Predicting the epidemic curve of the coronavirus (SARS-CoV-2) disease (COVID-19) using artificial intelligence: An application on the first and second waves

Abstract

Objectives The COVID-19 pandemic is considered a major threat to global public health. The aim of our study was to use the official epidemiological data to forecast the epidemic curves (daily new cases) of the COVID-19 using Artificial Intelligence (AI)-based Recurrent Neural Networks (RNNs), then to compare and validate the predicted models with the observed data. Methods We used publicly available datasets from the World Health Organization and Johns Hopkins University to create a training dataset, then we employed RNNs with gated recurring units (Long Short-Term Memory - LSTM units) to create two prediction models. Our proposed approach considers an ensemble-based system, which is realized by interconnecting several neural networks. To achieve the appropriate diversity, we froze some network layers that control the way how the model parameters are updated. In addition, we could provide country-specific predictions by transfer learning, and with extra feature injections from governmental constraints, better predictions in the longer term are achieved. We have calculated the Root Mean Squared Logarithmic Error (RMSLE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) to thoroughly compare our model predictions with the observed data. Results We reported the predicted curves for France, Germany, Hungary, Italy, Spain, the United Kingdom, and the United States of America. The result of our study underscores that the COVID-19 pandemic is a propagated source epidemic, therefore repeated peaks on the epidemic curve are to be anticipated. Besides, the errors between the predicted and validated data and trends seem to be low. Conclusion Our proposed model has shown satisfactory accuracy in predicting the new cases of COVID-19 in certain contexts. The influence of this pandemic is significant worldwide and has already impacted most life domains. Decision-makers must be aware, that even if strict public health measures are executed and sustained, future peaks of infections are possible. The AI-based models are useful tools for forecasting epidemics as these models can be recalculated according to the newly observed data to get a more precise forecasting.

The Experiences of Nurses and Physicians Caring for COVID-19 Patients: Findings from an Exploratory Phenomenological Study in a High Case-Load Country

“…However, the increasing demand led to reallocating healthcare providers who lack sufficient experience in dealing with such emerging diseases to serve in the frontlines to counter the disease, thus putting them at a higher risk for contracting the infection due to the high infectivity rate of the COVID-19 virus. Despite the recent approval of various vaccines against COVID-19, and the start of vaccine rollouts in many countries globally, the pandemic is still striking many countries very severely considering the lack of sufficient vaccine supplies, the evolving viral variants, and the effect of pandemic fatigue [3][4][5]. Additionally, the pandemic crisis has impacted various life domains and sectors, thus afflicting day-to-day activities and personal practices [6,7].…”

Beliefs Toward Smoking and COVID-19, and the Pandemic Impact on Smoking Behavior and Quit Intention: Findings from a Community-Based Cross-Sectional Study in Jordan

“…[1][2][3] To retard the spread of the disease, most countries have applied several mitigation strategies including restrictions on international travel, closing universities and schools, and enforcing face-masking, physical distancing, and quarantine/lockdowns. [4][5][6] Unfortunately, the influence of COVID-19 induced stress and lockdowns came up with substantial socio-economic and psychological consequences. [7][8][9][10][11][12][13][14] Despite various mitigation measures and vaccination rollouts, 15 the COVID-19-related morbidity and mortality are still rising globally, with over 216 million confirmed cases and more than 4.4 million deaths as of August 30, 2021.…”

A Deep Learning BiLSTM Encoding-Decoding Model for COVID-19 Pandemic Spread Forecasting

“…Generally speaking, the pandemic wave starts with the first confirmed case reported, and it ends with about zero cases reported before the regrowth of confirmed cases again [66]. For example, in Saudi Arabia, the first case of COVID-19 was reported on 2 March 2020 [67].…”

“…Then, we tune the dropout percentage with values (0.1, 0.2, 0.3, and 0.4). We set the (Adaptive Moment Estimation Algorithm) Adam optimizer as the training optimizer for this experiment as it proved its superior performance for forecasting the same task [32,42,66]. Therefore, we can deliver the optimal parameters for our proposed model.…”