Predict COVID-19 Case Research Articles

COVID-19 Epidemic Analysis in India with Multi-Source State-Level Datasets.

The COVID-19 pandemic has been a global crisis affecting billions of people and causing countless economic losses. Different approaches have been proposed for combating this crisis, including both medical measures and technical innovations, e.g., artificial intelligence technologies to diagnose and predict COVID-19 cases. While there is much attention being paid to the USA and China, little research attention has been drawn to less developed countries, e.g., India. In this study, I conduct an analysis of the COVID-19 epidemic in India, with datasets collected from different sources. Several machine learning models have been built to predict the COVID-19 spread, with different combinations of input features, in which the Transformer is proven as the most precise one. I also find that the Facebook mobility dataset is the most useful for predicting the number of confirmed cases. However, I find that the datasets from different sources are not very effective when predicting the number of deaths caused by the COVID-19 infection.

Analisis Prediksi Data Kasus Covid-19 di Provinsi Lampung Menggunakan Recurrent Neural Network (RNN)

This study aims to examine the architectural performance of the Recurrent Neural Network (RNN) model in predicting Covid-19 cases in Lampung Province. The RNN method is part of Deep Learning which will be used to model data on Covid-19 cases in Lampung Province from March 26, 2020 to March 28, 2021. The RNN model was chosen because the Covid-19 data is in the form of a time series and the advantages of RNN are that it can capture information on the data time series using multiple network layers which allow better modeling and resulting in high prediction accuracy. The data is divided into 3, namely active cases, recovered cases, and dead cases. After preparing the data, the 368 data were divided into 294 initial latih data and 74 test data. After latih on the data for each data, then a test is carried out on the data for each data as a reference for predicting the latest data. The most optimal results show the cumulative active case model with RMSE=0.0022; for cumulative recovery cases obtained RMSE = 0.0007; while the cumulative death cases obtained RMSE = 0.0012. Based on the modeling error, then make predictions on the three cases which results in RMSE = 0.001 for cumulative active cases; RMSE=0.0027 for cumulative recovery cases; and RMSE=0.001 for cumulative death cases.

Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA

Because of the large-scale impact of COVID-19 on human health, several investigations are being conducted to understand the underlying mechanisms affecting the spread and transmission of the disease. The present study aimed to assess the effects of selected environmental factors such as temperature, humidity, dew point, wind speed, pressure, and precipitation on the daily increase in COVID-19 cases in Mississippi, USA, during the period from January 2020 to August 2021. A machine learning model was used to predict COVID-19 cases and implement preventive measures if necessary. A statistical analysis using Python programming showed that the humidity ranged from 56% to 78%, and COVID-19 cases increased from 634 to 3546. Negative correlations were found between temperature and COVID-19 incidence rate (−0.22) and between humidity and COVID-19 incidence rate (−0.15). The linear regression model showed the model linear coefficients to be 0.92 and −1.29, respectively, with the intercept being 55.64. For the test dataset, the R2 score was 0.053. The statistical analysis and machine learning show that there is no linear dependence of temperature and humidity with the COVID-19 incidence rate.

Real-Time Forecasting of COVID-19 Cases Using Human Mobility in Ontario, Canada

PurposeTo minimize the impact of the COVID-19 pandemic, local public health authorities are often required to make prompt and informed decisions on anticipated case-loads, resource allocation for surveillance and testing, and public health intervention appropriateness. The objective of this research was to develop a near-term forecasting model to predict COVID-19 cases using real-time human mobility information in Ontario, Canada to assist public health authorities with outbreak response.Methods & MaterialsWe utilized a deep neural network model to generate a short-term forecast of new COVID-19 cases by two weeks from May to August 2021. Variable selection was informed by a recent literature review and our ongoing research associating COVID-19 cases with human mobility, demographic and socio-economic factors. A real-time human mobility statistics consisting of a weekly summary of short and long-distance movement, demographic characteristics, weather, vaccination coverage, geo-location, and reported COVID-19 cases two weeks prior were included as predictors. We considered weeks as temporal and health regions as geographic units to account for population-level variabilities. We used a holdout method for model validation of over 300 iterations. Average mean squared error (MSE) and 95% confidence interval (CI) along with overlaying forecasted COVID-19 cases over the reported were used to evaluate the overall model fit. The model predictions were summarized as means and 95% CIs.ResultsOur best forecasting model had a mean MSE of 0.53 (95% CI: 0.49 – 0.56). Since May 2021, the overall trend of the reported COVID-19 cases in Ontario closely followed the forecasted cases, about 89% of the reported cases were within 1.5 times the interquartile range (IQR) and all were within the entire range of the distribution of the predictions. Forecasting accuracy also varied by health region characteristics, such a population size and density, remoteness, and reported COVID-19 case volume during the most recent weeks.ConclusionA near-term prediction of new COVID-19 cases with real-time population-level data could help public health authorities anticipate, plan and monitor disease burden in a population. Such predictions also allow the assessment of population-level health interventions to minimize new COVID-19 cases on a real-time basis and inform prompt decision making.

