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While parasites serve as prey, it is unclear how the spatial distribution of parasite predators provides transmission control and influences patterns of parasitism. Because many of its organisms are sessile, the rocky intertidal zone is a valuable but little used system to understand spatial patterns of parasitism and elucidate the underlying mechanisms driving these patterns. Sea anemones and barnacles are important space competitors in the rocky intertidal zone along the Pacific coast of North America. Anemones are voracious, indiscriminate predators; thus, they may intercept infectious stages of parasites before they reach a host. We investigate whether a sea anemone protects an associated barnacle from parasitism by Hemioniscus balani, an isopod parasitic castrator. At Coal Oil Point, Santa Barbara, California USA, 29% of barnacles were within 1 cm from an anemone at the surveyed tidal height. Barnacles associated with anemones had reduced parasite prevalence and higher reproductive productivity than those remote from sea anemones. In the laboratory, anemones readily consumed the transmission stage of the parasite. Hence, anemone consumption of parasite transmission stages may provide a mechanism by which community context regulates parasite prevalence at a local scale. Our results suggest predation may be an important process providing parasite transmission control. [ABSTRACT FROM AUTHOR]

Host and/or vector movement patterns have been shown to have significant effects in both empirical studies and mathematical models of vector-borne diseases. The processes of economic development and globalization seem likely to make host movement even more important in the future. This article is a brief survey of some of the approaches that have been used to study the effects of host movement in analytic mathematical models for vector-borne diseases. It describes the formulation and interpretation of various types of spatial models and describes a few of the conclusions that can be drawn from them. It is not intended to be comprehensive but rather to provide sufficient background material and references to the literature to serve as an entry point into this area of research for interested readers. [ABSTRACT FROM AUTHOR]

In this paper, apart from the well-known second-order reaction–diffusion models in epidemiology and ecology, the third-order variants, called the reaction–dispersion models, are proposed. Using an ansatz method with the suitable function, the travelling wave solutions of the corresponding systems are investigated. The long-time behaviour of obtained solutions, which are actually kink type waves, are discussed. It is seen that the proposed system is able to simply display such epidemic model. • The third-order variants of models in Biology are studied. • Proposed systems are called as the reaction–dispersion systems. • Dispersion effect in epidemiology and ecology are discussed. • One of the efficient symbolic computation, called the tanh method, is used. • Kink-type waves are obtained. [ABSTRACT FROM AUTHOR]

Identification of high-risk regions of schistosomiasis is important for rational resource allocation and effective control strategies. We conducted the first study to apply the newly developed method of adaptive kernel density estimation (KDE)-based spatial relative risk function (sRRF) to detect the high-risk regions of schistosomiasis in the Guichi region of China and compared it with the fixed KDE-based sRRF. We found that the adaptive KDE-based sRRF had a better ability to depict the heterogeneity of risk regions, but was more sensitive to altering the user-defined smoothing parameters. Specifically, the impact of bandwidths on the estimated risk value and risk significance (P value) was higher for the adaptive KDE-based sRRF, but lower on the estimated risk variation standard error (s.e.) compared with the fixed KDE-based sRRF. Based on this application the adaptive and fixed KDE-based sRRF have their respective advantages and disadvantages and the joint application of the two approaches can warrant the best possible identification of high-risk subregions of diseases. [ABSTRACT FROM AUTHOR]

The distributions of parasitic diseases are determined by complex factors, including many that are distributed in space. A variety of statistical methods are now readily accessible to researchers providing opportunities for describing and ultimately understanding and predicting spatial distributions. This review provides an overview of the spatial statistical methods available to parasitologists, ecologists and epidemiologists and discusses how such methods have yielded new insights into the ecology and epidemiology of infection and disease. The review is structured according to the three major branches of spatial statistics: continuous spatial variation; discrete spatial variation; and spatial point processes. [ABSTRACT FROM AUTHOR]

Schistosomiasis control in China has, in general, been very successful during the past several decades. However, the rebounding of the epidemic situation in some areas in recent years raises concerns about a sustainable control strategy of which locating active transmission sites (ATS) is a necessary first step. This study presents a systematic approach for locating schistosomiasis ATS by combining the approaches of identifying high risk regions for schisotosmiasis and extracting snail habitats. Environmental, topographical, and human behavioural factors were included in the model. Four significant high-risk regions were detected and 6 ATS were located. We used the normalized difference water index (NDWI) combined with the normalized difference vegetation index (NDVI) to extract snail habitats, and the pointwise 'P-value surface' approach to test statistical significance of predicted disease risk. We found complicated non-linear relationships between predictors and schistosomiasis risk, which might result in serious biases if data were not properly treated. We also found that the associations were related to spatial scales, indicating that a well-designed series of studies were needed to relate the disease risk with predictors across various study scales. Our approach provides a useful tool, especially in the field of vector-borne or environment-related diseases. [ABSTRACT FROM AUTHOR]

The current note presents the power comparisons for disease clustering tests, as originally reported by Kulldorff et al. (Comput. Statist. Data Anal. 42 (2003) 665). A minor improvement to the implementation of the M-statistic, motivated by that work, results in dramatically higher power to detect clusters of disease. [Copyright &y& Elsevier]

Many different methods have been proposed to test for geographical disease clustering, and more generally, for spatial clustering of any type of observations while adjusting for an inhomogeneous background population generating the observations. Despite the many proposed test statistics, there has been few formal comparisons conducted. We present a collection of 1,220,000 simulated benchmark data sets generated under 51 different cluster models and the null hypothesis, to be used for power evaluations. We then use these data sets to compare the power of the spatial scan statistic, the maximized excess events test and the nonparametric M statistic. All have good power, the first having an advantage for localized hot-spot type clusters and the second for global clustering where randomly located cases generate other cases close by. By making the simulated data sets publicly available, new tests can easily be compared with previously evaluated tests by analyzing the same benchmark data. [Copyright &y& Elsevier]