Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN

IHME COVID-19 Forecasting Team. Modeling COVID-19 scenarios for the United States. Nat Med 94, 105 (2021).
Google Scholar 
Worldometers. COVID-19 Coronavirus Pandemic. https://www.worldometers.info/coronavirus/, (2022).Al Zobbi, M., Alsinglawi, B., Mubin, O. & Alnajjar, F. Measurement method for evaluating the lockdown policies during the COVID-19 pandemic. Int. J. Environ. Res. Public Health 17(15), 5574. https://doi.org/10.3390/ijerph17155574 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Schüler, L., Calabrese, J. M. & Attinger, S. Data driven high resolution modeling and spatial analyses of the COVID-19 pandemic in Germany. PLoS One https://doi.org/10.1101/2021.01.21.21250215 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ngonghala, C. N., Iboi, E. A. & Gumel, A. B. Could masks curtail the post-lockdown resurgence of COVID-19 in the US?. Math. Biosci. 1(329), 108452. https://doi.org/10.1016/j.mbs.2020.108452 (2020).Article 
MathSciNet 
CAS 

Google Scholar 
Jiang, P. et al. Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour. J. Clean. Prod. https://doi.org/10.1016/j.jclepro.2020.123673 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Kang, D., Choi, H., Kim, J. H. & Choi, J. Spatial epidemic dynamics of the COVID-19 outbreak in China. Int. J. Infect. Dis. https://doi.org/10.1016/j.ijid.2020.03.076 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Fatima, M., O’keefe, K. J., Wei, W. & Arshad, S. Geospatial analysis of COVID-19: A scoping review. Int. J. Environ. Res. Public Health https://doi.org/10.3390/ijerph18052336 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Giuliani, D., Dickson, M. M., Espa, G. & Santi, F. Modelling and predicting the spatio-temporal spread of COVID-19 in Italy. BMC Infect. Dis. 20(1), 1. https://doi.org/10.2139/ssrn.3559569 (2020).Article 

Google Scholar 
Kandel, N., Chungong, S., Omaar, A. & Xing, J. Health security capacities in the context of COVID-19 outbreak: An analysis of International Health Regulations annual report data from 182 countries. Lancet https://doi.org/10.1016/S0140-6736(20)30553-5 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Jalilian, A. & Mateu, J. A hierarchical spatio-temporal model to analyze relative risk variations of COVID-19: a focus on Spain, Italy and Germany. Stoch. Env. Res. Risk Assess. 35, 797–812. https://doi.org/10.1007/s00477-021-02003-2 (2021).Article 

Google Scholar 
Payedimarri AB, Concina D, Portinale L, Canonico M, Seys D, Vanhaecht K, Panella M. Prediction models for public health containment measures on COVID-19 using artificial intelligence and machine learning: a systematic review. Int. J. Environ. Res. Public Health. https://doi.org/10.3390/ijerph1809449, (2021).Van der Schaar, M. et al. How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Mach. Learn. 110, 1–4. https://doi.org/10.1007/s10994-020-05928-x (2021).Article 
MathSciNet 
PubMed 

Google Scholar 
Tuli, S., Tuli, S., Tuli, R. & Gill, S. S. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet Things. https://doi.org/10.1016/j.iot.2020.100222 (2020).Article 

Google Scholar 
Saba, T., Abunadi, I., Shahzad, M. N. & Khan, A. R. Machine learning techniques to detect and forecast the daily total COVID-19 infected and deaths cases under different lockdown types. Microsc. Res. Tech. 84(7), 1462–1474. https://doi.org/10.1002/jemt.23702 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Mansoor, M., Grimaccia, F., Leva, S. & Mussetta, M. Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs. Math. Comput. Simul. 1(184), 282–293. https://doi.org/10.1016/j.matcom.2020.07.011 (2021).Article 
MathSciNet 

Google Scholar 
Ahmed, I., Ahmad, M., Rodrigues, J. J., Jeon, G. & Din, S. A deep learning-based social distance monitoring framework for COVID-19. Sustain. Cities Soc. https://doi.org/10.1016/j.scs.2020.102571 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Zivkovic, M. et al. COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach. Sustain. Cities Soc. https://doi.org/10.1016/j.scs.2020.102669 (2021).Article 
PubMed 

Google Scholar 
Li, M. et al. Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.142810 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Mollalo, A., Rivera, K. M. & Vahedi, B. Artificial neural network modeling of novel coronavirus (COVID-19) incidence rates across the continental United States. Int. J. Environ. Res. Public Health 17(12), 4204. https://doi.org/10.3390/ijerph17124204 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Alsayed, A., Sadir, H., Kamil, R. & Sari, H. Prediction of epidemic peak and infected cases for COVID-19 disease in Malaysia, 2020. Int. J. Environ. Res. Public Health 17(11), 4076. https://doi.org/10.3390/ijerph17114076 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kannan, S., Subbaram, K., Ali, S. & Kannan, H. The role of artificial intelligence and machine learning techniques: Race for covid-19 vaccine. Arch. Clin. Infectious Diseases https://doi.org/10.5812/archcid.103232 (2020).Article 

