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The purpose of this study was to deepen our understanding of Plasmodium vivax malaria transmission patterns in the People's Republic of China (P.R. China). An integrated modeling approach was employed, combining biological and statistical models. A Delphi approach was used to determine environmental factors that govern malaria transmission. Key factors identified (i.e. temperature, rainfall and relative humidity) were utilized for subsequent mapping and modeling purposes. Yearly growing degree days, annual rainfall and effective yearly relative humidity were extracted from a 15-year time series (1981-1995) of daily environmental data readily available for 676 locations in P.R. China. A suite of eight multinomial regression models, ranging from the null model to a fully saturated one were constructed. Two different information criteria were used for model ranking, namely the corrected Akaike's information criterion and the Bayesian information criterion. Mapping was based on model output data, facilitated by using ArcGIS software. Temperature was found to be the most important environmental factor, followed by rainfall and relative humidity in the Delphi evaluation. However, relative humidity was found to be more important than rainfall and temperature in the ranking list according to the three single environmental factor regression models. We conclude that the distribution of the mosquito vector is mainly related to relative humidity, which thus determines the extent of malaria transmission. However, in regions with relative humidity >60%, temperature is the major driver of malaria transmission intensity. By integrating biology-driven models with statistical regression models, reliable risk maps indicating the distribution of transmission and the intensity can be produced. In a next step, we propose to integrate social and health systems factors into our modeling approach, which should provide a platform for rigorous surveillance and monitoring progress towards P. vivax malaria elimination in P.R. China.

Climate change-according to conventional wisdom-will result in an expansion of tropical parasitic diseases in terms of latitude and altitude, with vector-borne diseases particularly prone to change. However, although a significant rise in temperature occurred over the past century, there is little empirical evidence whether climate change has indeed favoured infectious diseases. This might be explained by the complex relationship between climate change and the frequency and the transmission dynamics of infectious diseases, which is characterised by nonlinear associations and countless other complex factors governing the distribution of infectious diseases. Here, we explore whether and how climate change might impact on diseases targeted by the Regional Network for Asian Schistosomiasis and Other Helminth Zoonoses (RNAS(+)). We start our review with a short summary of the current evidence-base how climate change affects the distribution of infectious diseases. Next, we introduce biology-based models for predicting the distribution of infectious diseases in a future, warmer world. Two case studies are presented: the classical RNAS(+) disease schistosomiasis and an emerging disease, angiostrongyliasis, focussing on their occurrences in the People's Republic of China. Strengths and limitations of current models for predicting the impact of climate change on infectious diseases are discussed, and we propose model extensions to include social and ecological factors. Finally, we recommend that mitigation and adaptation strategies to diminish potential negative effects of climate change need to be developed in concert with key stakeholders so that surveillance and early-warning systems can be strengthened and the most vulnerable population groups protected., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Background: Spatial modeling is increasingly utilized to elucidate relationships between demographic, environmental, and socioeconomic factors, and infectious disease prevalence data. However, there is a paucity of studies focusing on spatio-temporal modeling that take into account the uncertainty of diagnostic techniques., Methodology/principal Findings: We obtained Schistosoma japonicum prevalence data, based on a standardized indirect hemagglutination assay (IHA), from annual reports from 114 schistosome-endemic villages in Dangtu County, southeastern part of the People's Republic of China, for the period 1995 to 2004. Environmental data were extracted from satellite images. Socioeconomic data were available from village registries. We used Bayesian spatio-temporal models, accounting for the sensitivity and specificity of the IHA test via an equation derived from the law of total probability, to relate the observed with the 'true' prevalence. The risk of S. japonicum was positively associated with the mean land surface temperature, and negatively correlated with the mean normalized difference vegetation index and distance to the nearest water body. There was no significant association between S. japonicum and socioeconomic status of the villages surveyed. The spatial correlation structures of the observed S. japonicum seroprevalence and the estimated infection prevalence differed from one year to another. Variance estimates based on a model adjusted for the diagnostic error were larger than unadjusted models. The generated prediction map for 2005 showed that most of the former and current infections occur in close proximity to the Yangtze River., Conclusion/significance: Bayesian spatial-temporal modeling incorporating diagnostic uncertainty is a suitable approach for risk mapping S. japonicum prevalence data. The Yangtze River and its tributaries govern schistosomiasis transmission in Dangtu County, but spatial correlation needs to be taken into consideration when making risk prediction at small scales.

