The role of environmental factors in the spatial distribution of Japanese encephalitis in mainland China (original) (raw)
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Ecological Niche Modeling to Estimate the Distribution of Japanese Encephalitis Virus in Asia
PLoS Neglected Tropical Diseases, 2012
Background: Culex tritaeniorhynchus is the primary vector of Japanese encephalitis virus (JEV), a leading cause of encephalitis in Asia. JEV is transmitted in an enzootic cycle involving large wading birds as the reservoirs and swine as amplifying hosts. The development of a JEV vaccine reduced the number of JE cases in regions with comprehensive childhood vaccination programs, such as in Japan and the Republic of Korea. However, the lack of vaccine programs or insufficient coverage of populations in other endemic countries leaves many people susceptible to JEV. The aim of this study was to predict the distribution of Culex tritaeniorhynchus using ecological niche modeling. Methods/Principal Findings: An ecological niche model was constructed using the Maxent program to map the areas with suitable environmental conditions for the Cx. tritaeniorhynchus vector. Program input consisted of environmental data (temperature, elevation, rainfall) and known locations of vector presence resulting from an extensive literature search and records from MosquitoMap. The statistically significant Maxent model of the estimated probability of Cx. tritaeniorhynchus presence showed that the mean temperatures of the wettest quarter had the greatest impact on the model. Further, the majority of human Japanese encephalitis (JE) cases were located in regions with higher estimated probability of Cx. tritaeniorhynchus presence. Conclusions/Significance: Our ecological niche model of the estimated probability of Cx. tritaeniorhynchus presence provides a framework for better allocation of vector control resources, particularly in locations where JEV vaccinations are unavailable. Furthermore, this model provides estimates of vector probability that could improve vector surveillance programs and JE control efforts.
Culex tritaeniorhynchus is the primary vector of Japanese encephalitis virus (JEV) throughout much of the tropical and temperate climates of Asia. Several recent papers have used ecological niche modeling programs, e.g., Maxent and GARP, to predict the distribution of disease vectors (e.g. Peterson and Shaw 2003, Moffett et al. 2007). In this on-going study, we used the Maxent program to model the distribution of Cx. tritaeniorhynchus in the Republic of Korea. Using mosquito collection data, temperature, precipitation, elevation, land cover, and SPOT normalized difference vegetation index (NDVI), models were created for each month for a period of five years. Output maps from the models matched several known ecological characteristics of this species' distribution. The output maps show the highest probabilities of mosquito occurrence in August and September, which correlates to the observed mosquito population density peaks. The model demonstrated low probabilities for forest cov...
In recent times, Japanese Encephalitis (JE) has emerged as a serious public health problem. In India, JE outbreaks were recently reported in Uttar Pradesh, Gorakhpur. The present study presents an approach to use GIS for analyzing the reported cases of JE in the Gorakhpur district based on spatial analysis to bring out the spatial and temporal dynamics of the JE epidemic. The study investigates spatiotemporal pattern of the occurrence of disease and detection of the JE hotspot. Spatial patterns of the JE disease can provide an understanding of geographical changes. Geospatial distribution of the JE disease outbreak is being investigated since 2005 in this study. The JE incidence data for the years 2005 to 2010 is used. The data is then geo-coded at block level. Spatial analysis is used to evaluate autocorrelation in JE distribution and to test the cases that are clustered or dispersed in space. The Inverse Distance Weighting interpolation technique is used to predict the pattern of JE incidence distribution prevalent across the study area. Moran’s I Index (Moran’s I) statistics is used to evaluate autocorrelation in spatial distribution. The Getis-Ord Gi*(d) is used to identify the disease areas. The results represent spatial disease patterns from 2005 to 2010, depicting spatially clustered patterns with significant differences between the blocks. It is observed that the blocks on the built up areas reported higher incidences.
American Journal of Tropical Medicine and Hygiene, 2011
Hemorrhagic fever with renal syndrome (HFRS) is an important public health problem in Shandong Province, China. In this study, we combined ecologic niche modeling with geographic information systems (GIS) and remote sensing techniques to identify the risk factors and affected areas of hantavirus infections in rodent hosts. Land cover and elevation were found to be closely associated with the presence of hantavirus-infected rodent hosts. The averaged area under the receiver operating characteristic curve was 0.864, implying good performance. The predicted risk maps based on the model were validated both by the hantavirus-infected rodents' distribution and HFRS human case localities with a good fit. These findings have the applications for targeting control and prevention efforts. and fanglq@nic.bmi.ac.cn † The first two authors contributed equally to this study. * RH = relative humidity; NDVI = normalized difference vegetation index; LSTD = land surface temperature during daytime; LSTN = land surface temperature during nighttime. ** The variable was excluded from the final model.
Spatiotemporal Patterns of Japanese Encephalitis in China, 2002–2010
PLoS Neglected Tropical Diseases, 2013
Objective: The aim of the study is to examine the spatiotemporal pattern of Japanese Encephalitis (JE) in mainland China during [2002][2003][2004][2005][2006][2007][2008][2009][2010]. Specific objectives of the study were to quantify the temporal variation in incidence of JE cases, to determine if clustering of JE cases exists, to detect high risk spatiotemporal clusters of JE cases and to provide evidencebased preventive suggestions to relevant stakeholders.