Google Scholar 
Elsheikh, A. H. et al. Artificial intelligence for forecasting the prevalence of COVID-19 pandemic: An overview. InHealthcare https://doi.org/10.3390/healthcare9121614 (2021).Article 

Google Scholar 
Elsheikh, A. H. et al. Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia. Process Safety Environ. Protect. 1(149), 223–233 (2021).Article 

Google Scholar 
Saba, A. I. & Elsheikh, A. H. Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks. Process Safety Environ. Protect. 1(141), 1–8 (2020).Article 

Google Scholar 
Cihan, P. The machine learning approach for predicting the number of intensive care, intubated patients and death: The COVID-19 pandemic in Turkey. Sigma J. Eng. Nat. Sci. 40(1), 85–94 (2022).MathSciNet 

Google Scholar 
Al-Qaness, M. A. et al. Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil. Process Safety Environ Protect. 1(149), 399–409 (2021).Article 

Google Scholar 
Issa, M., Helmi, A. M., Elsheikh, A. H. & Abd, E. M. A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19. Expert Syst. Appl. 1(189), 116063 (2022).Article 

Google Scholar 
Abd Elaziz, M. et al. Boosting COVID-19 image classification using MobileNetV3 and aquila optimizer algorithm. Entropy 23(11), 1383 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cihan, P. Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World. Appl. Soft Comput. 1(111), 107708 (2021).Article 

Google Scholar 
Griva, K. et al. Evaluating rates and determinants of COVID-19 vaccine hesitancy for adults and children in the Singapore population: strengthening our community’s resilience against threats from emerging infections (SOCRATEs) cohort. Vaccines 9(12), 1415 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Choi, S. M. & Choi, H. Artificial Neural Network Modeling on PM10, PM2. 5, and NO2 Concentrations between Two Megacities without a Lockdown in Korea, for the COVID-19 Pandemic Period of 2020. Int. J. Environ. Res. Public Health https://doi.org/10.3390/ijerph192316338 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Adak, S., Majumder, R., Majee, S., Jana, S. & Kar, T. K. An ANFIS model-based approach to investigate the effect of lockdown due to COVID-19 on public health. Eur. Phys. J. Special Topics 231(18), 3317–3327. https://doi.org/10.1140/epjs/s11734-022-00621-7 (2022).Article 
ADS 
CAS 

Google Scholar 
Huang, X. et al. The impact of lockdown timing on COVID-19 transmission across US counties. EClinicalMedicine https://doi.org/10.1016/j.eclinm.2021.101035 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Siqueira, C. A. et al. The effect of lockdown on the outcomes of COVID-19 in Spain: An ecological study. Plos One https://doi.org/10.1371/journal.pone.0236779 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Plan, E. L., Thi, H. L., Le, D. M. & Phan, H. Temporal considerations in the 2021 COVID-19 lockdown of Ho Chi Minh City. Medrxiv https://doi.org/10.1101/2021.08.04.21261332 (2021).Article 

Google Scholar 
Singh, S., Shaikh, M., Hauck, K. & Miraldo, M. Impacts of introducing and lifting nonpharmaceutical interventions on COVID-19 daily growth rate and compliance in the United States. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.2021359118/-/DCSupplemental (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Guzzetta, G. et al. Impact of a nationwide lockdown on SARS-CoV-2 transmissibility, Italy. Emerg. Infect. Dis. 27(1), 267. https://doi.org/10.3201/eid2701.202114 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Di Domenico, L., Pullano, G., Sabbatini, C. E., Boëlle, P. Y. & Colizza, V. Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies. BMC Med. 18(1), 1–3. https://doi.org/10.1186/s12916-020-01698-4 (2020).Article 
CAS 

Google Scholar 
Zawbaa, H. M. et al. A study of the possible factors affecting COVID-19 spread, severity and mortality and the effect of social distancing on these factors: Machine learning forecasting model. Int. J. Clin. Pract. https://doi.org/10.1111/ijcp.14116 (2021).Article 
PubMed 

Google Scholar 
Di Nunno, F. & Granata, F. Groundwater level prediction in Apulia region (Southern Italy) using NARX neural network. Environ. Res. https://doi.org/10.1016/j.envres.2020.110062 (2020).Article 
PubMed 

Google Scholar 
Marzouk, M., Elshaboury, N., Abdel-Latif, A. & Azab, S. Deep learning model for forecasting COVID-19 outbreak in Egypt. Process Safety Environmen. Protect. 1(153), 363–375. https://doi.org/10.1016/j.psep.2021.07.034 (2021).Article 
CAS 

Google Scholar 
Raj, P. & Evangeline, P. The digital twin paradigm for smarter systems and environments: The industry use cases (Academic Press, 2020).
Google Scholar 
Okoro, E. E., Obomanu, T., Sanni, S. E., Olatunji, D. I. & Igbinedion, P. Application of artificial intelligence in predicting the dynamics of bottom hole pressure for under-balanced drilling: Extra tree compared with feed forward neural network model. Petroleum 8(2), 227–236. https://doi.org/10.1016/j.petlm.2021.03.001 (2022).Article 

