Skip to main content
SpringerLink
Log in

Modeling the Impact of Behavior Change on the Spread of Ebola

  • Chapter
  • First Online:
Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases

Abstract

We create a compartmental mathematical model to analyze the role of behavior change in slowing the spread of the Ebola virus disease (EVD) in the 2014–2015 Western Africa epidemic. Our model incorporates behavior change, modeled as decreased contact rates between susceptible and infectious individuals, the prevention of traditional funerals, and/or increased access to medical facilities. We derived the basic reproductive number for the model, and approximated the parameter values for the spread of the EVD in Monrovia. We used sensitivity analysis to quantify the relative importance of the timing, and magnitude, of the population reducing their contact rates, avoiding the traditional burial practices, and having access to medical treatment facilities. We found that reducing the number of contacts made by infectious individuals in the general population is the most effective intervention method for mitigating an EVD epidemic. While healthcare interventions delayed the onset of the epidemic, healthcare alone is insufficient to stop the epidemic in the model.

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
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. All Africa.: Liberia: Ellen Enforces Cremation as Measure Against Ebola (2014). Available from: http://allafrica.com/stories/201408051276.html

  2. Arino, J., Van den Driessche, P.: A multi-city epidemic model. Math. Popul. Stud. 10(3), 175–193 (2003)

    Google Scholar 

  3. Arriola, L., Chowell, G., Hyman, J.M., Bettencourt, L.M.A., Castillo-Chavez, C.: Sensitivity Analysis for Quantifying Uncertainty in Mathematical Models (2009)

    Google Scholar 

  4. Arriola, L.M., Hyman, J.M.: Being sensitive to uncertainty. Comput. Sci. Eng. 9(2), 10–20 (2007)

    Google Scholar 

  5. Browne, C., Huo, X., Magal, P., Seydi, M., Seydi, O., Webb, G.: A Model of the 2014 Ebola Epidemic in West Africa (2014). arXiv:1410.3817 [q-bio.PE]

  6. Bwaka, M.A., Bonnet, M.J., Calain, P., Colebunders, R., De Roo, A., Guimard, Y., Katwiki, K.R., Kibadi, K., Kipasa, M.A., Kuvula, K.J. et al.: Ebola Hemorrhagic Fever in Kikwit, Democratic Republic of the Congo: Clinical Observations in 103 Patients. J. Infect. Dis. 179(Supplement 1), S1–S7 (1999)

    Google Scholar 

  7. Camacho, A., Kucharski, A.J., Funk, S., Breman, J., Piot, P., Edmunds, W.J.: Potential for large outbreaks of ebola virus disease. Epidemics 9, 70–78 (2014)

    Google Scholar 

  8. Chowell, G., Hengartner, N.W., Castillo-Chavez, C., Fenimore, P.W., Hyman, J.M.: The basic reproductive number of ebola and the effects of public health measures: the cases of congo and uganda. J. Theor. Biol. 229(1), 119–126 (2004)

    Google Scholar 

  9. Althaus, C.L.: Estimating the reproduction number of ebola virus (EBOV) during the 2014 outbreak in West Africa. PLOS Curr. Outbreaks (2014)

    Google Scholar 

  10. Rivers, C.M., Lofgren, E.T., Marathe, M., Eubank, S., Lewis, B.L.: Modeling the impact of interventions on an epidemic of ebola in sierra leone and liberia. PLOS Curr. Outbreaks (2014)

    Google Scholar 

  11. Valle, S.Y.D., Hyman, J.M., Hethcote, H.W., Eubank, S.G.: Mixing patterns between age groups in social networks. Soc. Netw. 29(4), 539–554 (2007)

    Google Scholar 

  12. Diekmann, O., Heesterbeek, J.A.P., Metz, J.A.J.: On the definition and the computation of the basic reproduction ratio r 0 in models for infectious diseases in heterogeneous populations. J. Math. Biol. 28(4), 365–382 (1990)

