  14. Filipe, J.A., Riley, E.M., Drakeley, C.J., Sutherland, C.J., Ghani, A.C.: Determination of the processes driving the acquisition of immunity to malaria using a mathematical transmission model. PLoS Comput. Biol. 3(12), 1–11 (2007)

    Article  MathSciNet  Google Scholar 

  15. Gharbi, M., et al.: Surveillance of travellers: an additional tool for tracking antimalarial drug resistance in endemic countries. PLOS ONE 8(10), 1–11 (2013). https://doi.org/10.1371/journal.pone.0077775

    Article  Google Scholar 

  16. Gouvernement du Sénégal: National malaria control program (2017). http://www.pnlp.sn/. Accessed 25 Jan 2019

  17. Greenwood, B., Marsh, K., Snow, R.: Why do some african children develop severe malaria? Parasitol. Today 7(10), 277–281 (1991)

    Article  Google Scholar 

  18. Gu, W., Killeen, G.F., Mbogo, C.M., Regens, J.L., Githure, J.I., Beier, J.C.: An individual-based model of plasmodium falciparum malaria transmission on the coast of Kenya. Trans. Royal Soc. Trop. Med. Hyg. 97(1), 43–50 (2003). https://doi.org/10.1016/S0035-9203(03)90018-6

    Article  Google Scholar 

  19. Gu, W., Mbogo, C.M., Githure, J.I., Regens, J.L., Killeen, G.F., Swalm, C.M., Yan, G., Beier, J.C.: Low recovery rates stabilize malaria endemicity in areas of low transmission in coastal kenya. Acta Tropica 86(1), 71–81 (2003). https://doi.org/10.1016/S0001-706X(03)00020-2, http://www.sciencedirect.com/science/article/pii/S0001706X03000202

  20. He, S., Peng, Y., Sun, K.: Seir modeling of the COVID-19 and its dynamics. Nonlinear Dyn. 101(3), 1667–1680 (2020)

    Article  Google Scholar 

  21. Jolly, A.M., Wylie, J.L.: Gonorrhoea and chlamydia core groups and sexual networks in manitoba. Sex. Trans. Infect. 78(suppl 1), i145–i151 (2002). https://sti.bmj.com/content/78/suppl_1/i145

  22. Kermack, W.O., McKendrick, A.G.: Contributions to the mathematical theory of epidemics. III. Further studies of the problem of endemicity. R. Soc. of Lond. Ser. A 141(843), 94–122 (1933)

    Google Scholar 

  23. Kermack, W.O., McKendrick, A.G.: A contribution to the mathematical theory of epidemics. R. Soc. Lond. Ser. A 115(772), 700–721 (1927)

    MATH  Google Scholar 

  24. Khiri, L.B., Gueye, I., Naacke, H., Sarr, I., Gançarski, S.: A malaria control model using mobility data: an early explanation of kedougou case in senegal. In: Rango, F.D., Ören, T.I., Obaidat, M.S. (eds.) Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2020, Lieusaint, Paris, France, 8–10 July 2020, pp. 35–46. ScitePress (2020). https://doi.org/10.5220/0009591800350046

  25. Klovdahl, A.S.: Social networks and the spread of infectious diseases: the aids example. Soc. Sci. Med. 21(11), 1203–1216 (1985). https://doi.org/10.1016/0277-9536(85)90269-2, http://www.sciencedirect.com/science/article/pii/0277953685902692

  26. Koella, J.C.: On the use of mathematical models of malaria transmission. Acta Trop. 49(1), 1–25 (1991)

    Article  Google Scholar 

  27. Lechthaler, F., et al.: Trends in reported malaria cases and the effects of malaria control in the democratic republic of the congo. PLOS ONE 14(7), 1–20 (2019). https://doi.org/10.1371/journal.pone.0219853

    Article  Google Scholar 

  28. Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets, 2nd Ed. Cambridge University Press (2014). http://www.mmds.org/

  29. Lin, Q., Zhao, S., Gao, D., Lou, Y., Yang, S., Musa, S.S., Wang, M.H., Cai, Y., Wang, W., Yang, L., He, D.: A conceptual model for the coronavirus disease 2019 (COVID-19) outbreak in Wuhan, china with individual reaction and Governmental action. Int. J. Infect. Dis. 93, 211–216 (2020)

    Article  Google Scholar 

  30. MacQueen, K.M.: The epidemiology of HIV transmission: trends, structure and dynamics. Ann. Rev. Anthropol. 23, 509–526 (1994)

    Article  Google Scholar 

  31. Mandal, S., Sarkar, R.R., Sinha, S.: Mathematical models of malaria - a review. Malaria J. 10(1), 1–19 (2011). https://doi.org/10.1186/1475-2875-10-202