Bayesian spatial risk profiling holds promise to enhance our understanding of the epidemiology of parasitic diseases, and to target interventions in a cost-effective manner. Here, we present findings from a study using Bayesian variogram models to map and predict the seroprevalence of Schistosoma japonicum in Eryuan county, Yunnan province, China, including risk factor analysis. Questionnaire and serological data were obtained through a cross-sectional survey carried out in 35 randomly selected villages with 3,220 people enrolled. Remotely-sensed environmental data were derived from publicly available databases. Bivariate and non-spatial Bayesian multiple logistic regression models were used to identify associations between the local seroprevalence and demographic (i.e. age and sex), environmental (i.e. location of village, altitude, slope, land surface temperature and normalized difference vegetation index) and socio-economic factors. In the spatially-explicit Bayesian model, S. japonicum seroprevalence was significantly associated with sex, age and the location of the village. Males, those aged below 10 years and inhabitants of villages situated on steep slopes (inclination =20 degrees ) or on less precipitous slopes of >5 degrees above 2,150 m were at lower risk of seroconversion than their respective counterparts. Our final prediction model revealed an elevated risk for seroconversion in the plains of the eastern parts of Eryuan county. In conclusion, the prediction map can be utilized for spatial targeting of schistosomiasis control interventions in Eryuan county. Moreover, S. japonicum seroprevalence studies might offer a convenient means to assess the infection pressure experienced by local communities, and to improve risk profiling in areas where the prevalence and infection intensities have come down following repeated rounds of praziquantel administration.

Political and health sector reforms, along with demographic, environmental and socio-economic transformations in the face of global warming, could cause the re-emergence of schistosomiasis in areas where transmission has been successfully interrupted and its emergence in previously non-endemic areas in China. In the present study, we used geographic information systems and remote sensing techniques to predict potential habitats of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum. Focussing on the Hongze, Baima and Gaoyou lakes in Jiangsu province in eastern China, we developed a model using the normalized difference vegetation index, a tasseled-cap transformed wetness index, and flooding areas to predict snail habitats at a small scale. Data were extracted from two Landsat images, one taken during a typical dry year and the other obtained three years later during a flooding event. An area of approximately 163.6 km2 was predicted as potential O. hupensis habitats around the three lakes, which accounts for 4.3% of the estimated snail habitats in China. In turn, these predicted snail habitats are risk areas for transmission of schistosomiasis, and hence illustrate the scale of the possible impact of climate change and other ecological transformations. The generated risk map can be used by health policy makers to guide mitigation policies targetting the possible spread of O. hupensis, and with the aim of containing the transmission of S. japonicum.

It has been suggested that global warming may alter the frequency and transmission dynamics of vector-borne diseases. To test this claim for schistosomiasis, we conducted a time-series analysis from 1972-2002 for 39 of the 70 counties of Jiangsu province, eastern China, where Schistosoma japonicum is partially endemic. We used a modeling approach to estimate the annual growing degree-days (AGDD), employing a lower temperature threshold of 15.3 degrees C. Our final model included both temporal and spatial components, the former consisting of second order polynomials in time plus a seasonality component, whereas the spatial trend was formed by second order polynomials of the coordinates plus the thin-plate smoothing splines. We found that temperature increased over the past 30 years in all observing stations. There were distinct temporal trends with seasonality and periodicities of 12, 6, and 3 months, whereas only marginal spatial variation was observed. The predicted AGDDs for 2006 and 2003 showed increases for the entire Jiangsu province, with the AGDDs difference between these two time points exhibiting an increase from north to south. Our data suggest that changes in temperature will alter the extent and level of schistosomiasis transmission, which is relevant for the control of S. japonicum in a future warmer China.

