Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-18T19:51:44.242Z Has data issue: false hasContentIssue false
  • English
  • Français

A new approach to modelling schistosomiasis transmission based on stratified worm burden

Published online by Cambridge University Press:  13 July 2010

D. GURARIE ,
C. H. KING  and
X. WANG
D. GURARIE*
Affiliation:
Department of Mathematics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106 USA
C. H. KING
Affiliation:
Center for Global Health and Diseases, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106 USA
X. WANG
Affiliation:
Department of Mathematics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106 USA
*
*Corresponding author: Department of Mathematics, 220 Yost Hall, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106-7058, USA. Tel: 001 216 368 2857. Fax: 001 216 368 5163. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Background/Objective. Multiple factors affect schistosomiasis transmission in distributed meta-population systems including age, behaviour, and environment. The traditional approach to modelling macroparasite transmission often exploits the ‘mean worm burden’ (MWB) formulation for human hosts. However, typical worm distribution in humans is overdispersed, and classic models either ignore this characteristic or make ad hoc assumptions about its pattern (e.g., by assuming a negative binomial distribution). Such oversimplifications can give wrong predictions for the impact of control interventions. Methods. We propose a new modelling approach to macro-parasite transmission by stratifying human populations according to worm burden, and replacing MWB dynamics with that of ‘population strata’. We developed proper calibration procedures for such multi-component systems, based on typical epidemiological and demographic field data, and implemented them using Wolfram Mathematica. Results. Model programming and calibration proved to be straightforward. Our calibrated system provided good agreement with the individual level field data from the Msambweni region of eastern Kenya. Conclusion. The Stratified Worm Burden (SWB) approach offers many advantages, in that it accounts naturally for overdispersion and accommodates other important factors and measures of human infection and demographics. Future work will apply this model and methodology to evaluate innovative control intervention strategies, including expanded drug treatment programmes proposed by the World Health Organization and its partners.

