Title : ( Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN )
Authors: Reyhane Jalali , Hossein Etemadfard ,
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Abstract
This study aims to quantify the effectiveness of lockdown as a non-pharmacological solution for managing the COVID-19 pandemic. Daily COVID-19 death counts were collected for four states: California, Georgia, New Jersey, and South Carolina. The effectiveness of the lockdown was studied and the number of people saved during 7 days was evaluated. Five neural network models (MLP, FFNN, CFNN, ENN, and NARX) were implemented, and the results indicate that FFNN is the best prediction model. Based on this model, the total number of survivors over a 7-day period is 211, 270, 989, and 60 in California, Georgia, New Jersey, and South Carolina, respectively. The coefficients and weights of the FFNN for each state differ due to various factors, including socio-demographic conditions and the behavior of citizens towards lockdown laws. New Jersey and South Carolina have the most lockdowns and the least.
Keywords
, Spatiotemporal analysis, Lockdown, ANN, GIS, Healthcare
@article{paperid:1099732,
author = {Jalali, Reyhane and Etemadfard, Hossein},
title = {Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN},
journal = {Scientific Reports},
year = {2024},
volume = {14},
number = {1},
month = {August},
issn = {2045-2322},
keywords = {Spatiotemporal analysis; Lockdown; ANN; GIS; Healthcare},
}
%0 Journal Article
%T Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN
%A Jalali, Reyhane
%A Etemadfard, Hossein
%J Scientific Reports
%@ 2045-2322
%D 2024
