Environmental effects on parasitic disease transmission exemplified by schistosomiasis in western China

Abstract

Environmental effects on the transmission of many parasitic diseases are well recognized, but the role of specific factors like climate and agricultural practices in modulating transmission is seldom characterized quantitatively. Based on studies of Schistosoma japonicum transmission in irrigated agricultural environments in western China, a mathematical model was used to quantify environmental impacts on transmission intensity. The model was calibrated by using field data from intervention studies in three villages and simulated to predict the effects of alternative control options. Both the results of these interventions and earlier epidemiological findings confirm the central role of environmental factors, particularly those relating to snail habitat and agricultural and sanitation practices. Moreover, the findings indicate the inadequacy of current niclosamide-praziquantel strategies alone to achieve sustainable interruption of transmission in some endemic areas. More generally, the analysis suggests a village-specific index of transmission potential and how this potential is modulated by time-varying factors, including climatological variables, seasonal water-contact patterns, and irrigation practices. These time-variable factors, a village's internal potential, and its connectedness to its neighbors provide a framework for evaluating the likelihood of sustained schistosomiasis transmission and suggest an approach to quantifying the role of environmental factors for other parasitic diseases.disease control ͉ environment

Spatio-temporal correlation between human and bovine schistosomiasis in China: insight from three national sampling surveys

“…GIS and RS are suitable tools for the creation of spatially-explicit databases, and the display of relevant data for health policy planning and discussion, guiding interventions and costeffective monitoring at a range of spatial scales (Tatem and Hay, 2004). Schistosomiasis japonica is an important zoonosis in China and The Philippines, whose distribution, prevalence and intensity of infection are particularly sensitive to the climatic and geographical environment (Yang et al, 2005a;Zhou et al, 2005bZhou et al, , 2007bLi et al, 2007;Liang et al, 2007). Table 2.…”

“…Sustained control efforts are needed to consolidate the achievements made to date and stepped-up efforts may be necessary to further reduce the number of infections and force of transmission (Bian et al, 2004;Wang et al, 2005;Fenwick and Webster, 2006;Guo et al, 2006;Liang et al, 2006). Transmission of schistosomiasis is closely related to environmental factors (Steinmann et al, 2006;Li et al, 2007;Liang et al, 2007). However, despite a number of recent studies that employed geographical information system (GIS) applications to define transmission factors that relate to control (Bergquist, 2001;Zhou et al, 2001Zhou et al, , 2002Zhou et al, , 2007bYang et al, 2005b;Chen et al, 2007), little is known about the geospatial correlation between human and animal schistosomiasis and their relative contribution to the transmission of the disease.…”

Sensitivity analysis of infectious disease models: methods, advances and their application

“…For the present analysis, the model was simplified to two state variables and 14 parameters as follows: where W(t) is the average population worm burden, Z(t) is the density of infected snails and e, m and c are parasite eggs released into environment, miracidia density and cercaria density, respectively. Model parameters are summarized in table 2 and fully described elsewhere [40,41]. The model was run in Matlab (R 2011b) for 300 time steps with each parameter set, as described later.…”

“…A macroparasite model describing the transmission of schistosomiasis was previously used to simulate the worm burden in human hosts and the density of infected snails in China [40,41]. For the present analysis, the model was simplified to two state variables and 14 parameters as follows: where W(t) is the average population worm burden, Z(t) is the density of infected snails and e, m and c are parasite eggs released into environment, miracidia density and cercaria density, respectively.…”

“…Parameter values and ranges were adopted from previous work [24,40,41], and unspecified parameter ranges were assigned by dividing (lower bound) and multiplying (upper bound) the parameter point estimate by 2. A uniform distribution was assigned to parameters where the distribution was unspecified [21].…”