Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach

A growing number of studies show that the uneven spatial distribution of COVID-19 deaths is related to demographic and socioeconomic disparities across space. However, most studies fail to assess the relative importance of each factor to COVID-19 death rate and, more importantly, how this importance varies spatially. Here, we assess the variables that are more important locally using Geographical Random Forest (GRF), a local non-linear regression method. Through GRF, we estimated the non-linear relationships between the COVID-19 death rate and 29 socioeconomic and health-related factors during the first year of the pandemic in the USA (county level). GRF outputs are compared to global (Random Forest and OLS) and local (Geographically Weighted Regression) models. Results show that GRF outperforms all models and that the importance of variables highly varies by location. For example, lack of health insurance is the most important factor in one-third (34.86%) of the US counties. Most of these counties are (concentrated mainly in the Midwest region and South region). On the other hand, no leisure-time physical activity is the most important primary factor for 19.86% of the US counties. These counties are found in California, Oregon, Washington, and parts of the South region. Understanding the location-based characteristics and spatial patterns of socioeconomic and health factors linked to COVID-19 deaths is paramount for policy designing and decision making. In this way, interventions can be designed and implemented based on the most important factors locally, avoiding thus general guidelines addressed for the entire nation.

A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing

A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19.

Modelling Covid-19 infections in Zambia using data mining techniques

The outbreak of Covid-19 pandemic has been declared a global health crisis by the World Health Organization since its emergence. Several researchers have proposed a number of techniques to understand how the pandemic affects the populations. Reported among these techniques are data mining models which have been successfully applied in a wide range of situations before the advent of Covid-19 pandemic. In this work, the researchers have applied a number of existing data mining methods (classifiers) available in the Waikato Environment for Knowledge Analysis (WEKA) machine learning library. WEKA was used to gain a better understanding on how the epidemic spread within Zambia. The classifiers used are J48 decision tree, Multilayer Perceptron and Naïve Bayes among others. The predictions of these techniques are compared against simpler classifiers and those reported in related works.

Performance evaluation of regression models for COVID-19: A statistical and predictive perspective

Research is very important in the pandemic situation of COVID-19 to deliver a speedy solution to this problem. COVID-19 has presented governments, corporations and ordinary citizens around the world with technology playing an essential role to tackle the crisis. Moderate and flexible innovation arrangements that can speed up progress towards giving critical well-being ability are proved hourly. Knowledge with the aid of creativity must be obtained, accepted and analysed in a short time frame. In this example, the machine learning model has a major role to play in predicting the number of next positive COVID-19 cases to come. For government departments to take effective and strengthened future COVID-19 planning and innovation. The ongoing global pandemic of COVID-19 has been non-linear and dynamic. Due to the especially perplexing nature of the COVID-19 episode and its diversity from country to country, this study recommends machine learning as a convincing means to demonstrate flare-up. In this linear regression, polynomial regression, ridge regression, polynomial ridgeregression, support vector regression models, the COVID-19 data set from multiple on-line tools have been evaluated. During the work process comprehensive experiments were performed and each test was evaluated with the parameters mean square error (MSE), medium absolute error (MAE), root mean square error (RMSE) and R2 score. This study also offers a path for future research using regression models based on machine learning. Precise validation and data analysis can contribute to strategies for healing and disease prevention at an early stage. A systematic comprehensive strategy is a new philosophy in which statistical data for government agencies and community can be forecast.

A Decision-Level Fusion Method for COVID-19 Patient Health Prediction

With the continuous attempts to develop effective machine learning methods, information fusion approaches play an important role in integrating data from multiple sources and improving these methods' performance. Among the different fusion techniques, decision-level fusion has unique advantages to fuse the decisions of various classifiers and getting an effective outcome. In this paper, we propose a decision-level fusion method that combines three well-calibrated ensemble classifiers, namely, a random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) methods. It is used to predict the COVID-19 patient health for early monitoring and efficient treatment. A soft voting technique is used to generate the final decision result from the predictions of these calibrated classifiers. The method uses the COVID-19 patient's health information, travel demographic, and geographical data to predict the possible outcome of the COVID-19 case, recovered, or death. A different set of experiments is conducted on a public novel Corona Virus 2019 dataset using a different ratio of test sets. The experimental results show that the proposed fusion method achieved an accuracy of 97.24% and an F1-score of 0.97, which is higher than the current related work that has an accuracy of 94% and an F1-score 0.86, on 20% test set taken from the dataset.

