A spatiotemporal epidemiological investigation of the impact of environmental change on the transmission dynamics of Echinococcus spp. in Ningxia Hui Autonomous Region, China

Background: Human echinococcoses are zoonotic parasitic diseases of major public health importance globally. According to recent estimates, the geographical distribution of echinococcosis is expanding and becoming an emerging and re-emerging problem in several regions of the world. Echinococcosis endemicity is geographically heterogeneous and might be affected by global environmental change over time. The aims of my research were: 1) to assess and quantify the spatiotemporal variation in land cover and climate change in Ningxia Hui Autonomous Region (NHAR); 2) to identify highly endemic areas for human echinococcoses in NHAR, and to determine the environmental covariates that have shaped the local geographical distribution of the disease; 3) to develop spatial statistical models that explain and predict the spatiotemporal variation of human exposure to Echinococcus spp. in a highly endemic county of NHAR; and 4) to analyse associations between the environment and the spatiotemporal variation of human exposure to the parasites and dog infections with Echinococcus granulosus and Echinococcus multilocularis in four echinococcosis-endemic counties of NHAR. Methods: Data on echinococcosis infections and human exposure to E. granulosus and E. multilocularis were obtained from different sources: 1) A hospital-based retrospective survey of human echinococcosis cases in NHAR between 1992 and 2013; 2) three cross-sectional surveys of school children conducted in Xiji County in 2002–2003, 2006–2007 and 2012–2013; and 3) A cross-sectional survey of human exposure and dog infections with E. granulosus and E. multilocularis conducted in Xiji, Haiyuan, Guyuan and Tongxin Counties. Environmental data were derived from high-resolution (30 m) imagery from Landsat 4/5-TM and 8-OLI and meteorological reports provided by the Chinese Academy of Sciences. Image analysis techniques and a Bayesian statistical framework were used to conduct a land cover change detection analyses and to develop regression models that described and quantified climate trends and the environmental factors associated with echinococcosis risk at different spatial scales. Results: The land cover changes observed in NHAR from 1991 to 2015 concurred with the main goals of a national policy on payments for ecosystem services, implemented in the Autonomous Region, in increasing forest and herbaceous vegetation coverages and in regenerating bareland. Statistically significant positive trends were observed in annual, summer and winter temperatures in most of the region, and a small magnitude change was found in annual precipitation, in the same 25-year period. The south of NHAR was identified as a highly endemic area for cystic echinococcosis (CE; caused by E. granulosus) and alveolar echinococcosis (AE; caused by E. multilocularis). Selected environmental covariates explained most of the spatial variation in AE risk, while the risk of CE appeared to be less spatially variable at the township level. The risk of exposure to E. granulosus expanded across Xiji County from 2002–2013, while the risk of exposure to E. multilocularis became more confined in communities located in the south of this highly endemic area. In 2012–2013, the predicted seroprevalences of human exposure to E. granulosus and dog infection with this parasite were characterised by similar geographical patterns across Xiji, Haiyuan, Guyuan and Tongxin Counties. By contrast, the predicted high seroprevalence areas for human exposure and dog infection with E. multilocularis did not coincide spatially. Climate, land cover and landscape fragmentation played a key role in explaining some of the observed spatial variation in the risk of infection with Echinococcus spp. among schoolchildren and dogs in the south of NHAR at the village level. Conclusions: The findings of this research defined populations at a high risk of human exposure to E. granulosus and E. multilocularis in NHAR. The research provides evidence on the potential effects of landscape regeneration projects on the incidence of human echinococcoses due to the associations found between the infections and regenerated land. This information will be essential to track future requirements for scaling up and targeting the control strategies proposed by the National Action Plan for Echinococcosis Control in China and may facilitate the design of future ecosystem management and protection policies and a more effective response to emerging local environmental risks. The predictive models developed as part of this research can also be used to monitor echinococcosis infections and the emergence in Echinococcus spp. transmission in the most affected areas

Improving Air Pollution Modelling in Complex Terrain with a Coupled WRF–LOTOS–EUROS Approach: A Case Study in Aburrá Valley, Colombia

Chemical transport models (CTM) are crucial for simulating the distribution of air pollutants, such as particulate matter, and evaluating their impact on the environment and human health. However, these models rely heavily on accurate emission inventory and meteorological inputs, usually obtained from reanalyzed weather data, such as the European Centre for Medium-Range Weather Forecasts (ECMWF). These inputs do not accurately reflect the complex topography and micro-scale meteorology in tropical regions where air pollution can pose a severe public health threat. We propose coupling the LOTOS–EUROS CTM model and the weather research and forecasting (WRF) model to improve LOTOS–EUROS representation. Using WRF as a meteorological driver provides high-resolution inputs for accurate pollutant simulation. We compared LOTOS–EUROS results when WRF and ECMWF provided the meteorological inputs during low and high pollutant concentration periods. The findings indicate that the WRF–LOTOS–EUROS coupling offers a more precise representation of the meteorology and pollutant dispersion than the default input of ECMWF. The simulations also capture the spatio-temporal variability of pollutant concentration and emphasize the importance of accounting for micro-scale meteorology and topography in air pollution modelling