Spatiotemporal Patterns and Risk Factors for Scrub Typhus From 2007 to 2017 in Southern China

2018 ◽  
Vol 69 (7) ◽  
pp. 1205-1211 ◽  
Author(s):  
Canjun Zheng ◽  
Dong Jiang ◽  
Fangyu Ding ◽  
Jingying Fu ◽  
Mengmeng Hao
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Scrub Typhus ◽  
Southern China ◽  
Infection Risk ◽  
Spatiotemporal Patterns ◽  
Transmission Risk ◽  
Environmental Niche ◽  
Boosted Regression Tree ◽  
Risk Zones

Abstract Background Substantial outbreaks of scrub typhus, coupled with the discovery of this vector-borne disease in new areas, suggest that the disease remains remarkably neglected. The objectives of this study were to map the contemporary and potential transmission risk zones of the disease and to provide novel insights into the health burden imposed by scrub typhus in southern China. Methods Based on the assembled data sets of annual scrub typhus cases and maps of environmental and socioeconomic correlates, a boosted regression tree modeling procedure was used to identify the environmental niche of scrub typhus and to predict the potential infection zones of the disease. Additionally, we estimated the population living in the potential scrub typhus infection areas in southern China. Results Spatiotemporal patterns of the annual scrub typhus cases in southern China between 2007 and 2017 reveal a tremendous, wide spread of scrub typhus. Temperature, relative humidity, elevation, and the normalized difference vegetation index are the main factors that influence the spread of scrub typhus. In southern China, the predicted highest transmission risk areas of scrub typhus are mainly concentrated in several regions, such as Yunnan, Guangxi, Guangdong, Hainan, and Fujian. We estimated that 162 684 million people inhabit the potential infection risk zones in southern China. Conclusions Our results provide a better understanding of the environmental and socioeconomic factors driving scrub typhus spread, and estimate the potential infection risk zones beyond the disease’s current, limited geographical extent, which enhances our capacity to target biosurveillance and help public health authorities develop disease control strategies.

scholarly journals Mapping Environmental Suitability of Scrub Typhus in Nepal Using MaxEnt and Random Forest Models

2019 ◽  
Vol 16 (23) ◽  
pp. 4845
Author(s):  
Bipin Acharya ◽  
Wei Chen ◽  
Zengliang Ruan ◽  
Gobind Pant ◽  
Yin Yang ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Scrub Typhus ◽  
Control Strategies ◽  
National Level ◽  
Spatial Prediction ◽  
Health Concern ◽  
Environmental Suitability ◽  
And Control

Being a globally emerging mite-borne zoonotic disease, scrub typhus is a serious public health concern in Nepal. Mapping environmental suitability and quantifying the human population under risk of the disease is important for prevention and control efforts. In this study, we model and map the environmental suitability of scrub typhus using the ecological niche approach, machine learning modeling techniques, and report locations of scrub typhus along with several climatic, topographic, Normalized Difference Vegetation Index (NDVI), and proximity explanatory variables and estimated population under the risk of disease at a national level. Both MaxEnt and RF technique results reveal robust predictive power with test The area under curve (AUC) and true skill statistics (TSS) of above 0.8 and 0.6, respectively. Spatial prediction reveals that environmentally suitable areas of scrub typhus are widely distributed across the country particularly in the low-land Tarai and less elevated river valleys. We found that areas close to agricultural land with gentle slopes have higher suitability of scrub typhus occurrence. Despite several speculations on the association between scrub typhus and proximity to earthquake epicenters, we did not find a significant role of proximity to earthquake epicenters in the distribution of scrub typhus in Nepal. About 43% of the population living in highly suitable areas for scrub typhus are at higher risk of infection, followed by 29% living in suitable areas of moderate-risk, and about 22% living in moderately suitable areas of lower risk. These findings could be useful in selecting priority areas for surveillance and control strategies effectively.

scholarly journals Malaria risk map for India based on climate, ecology and geographical modelling

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Soma Sarkar ◽  
Poonam Singh ◽  
Mercy Aparna L. Lingala ◽  
Preeti Verma ◽  
Ramesh C. Dhiman
Keyword(s):  
Malaria Transmission ◽  
Vegetation Index ◽  
Forest Cover ◽  
Malaria Risk ◽  
Geographical Information ◽  
Risk Indicators ◽  
Transmission Risk ◽  
Climate Suitability ◽  
Risk Zones ◽  
Very High