Temperature Forecasting Correction Based on Operational GRAPES-3km Model Using Machine Learning Methods

Postprocess correction is essential to improving the model forecasting result, in which machine learning methods play more and more important roles. In this study, three machine learning (ML) methods of Linear Regression, LSTM-FCN and LightGBM were used to carry out the correction of temperature forecasting of an operational high-resolution model GRAPES-3km. The input parameters include 2 m temperature, relative humidity, local pressure and wind speed forecasting and observation data in Shaanxi province of China from 1 January 2019 to 31 December 2020. The dataset from September 2018 was used for model evaluation using the metrics of root mean square error (RMSE), average absolute error (MAE) and coefficient of determination (R2). All three machine learning methods perform very well in correcting the temperature forecast of GRAPES-3km model. The RMSE decreased by 33%, 32% and 40%, respectively, the MAE decreased by 33%, 34% and 41%, respectively, the R2 increased by 21.4%, 21.5% and 25.2%, respectively. Among the three methods, LightGBM performed the best with the forecast accuracy rate reaching above 84%.

Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model

An unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic's progress was uncertain, and thus, predicting it became crucial for public health researchers. These predictions help the effective allocation of health-care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public health policymakers after understanding the extent of the effect. The main objective of this paper is to develop a hybrid forecasting model that can generate real-time out-of-sample forecasts of COVID-19 outbreaks for five profoundly affected countries, namely the USA, Brazil, India, the UK, and Canada. A novel hybrid approach based on the Theta method and autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is developed. Daily new cases of COVID-19 are nonlinear, non-stationary, and volatile; thus, a single specific model cannot be ideal for future prediction of the pandemic. However, the newly introduced hybrid forecasting model with an acceptable prediction error rate can help healthcare and government for effective planning and resource allocation. The proposed method outperforms traditional univariate and hybrid forecasting models for the test datasets on an average.

Forecasting the Severity of COVID-19 Pandemic Amidst the Emerging SARS-CoV-2 Variants: Adoption of ARIMA Model

Currently, the global report of COVID-19 cases is around 110 million, and more than 2.43 million related death cases as of February 18, 2021. Viruses continuously change through mutation; hence, different virus of SARS-CoV-2 has been reported globally. The United Kingdom (UK), South Africa, Brazil, and Nigeria are the countries from which these emerged variants have been notified and now spreading globally. Therefore, these countries have been selected as a research sample for the present study. The datasets analyzed in this study spanned from March 1, 2020, to January 31, 2021, and were obtained from the World Health Organization website. The study used the Autoregressive Integrated Moving Average (ARIMA) model to forecast coronavirus incidence in the UK, South Africa, Brazil, and Nigeria. ARIMA models with minimum Akaike Information Criterion Correction (AICc) and statistically significant parameters were chosen as the best models in this research. Accordingly, for the new confirmed cases, ARIMA (3,1,14), ARIMA (0,1,11), ARIMA (1,0,10), and ARIMA (1,1,14) models were chosen for the UK, South Africa, Brazil, and Nigeria, respectively. Also, the model specification for the confirmed death cases was ARIMA (3,0,4), ARIMA (0,1,4), ARIMA (1,0,7), and ARIMA (Brown); models were selected for the UK, South Africa, Brazil, and Nigeria, respectively. The results of the ARIMA model forecasting showed that if the required measures are not taken by the respective governments and health practitioners in the days to come, the magnitude of the coronavirus pandemic is expected to increase in the study's selected countries.

Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty, percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes.

Forecasting COVID-19: Vector Autoregression-Based Model

Forecasting the spread of COVID-19 infection is an important aspect of public health management. In this paper, we propose an approach to forecasting the spread of the pandemic based on the vector autoregressive model. Concretely, we combine the time series for the number of new cases and the number of new deaths to obtain a joint forecasting model. We apply the proposed model to forecast the number of new cases and deaths in the UAE, Saudi Arabia, and Kuwait. Test results based on out-of-sample forecast show that the proposed model achieves a high level of accuracy that is superior to many existing methods. Concretely, our model achieves mean absolute percentage error (MAPE) of 0.35%, 2.03%, and 3.75% in predicting the number of daily new cases for the three countries, respectively. Furthermore, interpolating our predictions to forecast the cumulative number of cases, we obtain MAPE of 0.0017%, 0.002%, and 0.024%, respectively. The strong performance of the proposed approach indicates that it could be a valuable tool in managing the pandemic.