Spatial Delimitation, Forecasting and Control of Japanese Encephalitis: India - A Case Study
The Open Parasitology Journal, 2008
Japanese encephalitis (JE) is the leading cause of viral encephalitis through large parts of Asia with temperate and subtropical or tropical climate. In the present communication environmental determinants that influence the occurrence of JE have been enlisted, and based on which a conceptual frame for JE transmission was developed. The concept of endemic and epidemic has been defined using cluster analysis on JE occurrences in 175 districts over a period of 53 years in India. The average number (±standard deviation) of occurrences in endemic (7.4±3.5) and epidemic districts (3.4±2.9) was statistically significant ('t'=8.3; P=0.000). In the epidemic areas, JE immunization of target population in the risk area may be an effective preventive measure. In the endemic areas regular monitoring of vector population and viral activity, and implementing appropriate integrated methods of vector control are likely to reduce the transmission, besides the selective immunization of children.
Frontiers in Public Health, 2023
Introduction: As emerging infectious diseases (EIDs) increase, examining the underlying social and environmental conditions that drive EIDs is urgently needed. Ecological niche modeling (ENM) is increasingly employed to predict disease emergence based on the spatial distribution of biotic conditions and interactions, abiotic conditions, and the mobility or dispersal of vector-host species, as well as social factors that modify the host species’ spatial distribution. Still, ENM applied to EIDs is relatively new with varying algorithms and data types. We conducted a systematic review (PROSPERO: CRD42021251968) with the research question: What is the state of the science and practice of estimating ecological niches via ENM to predict the emergence and spread of vector-borne and/or zoonotic diseases? Methods: We searched five research databases and eight widely recognized One Health journals between 1995 and 2020. We screened 383 articles at the abstract level (included if study involved vector-borne or zoonotic disease and applied ENM) and 237 articles at the full-text level (included if study described ENM features and modeling processes). Our objectives were to: (1) describe the growth and distribution of studies across the types of infectious diseases, scientific fields, and geographic regions; (2) evaluate the likely effectiveness of the studies to represent ecological niches based on the biotic, abiotic, and mobility framework; (3) explain some potential pitfalls of ENM algorithms and techniques; and (4) provide specific recommendation for future studies on the analysis of ecological niches to predict EIDs. Results: We show that 99% of studies included mobility factors, 90% modeled abiotic factors with more than half in tropical climate zones, 54% modeled biotic conditions and interactions. Of the 121 studies, 7% include only biotic and mobility factors, 45% include only abiotic and mobility factors, and 45% fully integrated the biotic, abiotic, and mobility data. Only 13% of studies included modifying social factors such as land use. A majority of studies (77%) used well-recognized ENM algorithms (MaxEnt and GARP) and model selection procedures. Most studies (90%) reported model validation procedures, but only 7% reported uncertainty analysis. Discussion: Our findings bolster ENM to predict EIDs that can help inform the prevention of outbreaks and future epidemics.
International journal of environmental research and public health, 2017
Tick-borne encephalitis (TBE) is one of natural foci diseases transmitted by ticks. Its distribution and transmission are closely related to geographic and environmental factors. Identification of environmental determinates of TBE is of great importance to understanding the general distribution of existing and potential TBE natural foci. Hulunbuir, one of the most severe endemic areas of the disease, is selected as the study area. Statistical analysis, global and local spatial autocorrelation analysis, and regression methods were applied to detect the spatiotemporal characteristics, compare the impact degree of associated factors, and model the risk distribution using the heterogeneity. The statistical analysis of gridded geographic and environmental factors and TBE incidence show that the TBE patients mainly occurred during spring and summer and that there is a significant positive spatial autocorrelation between the distribution of TBE cases and environmental characteristics. The ...
Comparative Spatial Dynamics of Japanese Encephalitis and Acute Encephalitis Syndrome in Nepal
PLoS ONE, 2013
Japanese Encephalitis (JE) is a vector-borne disease of major importance in Asia. Recent increases in cases have spawned the development of more stringent JE surveillance. Due to the difficulty of making a clinical diagnosis, increased tracking of common symptoms associated with JE-generally classified as the umbrella term, acute encephalitis syndrome (AES) has been developed in many countries. In Nepal, there is some debate as to what AES cases are, and how JE risk factors relate to AES risk. Three parts of this analysis included investigating the temporal pattern of cases, examining the age and vaccination status patterns among AES surveillance data, and then focusing on spatial patterns of risk factors. AES and JE cases from 2007-2011 reported at a district level (n = 75) were examined in relation to landscape risk factors. Landscape pattern indices were used to quantify landscape patterns associated with JE risk. The relative spatial distribution of landscape risk factors were compared using geographically weighted regression. Pattern indices describing the amount of irrigated land edge density and the degree of landscape mixing for irrigated areas were positively associated with JE and AES, while fragmented forest measured by the number of forest patches were negatively associated with AES and JE. For both JE and AES, the local GWR models outperformed global models, indicating spatial heterogeneity in risks. Temporally, the patterns of JE and AES risk were almost identical; suggesting the relative higher caseload of AES compared to JE could provide a valuable earlywarning signal for JE surveillance and reduce diagnostic testing costs. Overall, the landscape variables associated with a high degree of landscape mixing and small scale irrigated agriculture were positively linked to JE and AES risk, highlighting the importance of integrating land management policies, disease prevention strategies and promoting healthy sustainable livelihoods in both rural and urban-fringe developing areas.