Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms
•The relative importance of influential variables on COVID-19 varies over time.•Unemployment and population density are highly correlated with COVID-19 prevalence.•Regarding COVID-19 mortality, diabetes is an influential variable worldwide.•Neural network algorithms can estimate the importance of va...
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| Published in: | Spatial and spatio-temporal epidemiology Vol. 40; p. 100471 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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01.02.2022
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| ISSN: | 1877-5845, 1877-5853, 1877-5853 |
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| Abstract | •The relative importance of influential variables on COVID-19 varies over time.•Unemployment and population density are highly correlated with COVID-19 prevalence.•Regarding COVID-19 mortality, diabetes is an influential variable worldwide.•Neural network algorithms can estimate the importance of variables more accurately.•Dealing with complex interactions, variable importance analysis tools are effective.
The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making. |
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| AbstractList | The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making. •The relative importance of influential variables on COVID-19 varies over time.•Unemployment and population density are highly correlated with COVID-19 prevalence.•Regarding COVID-19 mortality, diabetes is an influential variable worldwide.•Neural network algorithms can estimate the importance of variables more accurately.•Dealing with complex interactions, variable importance analysis tools are effective. The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making. The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making. |
| ArticleNumber | 100471 |
| Author | Kianfar, Nima Mollalo, Abolfazl Mesgari, Mohammad Saadi Kaveh, Mehrdad |
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| Keywords | COVID-19 GIS Spatio-temporal analysis Artificial neural network Variable importance analysis |
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| License | Copyright © 2021. Published by Elsevier Ltd. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
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| Snippet | •The relative importance of influential variables on COVID-19 varies over time.•Unemployment and population density are highly correlated with COVID-19... The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on... |
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| StartPage | 100471 |
| SubjectTerms | Algorithms Artificial neural network COVID-19 GIS Humans Neural Networks, Computer Prevalence SARS-CoV-2 Spatio-temporal analysis Variable importance analysis |
| Title | Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms |
| URI | https://dx.doi.org/10.1016/j.sste.2021.100471 https://www.ncbi.nlm.nih.gov/pubmed/35120681 https://www.proquest.com/docview/2626009007 https://pubmed.ncbi.nlm.nih.gov/PMC8580864 |
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