1887
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.

Modeling the Epidemiology of Cholera to Prevent Disease Transmission in Developing Countries

MyBook is a cheap paperback edition of the original book and will be sold at uniform, low price.
  • PDF
    273.45 Kb
  • XML
    50.27 Kb
  • HTML
    44.07 Kb
  • Authors: Zindoga Mukandavire1, J. Glenn Morris Jr2
  • Editor: Michael Sadowsky3
  • VIEW AFFILIATIONS HIDE AFFILIATIONS
    Affiliations: 1: Social and Mathematical Epidemiology Group, London School of Hygiene and Tropical Medicine, London, UK; 2: Emerging Pathogens Institute, University of Florida, Gainesville, FL 32610; 3: University of Minnesota, St. Paul, MN
  • Source: microbiolspec June 2015 vol. 3 no. 3 doi:10.1128/microbiolspec.VE-0011-2014
  • Received 23 December 2014 Accepted 03 March 2015 Published 19 June 2015
  • J. Glenn Morris Jr, jgmorris@epi.ufl.edu
image of Modeling the Epidemiology of Cholera to Prevent Disease Transmission in Developing Countries
    Preview this microbiology spectrum article:
    Zoom in
    Zoomout

    Modeling the Epidemiology of Cholera to Prevent Disease Transmission in Developing Countries, Page 1 of 2

    | /docserver/preview/fulltext/microbiolspec/3/3/VE-0011-2014-1.gif /docserver/preview/fulltext/microbiolspec/3/3/VE-0011-2014-2.gif
  • Abstract:

    Cholera remains an important global cause of morbidity and mortality, which is capable of causing periodic epidemic disease. A number of mathematical models have been developed to help in understanding the dynamics of cholera outbreaks and for use as a tool in planning interventions, including vaccination campaigns. We have explored the utility of models in assessing the spread of cholera in the recent epidemics in Zimbabwe and Haiti. In both instances, a mathematical model was formulated and fitted to cumulative cholera cases to estimate the basic reproductive number ℛ, and the partial reproductive numbers reflecting potential differences in environmental-to-human versus human-to-human transmission were quantified. In Zimbabwe, estimated ℛ for the epidemic using aggregated data at the national level was 1.15; in Haiti, it was 1.55. However, when calculated at a provincial/departmental level, estimated basic reproductive numbers were highly heterogeneous, with a range of 1.11 to 2.72 in Zimbabwe and 1.06 to 2.63 in Haiti. Our models suggest that the underlying patterns of cholera transmission varied widely from region to region, with a corresponding variation in the amenability of outbreaks to control measures such as immunization. These data underscore the heterogeneity of transmission dynamics, potentially linked to differences in environment, socio-economic conditions, and cultural practices. They also highlight the potential utility of these types of models in guiding development of public health intervention strategies.

  • Citation: Mukandavire Z, Morris J. 2015. Modeling the Epidemiology of Cholera to Prevent Disease Transmission in Developing Countries. Microbiol Spectrum 3(3):VE-0011-2014. doi:10.1128/microbiolspec.VE-0011-2014.

Key Concept Ranking

Infectious Diseases
0.55932313
Cholera
0.5
Infectious Dose
0.4877969
Vibrio cholerae
0.47724676
0.55932313