Geographic information system (GIS) and remote sensing (RS) technologies offer new opportunities for rapid assessment of endemic areas, provision of reliable estimates of populations at risk, prediction of disease distributions in areas that lack baseline data and are difficult to access, and guidance of intervention strategies, so that scarce resources can be allocated in a cost-effective manner. Here, we focus on the epidemiology and control of schistosomiasis in China and review GIS and RS applications to date. These include mapping prevalence and intensity data of Schistosoma japonicum at a large scale, and identifying and predicting suitable habitats for Oncomelania hupensis, the intermediate host snail of S. japonicum, at a small scale. Other prominent applications have been the prediction of infection risk due to ecological transformations, particularly those induced by floods and water resource developments, and the potential impact of climate change. We also discuss the limitations of the previous work, and outline potential new applications of GIS and RS techniques, namely quantitative GIS, WebGIS, and utilization of emerging satellite information, as they hold promise to further enhance infection risk mapping and disease prediction. Finally, we stress current research needs to overcome some of the remaining challenges of GIS and RS applications for schistosomiasis, so that further and sustained progress can be made to control this disease in China and elsewhere.

There is growing consensus among climate modellers that the unusual global warming observed in the last decades of the 20th century is primarily forced by human activities, namely greenhouse gas increases in the atmosphere. Global warming will trigger alterations in physical and biological systems, including shifts in the spatio-temporal distribution of disease vectors, but the nature and extent of these changes are poorly understood. The purpose of the present study was to assess the potential impact of climate change and water resource development on the distribution of Oncomelania hupensis, the intermediate host snail of Schistosoma japonicum. We employed two 30-year composite datasets comprising average monthly temperatures collected at 623 observing stations throughout China, spanning the periods 1961-1990 and 1971-2000. Temperature changes were assessed spatially between the 1960s and 1990s for January, as this is the critical month for survival of O. hupensis. Our database shows that January temperatures increased at 590 stations (94.7%), and that China's average January temperature in the 1990s was 0.96 degrees C higher than 30 years earlier. The historical 0-1 degrees C January isotherm, which was considered the approximate northern limit of S. japonicum transmission, has shifted from 33 degrees 15' N to 33 degrees 41' N, expanding the potential transmission area by 41,335 km2. This translates to an additional 20.7 million people at risk of schistosomiasis. Two lakes are located in this new transmission area that form part of the proposed South-North water transfer project. Climate change, coupled with water resource developments in China, may pose additional challenges for the control of schistosomiasis.

Spatio-temporal variations of Schistosoma japonicum infection risk in Jiangsu province, China, were examined and the relationships between key climatic factors and infection prevalence at the county level were determined. The parasitological data were collected annually by means of cross-sectional surveys carried out in 47 counties from 1990 to 1998. Climatic factors, namely land surface temperature (LST) and normalized difference vegetation index (NDVI), were obtained from remote sensing satellite sensors. Bayesian spatio-temporal models were employed to analyze the data. The best fitting model showed that spatial autocorrelation in Jiangsu province decreased dramatically from 1990 to 1992 and increased gradually thereafter. A likely explanation of this finding arises from the large-scale administration of praziquantel for morbidity control of schistosomiasis. Our analysis suggested a negative association between NDVI and risk of S. japonicum infection. On the other hand, an increase in LST contributed to a significant increase in S. japonicum infection prevalence. We conclude that combining geographic information system, remote sensing and Bayesian-based statistical approaches facilitate integrated risk modeling of S. japonicum, which in turn is of relevance for allocation of scarce resources for control of schistosomiasis japonica in Jiangsu province and elsewhere in China, where the disease remains of public health and economic significance.