Management of COVID-19 Pandemic Data in India: Challenges Faced and Lessons Learnt

COVID-19 is an ongoing pandemic, which has already claimed millions of lives worldwide. In the absence of prior information on the pandemic, the governments can use generated testing data to drive policy decisions. Thus, a one-stop repository is essential to ensure sharing of clean, de-duplicated, and updated records to all the stakeholders. In India, the government initiated the testing through a network of VRDLs headed by the Indian Council of Medical Research (ICMR). Initially, the generated data were captured and shared in Excel sheets. As the number of cases increased, there was a need for a data management system to ensure reliable and up-to-date data to drive policy decisions. Thus, the data management team at ICMR initiated the development of a national COVID-19 testing data management tool that is currently maintaining all the data in a central hub. The first version of the tool was released in March 2020 and was subsequently modified with the changing testing guidelines and strategies. On completing one and a half years of managing the data and collecting approximately 550 million records, the team analyzed the challenges faced and the strategies used to ensure a seamless flow of data to the system and its real-time analysis. In this study, the entire duration of the pandemic has been divided into four different phases based on the resourcefulness of the country. Since the pandemic is currently ongoing, this study can be useful for countries in a different phase of pandemic facing similar challenges.

Accuracy versus reliability-based modelling approaches for medical decision making

Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.

The role of data-driven artificial intelligence on COVID-19 disease management in public sphere: a review

Coronavirus disease 2019 (COVID-19) is an infectious disease with acute intense respiratory syndrome which spread around the world for the very first time impacting the way of life with drastic uncertainty. It rapidly reached almost every nook and corner of the world and the World Health Organization (WHO) has announced COVID-19 as a pandemic. The health care institutions around the globe are looking for viable and real-time technological solutions to handle the virus for evading its spread and circumvent probable demises. Importantly, the artificial intelligence tools and techniques are playing a major role in fighting the effect of virus on the economic jolt by mimicking human intelligence by screening, analyzing, predicting and tracking the existing and likely future patients. Since the first reported case, all the government organizations in the world jumped into action to prevent it and many studies reported the role of AI in taking decisions analyzing big data available in public sphere. Thereby, this review focuses on identifying the significant implication of AI techniques used for the COVID-19 disease management in the public sphere by agglomerating the latest available information. It also discusses the pitfalls and future directions in handling sensitive big data required for advanced neural networks.

A dynamical map to describe COVID-19 epidemics

Nonlinear dynamics perspective is an interesting approach to describe COVID-19 epidemics, providing information to support strategic decisions. This paper proposes a dynamical map to describe COVID-19 epidemics based on the classical susceptible-exposed-infected-recovered (SEIR) differential model, incorporating vaccinated population. On this basis, the novel map represents COVID-19 discrete-time dynamics by adopting three populations: infected, cumulative infected and vaccinated. The map promotes a dynamical description based on algebraic equations with a reduced number of variables and, due to its simplicity, it is easier to perform parameter adjustments. In addition, the map description allows analytical calculations of useful information to evaluate the epidemic scenario, being important to support strategic decisions. In this regard, it should be pointed out the estimation of the number deaths, infection rate and the herd immunization point. Numerical simulations show the model capability to describe COVID-19 dynamics, capturing the main features of the epidemic evolution. Reported data from Germany, Italy and Brazil are of concern showing the map ability to describe different scenario patterns that include multi-wave pattern with bell shape and plateaus characteristics. The effect of vaccination is analyzed considering different campaign strategies, showing its importance to control the epidemics.

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.

Using Daily Nighttime Lights to Monitor Spatiotemporal Patterns of Human Lifestyle under COVID-19: The Case of Saudi Arabia

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too.

Forecasting COVID-19 infections in the Arabian Gulf region

In this paper, an empirical analysis of linear state space models and long short-term memory neural networks is performed to compare the statistical performance of these models in predicting the spread of COVID-19 infections. Data on the pandemic daily infections from the Arabian Gulf countries from 2020/03/24 to 2021/05/20 are fitted to each model and a statistical analysis is conducted to assess their short-term prediction accuracy. The results show that state space model predictions are more accurate with notably smaller root mean square errors than the deep learning forecasting method. The results also indicate that the poorer forecast performance of long short-term memory neural networks occurs in particular when health surveillance data are characterized by high fluctuations of the daily infection records and frequent occurrences of abrupt changes. One important result of this study is the possible relationship between data complexity and forecast accuracy with different models as suggested in the entropy analysis. It is concluded that state space models perform better than long short-term memory networks with highly irregular and more complex surveillance data.

Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model

Although more than a year has passed since the coronavirus outbreak globally, the Covid-19 pandemic conditions still exist in many countries, including Iran. Predicting the number of future patients and deaths can help governments and policymakers make better decisions to enforce disease control restrictions. In this study, we aim to use a combined multilayer perceptron (MLP) neural network and Markov chain (MC) model to predict two indicators of the number of discharged and death cases according to their relationship with the number of hospitalized cases in Bushehr province, Iran. This hybrid model is called MLP-MC. In this study, 136 data (days) are collected from May 13, 2020, to April 1, 2021, divided into two parts: training and test. The training data are used to train the MLP network, and the trained MLP network is used to predict the test data and the next 40 days. Then the residual errors of actual and predicted values are calculated. In the next step, the MC model is used to classify the errors and predict the values of the indicators according to the probabilities related to the error states and improve the performance of the MLP model in forecasting. Finally, the prediction accuracy of MLP and MLP-MC models are compared using three evaluation metrics: MAD, MSE and RMSE. This comparison showed that the MLP-MC model has slightly higher prediction accuracy than the MLP model.

A predictive analytics model for COVID-19 pandemic using artificial neural networks

The COVID-19 pandemic spread rapidly around the world and is currently one of the most leading causes of death and heath disaster in the world. Turkey, like most of the countries, has been negatively affected by COVID-19. The aim of this study is to design a predictive model based on artificial neural network (ANN) model to predict the future number of daily cases and deaths caused by COVID-19 in a generalized way to fit different countries’ spreads. In this study, we used a dataset between 11 March 2020 and 23 January 2021 for different countries. This study provides an ANN model to assist the government to take preventive action for hospitals and medical facilities. The results show that there is an 86% overall accuracy in predicting the mortality rate and 87% in predicting the number of cases.

Weighted butterfly optimization algorithm with intuitionistic fuzzy gaussian function based adaptive-neuro fuzzy inference system for covid-19 prediction

Covid-19 cases are increasing each day, however none of the countries successfully came up with a proper approved vaccine. Studies suggest that the virus enters the body causing a respiratory infection post contact with a disease. Measures like screening and early diagnosis contribute towards the management of COVID- 19 thereby reducing the load of health care systems. Recent studies have provided promising methods that will be applicable for the current pandemic situation. The previous system designed a various Machine Learning (ML) algorithms such as Decision Tree (DT), Random Forest (RF), XGBoost, Gradient Boosting Machine (GBM) and Support Vector Machine (SVM) for predicting COVID-19 disease with symptoms. However, it does not produce satisfactory results in terms of true positive rate. And also, better optimization methods are required to enhance the precision rate with minimum execution time. To solve this problem the proposed system designed a Weighted Butterfly Optimization Algorithm (WBOA) with Intuitionistic fuzzy Gaussian function based Adaptive-Neuro Fuzzy Inference System (IFGF-ANFIS) classifier for predicting the magnitude of COVID- 19 disease. The principle aim of this method is to design an algorithm that could predict and assess the COVID-19 parameters. Initially, the dataset regarding COVID-19 is taken as an input and preprocessed. The parameters included are age, sex, history of fever, travel history, presence of cough and lung infection. Then the optimal features are selected by using Weighted Butterfly Optimization Algorithm (WBOA) to improve the classification accuracy. Based on the selected features, an Intuitionistic fuzzy Gaussian function based Adaptive-Neuro Fuzzy Inference System (IFGF-ANFIS) classifier is utilized for classifying the people having infection possibility. The studies conducted on this proposed system indicates that it is capable of producing better results than the other systems especially in terms of accuracy, precision, recall and f-measure.

Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology

Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

COVID-19: A Comprehensive Review of Learning Models

Coronavirus disease is communicable and inhibits the infected person's immune system. It belongs to the Coronaviridae family and has affected 213 nations and territories so far. Many kinds of studies are being carried out to filter advice and provide oversight to monitor this outbreak. A comparative and brief review was carried out in this paper on research concerning the early identification of symptoms, estimation of the end of the pandemic, and examination of user-generated conversations. Chest X-ray images, abdominal computed tomography scan, tweets shared on social media are several of the datasets used by researchers. Using machine learning and deep learning methods such as K-means clustering, Random Forest, Convolutional Neural Network, Long Short-Term Memory, Auto-Encoder, and Regression approaches, the above-mentioned datasets are processed. The studies on COVID-19 with machine learning and deep learning models with their results and limitations are outlined in this article. The challenges with open future research directions are discussed at the end.