    Article  Google Scholar 

  32. National Agency of Statistics and Demography (ANSD): Projection de la population de la région de kedougou - 2013–2025 (2013). http://www.ansd.sn/ressources/publications/indicateurs/Projections-demographiques-2013-2025+.htm. Accessed 02 May 2019

  33. Odell, P.R.: Book Review: Bailey, n.t.j. 1975: The Mathematical Theory of Infectious Diseases and its Application. London: Griffin: Thom, r. 1975: Structural Stability and Morphogenesis. Reading, Massachusetts: Benjamin. Progress in Human Geography, 7(3), 442–444 (1983)

    Google Scholar 

  34. Oldham, P.D.: The mathematical theory of epidemics. J. R. Stat. Soc. Ser. C 8(1), 60–61 (1959). https://ideas.repec.org/a/bla/jorssc/v8y1959i1p60-61.htm

  35. Packer, S., Green, C., Brooks-Pollock, E., Chaintarli, K., Harrison, S., Beck, C.: Social network analysis and whole genome sequencing in a cohort study to investigate tb transmission in an educational setting. BMC Infect. Dis. 19 (2019)

    Google Scholar 

  36. Parham, P.: The Immune System. No. in The Immune System, Garland Science, p. 246 (2009). https://books.google.sn/books?id=mVRRAAAACAAJ

  37. Parham, P., Ferguson, N.: Space and contact networks: capturing the locality of disease transmission. J. R. Soc. Interface 3, 483–493 (2006). https://doi.org/10.1098/rsif.2005.0105

    Article  Google Scholar 

  38. PNLP: Bulletin de surveillance sentinelle du paludisme (2017). http://www.pnlp.sn/bulletin-de-surveillance-sentinelle-du-paludisme/. Accessed 30 Aug 2019

  39. Ross, R.: The Prevention of Malaria. John Murray, Albemarle Street (1911). https://books.google.fr/books?id=GDUzAQAAMAAJ

  40. Ruktanonchai, N.W., et al.: Identifying malaria transmission foci for elimination using human mobility data. PLOS Comput. Biol. 12(4), 1–19 (2016). https://doi.org/10.1371/journal.pcbi.1004846

    Article  Google Scholar 

  41. Sattenspiel, L., Dietz, K.: A structured epidemic model incorporating geographic mobility among regions. Math. Biosci. 128(1), 71–91 (1995). https://doi.org/10.1016/0025-5564(94)00068-B

    Article  MATH  Google Scholar 

  42. Stattner, E., Vidot, N.: Social network analysis in epidemiology: Current trends and perspectives. In: International Conference on Research Challenges in Information Science, pp. 1–11 (2011)

    Google Scholar 

  43. Thiam, S., et al.: Major reduction in anti-malarial drug consumption in Senegal after nation-wide introduction of malaria rapid diagnostic tests. PLOS ONE 6(4), 1–7 (2011)

    Article  Google Scholar 

  44. Watts, D.J., Muhamad, R., Medina, D.C., Dodds, P.S.: Multiscale, resurgent epidemics in a hierarchical metapopulation model. Proc. Natl. Acad. Sci. 102(32), 11157–11162 (2005). https://doi.org/10.1073/pnas.0501226102

    Article  Google Scholar 

  45. WHO Inc.: OMS | 10 faits sur le paludisme (2016). http://www.who.int/features/factfiles/malaria/fr/ Accessed 15 May 2017

Download references

Author information

Authors and Affiliations

  1. Ecole Polytechnique de Thiès, LTISI, Thies, Senegal

    Ibrahima Gueye

  2. Sorbonne Université, Laboratoire d’Informatique de Paris 6, LIP6, 75005, Paris, France

    Hubert Naacke, Lynda Bouzid Khiri & Stéphane Gancarski

  3. Department of Mathematics and Computer Science, Cheikh Anta Diop University, Dakar - Fann BP, 5005, Senegal

    Idrissa Sarr

Authors
  1. Ibrahima Gueye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hubert Naacke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Idrissa Sarr
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lynda Bouzid Khiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Stéphane Gancarski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ibrahima Gueye .

Editor information

Editors and Affiliations

  1. KASIT, University of Jordan, Amman, Jordan

    Mohammad S. Obaidat

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Oren

  3. University of Calabria, Rende, Cosenza, Italy

    Floriano De Rango

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gueye, I., Naacke, H., Sarr, I., Khiri, L.B., Gancarski, S. (2022). Malaria Control: Epidemic Progression Calculation Based on Individual Mobility Data. In: Obaidat, M.S., Oren, T., Rango, F.D. (eds) Simulation and Modeling Methodologies, Technologies and Applications. SIMULTECH 2020. Lecture Notes in Networks and Systems, vol 306. Springer, Cham. https://doi.org/10.1007/978-3-030-84811-8_8

Download citation

Publish with us

Policies and ethics

Search

Navigation