Premium
A multi‐group model of Schistosoma japonicum transmission dynamics and control: model calibration and control prediction
Author(s) -
Liang Song,
Spear Robert C.,
Seto Edmund,
Hubbard Alan,
Qiu Dongchuan
Publication year - 2005
Publication title -
tropical medicine and international health
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.056
H-Index - 114
eISSN - 1365-3156
pISSN - 1360-2276
DOI - 10.1111/j.1365-3156.2005.01386.x
Subject(s) - transmission (telecommunications) , schistosomiasis , schistosoma japonicum , calibration , parametric statistics , confidence interval , statistics , geography , computer science , biology , mathematics , telecommunications , helminths , zoology
Summary Previously we formulated a quantitative model to characterize site‐specific schistosomiasis transmission. In this paper, we present a procedure to calibrate the model to data collected in endemic villages of south‐western Sichuan, China, with the objective of reducing parametric uncertainty to allow the model to describe local transmission with relative confidence. A Bayesian approach using local epidemiological data and expert opinion is employed to calibrate the model. Results indicate that, after calibration, the output uncertainty is reduced substantially. The calibrated model is then used for prediction of the effects of different intervention options. Simulations reflect a bimodal transmission in both human (early summer and early fall) and snail (late summer and late fall) infections in this area, for which there is some field evidence. Also shown in the simulations are relatively high reinfection rates following chemotherapy in these endemic villages. These results suggest that a sustainable control strategy is essential in reducing transmission, and that transmission can be reduced by chemotherapy, focal snail (e.g. snail clusters) control, and egg control. Our work demonstrates the feasibility of characterizing site‐specific schistosomiasis transmission using a mathematical model and a calibration approach that integrates diverse field data, and the use of the calibrated model to design control strategies.