Spatial and Temporal Epidemiologic features analysis of pulmonary tuberculosis in Nanjing, China

2019 ◽  
Author(s):  
Hongyu Zhao ◽  
Changkui Sun ◽  
Wei Ma ◽  
Guoping Yin ◽  
Xia Zhang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Environmental Factors ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Central City ◽  
Floating Population ◽  
Distribution Model ◽  
Jackknife Test ◽  
Factors Associated ◽  
Epidemiologic Features

Abstract Background This study aims to analyse the epidemiological features of tuberculosis in Nanjing and to identify possible risk factors associated with the spatial-temporal distribution.Methods Firstly, we used descriptive statistical method to study the epidemiologic features of confirmed PTB cases in 2017. Secondly, we explored Maxent method to construct the species distribution model with a high spatial resolution of 1km based on those confirmed cases and environmental features. Once again, we validated the performance of the species distribution model using ROC approach and spatial jackknife test, identified the key environmental factors associated with PTB occurrence. Finally, we created a prediction map under specific environmental factors by projecting the training model onto the research areas.Results The seasonal amount of PTB was not obvious. The key risk factors associated with PTB occurrence are monthly mean vapor pressure, solar radiation, monthly average temperature, and precipitation with suitable ranges. The economically developed central city is the high-burden area of tuberculosis with a high risk probability, and the economically backward Gaochun district is the high transmission area of tuberculosis with a high incidence. Epidemiologically, the farmers, the elderly, the floating population, and males are the focus groups for PTB prevention and control.Conclusion The combination of environmental factors with Maxent methods is an appropriate option to analyse and estimate the spatial and temporal distribution of PTB cases.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

scholarly journals The impact of data quality filtering of opportunistic citizen science data on species distribution model performance

2021 ◽  
Vol 444 ◽  
pp. 109453
Author(s):  
Camille Van Eupen ◽  
Dirk Maes ◽  
Marc Herremans ◽  
Kristijn R.R. Swinnen ◽  
Ben Somers ◽  
...  
Keyword(s):  
Data Quality ◽  
Citizen Science ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Model Performance ◽  
Distribution Model ◽  
Science Data ◽  
Quality Filtering ◽  
The Impact

scholarly journals Application Natura 2000 Data For The Invasive Plants Spread Prediction

2015 ◽  
Vol 46 (4) ◽  
pp. 159-166 ◽  
Author(s):  
J. Pěknicová ◽  
D. Petrus ◽  
K. Berchová-Bímová
Keyword(s):  
Environmental Factors ◽  
Invasive Plants ◽  
Species Distribution ◽  
Natura 2000 ◽  
Distribution Model ◽  
Distribution Data ◽  
Habitat Types ◽  
Distribution Models ◽  
Occurrence Data ◽  
Heracleum Mantegazzianum

AbstractThe distribution of invasive plants depends on several environmental factors, e.g. on the distance from the vector of spreading, invaded community composition, land-use, etc. The species distribution models, a research tool for invasive plants spread prediction, involve the combination of environmental factors, occurrence data, and statistical approach. For the construction of the presented distribution model, the occurrence data on invasive plants (Solidagosp.,Fallopiasp.,Robinia pseudoaccacia,andHeracleum mantegazzianum) and Natura 2000 habitat types from the Protected Landscape Area Kokořínsko have been intersected in ArcGIS and statistically analyzed. The data analysis was focused on (1) verification of the accuracy of the Natura 2000 habitat map layer, and the accordance with the habitats occupied by invasive species and (2) identification of a suitable scale of intersection between the habitat and species distribution. Data suitability was evaluated for the construction of the model on local scale. Based on the data, the invaded habitat types were described and the optimal scale grid was evaluated. The results show the suitability of Natura 2000 habitat types for modelling, however more input data (e.g. on soil types, elevation) are needed.

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