Mapping the malaria risk at various geographical levels is often undertaken considering climate suitability, infection rate and/or malaria vector distribution, while the ecological factors related to topography and vegetation cover are generally neglected. The present study abides a holistic approach to risk mapping by including topographic, climatic and vegetation components into the framework of malaria risk modelling. This work attempts to delineate the areas of Plasmodium falciparum and Plasmodium vivax malaria transmission risk in India using seven geo-ecological indicators: temperature, relative humidity, rainfall, forest cover, soil, slope, altitude and the normalized difference vegetation index using multi-criteria decision analysis based on geographical information system (GIS). The weight of the risk indicators was assigned by an analytical hierarchical process with the climate suitability (temperature and humidity) data generated using fuzzy logic. Model validation was done through both primary and secondary datasets. The spatio-ecological model was based on GIS to classify the country into five zones characterized by various levels of malaria transmission risk (very high; high; moderate; low; and very low. The study found that about 13% of the country is under very high malaria risk, which includes the malaria- endemic districts of the states of Chhattisgarh, Odisha, Jharkhand, Tripura, Assam, Meghalaya and Manipur. The study also showed that the transmission risk suitability for P. vivax is higher than that for P. falciparum in the Himalayan region. The field study corroborates the identified malaria risk zones and highlights that the low to moderate risk zones are outbreak-prone. It is expected that this information will help the National Vector Borne Disease Control Programme in India to undertake improved surveillance and conduct target based interventions.

scholarly journals THE ROLES OF URBAN BUILDINGS AND VEGETATION IN ADJUSTING SEASONAL AND DAILY AIR TEMPERATURE

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1307-1312
Author(s):  
Y. Lan ◽  
Z. Huang ◽  
R. Guo ◽  
Q. Zhan
Keyword(s):  
Air Temperature ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Environment ◽  
Mitigation Strategies ◽  
Spatiotemporal Patterns ◽  
Gis Technology ◽  
Night Time ◽  
Urban Buildings ◽  
Urban Heat

<p><strong>Abstract.</strong> Exploring the spatiotemporal patterns of the relationships between urban indicators and urban temperature is essential to improve the mitigation effectiveness when we intend to adjust built environment for moderating urban thermal environment. In this study, RS, GIS technology and statistical methods were involved to investigate the spatiotemporal patterns of the impacts of urban buildings and vegetation on Air Temperature (AT). Building Density (BD) and Normalized Difference Vegetation Index (NDVI) are the indicators for urban buildings and vegetation respectively. The objectives of this study are: 1) to determine an appropriate scale for examining the building-AT relationships and vegetation-AT relationships; 2) to explore the seasonal and daily characteristics of these relationships; and 3) to compare the effects of urban buildings and vegetation. The results show that, for both summer and winter, a scale of 200&amp;ndash;250&amp;thinsp;m is optimal for examining building-AT relationships, and 960&amp;ndash;1020&amp;thinsp;m is the desirable scale for studying vegetation-AT relationships. Based on the optimal scales, we find that for both buildings and vegetation, they only significantly impact night-time temperature in both summer and winter. For seasonal comparison, the building-AT relationships and vegetation-AT relationships are relatively stronger in summer than in winter, which are indicated by R-square of the regression results. When comparing the effects of urban building and vegetation, we find that increasing vegetation is more effective than reduce buildings to achieve the same air temperature reduction. Our findings are conducive to generating space-time targeted Urban Heat Island (UHI) mitigation strategies.</p>

scholarly journals Rice Mapping and Growth Monitoring Based on Time Series GF-6 Images and Red-Edge Bands

2021 ◽  
Vol 13 (4) ◽  
pp. 579
Author(s):  
Xueqin Jiang ◽  
Shenghui Fang ◽  
Xia Huang ◽  
Yanghua Liu ◽  
Linlin Guo
Keyword(s):  
Time Series ◽  
Precision Agriculture ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Southern China ◽  
Growth Period ◽  
Growth Monitoring ◽  
Rice Growth ◽  
Red Edge