Keywords

modelstheoreticalschistosomiasisdisease transmissioninfectionsenvironmental and behavioural risk
Type
Research Article
Information
Parasitology , Volume 137 , Issue 13 , November 2010 , pp. 1951 - 1965
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Alexander, N., Moyeed, R. and Stander, J. (2000). Spatial modelling of individual-level parasite counts using the negative binomial distribution. Biostatistics 1, 453–463.Google Scholar
Anderson, R. M. and May, R. M. (1978 a). Regulation and stability of host-parasite population interactions. I. Journal of Animal Ecology 47, 219–247.Google Scholar
Anderson, R. M. and May, R. M. (1978 b). Regulation and stability of host-parasite population interactions. II. Journal of Animal Ecology 47, 249–267.CrossRefGoogle Scholar
Anderson, R. M. and May, R. M. (1991). Infectious Diseases of Humans. Dynamics and Control. Oxford University Press, New York, USA.CrossRefGoogle Scholar
Chan, M. S. and Bundy, D. A. (1997). Modelling the dynamic effects of community chemotherapy on patterns of morbidity due to Schistosoma mansoni. Transactions of the Royal Society of Tropical Medicine and Hygiene 91, 216–220.Google Scholar
Chan, M. S., Guyatt, H. L., Bundy, D. A. and Medley, G. F. (1996). Dynamic models of schistosomiasis morbidity. American Journal of Tropical Medicine and Hygiene 55, 52–62.Google Scholar
Clennon, J. A., Mungai, P. L., Muchiri, E. M., King, C. H. and Kitron, U. (2006). Spatial and temporal variations in local transmission of Schistosoma haematobium in Msambweni, Kenya. American Journal of Tropical Medicine and Hygiene 75, 1034–1041.Google Scholar
Dobson, A. and Roberts, M. (1994). The population dynamics of parasitic helminth communities. Parasitology 109, (Suppl.) S97–S108.CrossRefGoogle ScholarPubMed
Feng, Z., Curtis, J. and Minchella, D. J. (2001). The influence of drug treatment on the maintenance of schistosome genetic diversity. Journal of Mathematical Biology 43, 52–68.CrossRefGoogle ScholarPubMed
Gabrielli, A. F., Toure, S., Sellin, B., Sellin, E., Ky, C., Ouedraogo, H., Yaogho, M., Wilson, M. D., Thompson, H., Sanou, S. and Fenwick, A. (2006). A combined school- and community-based campaign targeting all school-age children of Burkina Faso against schistosomiasis and soil-transmitted helminthiasis: performance, financial costs and implications for sustainability. Acta Tropica 99, 234–242.Google Scholar
Gurarie, D. and King, C. H. (2005). Heterogeneous model of schistosomiasis transmission and long-term control: the combined influence of spatial variation and age-dependent factors on optimal allocation of drug therapy. Parasitology 130, 49–65.Google Scholar
Gurarie, D. and King, C. H. (2008). Age- and risk-targeted control of schistosomiasis-associated morbidity among children and adult age groups. The Open Tropical Medicine Journal 1, 21–30.Google Scholar
Gurarie, D. and Seto, E. Y. (2009). Connectivity sustains disease transmission in environments with low potential for endemicity: modelling schistosomiasis with hydrologic and social connectivities. Journal of the Royal Society Interface 6, 495–508. doi: 10.1098/rsif.2008.0265.Google Scholar
Hamburger, J., Hoffman, O., Kariuki, H. C., Muchiri, E. M., Ouma, J. H., Koech, D. K., Sturrock, R. F. and King, C. H. (2004). Large-scale, polymerase chain reaction-based surveillance of Schistosoma haematobium DNA in snails from transmission sites in coastal Kenya: A new tool for studying the dynamics of snail infection. American Journal of Tropical Medicine and Hygiene 71, 765–773.Google Scholar
Kariuki, H. C., Clennon, J. A., Brady, M. S., Kitron, U., Sturrock, R. F., Ouma, J. H., Ndzovu, S. T., Mungai, P., Hoffman, O., Hamburger, J., Pellegrini, C., Muchiri, E. M. and King, C. H. (2004). Distribution patterns and cercarial shedding of Bulinus nasutus and other snails in the Msambweni area, Coast Province, Kenya. American Journal of Tropical Medicine and Hygiene 70, 449–456.Google Scholar
Macdonald, G. (1965). The dynamics of helminth infections, with special reference to schistosomes. Transactions of the Royal Society of Tropical Medicine and Hygiene 59, 489–506.Google Scholar
Medley, G. F. and Bundy, D. A. (1996). Dynamic modeling of epidemiologic patterns of schistosomiasis morbidity. American Journal of Tropical Medicine and Hygiene 55, 149–158.CrossRefGoogle ScholarPubMed
Muchiri, E. M., Ouma, J. H. and King, C. H. (1996). Dynamics and control of Schistosoma haematobium transmission in Kenya: an overview of the Msambweni Project. American Journal of Tropical Medicine and Hygiene 55, 127–134.Google Scholar
Nasell, I. (1978). Mating for schistosomes. Journal of Mathematical Biology 6, 21–35.CrossRefGoogle ScholarPubMed
Pugliese, A. (2000). Coexistence of macroparasites without direct interactions. Theoretical Population Biology 57, 145–165. doi: S0040-5809(99)91443-0 [pii].CrossRefGoogle ScholarPubMed
Riley, S., Carabin, H., Belisle, P., Joseph, L., Tallo, V., Balolong, E., Willingham, A. L., Fernandez, T. J., Gonzales, R. O., Olveda, R. and Mcgarvey, S. T. (2008). Multi-host transmission dynamics of Schistosoma japonicum in Samar province, the Philippines. PLoS Med 5, e18. doi: 10.1371/journal.pmed.0050018.Google Scholar
Rosa, R. and Pugliese, A. (2002). Aggregation, stability, and oscillations in different models for host-macroparasite interactions. Theoretical Population Biology 61, 319–334. doi: 10.1006/tpbi.2002.1575CrossRefGoogle ScholarPubMed
Sturrock, R. F., Kinyanjui, H., Thiongo, F. W., Tosha, S., Ouma, J. H., King, C. H., Koech, D., Siongok, T. K. and Mahmoud, A. A. (1990). Chemotherapy-based control of schistosomiasis haematobia. 3. Snail studies monitoring the effect of chemotherapy on transmission in the Msambweni area, Kenya. Transactions of the Royal Society of Tropical Medicine and Hygiene 84, 257–261.CrossRefGoogle ScholarPubMed
Van Der Werf, M. J. and De Vlas, S. J. (2001). Morbidity and Infection with Schistosomes or Soil-Transmitted Helminths. Report for WHO Parasitic Diseases and Vector Contol. pp. 1–103. Erasmus University, Rotterdam, The Netherlands.Google Scholar
de Vlas, S. J., Engels, D., Rabello, A. L., Oostburg, B. F., Van Lieshout, L., Polderman, A. M., Van Oortmarssen, G. J., Habbema, J. D. and Gryseels, B. (1997). Validation of a chart to estimate true Schistosoma mansoni prevalences from simple egg counts. Parasitology 114, 113–121.CrossRefGoogle ScholarPubMed
Wilson, R. A., Dam, G. J., Kariuki, T. M., Farah, I. O., Deedler, A. M. and Coulson, P. S. (2006). The detection limits for estimates of infection intensity in schistosomiasis mansoni established by a study in non-human primates. International Journal for Parasitology 36, 1241–1244.Google Scholar
World Health Organization (2006). Preventive Chemotherapy in Human Helminthiasis: Coordinated Use of Anthelminthic Drugs in Control Interventions: a Manual for Health Professionals and Programme Managers. World Health Organization Press, Geneva, Switzerland.Google Scholar
Woolhouse, M. E. (1991). On the application of mathematical models of schistosome transmission dynamics. I. Natural transmission. Acta Tropica 49, 241–270.Google Scholar
Woolhouse, M. E., Watts, C. H. and Chandiwana, S. K. (1991). Heterogeneities in transmission rates and the epidemiology of schistosome infection. Proceedings of the Royal Society of London, B 245, 109–114.Google Scholar