Analyzing the Impact of Demographic Variables on Spreading and Forecasting COVID-19

The aim of this study is to analyse the coronavirus disease 2019 (COVID-19) outbreak in Bangladesh. This study investigates the impact of demographic variables on the spread of COVID-19 as well as tries to forecast the COVID-19 infected numbers. First of all, this study uses Fisher's Exact test to investigate the association between the infected groups of COVID-19 and demographical variables. Second, it exploits the ANOVA test to examine significant difference in the mean infected number of COVID-19 cases across the population density, literacy rate, and regions/divisions in Bangladesh. Third, this research predicts the number of infected cases in the epidemic peak region of Bangladesh for the year 2021. As a result, from the Fisher's Exact test, we find a very strong significant association between the population density groups and infected groups of COVID-19. And, from the ANOVA test, we observe a significant difference in the mean infected number of COVID-19 cases across the five different population density groups. Besides, the prediction model shows that the cumulative number of infected cases would be raised to around 500,000 in the most densely region of Bangladesh, Dhaka division.

An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images

In recent times, COVID-19, has a great impact on the healthcare sector and results in a wide range of respiratory illnesses. It is a type of Ribonucleic acid (RNA) virus, which affects humans as well as animals. Though several artificial intelligence-based COVID-19 diagnosis models have been presented in the literature, most of the works have not focused on the hyperparameter tuning process. Therefore, this paper proposes an intelligent COVID-19 diagnosis model using a barnacle mating optimization (BMO) algorithm with a cascaded recurrent neural network (CRNN) model, named BMO-CRNN. The proposed BMO-CRNN model aims to detect and classify the existence of COVID-19 from Chest X-ray images. Initially, pre-processing is applied to enhance the quality of the image. Next, the CRNN model is used for feature extraction, followed by hyperparameter tuning of CRNN via the BMO algorithm to improve the classification performance. The BMO algorithm determines the optimal values of the CRNN hyperparameters namely learning rate, batch size, activation function, and epoch count. The application of CRNN and hyperparameter tuning using the BMO algorithm shows the novelty of this work. A comprehensive simulation analysis is carried out to ensure the better performance of the BMO-CRNN model, and the experimental outcome is investigated using several performance metrics. The simulation results portrayed that the BMO-CRNN model has showcased optimal performance with an average sensitivity of 97.01%, specificity of 98.15%, accuracy of 97.31%, and F-measure of 97.73% compared to state-of-the-art methods.

Performance Evaluation of Regression Models for the Prediction of the COVID-19 Reproduction Rate

This paper aims to evaluate the performance of multiple non-linear regression techniques, such as support-vector regression (SVR), k-nearest neighbor (KNN), Random Forest Regressor, Gradient Boosting, and XGBOOST for COVID-19 reproduction rate prediction and to study the impact of feature selection algorithms and hyperparameter tuning on prediction. Sixteen features (for example, Total_cases_per_million and Total_deaths_per_million) related to significant factors, such as testing, death, positivity rate, active cases, stringency index, and population density are considered for the COVID-19 reproduction rate prediction. These 16 features are ranked using Random Forest, Gradient Boosting, and XGBOOST feature selection algorithms. Seven features are selected from the 16 features according to the ranks assigned by most of the above mentioned feature-selection algorithms. Predictions by historical statistical models are based solely on the predicted feature and the assumption that future instances resemble past occurrences. However, techniques, such as Random Forest, XGBOOST, Gradient Boosting, KNN, and SVR considered the influence of other significant features for predicting the result. The performance of reproduction rate prediction is measured by mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), R-Squared, relative absolute error (RAE), and root relative squared error (RRSE) metrics. The performances of algorithms with and without feature selection are similar, but a remarkable difference is seen with hyperparameter tuning. The results suggest that the reproduction rate is highly dependent on many features, and the prediction should not be based solely upon past values. In the case without hyperparameter tuning, the minimum value of RAE is 0.117315935 with feature selection and 0.0968989 without feature selection, respectively. The KNN attains a low MAE value of 0.0008 and performs well without feature selection and with hyperparameter tuning. The results show that predictions performed using all features and hyperparameter tuning is more accurate than predictions performed using selected features.

