Analysis and Prediction of COVID-19 Pandemic in Bangladesh by Using ANFIS and LSTM Network

Forecasting of coronavirus active cases by utilizing logistic growth model and fuzzy time series techniques

Coronavirus has long been considered a global epidemic. It caused the deaths of nearly 7.01 million individuals and caused an economic downturn. The number of verified coronavirus cases is increasing daily, putting the whole human race at danger and putting strain on medical experts to eradicate the disease as rapidly as possible. As a consequence, it is vital to predict the upcoming coronavirus positive patients in order to plan actions in the future. Furthermore, it has been discovered all across the globe that asymptomatic coronavirus patients play a significant part in the disease's transmission. This prompted us to incorporate similar examples in order to accurately forecast trends. A typical strategy for analysing the rate of pandemic infection is to use time-series forecasting technique. This would assist us in developing better decision support systems. To anticipate COVID-19 active cases for a few countries, we recommended a hybrid model utilizing a fuzzy time series (FTS) model mixed with a non-linear growth model. The coronavirus positive case outbreak has been evaluated for Italy, Brazil, India, Germany, Pakistan, and Myanmar through June 5, 2020 in phase-1, and January 15, 2022 in phase-2, and forecasts active cases for the next 26 and 14 days respectively. The proposed framework fitting effect outperforms individual logistic growth and the fuzzy time series techniques, with R-scores of 0.9992 in phase-1 and 0.9784 in phase-2. The proposed model provided in this article may be utilised to comprehend a country's epidemic pattern and assist the government in developing better effective interventions.

Deep learning in public health: Comparative predictive models for COVID-19 case forecasting

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings.

DeepCov: Effective Prediction Model of COVID-19 Using CNN Algorithm

COVID-19 outbreak prediction is a challenging and complicated problem in a vast dataset. Several communities have proposed various methods to predict the COVID-19-positive cases. However, conventional techniques remain drawbacks to predicting the actual trend cases. In this experiment, we adopt CNN to build our model by analyzing features from the vast COVID-19 dataset to predict long-term outbreaks to present early prevention. Our model can achieve adequate accuracy with a tiny loss based on the experiment results. In this study, we calculate the function which produces RMSE 0.00070 and MAPE 0.02440 to predict new cases and get RMSE 0.00468 and MAPE 0.06446 for predicting new deaths. Therefore, our proposed method can accurately predict the trend of positive cases in the COVID-19 outbreak.

A Hybridized Machine Learning Approach for Predicting COVID-19 Using Adaptive Neuro-Fuzzy Inference System and Reptile Search Algorithm

This research is aimed to escalate Adaptive Neuro-Fuzzy Inference System (ANFIS) functioning in order to ensure the veracity of existing time-series modeling. The COVID-19 pandemic has been a global threat for the past three years. Therefore, advanced forecasting of confirmed infection cases is extremely essential to alleviate the crisis brought out by COVID-19. An adaptive neuro-fuzzy inference system-reptile search algorithm (ANFIS-RSA) is developed to effectively anticipate COVID-19 cases. The proposed model integrates a machine-learning model (ANFIS) with a nature-inspired Reptile Search Algorithm (RSA). The RSA technique is used to modulate the parameters in order to improve the ANFIS modeling. Since the performance of the ANFIS model is dependent on optimizing parameters, the statistics of infected cases in China and India were employed through data obtained from WHO reports. To ensure the accuracy of our estimations, corresponding error indicators such as RMSE, RMSRE, MAE, and MAPE were evaluated using the coefficient of determination (R2). The recommended approach employed on the China dataset was compared with other upgraded ANFIS methods to identify the best error metrics, resulting in an R2 value of 0.9775. ANFIS-CEBAS and Flower Pollination Algorithm and Salp Swarm Algorithm (FPASSA-ANFIS) attained values of 0.9645 and 0.9763, respectively. Furthermore, the ANFIS-RSA technique was used on the India dataset to examine its efficiency and acquired the best R2 value (0.98). Consequently, the suggested technique was found to be more beneficial for high-precision forecasting of COVID-19 on time-series data.

