Zgheib, RitaChahbandarian, GhazarKamalov, FiruzLabban, Osman El2022-05-222022-05-22© 20212021Zgheib, R., Chahbandarian, G., Kamalov, F., & Labban, O. E. (2021). Neural networks architecture for COVID-19 early detection. 2021 International Symposium on Networks, Computers and Communications (ISNCC). https://doi.org/10.1109/ISNCC52172.2021.9615883978-073811316-6https://doi.org/10.1109/ISNCC52172.2021.9615883http://hdl.handle.net/20.500.12519/647Coronavirus fight seems far from being won. Governments are trying to balance the necessity to enforce restrictions on travel outside the home and the impact of these restrictions on the economy. Healthcare workers are overloaded, a considerable number of unnecessary and costly PCR tests are performed to serve as a certificate to go to work. At this stage, going back to everyday life safely requires the companies and public places to adopt AI-based solutions to assist the public authorities and the hospitals with the COVID detection. The most important issue that we tackle in this paper is the prediction to be very accurate. As a result, we propose an AI system based on Neural Networks (NN) method to predict whether a person has caught COVID19 disease or not. In this study, we used a real data set of 9416 patients tested for COVID19 at a hospital in Dubai. After training the NN model, the average error function of the neural network was equal to 0.01, and the accuracy of the prediction of whether a person has COVID or not was 97.6%. © 2021 IEEE.Permission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.CoronavirusForecastingAI systemsCoronavirusesGovernment ISHealthcare workersNeural network architectureNeural network methodNeural network modelPublic authoritiesPublic placesReal data setsHospitalsConference PaperCopyright : © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.