CASE-CF: Context Aware Smart Epidemic Control Framework

Novel Coronavirus (COVID-19) has become one of the deadliest pandemics that has affected almost all the nations in the world. Lockdown and systematic re-opening of shopping malls, offices, etc. is still one of the major weapons against this virus. However, the government and medical agencies take long time to reopen the places due to risks involved in this deadly virus. The delay to reopen places has resulted in sharp decline in the growth of economy. In this paper a current context aware framework is proposed which uses multiple inputs for a specific region to decide whether to open it or not. The proposed framework used series of deep neural network models to generate recommendations specific to a particular region. Most of the inputs are real-time and readily available with the government. The main aim is to develop framework which can be used in any kind of pandemic even in small region to easily contain it. However, it has been tested using opensource data available for COVID-19. Data was crawled from web for 22 districts of Haryana state of India. Experimental result proved the efficiency of proposed framework.

COVID-19 prediction models: a systematic literature review

As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.

COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty

The abundance of type and quantity of available data in the healthcare field has led many to utilize machine learning approaches to keep up with this influx of data. Data pertaining to COVID-19 is an area of recent interest. The widespread influence of the virus across the United States creates an obvious need to identify groups of individuals that are at an increased risk of mortality from the virus. We propose a so-called clustered random forest approach to predict COVID-19 patient mortality. We use this approach to examine the hidden heterogeneity of patient frailty by examining demographic information for COVID-19 patients. We find that our clustered random forest approach attains predictive performance comparable to other published methods. We also find that follow-up analysis with neural network modeling and k-means clustering provide insight into the type and magnitude of mortality risks associated with COVID-19.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Machine Learning Approaches for Tackling Novel Coronavirus (COVID-19) Pandemic

Novel coronavirus (COVID-19) has become a global problem in recent times due to the rapid spread of this disease. Almost all the countries of the world have been affected by this pandemic that made a major consequence on the medical system and healthcare facilities. The healthcare system is going through a critical time because of the COVID-19 pandemic. Modern technologies such as deep learning, machine learning, and data science are contributing to fight COVID-19. The paper aims to highlight the role of machine learning approaches in this pandemic situation. We searched for the latest literature regarding machine learning approaches for COVID-19 from various sources like IEEE Xplore, PubMed, Google Scholar, Research Gate, and Scopus. Then, we analyzed this literature and described them throughout the study. In this study, we noticed four different applications of machine learning methods to combat COVID-19. These applications are trying to contribute in various aspects like helping physicians to make confident decisions, policymakers to take fruitful decisions, and identifying potentially infected people. The major challenges of existing systems with possible future trends are outlined in this paper. The researchers are coming with various technologies using machine learning techniques to face the COVID-19 pandemic. These techniques are serving the healthcare system in a great deal. We recommend that machine learning can be a useful tool for proper analyzing, screening, tracking, forecasting, and predicting the characteristics and trends of COVID-19.

Forecasting of COVID 19 Cases in Karnataka State using Artificial Neural Network (ANN)

COVID-19 is a pandemic that has caused lot of deaths and infections in the last 2 months and is showing an increasing trend not only in the number of infections and deaths, but also in the recovery rate. Accurate prediction models are very much essential to make proper forecasts and take necessary actions. This study demonstrates the capability of Multilayer Perceptron (MLP), an Artificial Neural network (ANN) model for forecasting the number of infected cases in the state of Karnataka in India. It is trained using a fast training algorithm namely, Extreme Learning machine to reduce the training time required. The parameters required for the forecasting model have been selected using partial autocorrelation function, which is a conventional method, and its performance has been compared with parameters selected using cuckoo search (CS) algorithm, which is a very popular metaheuristic optimization algorithm. The testing of the forecasting model has been done, and comparison between the two parameter selection methods as well as with MLP with conventional backpropagation has been carried out. Use of CS algorithm has resulted in a better forecasting performance based on mean absolute percentage error (MAPE), with a value of 6.62% on training data and 7.03% on the test data. Further to check the efficacy of the model, the data of COVID-19 cases of Hungary from 4 March to 19 April 2020 have been used, which resulted in a MAPE of 1.55%, thereby establishing the robustness of the proposed ANN model for forecasting COVID-19 cases for the state of Karnataka.

