Prediction of COVID-19 cases using the weather integrated deep learning approach for India

LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach

The COVID-19 pandemic has significantly impacted public health and necessitated urgent actions to mitigate its spread. Monitoring and predicting the outbreak's progression have become vital to devise effective strategies and allocate resources efficiently. This study presents a novel approach utilizing Multivariate Long Short-Term Memory (LSTM) to analyze and predict COVID-19 trends in Central Thailand, particularly emphasizing the multi-feature selection process. To consider a comprehensive view of the pandemic's dynamics, our research dataset encompasses epidemiological, meteorological, and particulate matter features, which were gathered from reliable sources. We propose a multi-feature selection technique to identify the most relevant and influential features that significantly impact the spread of COVID-19 in the region to enhance the model's performance. Our results highlight that relative humidity is the key factor driving COVID-19 transmission in Central Thailand. The proposed multi-feature selection technique significantly improves the model's accuracy, ensuring that only the most informative variables contribute to the predictions, avoiding the potential noise or redundancy from less relevant features. The proposed LSTM model demonstrates its capability to forecast COVID-19 cases, facilitating informed decision-making for public health authorities and policymakers.

Deep learning framework for epidemiological forecasting: A study on COVID-19 cases and deaths in the Amazon state of Pará, Brazil

Modeling time series has been a particularly challenging aspect due to the need for constant adjustments in a rapidly changing environment, data uncertainty, dependencies between variables, volatile fluctuations, and the need to identify ideal hyperparameters. The present study presents a Framework capable of making projections from time series related to cases and deaths by COVID-19 in the Amazonian state of Pará, in Brazil. For the first time, deep learning models such as TCN, TRANSFORMER, TFT, N-BEATS, and N-HiTS were assessed for this purpose. The ARIMA statistical model was also used in post-processing for residual adjustment and short-term smoothing of the generated forecasts. The Framework generates probabilistic forecasts, with multivariate support, considering the following variables: daily cases per day of the first symptom, cases published daily, the occurrence of deaths, deaths published daily, and percentage of daily vaccination. The generated predictions are statistically evaluated by determining the best model for 7-day moving average projections using evaluating metrics such as MSE, RMSE, MAPE, sMAPE, r2, Coefficient of Variation, and residual analysis. As a result, the generated projections showed an average error of 5.4% for Cases Publication, 8.0% for Cases Symptoms, 11.12% for Deaths Publication, and 4.6% for Deaths Occurrence, with the N-HiTS and N-BEATS models obtaining better results. In general terms, the use of deep learning models to predict cases and deaths from COVID-19 has proven to be a valuable practice for analyzing the spread of the virus, which allows health managers to better understand and respond to this kind of pandemic outbreak.

STG-Net: A COVID-19 prediction network based on multivariate spatio-temporal information

The modern urban population features a high population density and a fast population flow, and COVID-19 has strong transmission ability, long incubation period, and other characteristics. Considering only the time sequence of COVID-19 transmission cannot effectively respond to the current epidemic transmission situation. The distance between cities and population density information also have a significant impact on the transmission of the virus. Currently, cross-domain transmission prediction models do not fully exploit the time-space information and fluctuation trend of data, and cannot reasonably predict the trend of infectious diseases by integrating time-space multi-source information. To solve this problem, this paper proposes the COVID-19 prediction network (STG-Net) based on multivariate spatio-temporal information, which introduces the Spatial Information Mining module (SIM) and the Temporal Information Mining module (TIM) to mine the spatio-temporal information of the data in a deeper level, and uses the slope feature method to further mine the fluctuation trend of the data. Also, we introduce the Gramian Angular Field module (GAF), which converts one-dimensional data into two-dimensional images, further enhancing the network's feature mining capability in the time and feature dimension, ultimately combining spatiotemporal information to predict daily newly confirmed cases. We tested the network on datasets from China, Australia, the United Kingdom, France, and Netherlands. The experimental results show that STG-Net has better prediction performance than existing prediction models, with an average decision coefficient R2 of 98.23% on the datasets from five countries, as well as good long- and short-term prediction ability and overall good robustness.

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters

The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.

Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models

Deep learning methods have become the state of the art for spatio-temporal predictive analysis in a wide range of fields, including environmental management, public health, urban planning, pollution monitoring, and so on. Despite the fact that a variety of powerful deep learning-based models can address various problem-specific issues in different research domain, it has been found that no single optimal model can outperform everywhere. Now, in the last two years, various deep learning-based studies have provided a variety of best-performing techniques for predicting COVID-19 health outcomes. In this context, this study attempts to perform a case study that investigates the spatio-temporal variation in the performance of deep-learning-based methods for predicting COVID-19 health outcomes in India. Various widely applied deep learning models namely CNN (convolutional neural network), RNN (recurrent neural network), Vanilla LSTM (long short-term memory), LSTM Autoencoder, and Bidirectional LSTM are considered to investigate their spatio-temporal performance variation. The effectiveness of the models is assessed using various metrics based on COVID-19 mortality time-series from 36 states and union territories of India.