Accurate rice mapping and growth monitoring are of great significance for ensuring food security and agricultural sustainable development. Remote sensing (RS), as an efficient observation technology, is expected to be useful for rice mapping and growth monitoring. Due to the fragmented distribution of paddy fields and the undulating terrain in Southern China, it is very difficult in rice mapping. Moreover, there are many crops with the same growth period as rice, resulting in low accuracy of rice mapping. We proposed a red-edge decision tree (REDT) method based on the combination of time series GF-6 images and red-edge bands to solve this problem. The red-edge integral and red-edge vegetation index integral were computed by using two red-edge bands derived from GF-6 images to construct the REDT. Meanwhile, the conventional method based on time series normalized difference vegetation index (NDVI), normalized difference water index (NDWI), enhanced vegetation index (EVI) (NNE) was employed to compare the effectiveness of rice mapping. The results indicated that the overall accuracy and Kappa coefficient of REDT ranged from 91%–94% and 0.82–0.87, improving about 7% and 0.15 compared with the NNE method. This proved that the proposed technology was able to efficiently solve the problem of rice mapping on a large scale and regions with fragmented landscapes. Additionally, two red-edge bands of GF-6 images were applied to monitor rice growth. It concluded that the two red-edge bands played different roles in rice growth monitoring. The red-edge bands of GF-6 images were superior in rice mapping and growth monitoring. Further study needs to develop more vegetation indices (VIs) related to the red-edge to make the best use of red-edge characteristics in precision agriculture.

scholarly journals Decreasing Rice Cropping Intensity in Southern China from 1990 to 2015

2018 ◽  
Vol 11 (1) ◽  
pp. 35 ◽  
Author(s):  
Min Jiang ◽  
Liangjie Xin ◽  
Xiubin Li ◽  
Minghong Tan ◽  
Renjing Wang
Keyword(s):  
Food Security ◽  
High Resolution ◽  
Greenhouse Gas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Southern China ◽  
Cropping Systems ◽  
Data Availability ◽  
Rice Cropping Systems ◽  
Sown Area

Assessing changes in rice cropping systems is essential for ensuring food security, greenhouse gas emissions, and sustainable water management. However, due to the insufficient availability of images with moderate to high spatial resolution, caused by frequent cloud cover and coarse temporal resolution, high-resolution maps of rice cropping systems at a large scale are relatively limited, especially in tropical and subtropical regions. This study combined the difference of Normalized Difference Vegetation Index (dNDVI) method and the Normalized Difference Vegetation Index (NDVI) threshold method to monitor changes in rice cropping systems of Southern China using Landsat images, based on the phenological differences between different rice cropping systems. From 1990–2015, the sown area of double cropping rice (DCR) in Southern China decreased by 61054.5 km2, the sown area of single cropping rice (SCR) increased by 20,110.7 km2, the index of multiple cropping decreased from 148.3% to 129.3%, and the proportion of DCR decreased by 20%. The rice cropping systems in Southern China showed a “double rice shrinking and single rice expanding” change pattern from north to south, and the most dramatic changes occurred in the Middle-Lower Yangtze Plain. This study provided an efficient strategy that can be applied to moderate to high resolution images with deficient data availability, and the resulting maps can be used as data support to adjust agricultural structures, formulate food security strategies, and compile a greenhouse gas emission inventory.

scholarly journals Performance of Various Forms of the Palmer Drought Severity Index in China from 1961 to 2013

2019 ◽  
Vol 20 (9) ◽  
pp. 1867-1885 ◽  
Author(s):  
Ziqian Zhong ◽  
Bin He ◽  
Lanlan Guo ◽  
Yafeng Zhang
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Potential Evapotranspiration ◽  
Southern China ◽  
Northern China ◽  
Severity Index ◽  
Drought Severity ◽  
Average Rainfall ◽  
Terrestrial Water ◽  
Better Than

