In this study, the ANN approach was applied to
analyze COVID-19 new cases in El Salvador. The employed data covers the period
1 January 2020 – 25 March 2021 and the out-of-sample period ranges over the
period 26 March – 31 July 2021. The residuals and forecast evaluation criteria
(Error, MSE and MAE) of the applied model indicate that the model is quite
stable. The results of the study indicate that COVID-19 cases are likely to
vanish around late April 2021. Amongst other suggested policy directions, there
is need for the government of El Salvador to ensure adherence to safety
guidelines while continuing to create awareness about the COVID-19 pandemic.
Country : Zimbabwe
1 Dr. Smartson. P. NYONI2 Mr. Thabani NYONI3 Mr. Tatenda. A. CHIHOHO
ZICHIRe Project, University of Zimbabwe, Harare, Zimbabwe
Al-Waeli AH., Kazem
HA., Yousif JH., Chaichan MT., &Sopian K (2020). Mathematical and neural
network modeling for predicting and analyzing of nanofluid-nano PCM
photovoltaic thermal systems performance. Renewable Energy. 145:963–980. https://doi.org/10.1016/j.renene.2019.06.099
Bogard N., Linder J.,
Rosenberg AB., &Seelig G (2019). A deep neural network for predicting and
engineering alternative polyadenylation. Cell, 178(1):91–106. https://doi.org/10.1016/j.cell.2019.04.046
Bullock J., Alexandra,
Luccioni, Pham KH, Lam CSN, Luengo-Oroz M. Mapping the Landscape of Artificial
Intelligence Applications against COVID-19. 2020 [cited 14 Apr 2020].
http://arxiv.org/abs/2003. 11336.
Castro J., Souza GN.,
Brito SR., Folador AR., Ramos RT., & Braga M de B (2020).
Redesneuraisartificiaisnaprevisão de conta´gio e o´bitospor COVID-19: um estudo
no Estado do Para´, Brasil. Int J Dev Res. 10: 35416–35421.
https://www.journalijdr.com/redes-neurais-artificiais-na-previsão-de-conta´gioe-o´bitos-por-covid-19
DaoudM.,& Mayo M
(2019). A survey of neural network-based cancer prediction models from
microarray data. Artificial intelligence in medicine.
Das A., Rad P., Choo
KKR., Nouhi B., Lish J., & Martel J(2019). Distributed machine learning
cloud teleophthalmology IoT for predicting AMD disease progression. Future
Generation Computer Systems. 2019; 93:486– 498. https://doi.org/10.1016/j.future.2018.10.050
Hannun AY., Rajpurkar
P., Haghpanahi M., Tison GH., Bourn C., &Turakhia MP (2019) Cardiologist-level
arrhythmia detection and classification in ambulatory electrocardiograms using
a deep neural network. Nature medicine, 25(1):65.
https://doi.org/10.1038/s41591-018-0268-3 PMID: 30617320
Luengo-Oroz M.,
Hoffmann Pham K., Bullock J., Kirkpatrick R., Luccioni A., &Rubel (2020).
Artificial intelligence cooperation to support the global response to COVID-19.
Nat Mach Intell. 2: 295–297. https://doi.org/10.1038/s42256-020-0184-3
Manliura Datilo P.,
Ismail Z., & Dare J (2020). A Review of Epidemic Forecasting Using
Artificial Neural Networks. Int J Epidemiol Res. 2019. https://doi.org/10.15171/ijer.2019.24
MaragathamG.,& Devi
S (2019). LSTM model for prediction of heart failure in big data. Journal of
medical systems, 43(5):111. https://doi.org/10.1007/s10916-019-1243-3
Pal R., Sekh AA., Kar
S., & Prasad DK (2020). Neural network based country wise risk prediction
of COVID-19. 2020 [cited 30 Jun 2020]. http://arxiv.org/abs/2004.00959.