References

1. Pollitzer R. 1959. Cholera. World Health Organization, Geneva, Switzerland.
2. WHO. 2008. Outbreak news. Cholera, Zimbabwe. Wkly Epidemiol Rec 83:449–450. [PubMed]
3. Koenig R. 2009. Public health, international groups battle cholera in Zimbabwe. Science 323:860–861. [PubMed][CrossRef]
4. Centers for Disease Control and Prevention. 2011. Haiti cholera outbreak: cholera confirmed in Haiti, October 21, 2010. http://www.cdc.gov/haiticholera/situation/. Accessed February 10, 2011.
5. Mukandavire Z, Liao S, Wang J, Gaff H, Smith DL, Morris JG Jr. 2011. Estimating the reproductive numbers for the 2008–2009 cholera outbreaks in Zimbabwe. Proc Natl Acad Sci USA 108:8767–8772. [PubMed][CrossRef]
6. Mukandavire Z, Smith DL, Morris JG Jr. 2013. Cholera in Haiti: reproductive numbers and vaccination coverage estimates. Sci Rep 3:997. [PubMed][CrossRef]
7. Merrell DS, Butler SM, Qadri F, Dolganov NA, Alam A, Cohen MB, Calderwood SB, Schoolnik GK, Camilli A. 2002. Host-induced epidemic spread of the cholera bacterium. Nature 417:642–645. [PubMed][CrossRef]
8. Hartley DM, Morris JG Jr, Smith DL. 2006. Hyperinfectivity: A critical element in the ability of V. cholerae to cause epidemics? PLoS Med 3:e7. [PubMed][CrossRef]
9. Nelson EJ, Harris JB, Morris JG Jr, Calderwood SB, Camilli A. 2009. Cholera transmission: the host, pathogen, and bacteriophage dynamic. Nat Rev Microbiol 7:693–702. [PubMed][CrossRef]
10. Morris JG Jr. 2011. Cholera: modern pandemic disease of ancient lineage. Emerg Infect Dis 17:2099–2104. [PubMed][CrossRef]
11. Franco AA, Fix AD, Prada A, Paredes E, Palomino JC, Wright AC, Johnson JA, McCarter R, Guerra H, Morris JG Jr. 1997. Cholera in Lima, Peru, correlates with prior isolation of Vibrio cholerae from the environment. Am J Epidemiol 146:1067–1075. [PubMed][CrossRef]
12. Huq A, Sack RB, Nizam A, Longini IM, Nair GB, Ali A, Morris JG Jr, Khan MN, Siddique AK, Yunus M, Albert MJ, Sack DA, Colwell RR. 2005. Critical factors influencing the occurrence of Vibrio cholerae in the environment in Bangladesh. Appl Environ Microbiol 71:4645–4654. [PubMed][CrossRef]
13. Rodo X, Pascual M, Fuchs G, Faruque AS. 2002. ENSO and cholera: a nonstationary link related to climate change? Proc Natl Acad Sci USA 99:12901–12906. [PubMed][CrossRef]
14. Nelson EJ, Chowdhury A, Flynn J, Schild S, Bourassa L, Shao Y, LaRocque RC, Calderwood SB, Qadri F, Camilli A. 2008. Transmission of Vibrio cholerae is antagonized by lytic phage and entry into the aquatic environment. PLoS Pathog 4:e1000187. [PubMed][CrossRef]
15. Martinelli Filho JE, Lopes RM, Rivera ING, Colwell RR. 2011. Vibrio cholerae O1 detection in estuarine and coatal zooplankton. J Plankton Res 33:51–62. [CrossRef]
16. Ministry of Public Health and Population. 2011. MSPP website. http://mspp.gouv.ht/site/index.php?option=com_content&view=article&id=57& Itemid=1; Accessed February 2, 2011.
17. Lucas M, Deen JL, von Seidlein L, Wang XY, Ampuero J, Puri M, Ali M, Ansaruzzaman M, Amos J, Cavailler P, Guerin P, Mahoudeau C, Kahozi P, Chaignat CL, Barreto A, Songane FF, Clemens JD. 2005. Effectiveness of mass oral cholera vaccination in Beira, Mozambique. N Engl J Med 352:757–767. [PubMed][CrossRef]
18. Gangarosa EJ, Mosley WH. 1974. Epidemiology and surveillance of cholera, p 381–403. In Barua D, Burrows W (ed), Cholera. W. B. Saunders, Philadelphia, PA.
19. Li J, Blakeley D, Smith RJ. 2011. The failure of R0. Comput Math Methods Med 2011:527610. [PubMed][CrossRef]
20. Chunara R, Andrews JR, Brownstein JS. 2012. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. Am J Trop Med Hyg 86:39–45. [PubMed][CrossRef]
21. Bertuzzo E, Mari L, Righetto L, Gatto M, Casagrandi R, Blokesch M, Rodriguez-Iturbe I, Rinaldo A. 2011. Prediction of the spatial evolution and effects of control measures for the unfolding Haiti cholera outbreak. Geophys Res Lett 38:L06403. [CrossRef]
22. Chao DL, Halloran ME, Longini IM. 2011. Vaccination strategies for epidemic cholera in Haiti with implications for the developing world. Proc Natl Acad Sci USA 108:7081–7085. [PubMed][CrossRef]
23. Tuite AR, Tien J, Eisenberg M, Earn DJD, Ma J, Fisman DN. 2011. Cholera epidemic in Haiti, 2010: using a transmission model to explain spatial spread of disease and identify optimal control interventions. Ann Intern Med 154:293–302. [PubMed][CrossRef]
microbiolspec.VE-0011-2014.citations
cm/3/3
content/journal/microbiolspec/10.1128/microbiolspec.VE-0011-2014
Loading