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.

COVID-19 detection based on pre-trained deep networks and LSTM model using X-ray images enhanced contrast with artificial bee colony algorithm

Coronavirus (COVID-19) is an infectious disease that has spread across the world within a short period of time and is causing rapid casualties. The main symptoms of this virus are shortness of breath, fever, cough, and a sore throat. The virus is detected through samples, such as throat swabs and sputum, taken from people who meet the possible case definition and the results are usually obtained within a few hours or a day. The development of test kits to detect the COVID-19 virus is still an open research topic, and automated and faster diagnostic tools are needed. Recent studies have shown that biomedical images can be used for COVID-19 testing. This study proposes the hybrid use of pre-trained deep networks and the long short-term memory (LSTM) for the classification of COVID-19 from contrast-enhanced chest X-rays. In the proposed system, a transformation function is applied to X-ray images first. Then, the artificial bee colony (ABC) algorithm is used to optimize the parameters obtained from the transformation function. The pre-trained deep network models and LSTM are preferred to extract features from the contrast-enhanced chest X-rays. At the final stage, COVID-19, normal (healthy), and pneumonia chest X-ray are classified using softmax. To evaluate the performance of the proposed method, the "COVID-19 radiography" dataset, which is widely used in the literature, is preferred. From the proposed model, 98.97% accuracy, 98.80% precision, and 98.70% sensitivity rates are obtained. Experimental results reveal that the proposed model provides efficient results compared to other methods. Thanks to the application of ABC-based image enhancement, increased classification of 2.5% has been achieved against other state-of-the-art models.

Vaccination and isolation based control design of the COVID-19 pandemic based on adaptive neuro fuzzy inference system optimized with the genetic algorithm

The study of the COVID-19 pandemic is of pivotal importance due to its tremendous global impacts. This paper aims to control this disease using an optimal strategy comprising two methods: isolation and vaccination. In this regard, an optimized Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed using the Genetic Algorithm (GA) to control the dynamic model of the COVID-19 termed SIDARTHE (Susceptible, Infected, Diagnosed, Ailing, Recognized, Threatened, Healed, and Extinct). The number of diagnosed and recognized people is reduced by isolation, and the number of susceptible people is reduced by vaccination. The GA generates optimal control efforts related to the random initial number of each chosen group as the input data for ANFIS to train Takagi-Sugeno (T-S) fuzzy structure coefficients. Also, three theorems are presented to indicate the positivity, boundedness, and existence of the solutions in the presence of the controller. The performance of the proposed system is evaluated through the mean squared error (MSE) and the root-mean-square error (RMSE). The simulation results show a significant decrease in the number of diagnosed, recognized, and susceptible individuals by employing the proposed controller, even with a 70% increase in transmissibility caused by various variants.

A COVID-19 forecasting system for hospital needs using ANFIS and LSTM models: A graphical user interface unit

Background

Centers for Disease Control and Prevention data showed that about 40% of coronavirus disease 2019 (COVID-19) patients had been suffering from at least one underlying medical condition were hospitalized; in which nearly 33% of them needed to be admitted to the intensive care unit (ICU) to receive specialized medical services. Our study aimed to find a proper machine learning algorithm that can predict confirmed COVID-19 hospital admissions with high accuracy.

Methods

We obtained data on daily COVID-19 cases in regular medical inpatient units, emergency department, and ICU in the time window between 21 July 2020 and 21 November 2021. Data for the first 183 days (training data set) were used for long short-term memory (LSTM) network, adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR) and decision tree model training, whilst the remaining data for the last 60 days (test data set) were used for model validation. To predict the number of ICU and non-ICU patients, we used these models. Finally, a user-friendly graphical user interface unit was designed to load any time series data (here the trend of population of COVID-19 patients) and train LSTM, ANFIS, SVR or tree models for the prediction of COVID-19 cases for one week ahead.

Results

All models predicted the dynamics of COVID-19 cases in ICU and non- wards. The values of root-mean-square error and R2 as model assessment metrics showed that ANFIS model had better predictive power among all models.

Conclusion

Artificial intelligence-based forecasting models such as ANFIS system or deep learning approach based on LSTM or regression models including SVR or tree regression play a key role in forecasting the required number of beds or other types of medical facilities during the coronavirus pandemic. Thus, the designed graphical user interface of the present study can be used for optimum management of resources by health care systems amid COVID-19 pandemic.

Forecasting the spread of the third wave of COVID-19 pandemic using time series analysis in Bangladesh

During the third wave of the coronavirus epidemic in Bangladesh, the death and infection rate due to this devastating virus has increased dramatically. The rapid spread of the virus is one of the reasons for this terrible condition. So, identifying the subsequent cases of coronavirus can be a great tool to reduce the mortality and infection rate. In this article, we used the autoregressive integrated moving average-ARIMA(8,1,7) model to estimate the expected daily number of COVID-19 cases in Bangladesh based on the data from April 20, 2021, to July 4, 2021. The ARIMA model showed the best results among the five executed models over Autoregressive Model (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Rolling Forest Origin. The findings of this article were used to anticipate a rise in daily cases for the next month in Bangladesh, which can help governments plan policies to prevent the spread of the virus. The forecasting outcome indicated that this new trend(named delta variant) in Bangladesh would continue increasing and might reach 18327 daily new cases within four weeks if strict rules and regulations are not applied to control the spread of COVID-19.

Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

As the number of COVID-19 cases increases day by day, the situation and livelihood of people throughout the world deteriorates. The goal of this study is to use machine learning models to identify disease and forecast whether or not a person is infected with the virus or another common illness. More articles about COVID-19 will be released starting in 2020, but we still do not have a reliable prediction mechanism to diagnose the disease with 100% accuracy. This comparison is done to see which model is the most effective in detecting and predicting disease. Despite the fact that we have immunizations, we require a best-prediction strategy to assist all humans in surviving. Researchers claimed that the supervised learning method predicts more accurately than the unsupervised learning method in the majority of studies. Supervised learning is the process of mapping inputs to derived outputs using a set of variables and created functions. This will also help us to optimize performance criteria using experience. It is further divided into two categories: classification and regression. According to recent studies, classification models are more accurate than other models.

Application of machine learning in the prediction of COVID-19 daily new cases: A scoping review

COVID-19 has produced a global pandemic affecting all over of the world. Prediction of the rate of COVID-19 spread and modeling of its course have critical impact on both health system and policy makers. Indeed, policy making depends on judgments formed by the prediction models to propose new strategies and to measure the efficiency of the imposed policies. Based on the nonlinear and complex nature of this disorder and difficulties in estimation of virus transmission features using traditional epidemic models, artificial intelligence methods have been applied for prediction of its spread. Based on the importance of machine and deep learning approaches in the estimation of COVID-19 spreading trend, in the present study, we review studies which used these strategies to predict the number of new cases of COVID-19. Adaptive neuro-fuzzy inference system, long short-term memory, recurrent neural network and multilayer perceptron are among the mostly used strategies in this regard. We compared the performance of several machine learning methods in prediction of COVID-19 spread. Root means squared error (RMSE), mean absolute error (MAE), R2 coefficient of determination (R2), and mean absolute percentage error (MAPE) parameters were selected as performance measures for comparison of the accuracy of models. R2 values have ranged from 0.64 to 1 for artificial neural network (ANN) and Bidirectional long short-term memory (LSTM), respectively. Adaptive neuro-fuzzy inference system (ANFIS), Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptron (MLP) have also have R2 values near 1. ARIMA and LSTM had the highest MAPE values. Collectively, these models are capable of identification of learning parameters that affect dissimilarities in COVID-19 spread across various regions or populations, combining numerous intervention methods and implementing what-if scenarios by integrating data from diseases having analogous trends with COVID-19. Therefore, application of these methods would help in precise policy making to design the most appropriate interventions and avoid non-efficient restrictions.