Predicting COVID-19 Cases in India Based on Web Data Mining

Predicting COVID-19 Cases in India Based on Web Data Mining

Analysis of Government Policy in Handling Covid-19 in Indonesia

The Covid-19 pandemic has affected the economy in many countries, including Indonesia. Until July 2021, the Government has implemented social activity policies for the community, starting from Large-Scale Social Restrictions in the first semester of last year to PPKM Level 4 to stop the spread of Covid-19. Responding to the Covid-19 pandemic, Google released data from people who access google applications using mobile devices. The Google Mobility report shows changes in population activity and mobility in several locations. This study aims to examine the effect of the PSBB and PPKM policies in Indonesia on the decline in COVID-19 cases in Indonesia using the Google Mobility Index and their impact on the economy in Indonesia. The analysis uses graphs and Pearson Correlation and Long Short-Term Memory (LSTM) method to predict Covid-19 cases and mobility data. The result shows that the mobility of people to five places has a significant effect on the number of daily cases of Covid-19, while there is a significant effect on three places of community mobility on Indonesian economic. As the results, controlling the spread of Covid-19 is better prioritized than economic condition.

Usefulness of laboratory parameters and chest CT in the early diagnosis of COVID-19.

ABSTRACTIn the present study, the importance of laboratory parameters and CT findings in the early diagnosis of COVID-19 was investigated. To this end, 245 patients admitted between April 1st, and May 30th, 2020 with suspected COVID-19 were enrolled. The patients were divided into three groups according to chest CT findings and RT-PCR results. The non-COVID-19 group consisted of 71 patients with negative RT-PCR results and no chest CT findings. Ninety-five patients with positive RT-PCR results and negativechest CT findings were included in the COVID-19 group; 79 patients with positive RT-PCR results and chest CT findings consistent with COVID-19 manifestations were included in COVID-19 pneumonia group. Chest CT findings were positive in 45% of all COVID-19 patients. Patients with positive chest CT findings had mild (n=30), moderate (n=21) andor severe (n=28) lung involvement. In the COVID-19 group, CRP levels and the percentage of monocytes increased significantly. As disease progressed from mild to severe, CRP, LDH and ferritin levels gradually increased. In the ROC analysis, the area under the curve corresponding to the percentage value of monocytes (AUC=0.887) had a very good accuracy in predicting COVID-19 cases. The multinomial logistic regression analysis showed that CRP, LYM and % MONO were independent factors for COVID-19. Furthermore, the chest CT evaluation is a relevant tool in patients with clinical suspicion of COVID-19 pneumonia and negative RT-PCR results. In addition to decreased lymphocyte count, the increased percentage of monocytes may also guide the diagnosis.

Predicting COVID-19 cases in various scenarios using RNN-LSTM models aided by adaptive linear regression to identify data anomalies.

The evolution of the Sars-CoV-2 (COVID-19) virus pandemic has revealed that the problems of social inequality, poverty, public and private health systems guided by controversial public policies are much more complex than was conceived before the pandemic. Therefore, understanding how COVID-19 evolves in society and looking at the infection spread is a critical task to support efficient epidemiological actions capable of suppressing the rates of infections and deaths. In this article, we analyze daily COVID-19 infection data with two objectives: (i) to test the predictive power of a Recurrent Neural Network - Long Short Term Memory (RNN-LSTM) on the daily stochastic fluctuation in different scenarios, and (ii) analyze, through adaptive linear regression, possible anomalies in the reported data to provide a more realistic and reliable scenario to support epidemic control actions. Our results show that the approach is even more suitable for countries, states or cities where the rate of testing, diagnosis and prevention were low during the virus dissemination. In this sense, we focused on investigating countries and regions where the disease evolved in a severe and poorly controlled way, as in Brazil, highlighting the favelas in Rio de Janeiro as a regional scenario.