Citations loading...

Loading

Article metrics loading...

/content/journal/microbiolspec/10.1128/microbiolspec.VE-0011-2014
2015-06-19
2017-03-25

Abstract:

Cholera remains an important global cause of morbidity and mortality, which is capable of causing periodic epidemic disease. A number of mathematical models have been developed to help in understanding the dynamics of cholera outbreaks and for use as a tool in planning interventions, including vaccination campaigns. We have explored the utility of models in assessing the spread of cholera in the recent epidemics in Zimbabwe and Haiti. In both instances, a mathematical model was formulated and fitted to cumulative cholera cases to estimate the basic reproductive number ℛ, and the partial reproductive numbers reflecting potential differences in environmental-to-human versus human-to-human transmission were quantified. In Zimbabwe, estimated ℛ for the epidemic using aggregated data at the national level was 1.15; in Haiti, it was 1.55. However, when calculated at a provincial/departmental level, estimated basic reproductive numbers were highly heterogeneous, with a range of 1.11 to 2.72 in Zimbabwe and 1.06 to 2.63 in Haiti. Our models suggest that the underlying patterns of cholera transmission varied widely from region to region, with a corresponding variation in the amenability of outbreaks to control measures such as immunization. These data underscore the heterogeneity of transmission dynamics, potentially linked to differences in environment, socio-economic conditions, and cultural practices. They also highlight the potential utility of these types of models in guiding development of public health intervention strategies.

Highlighted Text: Show | Hide
Loading full text...

Full text loading...

/deliver/fulltext/microbiolspec/3/3/VE-0011-2014.html?itemId=/content/journal/microbiolspec/10.1128/microbiolspec.VE-0011-2014&mimeType=html&fmt=ahah

Figures

Image of FIGURE 1

Click to view

FIGURE 1

Conceptual framework for cholera transmission. From Morris ( 10 ). doi:10.1128/microbiolspec.VE-0011-2014.f1

Source: microbiolspec June 2015 vol. 3 no. 3 doi:10.1128/microbiolspec.VE-0011-2014
Permissions and Reprints Request Permissions
Download as Powerpoint
Image of FIGURE 2

Click to view

FIGURE 2

Model flow diagram. S, susceptibles; I, infected; R, recovered individuals; B, concentration of vibrios in contaminated water. From Mukandavire et al. ( 6 ). doi:10.1128/microbiolspec.VE-0011-2014.f2

Source: microbiolspec June 2015 vol. 3 no. 3 doi:10.1128/microbiolspec.VE-0011-2014
Permissions and Reprints Request Permissions
Download as Powerpoint

Tables

Generic image for table

Click to view

TABLE 1

Estimates of ℛ, ℛ, ℛ, and minimum vaccination coverages for Zimbabwe

Source: microbiolspec June 2015 vol. 3 no. 3 doi:10.1128/microbiolspec.VE-0011-2014
Generic image for table

Click to view

TABLE 2

Estimates of ℛ, ℛ, ℛ, and minimum vaccination coverages for Haiti

Source: microbiolspec June 2015 vol. 3 no. 3 doi:10.1128/microbiolspec.VE-0011-2014

Supplemental Material

No supplementary material available for this content.

This is a required field
Please enter a valid email address
Please check the format of the address you have entered.
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error