Abstract A topic of ongoing debate on the application of PDSI is whether to use the original version of the PDSI or a self-calibrating form, as well as which method to use for calculating potential evapotranspiration (PET). In this study, the performances of four forms of the PDSI, including the original PDSI based on the Penman–Monteith method for calculating PET (ETp), the PDSI based on the crop reference evapotranspiration method for calculating PET (ET0), the self-calibrating PDSI (scPDSI) based on ETp, and the scPDSI based on ET0, were evaluated in China using the normalized difference vegetation index (NDVI), modeled soil moisture anomalies (SMA), and the terrestrial water storage deficit index (WSDI). The interannual variations of all forms of PDSI agreed well with each other and presented a weak increasing trend, suggesting a climate wetting in China from 1961 to 2013. PDSI-ET0 correlated more closely with NDVI anomalies, SMA, and WSDI than did PDSI-ETp in northern China, especially in northeastern China, while PDSI-ETp correlated more closely with SMA and WSDI in southern China. PDSI-ET0 performed better than PDSI-ETp in regions where the annual average rainfall is between 350 and 750 mm yr−1. The spatial comparability of scPDSI was better than that of PDSI, while the PDSI correlated more closely with NDVI anomalies, SMA, and WSDI than did scPDSI in most regions of China. Knowledge from this study provides important information for the choice of PDSI forms when it is applied for different practices.

scholarly journals Risk mapping of scrub typhus infections in Qingdao city, China

2020 ◽  
Vol 14 (12) ◽  
pp. e0008757
Author(s):  
Hualei Xin ◽  
Peng Fu ◽  
Junling Sun ◽  
Shengjie Lai ◽  
Wenbiao Hu ◽  
...  
Keyword(s):  
At Risk ◽  
Scrub Typhus ◽  
Control Strategies ◽  
Regression Tree ◽  
Infection Risk ◽  
Tree Model ◽  
Boosted Regression Tree ◽  
Risk Areas ◽  
Qingdao City ◽  
Favourable Environment

Background The emergence and re-emergence of scrub typhus has been reported in the past decade in many global regions. In this study, we aim to identify potential scrub typhus infection risk zones with high spatial resolution in Qingdao city, in which scrub typhus is endemic, to guide local prevention and control strategies. Methodology/Principal findings Scrub typhus cases in Qingdao city during 2006–2018 were retrieved from the Chinese National Infectious Diseases Reporting System. We divided Qingdao city into 1,101 gridded squares and classified them into two categories: areas with and without recorded scrub typhus cases. A boosted regression tree model was used to explore environmental and socioeconomic covariates associated with scrub typhus occurrence and predict the risk of scrub typhus infection across the whole area of Qingdao city. A total of 989 scrub typhus cases were reported in Qingdao from 2006–2018, with most cases located in rural and suburban areas. The predicted risk map generated by the boosted regression tree models indicated that the highest infection risk areas were mainly concentrated in the mid-east and northeast regions of Qingdao, with gross domestic product (20.9%±1.8% standard error) and annual cumulative precipitation (20.3%±1.1%) contributing the most to the variation in the models. By using a threshold environmental suitability value of 0.26, we identified 757 squares (68.7% of the total) with a favourable environment for scrub typhus infection; 66.2% (501/757) of the squares had not yet recorded cases. It is estimated that 6.32 million people (72.5% of the total population) reside in areas with a high risk of scrub typhus infection. Conclusions/Significance Many locations in Qingdao city with no recorded scrub typhus cases were identified as being at risk for scrub typhus occurrence. In these at-risk areas, awareness and capacity for case diagnosis and treatment should be enhanced in the local medical service institutes.

scholarly journals Microspatial Distributional Patterns of Vectors of Cutaneous Leishmaniasis in Pernambuco, Northeastern Brazil

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Maria Rita Donalisio ◽  
A. Townsend Peterson ◽  
Pietra Lemos Costa ◽  
Fernando José da Silva ◽  
Hélio França Valença ◽  
...  
Keyword(s):  
Cutaneous Leishmaniasis ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Northeastern Brazil ◽  
Transmission Risk ◽  
Sand Fly ◽  
Ecological Niches ◽  
Landsat Images ◽  
Forested Areas ◽  
Species Occurrences

The purpose of this study is to analyze the spatial distribution and population trends through time ofLutzomyiaspecies in a long-term focus of cutaneous leishmaniasis transmission in an Atlantic Forest area, northeastern Brazil. Sand fly populations of different ecological niches were monitored spatiotemporally in 2009. To summarize vegetation characteristics and phenology, we calculated the Normalized Difference Vegetation Index from Landsat images. Using niche modeling approaches, we assessed suites of environmental factors to identify areas of transmission risk. Although 12 species were detected,L. whitmaniwas the most abundant and broadly distributed across the area, particularly in peridomiciliary locations, and associated negatively with denser vegetation areas. On the other hand,L. complexa,L. sordelli, andL. tupynambaiwere found almost exclusively in forested areas (), and associated positively with denser vegetation.Lutzomyiaspecies' occurrences are related to specific environmental combinations (with contrast among species) in the region.