Google Scholar 
Tran HD, Musau P, Lopez DM, Yang X, Nguyen LV, Xiang W, Johnson TT. Parallelizable reachability analysis algorithms for feed-forward neural networks. In2019 IEEE/ACM 7th International Conference on Formal Methods in Software Engineering (FormaliSE), IEEE. https://doi.org/10.1109/FormaliSE.2019.00012. (2019).Hayder, G., Solihin, M. I. & Mustafa, H. M. Modelling of river flow using particle swarm optimized cascade-forward neural networks: A case study of Kelantan River in Malaysia. Appl. Sci. 10(23), 8670. https://doi.org/10.3390/app10238670 (2020).Article 
CAS 

Google Scholar 
Zeng, J., Jamei, M., Nait Amar, M., Hasanipanah, M. & Bayat, P. A novel solution for simulating air overpressure resulting from blasting using an efficient cascaded forward neural network. Eng. Comput. 38(Suppl 3), 2069–2081. https://doi.org/10.1007/s00366-021-01381-z (2022).Article 

Google Scholar 
Selvi, M. V. & Mishra, S. Investigation of Weather Impact on Electric Load Power Forecasting based on Cascade Forward Neural Network Technique. In 2020 IEEE 5th International Conference on Computing Communication and Automation (ed. Selvi, M. V.) (IEEE, 2020).
Google Scholar 
Abujazar, M. S., Fatihah, S., Ibrahim, I. A., Kabeel, A. E. & Sharil, S. Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model. J. Clean. Prod. 1(170), 147–159. https://doi.org/10.1016/j.jclepro.2017.09.092 (2018).Article 

Google Scholar 
Yu, D., Wang, Y., Liu, H., Jermsittiparsert, K. & Razmjooy, N. System identification of PEM fuel cells using an improved Elman neural network and a new hybrid optimization algorithm. Energy Rep. 1(5), 1365–1374. https://doi.org/10.1016/j.egyr.2019.09.039 (2019).Article 

Google Scholar 
Nawab, F. et al. Evaluation of Artificial Neural Networks with Satellite Data Inputs for Daily, Monthly, and Yearly Solar Irradiation Prediction for Pakistan. Sustainability 14(13), 7945. https://doi.org/10.3390/su14137945 (2022).Article 

Google Scholar 
Li, X., Zhang, L., Wang, Z. & Dong, P. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks. J. Energy Storage 1(21), 510–518. https://doi.org/10.1016/j.est.2018.12.011 (2019).Article 

Google Scholar 
Xie, K., Yi, H., Hu, G., Li, L. & Fan, Z. Short-term power load forecasting based on Elman neural network with particle swarm optimization. Neurocomputing. 27(416), 136–142. https://doi.org/10.1016/j.neucom.2019.02.063 (2020).Article 

Google Scholar 
Han, J. B., Kim, S. H., Jang, M. H. & Ri, K. S. Using genetic algorithm and NARX neural network to forecast daily bitcoin price. Comput. Econ. 56, 337–353. https://doi.org/10.1007/s10614-019-09928-5 (2020).Article 

Google Scholar 
Liu, Q., Chen, W., Hu, H., Zhu, Q. & Xie, Z. An optimal NARX neural network identification model for a magnetorheological damper with force-distortion behavior. Front. Mater. 14(7), 10. https://doi.org/10.3389/fmats.2020.00010 (2020).Article 
ADS 

Google Scholar 
Wei, M., Ye, M., Li, J. B., Wang, Q. & Xu, X. State of charge estimation of lithium-ion batteries using LSTM and NARX neural networks. Ieee Access. 15(8), 189236–189245. https://doi.org/10.1109/ACCESS.2020.3031340 (2020).Article 

Google Scholar 
Di Nunno, F., Granata, F., Gargano, R. & de Marinis, G. Prediction of spring flows using nonlinear autoregressive exogenous (NARX) neural network models. Environ. Monitor. Assess. 193(6), 350. https://doi.org/10.1007/s10661-021-09135-6 (2021).Article 

Google Scholar 
Executive Department State of California. Executive order N-33–20, https://www.ca.gov, (2020)Executive Department State of Georgia. Executive order 03.23.20.01, https://www.georgia.gov, (2020).Executive Department State of New Jersey. Executive order No. 107, https://www.nj.gov, (2020).Executive Department State of South Carolina. Executive order No. 2020–21, https://www.sc.gov, (2020).The New York Times. Coronavirus (Covid-19) Data in the United States; 2021. From: https://www.kaggle.com/imoore/us-covid19-dataset-live-hourlydaily-updates.The United States Census. Census, https://www.census.gov, (2020).Maryland Transportation Institute. University of Maryland COVID-19 Impact Analysis Platform, University of Maryland, College Park, USA, https://data.covid.umd.edu, (2022).The United States Census Bureau. Cartographic Boundary Files Naming Convention; 2020. Form: https://www2.census.gov/geo/tiger/GENZ2020, (2020).