Pathak Y., Shukla PK.,
Tiwari A., Stalin S., Singh S., & Shukla PK (2020). Deep Transfer Learning based
Classification Model for COVID-19Disease. IRBM. 2020;.
Reddy BK., Delen D.,
& Agrawal RK (2019). Predicting and explaining inflammation in Crohn’s
disease patients using predictive analytics methods and electronic medical
record data. Health informatics journal. 2019; 25(4):1201–1218. https://doi.org/10.1177/1460458217751015
Ren Y., Fei H., Liang
X., Ji D., & Cheng M (2019). A hybrid neural network model for predicting
kidney disease in hypertension patients based on electronic health records. BMC
medical informatics and decision making, 19(2):51.
Sanderson M., Bulloch
AG., Wang J., Williamson T., & Patten SB (2020). Predicting death by
suicide using administrative health care system data: Can recurrent neural
network, one-dimensional convolutional neural network, and gradient boosted
trees models improve prediction performance? Journal of Affective Disorders,
264:107–114. https://doi.org/10.1016/j.jad.2019.12.024
Sayeed A., Choi Y.,
Eslami E., Lops Y., Roy A., & Jung J (2020). Using a deep convolutional
neural network to predict 2017 ozone concentrations, 24 hours in advance.
Neural Networks. 121:396–408.
https://doi. org/10.1016/j.neunet.2019.09.033
Schmitt F., Banu R.,
&Yeom IT., Do KU (2018). Development of artificial neural networks to
predict membrane fouling in an anoxic-aerobic membrane bioreactor treating
domestic wastewater. BiochemEng J. 133: 47–58. https://doi.org/10.1016/j.bej.2018.02.001
Singh D., Kumar V.,
& Kaur M (2020). Classification of COVID-19 patients from chest CT images
using multi-objective differential evolution–based convolutional neural
networks. European Journal of Clinical Microbiology & Infectious Diseases.
2020; p. 1–11.
Singh G., Pal M., Yadav
Y., &Singla T (2020). Deep neural network-based predictive modeling of road
accidents. Neural Computing and Applications. 2020; p. 1–10.
Smartson P Nyoni.,
Thabani Nyoni & Tatenda A Chihoho (2020) Prediction of new Covid-19 cases
in Ghana using artificial neural networks. IJARIIE Vol-6 Issue-6 2395-4396
Torrealba-Rodriguez O.,
Conde-Gutie´rrez RA., &Herna´ndez-Javier AL (2020). Modeling and prediction
of COVID-19 in Mexico applying mathematical and computational models. Chaos,
Solitons and Fractals. 138: 109946. https://doi.org/10.1016/j.chaos.2020.109946
PMID: 32836915
Va´squez-Morales GR.,
Martı´nez-Monterrubio SM., Moreno-Ger P., &Recio-Garc´ıa JA (2019).
Explainable Prediction of Chronic Renal Disease in the Colombian Population
Using Neural Networks and Case-Based Reasoning. IEEE Access. 2019;
7:152900–152910. https://doi.org/10.1109/ACCESS.2019.2948430
Wahyunggoro O.,
Permanasari AE., &Chamsudin A (2013). Utilization of Neural Network for
Disease Forecasting. Proceedings 59th ISI World Statistics Congress. Hong Kong;
2013. p. 549. https://www.
semanticscholar.org/paper/Utilization-of-Neural-Network-for-Disease-Wahyunggoro-Permanasari/
88b515658b38e404dfffe8fdc55da519076c848d.
Wieczorek M., Sink J.,
Połap D., Woźniak M., &Damasˇevičius R (2020) Real-time neural network
based predictor for cov19 virus spread. PLoS ONE 15(12): e0243189.
https://doi.org/ 10.1371/journal.pone.0243189
Wu JT., & Cowling
BJ (2018). Real-time forecasting of infectious disease epidemics. Hong Kong Med
J. 2018. www.hkmj.org