Determining the efficiency of data analysis systems in predicting COVID-19 infected cases.

ABSTRACTAfter the outbreak of the novel coronavirus disease (2019) (COVID-19), a lot of people have been affected around the world. Due to the large number of affected patients in the world, the global health care system has been disrupted and nearly all hospitals around the world has faced a shortage of bed spaces. As a consequence, being able of prediction of the number of COVID-19 cases is extremely important for taking appropriate decision for management of the affected patients. An accurate prediction of the number of COVID-19 cases Can be obtained using the historical data of reported cases as well as some other data affecting the virus outbreak. However, most of the literature has used only historical data to provide a method of predicting COVID-19 cases and has neglected other influential factors. This has led to inaccurate estimates of the number of infected cases with COVID-19. Thus, the present study tries to provide a more accurate estimation of the number of COVID-19 cases by considering both historical data and other effective factors on the virus. For this purpose, data analysis including the development of a network-based neural algorithm [i.e., nonlinear autonomous exogenous input (NARX)] can be adopted. To examine the viability of this algorithm, experiments were conducted using data collected for the number of COVID-19 cases in the five most affected countries on each continent. Our method led to a more accurate prediction than those obtained by the existing methods. Moreover, we performed experiments to extend our method to predict the number of COVID-19 cases in the future during a period between August 2020 and September 2020. Such predictions can be utilized by the government or people in the affected countries to take precautionary measures against the pandemic.

Comparative study on performance of different artificial neural network methods for prediction of the Covid19

PurposeThis study aims to compare many artificial neural network (ANN) methods to find out which method is better for the prediction of Covid19 number of cases in N steps ahead of the current time. Therefore, the authors can be more ready for similar issues in the future.Design/methodology/approachThe authors are going to use many ANNs in this study including, five different long short-term memory (LSTM) methods, polynomial regression (from degree 2 to 5) and online dynamic unsupervised feedforward neural network (ODUFFNN). The authors are going to use these networks over a data set of Covid19 number of cases gathered by World Health Organization. After 1,000 epochs for each network, the authors are going to calculate the accuracy of each network, to be able to compare these networks by their performance and choose the best method for the prediction of Covid19.FindingsThe authors concluded that for most of the cases LSTM could predict Covid19 cases with an accuracy of more than 85% after LSTM networks ODUFFNN had medium accuracy of 45% but this network is highly flexible and fast computing. The authors concluded that polynomial regression cant is a good method for the specific purpose.Originality/valueConsidering the fact that Covid19 is a new global issue, less studies have been conducted with a comparative approach toward the prediction of Covid19 using ANN methods to introduce the best model of the prediction of this virus.

Analysis of Confirmed Cases of Corona Virus Diseases (COVID-19) in Nepal

An outbreak of COVID-19 pandemic disease, caused by a novel coronavirus SARS-coV-2, has posed a serious threat to global human health and economic activities. Nepal is also severely affected by this viral disease. The first case of COVID-19 patient was detected in Nepal on January 23, 2020. Since then, a total of 976,105 cases were reported as COVID-19 infected and the recovered cases were 953,430 with a recovery rate of 97.7 per cent with 11,928 deaths and a case fatality rate of 1.2 per cent until February 22, 2022. It would help the government to manage this crisis if the proper mechanism to predict COVID-19 cases has been developed. To combat COVID-19, the government of Nepal has taken various steps viz. diagnosis of the suspected cases, quarantine for people coming from other countries and isolation of the infected patients, lockdown, closure of the international boarder, increase public awareness, enforced social distancing, in the first phase, the addition of ICU beds, ventilator, oxygen concentrator and oxygen plant in a different hospital in the second phase and then vaccination of the target population, booster dose after the sixth month of a full dose of vaccination. This study aims to analyze the pattern of COVID-19 cases and death cases and forecast their values based on the data from January 23, 2020, to February 22, 2022, obtained from the website World Bank based on the ARMA model. This study indicates that the patients of COVID-19 are crossed three waves in Nepal, in the first wave the maximum number of cases was 4187, in second-wave the maximum number of cases was 9226 due to delta variant and in the third wave the maximum number of cases were 10052 is due to omicron. The forecasted value for March 18, 2022 for total cases is 984,595 with UCL values 1,056,143 and LCL values 913,047. Similarly, the forecasted value for new cases is 146 with UCL values 4,222 and LCL values 0 and for new death, the forecasted value is 4 with UCL values 78 and LCL values 0. These forecasted values indicate that the impact of Covid-19 is decreased rapidly due to vaccinations.

