Cargando…
A GIS-Based Artificial Neural Network Model for Spatial Distribution of Tuberculosis across the Continental United States
Despite the usefulness of artificial neural networks (ANNs) in the study of various complex problems, ANNs have not been applied for modeling the geographic distribution of tuberculosis (TB) in the US. Likewise, ecological level researches on TB incidence rate at the national level are inadequate fo...
Autores principales: | Mollalo, Abolfazl, Mao, Liang, Rashidi, Parisa, Glass, Gregory E. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338935/ https://www.ncbi.nlm.nih.gov/pubmed/30626123 http://dx.doi.org/10.3390/ijerph16010157 |
Ejemplares similares
-
GIS-based spatial modeling of COVID-19 incidence rate in the continental United States
por: Mollalo, Abolfazl, et al.
Publicado: (2020) -
Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States
por: Mollalo, Abolfazl, et al.
Publicado: (2020) -
Predicting the hotspots of age-adjusted mortality rates of lower respiratory infection across the continental United States: Integration of GIS, spatial statistics and machine learning algorithms
por: Mollalo, Abolfazl, et al.
Publicado: (2020) -
Spatial statistical analysis of pre-existing mortalities of 20 diseases with COVID-19 mortalities in the continental United States
por: Mollalo, Abolfazl, et al.
Publicado: (2021) -
Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States
por: Mollalo, Abolfazl, et al.
Publicado: (2021)