Prediction and analysis of COVID-19 daily new cases and cumulative cases: times series forecasting and machine learning models

BACKGROUND

COVID-19 poses a severe threat to global human health, especially the USA, Brazil, and India cases continue to increase dynamically, which has a far-reaching impact on people's health, social activities, and the local economic situation.

METHODS

The study proposed the ARIMA, SARIMA and Prophet models to predict daily new cases and cumulative confirmed cases in the USA, Brazil and India over the next 30 days based on the COVID-19 new confirmed cases and cumulative confirmed cases data set(May 1, 2020, and November 30, 2021) published by the official WHO, Three models were implemented in the R 4.1.1 software with forecast and prophet package. The performance of different models was evaluated by using root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

RESULTS

Through the fitting and prediction of daily new case data, we reveal that the Prophet model has more advantages in the prediction of the COVID-19 of the USA, which could compose data components and capture periodic characteristics when the data changes significantly, while SARIMA is more likely to appear over-fitting in the USA. And the SARIMA model captured a seven-day period hidden in daily COVID-19 new cases from 3 countries. While in the prediction of new cumulative cases, the ARIMA model has a better ability to fit and predict the data with a positive growth trend in different countries(Brazil and India).

CONCLUSIONS

This study can shed light on understanding the outbreak trends and give an insight into the epidemiological control of these regions. Further, the prediction of the Prophet model showed sufficient accuracy in the daily COVID-19 new cases of the USA. The ARIMA model is suitable for predicting Brazil and India, which can help take precautions and policy formulation for this epidemic in other countries.

A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic

The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19.

Predicting COVID-19 future trends for different European countries using Pearson correlation

The ability to accurately forecast the number of COVID-19 cases and future case trends would certainly assist governments and various organisations in strategising and preparing for the newly infected cases well in advance. Many predictions have failed to foresee future COVID-19 cases due to the lack of reliable data; however, such data are now widely available for predicting future trends in COVID-19 after more than one and a half years of the pandemic. Also, various countries are closely monitoring other countries that are experiencing a surge in COVID-19 cases in the expectation of similar scenarios, but this does not always produce correct results, as no research has identified specific correlations between different countries in terms of COVID-19 cases. During the past 18 months, many nations have watched countries whose COVID-19 cases have risen sharply, in anticipation of handling the situation themselves. However, this did not provide accurate results, as no research was conducted that compared countries to determine if their COVID-19 case trends were correlated. As official data on COVID-19 cases has become increasingly available, using the Pearson correlation technique to pinpoint the countries that should be closely monitored will help governments plan and prepare for the number of infections that are expected in the future at an early stage. In this study, a simple and real-time prediction of COVID-19 cases incorporating existing variables of coronavirus variants was used to explore the correlation among different European countries in terms of the number of COVID-19 cases officially recorded on a daily basis. Data from selected countries over the past 76 weeks were analysed using a Pearson correlation technique to determine if there were correlations between case trends and geographical position. The correlation coefficient (r) was employed for identifying whether the different countries in Europe were interrelated, with r > 0.85 indicating they were very strongly correlated, 0.85 > r > 0.8 indicating that they were strongly correlated, 0.8 > r > 0.7 indicating that they were moderately correlated, and r < 0.7 indicating that the examined countries were either weakly correlated or that a correlation did not exist. The results showed that although some neighbouring countries are strongly correlated, other countries that are not geographically close are also correlated. In addition, some countries on opposite sides of Europe (Belgium and Armenia) are also correlated. Other countries (France, Iceland, Israel, Kosovo, San Marino, Spain, Sweden and Turkey) were either weakly correlated or had no relationship at all.

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

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.

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

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.

Prediction of COVID-19 cases using the weather integrated deep learning approach for India

Advanced and accurate forecasting of COVID‐19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non‐linear problems. In the present study, the relationship between weather factor and COVID‐19 cases was assessed, and also developed a forecasting model using long short‐term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID‐19 confirmed case data (1 April to 30 June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID‐19 cases for the period 1 July 2020 to 31 July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short‐term (1 day lead) forecast of COVID‐19 cases (relative error <20%). Moreover, the multivariate LSTM model improved the medium‐range forecast skill (1–7 days lead) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India.