    Google Scholar 

  13. Eubank, S., Guclu, H., Kumar, V.S.A., Marathe, M.V., Srinivasan, A., Toroczkai, Z., Wang, N.: Modelling disease outbreaks in realistic urban social networks. Nature 429(6988), 180–184 (2004)

    Google Scholar 

  14. Tuite, A., Fisman, D., Khoo, E.: Early epidemic dynamics of the West African 2014 ebola outbreak: Estimates derived with a simple two-parameter model. PLOS Curr. Outbreaks (2014)

    Google Scholar 

  15. Garrett, L.: Ebola: Story of an Outbreak. Hachette Books, New York (2014)

    Google Scholar 

  16. Gomes, M.F.C., Piontti, A.P., Rossi, L., Chao, D., Longini, I., Halloran, M.E., Vespignani, A.: Assessing the international spreading risk associated with the 2014 West African ebola outbreak. PLOS Curr. Outbreaks (2014)

    Google Scholar 

  17. Thomas House. Epidemiological Dynamics of Ebola Outbreaks. eLife (September 2014)

    Google Scholar 

  18. Hyman, J.M., LaForce, T.: Modeling the spread of influenza among cities. Bioterrorism Math. Model. Appl. Homel. Secur. 211–236 (2003)

    Google Scholar 

  19. Hyman, J.M., Li, J.: Disease transmission models with biased partnership selection. Appl. Numer. Math. 24(2), 379–392 (1997)

    Google Scholar 

  20. Hyman, J.M., Li, J., Stanley, E.A.: The initialization and sensitivity of multigroup models for the transmission of HIV. J. Theor. Biol. 208(2), 227–249 (2001)

    Google Scholar 

  21. Legrand, J., Grais, R.F., Boelle, P.Y., Valleron, A.J., Flahault, A.: Understanding the dynamics of ebola epidemics. Epidemiol. Infect. 135(04), 610–621 (2007)

    Google Scholar 

  22. Lofgren, E.T., Rivers, C.M., Marathe, M.V., Eubank, S.G., Lewis, B.L.: The Potential Impact of Increased Hospital Capacity to Contain and Control Ebola in Liberia (2014). arXiv:1410.8207

  23. Maganga, G.D., Kapetshi, J., Berthet, N., Ilunga, B.K., Kabange, F., Kingebeni, P.M., Mondonge, V., Muyembe, J.-J.T., Bertherat, E., Briand, S., et al.: Ebola virus disease in the democratic republic of congo. N. Engl. J. Med. 371(22), 2083–2091 (2014)

    Google Scholar 

  24. Manore, C., McMahon, B., Fair, J., Hyman, J.M., Brown, M., LaBute, M.: Disease properties, geography, and mitigation strategies in a simulation spread of rinderpest across the United States. Vet. Res. 42(1), 1–12 (2011)

    Google Scholar 

  25. Manore, C.A., Hickmann, K.S., Xu, S., Wearing, H.J., Hyman, J.M.: Comparing dengue and chikungunya emergence and endemic transmission in a. aegypti and a. albopictus. J. Theor. Biol. 356, 174–191 (2014)

    Google Scholar 

  26. McMahon, B., Manore, C., Hyman, J., LaBute, M., Fair, J.: Coupling Vector-host Dynamics with Weather, Geography and Mitigation Measures to Model Rift Valley Fever in Africa. Submitted (2014)

    Google Scholar 

  27. Martin, I.M., Atkins, C.Y., Santibanez, S., Knust, B., Petersen, B.W., Ervin, E.D., Nichol, S.T., Damon, I.K., Washington, M.L.: Estimating the future number of cases in the ebola epidemicliberia and sierra leone, 2014–2015. MMWR Surveill Summ 63(suppl 3), 1–14 (2014)