scholarly journals A double peak in the seasonality of California's photosynthesis as observed from space

2020 ◽  
Vol 17 (2) ◽  
pp. 405-422 ◽  
Author(s):  
Alexander J. Turner ◽  
Philipp Köhler ◽  
Troy S. Magney ◽  
Christian Frankenberg ◽  
Inez Fung ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Temporal Dynamics ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Spatiotemporal Patterns ◽  
Eof Analysis ◽  
Near Infrared Reflectance ◽  
Double Peak

Abstract. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a powerful proxy for photosynthesis and gross primary productivity (GPP). The recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal-to-noise ratio to retrieve SIF from space. Here, we present a downscaling method to obtain 500 m spatial resolution SIF over California. We report daily values based on a 14 d window. TROPOMI SIF data show a strong correspondence with daily GPP estimates at AmeriFlux sites across multiple ecosystems in California. We find a linear relationship between SIF and GPP that is largely invariant across ecosystems with an intercept that is not significantly different from zero. Measurements of SIF from TROPOMI agree with MODerate Resolution Imaging Spectroradiometer (MODIS) vegetation indices – the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation index (NIRv) – at annual timescales but indicate different temporal dynamics at monthly and daily timescales. TROPOMI SIF data show a double peak in the seasonality of photosynthesis, a feature that is not present in the MODIS vegetation indices. The different seasonality in the vegetation indices may be due to a clear-sky bias in the vegetation indices, whereas previous work has shown SIF to have a low sensitivity to clouds and to detect the downregulation of photosynthesis even when plants appear green. We further decompose the spatiotemporal patterns in the SIF data based on land cover. The double peak in the seasonality of California's photosynthesis is due to two processes that are out of phase: grasses, chaparral, and oak savanna ecosystems show an April maximum, while evergreen forests peak in June. An empirical orthogonal function (EOF) analysis corroborates the phase offset and spatial patterns driving the double peak. The EOF analysis further indicates that two spatiotemporal patterns explain 84 % of the variability in the SIF data. Results shown here are promising for obtaining global GPP at sub-kilometer spatial scales and identifying the processes driving carbon uptake.

scholarly journals Risk factors and predicted distribution of visceral leishmaniasis in the Xinjiang Uygur Autonomous Region, China, 2005–2015

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Fangyu Ding ◽  
Qian Wang ◽  
Jingying Fu ◽  
Shuai Chen ◽  
Mengmeng Hao ◽  
...  
Keyword(s):  
Visceral Leishmaniasis ◽  
Spatial Epidemiology ◽  
Infection Risk ◽  
Boosted Regression Tree ◽  
Infected Female ◽  
Autonomous Region ◽  
Xinjiang Uygur Autonomous Region ◽  
Risk Areas ◽  
Risk Zones ◽  
Kashgar Prefecture

Abstract Background Visceral leishmaniasis (VL) is a neglected disease that is spread to humans by the bites of infected female phlebotomine sand flies. Although this vector-borne disease has been eliminated in most parts of China, it still poses a significant public health burden in the Xinjiang Uygur Autonomous Region. Understanding of the spatial epidemiology of the disease remains vague in the local community. In the present study, we investigated the spatiotemporal distribution of VL in the region in order to assess the potential threat of the disease. Methods Based on comprehensive infection records, the spatiotemporal patterns of new cases of VL in the region between 2005 and 2015 were analysed. By combining maps of environmental and socioeconomic correlates, the boosted regression tree (BRT) model was adopted to identify the environmental niche of VL. Results The fitted BRT models were used to map potential infection risk zones of VL in the Xinjiang Uygur Autonomous Region, revealing that the predicted high infection risk zones were mainly concentrated in central and northern Kashgar Prefecture, south of Atushi City bordering Kashgar Prefecture and regions of the northern Bayingolin Mongol Autonomous Prefecture. The final result revealed that approximately 16.64 million people inhabited the predicted potential infection risk areas in the region. Conclusions Our results provide a better understanding of the potential endemic foci of VL in the Xinjiang Uygur Autonomous Region with a 1 km spatial resolution, thereby enhancing our capacity to target the potential risk areas, to develop disease control strategies and to allocate medical supplies.

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