Spatio-Temporal Patterns of Schistosomiasis Japonica in Lake and Marshland Areas in China: The Effect of Snail Habitats

Abstract

Abstract. The progress of the integrated control policy for schistosomiasis implemented since 2005 in China, which is aiming at reducing the roles of bovines and humans as infection sources, may be challenged by persistent presence of infected snails in lake and marshland areas. Based on annual parasitologic data for schistosomiasis during 2004-2011 in Xingzi County, a spatio-temporal kriging model was used to investigate the spatio-temporal pattern of schistosomiasis risk. Results showed that environmental factors related to snail habitats can explain the spatio-temporal variation of schistosomiasis. Predictive maps of schistosomiasis risk illustrated that clusters of the disease fluctuated during 2004-2008; there was an extensive outbreak in 2008 and attenuated disease occurrences afterwards. An area with an annually constant cluster of schistosomiasis was identified. Our study suggests that targeting snail habitats located within high-risk areas for schistosomiasis would be an economic and sustainable way of schistosomiasis control in the future.

Spatial characteristics and the epidemiology of human infections with avian influenza A(H7N9) virus in five waves from 2013 to 2017 in Zhejiang Province, China

“…It has been proven to be the best linear unbiased estimate for variables in regions where the data are spatially autocorrelative[ 18 ]. The unsampled value is computed as a linear combination of neighboring observations[ 19 , 20 ]: where z * (x 0 ,y 0 ) is the predicted value that is spatially located at point (x 0 ,y 0 ), λ i is the weight associated with the measured value of Z at the location (x i ,y i ), and the weights are derived from the kriging equation using a semivariance function. The parameters of the semivariance function and the nugget effect can be estimated by an empirical semivariance function[ 19 ]: where γ(h) is the semivariance value at the distance interval h, N(h) is the number of sample pairs within the distance interval h, and Z obs (x i + h x ,y i + h y ) Z obs (x i ,y i ) are sample values at two points separated by the distance interval h.…”

Spatial measurement errors in the field of spatial epidemiology

“…Together, these developments have created unprecedented new opportunities for researchers to investigate the association of geographically indexed health events with various demographic, environmental, behavioral, socioeconomic, and genetic risk factors to explore and explain geographic variation in disease risk. Researchers are becoming more familiar with space-related epidemiological studies (hereafter referred to as “spatial epidemiology”) [ 5 – 7 ].…”

Monitoring schistosomiasis risk in East China over space and time using a Bayesian hierarchical modeling approach

“…This model characterizes the evolution of schistosomiasis risk over space and time. Another way to model the spatio-temporal dependence is to build a moment-based model (e.g., Kriging approach) and it has been used in some previous schistosomiasis risk studies 33 34 . The latter approach ignores the directionality of time and specifies only the first and second moments of η it in the temporal domain of interest.…”