    Google Scholar 

  28. Nishiura, H., Chowell, G.: Early transmission dynamics of ebola virus disease (EVD), West Africa, March to August 2014. Eurosurveillance, 19(36) (2014)

    Google Scholar 

  29. University of Pittsburg MIDAS National Center of Excellence. Framework for Reconstructing Epidemiological Dynamics (2012). https://midas.pitt.edu/index.php?option=com_content&view=article&id=78&Itemid=72

  30. World Health Organization. Ebola Epidemic Liberia, Marchoctober 2014 (2014). Available from: http://www.cdc.gov/mmwr/preview/mmwrhtml/mm63e1114a4.htm

  31. World Health Organization. Who Ebola Data and Statistics (2016). Available from: http://time.com/3478238/ebola-liberia-burials-cremation-burned

  32. Parpia, A.S., Ndeffo-Mbah, M.L., Wenzel, N.S., Galvani, A.P.: Effects of response to 2014–2015 ebola outbreak on deaths from malaria, HIV/AIDS, and tuberculosis, West Africa. Emerg. Infect. Dis. 22(3), 433–441 (2016)

    Google Scholar 

  33. Stroud, P., Del Valle, S., Sydoriak, S., Riese, J., Mniszewski, S.: Spatial dynamics of pandemic influenza in a massive artificial society. J. Artif. Soc. Soc. Simul. 10(4), 9 (2007)

    Google Scholar 

  34. WHO Ebola Response Team. Ebola virus disease in west africathe first 9 months of the epidemic and forward projections. N. Engl. J. Med. 371(16), 1481–1495 (2014)

    Google Scholar 

  35. Time. Liberia burns its bodies as ebola fears run rampant (2014). Available from: http://apps.who.int/gho/data/node.ebola-sitrep

  36. Troncoso, A.: Ebola outbreak in West Africa: a neglected tropical disease. Asian Pac. J. Tropical Biomed. 5(4), 255–259 (2015)

    Google Scholar 

  37. Van den Driessche, P., Watmough, J.: Reproduction numbers and sub-threshold endemic equilibria for compartmental models of disease transmission. Math. Biosci. 180(1), 29–48 (2002)

    Google Scholar 

  38. Van Kerkhove, M.D., Bento, A.I., Mills, H.L., Ferguson, N.M., Donnelly, C.A.: A review of epidemiological parameters from ebola outbreaks to inform early public health decision-making. Sci. Data, 2 (2015)

    Google Scholar 

  39. Xue, L., Scott, H.M., Cohnstaedt, L.W., Scoglio, C.: A network-based meta-population approach to model Rift Valley fever epidemics. J. Theor. Biol. (2012)

    Google Scholar 

Download references

Acknowledgments

MH, LX, and JD were partially supported by NIH/NIGMS Models of Infectious Disease Agent Study (MIDAS) grants U01-GM097658-01 and U01-GM097661-01. This work was also partially supported by the NSF/DEB RAPID award B53035G and the Louisiana Board of Regents, SURE program.

Author information

Authors and Affiliations

  1. Department of Mathematics, Tulane University, New Orleans, LA, 70118, USA

    Jessica R. Conrad, Ling Xue, Jeremy Dewar & James M. Hyman

Authors
  1. Jessica R. Conrad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ling Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeremy Dewar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. James M. Hyman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James M. Hyman .

Editor information

Editors and Affiliations

  1. School of Public Health, Georgia State University, Atlanta, Georgia, USA

    Gerardo Chowell

  2. Department of Mathematics, Tulane University, New Orleans, Louisiana, USA

    James M. Hyman

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Conrad, J.R., Xue, L., Dewar, J., Hyman, J.M. (2016). Modeling the Impact of Behavior Change on the Spread of Ebola. In: Chowell, G., Hyman, J. (eds) Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases. Springer, Cham. https://doi.org/10.1007/978-3-319-40413-4_2

Download citation

Publish with us

Policies and ethics

Search

Navigation