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent

In the field of machine learning, building models and measuring their performance are two equally important tasks. Currently, measures of precision of regression models' predictions are usually based on the notion of mean error, where by error we mean a deviation of a prediction from an observation. However, these mean based measures of models' performance have two drawbacks. Firstly, they ignore the length of the prediction, which is crucial when dealing with chaotic systems, where a small deviation at the beginning grows exponentially with time. Secondly, these measures are not suitable in situations where a prediction is made for a specific point in time (e.g. a date), since they average all errors from the start of the prediction to its end. Therefore, the aim of this paper is to propose a new measure of models' prediction precision, a divergence exponent, based on the notion of the Lyapunov exponent which overcomes the aforementioned drawbacks. The proposed approach enables the measuring and comparison of models' prediction precision for time series with unequal length and a given target date in the framework of chaotic phenomena. Application of the divergence exponent to the evaluation of models' accuracy is demonstrated by two examples and then a set of selected predictions of COVID-19 spread from other studies is evaluated to show its potential.

Machine Learning on the COVID-19 Pandemic, Human Mobility and Air Quality: A Review

The ongoing COVID-19 global pandemic is touching every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and non-pharmaceutical interventions of lockdown and confinement implemented citywide, regionally or nationally are affecting virus transmission, people's travel patterns, and air quality. Many studies have been conducted to predict the diffusion of the COVID-19 disease, assess the impacts of the pandemic on human mobility and on air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This literature review aims to analyze the results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 viral transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also explores the spatio-temporal aspects of lockdown and confinement measures on coronavirus diffusion, human mobility, and air quality. Additionally, we discuss policy implications, which will be helpful for policy makers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

Predictability of COVID-19 Hospitalizations, Intensive Care Unit Admissions, and Respiratory Assistance in Portugal: Longitudinal Cohort Study

BACKGROUND

In the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems.

OBJECTIVE

This work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal.

METHODS

A retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization.

RESULTS

For the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision.

CONCLUSIONS

The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end.

COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning

A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-021-02021-0.

Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics.

Network dynamic model of epidemic transmission introducing a heterogeneous control factor

The COVID-19 epidemic is not only a medical issue but also a sophisticated social problem. We propose a network dynamics model of epidemic transmission introducing a heterogeneous control factor. The proposed model applied the classical susceptible- exposed-infectious-recovered model to the network based on effective distance and was modified by introducing a heterogeneous control factor with temporal and spatial characteristics. International aviation data were approximately used to estimate the flux fraction matrix, and the effective distance was calculated. Through parameter estimation and simulation, the theoretical values of the modified model fit well with practical values. By adjusting the parameters and observing the change of the results, we found that the modified model is more in line with the actual needs and has higher credibility in the comprehensive analysis. The assessment shows that the number of confirmed cases worldwide will reach about 20 million optimistically. In severe cases, the peak value will exceed 80 million, and the late stage of the epidemic shows a long tail shape, lasting more than one and a half years. The effective way to control the global epidemic is to strengthen international cooperation and to impose international travel restrictions and other measures.

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

The dangerously contagious virus named "COVID-19" has struck the world strong and has locked down billions of people in their homes to stop the further spread. All the researchers and scientists in various fields are continually developing a vaccine and prevention methods to aid the world from this challenging situation. However, a reliable prediction of the epidemic may help control this contiguous disease until the cure is available. The machine learning techniques are one of the frontiers in predicting this outbreak's future trend and behavior. Our research is focused on finding a suitable machine learning algorithm that can predict the COVID-19 daily new cases with higher accuracy. This research has used the adaptive neuro-fuzzy inference system (ANFIS) and the long short-term memory (LSTM) to foresee the newly infected cases in Bangladesh. We have compared both the experiments' results, and it can be forenamed that LSTM has shown more satisfactory results. Upon study and testing on several models, we have shown that LSTM works better on a scenario-based model for Bangladesh with mean absolute percentage error (MAPE)-4.51, root-mean-square error (RMSE)-6.55, and correlation coefficient-0.75. This study is expected to shed light on COVID-19 prediction models for researchers working with machine learning techniques and avoid proven failures, especially for small imprecise datasets.

Big data driven COVID-19 pandemic crisis management: potential approach for global health

INTRODUCTION

Information has the power to protect against unexpected events and control any crisis such as the COVID-19 pandemic. Since COVID-19 has already rapidly spread all over the world, only technology-driven data management can provide accurate information to manage the crisis. This study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing it effectively.

METHODS

A systematic review guided by PRISMA guidelines has been performed to obtain the key elements.

RESULTS

This study identified the thirty-two most relevant documents for qualitative analysis. This study also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications. It also explores several limitations of big data usage including unethical use, privacy, and exploitative use of data.

CONCLUSIONS

The findings of the study will provide new insight and help policymakers and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis.

Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon

The first case of the novel coronavirus in Brazil was notified on February 26, 2020. After 21 days, the first case was reported in the second largest State of the Brazilian Amazon. The State of Pará presented difficulties in combating the pandemic, ranging from underreporting and a low number of tests to a large territorial distance between cities with installed hospital capacity. Due to these factors, mathematical data-driven short-term forecasting models can be a promising initiative to assist government officials in more agile and reliable actions. This study presents an approach based on artificial neural networks for the daily and cumulative forecasts of cases and deaths caused by COVID-19, and the forecast of demand for hospital beds. Six scenarios with different periods were used to identify the quality of the generated forecasting and the period in which they start to deteriorate. Results indicated that the computational model adapted capably to the training period and was able to make consistent short-term forecasts, especially for the cumulative variables and for demand hospital beds.

Evaluation of Performance of an LR and SVR models to predict COVID-19 Pandemic

Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days.

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide since December 2019. From the beginning of infection, patients will progress through different symptom stages, such as fever, dyspnea or even death. Identifying disease progression and predicting patient outcome at an early stage helps target treatment and resource allocation. However, there is no clear COVID-19 stage definition, and few studies have addressed characterizing COVID-19 progression, making the need for this study evident.

METHODS

We proposed a temporal deep learning method, based on a time-aware long short-term memory (T-LSTM) neural network and used an online open dataset, including blood samples of 485 patients from Wuhan, China, to train the model. Our method can grasp the dynamic relations in irregularly sampled time series, which is ignored by existing works. Specifically, our method predicted the outcome of COVID-19 patients by considering both the biomarkers and the irregular time intervals. Then, we used the patient representations, extracted from T-LSTM units, to subtype the patient stages and describe the disease progression of COVID-19.

RESULTS

Using our method, the accuracy of the outcome of prediction results was more than 90% at 12 days and 98, 95 and 93% at 3, 6, and 9 days, respectively. Most importantly, we found 4 stages of COVID-19 progression with different patient statuses and mortality risks. We ranked 40 biomarkers related to disease and gave the reference values of them for each stage. Top 5 is Lymph, LDH, hs-CRP, Indirect Bilirubin, Creatinine. Besides, we have found 3 complications - myocardial injury, liver function injury and renal function injury. Predicting which of the 4 stages the patient is currently in can help doctors better assess and cure the patient.

CONCLUSIONS

To combat the COVID-19 epidemic, this paper aims to help clinicians better assess and treat infected patients, provide relevant researchers with potential disease progression patterns, and enable more effective use of medical resources. Our method predicted patient outcomes with high accuracy and identified a four-stage disease progression. We hope that the obtained results and patterns will aid in fighting the disease.

A review on COVID-19 forecasting models

The novel coronavirus (COVID-19) has spread to more than 200 countries worldwide, leading to more than 36 million confirmed cases as of October 10, 2020. As such, several machine learning models that can forecast the outbreak globally have been released. This work presents a review and brief analysis of the most important machine learning forecasting models against COVID-19. The work presented in this study possesses two parts. In the first section, a detailed scientometric analysis presents an influential tool for bibliometric analyses, which were performed on COVID-19 data from the Scopus and Web of Science databases. For the above-mentioned analysis, keywords and subject areas are addressed, while the classification of machine learning forecasting models, criteria evaluation, and comparison of solution approaches are discussed in the second section of the work. The conclusion and discussion are provided as the final sections of this study.

A city cluster risk-based approach for Sars-CoV-2 and isolation barriers based on anonymized mobile phone users' location data

Given the recent outbreak of Sars-CoV-2, several countries started to seek different strategies to control contamination and minimize fatalities, which are usually the primary objectives for all strategies. Secondary objectives are related to economic factors, therefore ensuring that society would be able is to keep its essential activities and avoid supply disruptions. This paper presents an application of anonymized mobile phone users' location data to estimate population flow amongst cities with an origin-destination matrix. The work includes a clustering analysis of cities, which may enable policymakers (and epidemiologists) to develop public policies giving the appropriate consideration for each set of cities within a Province or State. Risk measures are included to analyze the severity of the spread among the clusters, which can be ranked. Then, intelligence can be obtained from the analysis, and some clusters could be isolated to avoid contagion while keeping their economic activities. Therefore, this analysis is reproducible for other states of Brazil and other countries and can be adapted for districts within a city, especially considering the possibility of a second wave COVID-19 pandemic.