Predicting COVID-19 cases, deaths and recoveries using machine learning methods

In the presented work we applied three machine learning techniques to forecast and predict COVID-19 cases, deaths ad recoveries numbers in Algeria for the next six months using data from February 25th, 2020 to April 26th , 2021. These models are represented by the Gaussian process regression (GPR), the support vector machine (SVM) and the decision tree (DT). The plotting results and parameters evaluation pointed out that the Gaussian Process Regression (GPR) has the best performance. Prediction with this model showed that the number of cases, deaths and recoveries will increase in the next months Algeria recording a peak in the month of August and the curve will tend to decrease later.

Predicting COVID-19 Cases in South Korea with All K-Edited Nearest Neighbors Noise Filter and Machine Learning Techniques

The application of machine learning techniques to the epidemiology of COVID-19 is a necessary measure that can be exploited to curtail the further spread of this endemic. Conventional techniques used to determine the epidemiology of COVID-19 are slow and costly, and data are scarce. We investigate the effects of noise filters on the performance of machine learning algorithms on the COVID-19 epidemiology dataset. Noise filter algorithms are used to remove noise from the datasets utilized in this study. We applied nine machine learning techniques to classify the epidemiology of COVID-19, which are bagging, boosting, support vector machine, bidirectional long short-term memory, decision tree, naïve Bayes, k-nearest neighbor, random forest, and multinomial logistic regression. Data from patients who contracted coronavirus disease were collected from the Kaggle database between 23 January 2020 and 24 June 2020. Noisy and filtered data were used in our experiments. As a result of denoising, machine learning models have produced high results for the prediction of COVID-19 cases in South Korea. For isolated cases after performing noise filtering operations, machine learning techniques achieved an accuracy between 98–100%. The results indicate that filtering noise from the dataset can improve the accuracy of COVID-19 case prediction algorithms.

Development of novel hybrid models for the prediction of COVID-19 in Kuwait

The first case of COVID-19 in Kuwait was reported on February 24, 2020. There is a need to develop a prediction model for estimating this epidemic size. In this study, we aimed to develop and compare several prediction models using real-time data of COVID-19 from February 24 to June 12, 2020. We modeled the uncertainty and non-stationary real-time data of COVID-19 cases using a multilayer model with different decomposition techniques. We applied our proposed hybrid methodology to predict COVID-19 cases in Kuwait. We further evaluated the performance of the novel hybrid model with others using mean relative error, mean absolute error, and mean square error. We found that our proposed hybrid approach performed better than others for predicting COVID-19 cases.

Predictive Analysis of Real-Time Strategy using Face book’s Prophet Model on Covid-19 Dataset of India

The global epidemic of the novel coronavirus (COVID-19) called SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has infected millions and killed millions. The prevalence of the virus is of paramount importance in identifying future infections and preparing healthcare facilities to avoid death. Accurately predicting the spread of COVID-19 is a challenging analytical and practical task for the research community. We can learn to use predictive analytics to predict the positive outcomes of these risks. These predictive analytics can look at the risks of past successes and failures.
 In this paper, the Facebook prophet model discusses the number of large-scale cases and deaths in India based on daily time-series data from 30 January 2020 to 30 April 2021, for forecasting and visualization. The covid-19 pandemic could end prematurely if social distancing and safety measures are required to stabilize and control is required to achieve treatment in India. This paper suggests that the Prophet Model is more effective in predicting COVID-19 cases. The forecast results will help the government plan strategies to prevent the spread of the coronavirus.

Predicting COVID-19 cases in diverse population groups using SARS-CoV-2 wastewater monitoring across Oklahoma City

SARS-CoV-2 was discovered among humans in late 2019 and rapidly spread across the world. Although the virus is transmitted by respiratory droplets, most infected persons also excrete viral particles in their feces. This fact prompted a range of studies assessing the usefulness of wastewater surveillance to determine levels of infection and transmission and produce early warnings of outbreaks in local communities, independently of human testing. In this study, we collected samples of wastewater from 13 locations across Oklahoma City, representing different population types, twice per week from November 2020 to end of March 2021. Wastewater samples were collected and analyzed for the presence and concentration of SARS-CoV-2 RNA using RT-qPCR. The concentration of SARS-CoV-2 in the wastewater showed notable peaks, preceding the number of reported COVID-19 cases by an average of one week (ranging between 4 and 10 days). The early warning lead-time for an outbreak or increase in cases was significantly higher in areas with larger Hispanic populations and lower in areas with a higher household income or higher proportion of persons aged 65 years or older. Using this relationship, we predicted the number of cases with an accuracy of 81–92% compared to reported cases. These results confirm the validity and timeliness of using wastewater surveillance for monitoring local disease transmission and highlight the importance of differences in population structures when interpreting surveillance outputs and planning preventive action.

