A Remote Sensing Data Based Artificial Neural Network Approach for Predicting Climate-Sensitive Infectious Disease Outbreaks: A Case Study of Human Brucellosis
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Summary
The study suggests that HB outbreaks may be predicted, with a reasonable degree of accuracy, using the ANN model and environmental variables obtained from satellite data, and deepened the understanding of environmental determinants of HB and advanced the methodology for prediction of climate-sensitive infectious disease outbreaks.
- Type
- article
- Published
- 2017-09-30
- Cited by
- 19
- References
- 46
- Access
- Open access
- OpenAlex
- https://openalex.org/W2758580191
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:37494692
Keywords
Outbreak, Brucellosis, Environmental data, Infectious disease (medical specialty), Environmental science
References
- Epidemiological features and risk factors associated with the spatial and temporal distribution of human brucellosis in China
- Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
- A logical calculus of the ideas immanent in nervous activity
- Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences
- Likelihood analysis of species occurrence probability from presence‐only data for modelling species distributions
- Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network.
- A comparison of Maxlike and Maxent for modelling species distributions
- Epidemiology and control of brucellosis in China.
- Human brucellosis in the People's Republic of China during 2005-2010.
- A fine-scale spatial population distribution on the High-resolution Gridded Population Surface and application in Alachua County, Florida
- Networks in Cognitive Science
- Human brucellosis occurrences in inner mongolia, China: a spatio-temporal distribution and ecological niche modeling approach
- Analysis of the ancient river system in Loulan period in Lop Nur region
- Climate Change and Risk of Leishmaniasis in North America: Predictions from Ecological Niche Models of Vector and Reservoir Species
- Modeling Fecal Coliform Bacteria Levels at Gulf Coast Beaches
- High Resolution Population Distribution Maps for Southeast Asia in 2010 and 2015
- The new global map of human brucellosis.
- An introduction to computing with neural nets
- Recognition and extraction of the ancient sites covered by thick vegetation in Hainan Province of China
- The Impact of Desertification in the Mongolian and the Inner Mongolian Grassland on the Regional Climate
Cited by
- Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery
- Spatial lifecourse epidemiology.
- Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data
- How decision makers can use quantitative approaches to guide outbreak responses
- A survey on prediction approaches for epidemic disease outbreaks based on social media data
- Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches
- Prediction of Human Brucellosis in China Based on Temperature and NDVI
- Spatial Lifecourse Epidemiology and Infectious Disease Research
- Predicting population health with machine learning: a scoping review
- Geomatics and EO Data to Support Wildlife Diseases Assessment at Landscape Level: A Pilot Experience to Map Infectious Keratoconjunctivitis in Chamois and Phenological Trends in Aosta Valley (NW Italy)
- Spatial prediction of human brucellosis (HB) using a GIS-based adaptive neuro-fuzzy inference system (ANFIS).
- Spatial modeling of zoonotic cutaneous leishmaniasis with regard to potential environmental factors using ANFIS and PCA-ANFIS methods.
- Synergising decision making and interventions across human health and environment: concepts for designing a model for infectious diseases
- Predicting the Spatial-Temporal Distribution of Human Brucellosis in Europe Based on Convolutional Long Short-Term Memory Network
- Predicting COVID-19 using lioness optimization algorithm and graph convolution network
- Prediction of Foodborne Norovirus Outbreaks in Coastal Areas in China in 2008–2018
- Emergencies of zoonotic diseases, drivers, and the role of artificial intelligence in tracking the epidemic and pandemics
- A Hybrid Prediction Model Based on Decomposition-Integration for Foodborne Disease Risks
- Rede neural artificial para predic¸˜ao de brucelose bovina a partir de dados desbalanceados
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