Forecasting COVID-19 Cases in Indonesia Using Hybrid Double Exponential Smoothing

The COVID-19 epidemic has spread throughout countries around the world. In Indonesia, this case was detected in early March 2020, and until now, there is still an increase in positive cases of COVID-19. The purpose of this paper is to predict COVID-19 cases in Indonesia using a time series approach. The method used is H-WEMA method because this method can capture trend data patterns following the conditions of COVID-19 cases in Indonesia. Based on the analysis results, H-WEMA can predict COVID-19 cases very well. The forecasted results of the COVID-19 cases in Indonesia still have an upward trend, so it needs the cooperation of all elements of community to reduce the spread of COVID-19.

A Local Health Situation Room for COVID-19: Recommendations for Decision-Making From a Higher Education Institution in Mexico

Introduction: The Situation Room is a physical or virtual space where experts systematically analyze information to characterize a health situation, especially during emergencies. Decision-making processes are made toward solving health needs and promoting collaboration among institutions and social sectors. This paper presents the context and circumstances that led the University of Guadalajara (UdeG) to install a local health situation room (HSR) to address the COVID-19 pandemic at this institution based in the state of Jalisco, Mexico, a narrative is also made of its working processes and some of its results.Methods: The design of this situation room for COVID-19 was based on the methodology established by the Pan American Health Organization (PAHO)/WHO. This local-type situation room was installed on February 12, 2020. The health problem was characterized, and strategic lines, objectives, and goals were established; the first analysis was derived from an action plan deployed at the UdeG. The strategic lines were situational diagnosis, preventive actions, and containment strategies.Results: The situation room influenced the activities of the UdeG before the epidemic cases started in the state. One of the actions with the greatest impact was developing a mathematical model for predicting COVID-19 cases. Subsequently, new models have been developed according to the epidemiological evolution of the disease, helping manage the epidemic in the state. Another important result was the early closing of face-to-face university activities, reducing contagion risks and the mobility of more than 310,000 students, faculty, and administrative personnel throughout Jalisco.Conclusions: A consequence of the closure was that the confinement generated by the pandemic was the change to virtual meetings from April 2020 to date; but at the same time, this working format was a strength, since it influenced the decision of the university board to change all the academic activities to virtual format before other educational, economic, and social activities in the state did. By April 2020, the situation room transcended its institutional boundaries and was invited to participate at the Jalisco State's Health Committee. Its recommendations have helped to maintain the state with one of Mexico's lowest COVID-19 incidence and mortality rates.

Hybrid of K-Means and partitioning around medoids for predicting COVID-19 cases: Iraq case study

COVID-19 was discovered near the end of 2019 in Wuhan, China. In a short period, the virus had spread throughout the entire world. One of the primary concerns of managers and decision-makers in all types of hospitals nowadays is to implement detection plans for status of patient (Negative, Positive) in order to provide enough care at the proper moment. To reduce a pandemic of COVID-19, improving health care quality could be advantageous. Making clusters of patients with similar features and symptoms supplies an overview of health quality given to similar patients. In the scope of medical machine learning, the K-means and Partitioning Around Medoids (PAM) clustering algorithms are usually used to produce clusters depend on similarity and to detect helpful patterns from sizes of data. In this paper, we proposed a hybrid algorithm of K-Means and Partitioning Around Medoids (PAM) called K-MP to take benefits of both PAM and K-Means to construct an efficient model for predicting patient status. The suggested model for the real dataset was collected from 400 patients in the many Iraqi clinics using a questionnaire. We evaluated the proposed K-MP by using true negative rate, balance accuracy, precision, accuracy, recall, mean absolute error, F1 score, and root mean square error. From these performance measures, we found that K-MP is more efficient in discovering patient status comparing to K-Means and PAM.

Review of Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

Review of Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee