Predict COVID-19 Case Research Articles

Wastewater SARS-CoV-2 monitoring as a community-level COVID-19 trend tracker and variants in Ohio, United States

The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in more than 129 million confirm cases. Many health authorities around the world have implemented wastewater-based epidemiology as a rapid and complementary tool for the COVID-19 surveillance system and more recently for variants of concern emergence tracking. In this study, three SARS-CoV-2 target genes (N1 and N2 gene regions, and E gene) were quantified from wastewater influent samples (n = 250) obtained from the capital city and 7 other cities in various size in central Ohio from July 2020 to January 2021. To determine human-specific fecal strength in wastewater samples more accurately, two human fecal viruses (PMMoV and crAssphage) were quantified to normalize the SARS-CoV-2 gene concentrations in wastewater. To estimate the trend of new case numbers from SARS-CoV-2 gene levels, different statistical models were built and evaluated. From the longitudinal data, SARS-CoV-2 gene concentrations in wastewater strongly correlated with daily new confirmed COVID-19 cases (average Spearman's r = 0.70, p < 0.05), with the N2 gene region being the best predictor of the trend of confirmed cases. Moreover, average daily case numbers can help reduce the noise and variation from the clinical data. Among the models tested, the quadratic polynomial model performed best in correlating and predicting COVID-19 cases from the wastewater surveillance data, which can be used to track the effectiveness of vaccination in the later stage of the pandemic. Interestingly, neither of the normalization methods using PMMoV or crAssphage significantly enhanced the correlation with new case numbers, nor improved the estimation models. Viral sequencing showed that shifts in strain-defining variants of SARS-CoV-2 in wastewater samples matched those in clinical isolates from the same time periods. The findings from this study support that wastewater surveillance is effective in COVID-19 trend tracking and provide sentinel warning of variant emergence and transmission within various types of communities.

Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility.

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID-19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, large-effect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak.

Development of Algorithms for Choosing the Best Time Series Models and Neural Networks to Predict COVID-19 Cases

Time series analysis became one of the most investigated fields of knowledge during spreading of the COVID-19 around the world. The problem of modeling and forecasting infection cases of COVID-19, deaths, recoveries and other parameters is still urgent. Purpose of the study. Our article is devoted to investigation of classical statistical and neural network models that can be used for forecasting COVID-19 cases. Materials and methods. We discuss neural network model NNAR, compare it with linear and nonlinear models (BATS, TBATS, Holt's linear trend, ARIMA, classical epidemiological SIR model). In our article we discuss the Epemedic.Network algorithm using the R programming language. This algorithm takes the time series as input data and chooses the best model from SIR, statistical models and neural network model. The model selection criterion is the MAPE error. We consider the implementation of our algorithm for analysis of time series for COVID -19 spreading in Chelyabinsk region, and predicting the possible peak of the third wave using three possible scenarios. We mention that the considered algorithm can work for any time se-ries, not only for epidemiological ones. Results. The developed algorithm helped to identify the pat-tern of COVID -19 infection for Chelyabinsk region using the models realized as parts of the consi-dered algorithm. It should be noted that the considered models make it possible to form short-term forecasts with sufficient accuracy. We show that the increase in the number of neurons led to in-creasing accuracy, as there are other cases where the error is reduced in case of reducing the number of neurons, and this depends on COVID -19 infection spreading pattern. Conclusion. Hence, to get a very accurate forecast, we recommend re-running the algorithm weekly. For medium-range fore-casting, only the NNAR model can be used from among those considered but it also allows to get good forecasts only with horizon 1–2 weeks.

Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets.

BackgroundThe COVID-19 pandemic has changed public health policies and human and community behaviors through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment and other equipment to mitigate disease spread in affected regions. Current models that predict COVID-19 case counts and spread are complex by nature and offer limited explainability and generalizability. This has highlighted the need for accurate and robust outbreak prediction models that balance model parsimony and performance.ObjectiveWe sought to leverage readily accessible data sets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis.MethodsOur modeling approach leveraged the following data inputs: COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model utilized a per capita running 7-day sum of the case counts per county per day and the mean cumulative case count to develop baseline values. The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020.ResultsThe model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%.ConclusionsOur parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.

Predicting COVID-19 cases with unknown homogeneous or heterogeneous resistance to infectivity.

We present a restricted infection rate inverse binomial-based approach to better predict COVID-19 cases after a family gathering. The traditional inverse binomial (IB) model is inappropriate to match the reality of COVID-19, because the collected data contradicts the model’s requirement that variance should be larger than the expected value. Our version of an IB model is more appropriate, as it can accommodate all potential data scenarios in which the variance is smaller, equal, or larger than the mean. This is unlike the usual IB, which accommodates only the scenario in which the variance is more than the mean. Therefore, we propose a refined version of an IB model to be able to accommodate all potential data scenarios. The application of the approach is based on a restricted infectivity rate and methodology on COVID-19 data, which exhibit two clusters of infectivity. Cluster 1 has a smaller number of primary cases and exhibits larger variance than the expected cases with a negative correlation of 28%, implying that the number of secondary cases is lesser when the number of primary cases increases and vice versa. The traditional IB model is appropriate for Cluster 1. The probability of contracting COVID-19 is estimated to be 0.13 among the primary, but is 0.75 among the secondary in Cluster 1, with a wider gap. Cluster 2, with a larger number of primary cases, exhibits smaller variance than the expected cases with a correlation of 79%, implying that the number of primary and secondary cases do increase or decrease together. Cluster 2 disqualifies the traditional IB model and requires its refined version. The probability of contracting COVID-19 is estimated to be 0.74 among the primary, but is 0.72 among the secondary in Cluster 2, with a narrower gap. The advantages of the proposed approach include the model’s ability to estimate the community’s health system memory, as future policies might reduce COVID’s spread. In our approach, the current hazard level to be infected with COVID-19 and the odds of not contracting COVID-19 among the primary in comparison to the secondary groups are estimable and interpretable.

Forecasting model of Covid-19 cases using fuzzy time series using persentage change

The pandemic caused by the novel corona virus (covid-19) has affected various aspects of life throughout the world. Indonesia is one of the country with a daily high number of Covid-19-19 spread cases. This study aims to obtain a forecasting model of Covid-19 cases that can be used to predict Covid-19 cases daily and it can increase the readiness of Covid-19 health protocols system. In this study, we get a very good model for Covid-19 forecasting in Indonesia obtained by the fuzzy time series method using frequency density-based partitioning. The universe of this method is the percentage of case changes from day to day. The percentage change as a universe in fuzzy time series forecasting method makes the results of comparison of actual data and predictions increasingly similar. We use data of the Covid 19 cases taken from the Nasional Kompas website during June 2020. Forecast results show very good with MSE value of 457,83 and small AFER value of 0,0425138%.

Short-Term Forecasting of Daily Confirmed COVID-19 Cases in Malaysia Using RF-SSA Model.

Novel coronavirus (COVID-19) was discovered in Wuhan, China in December 2019, and has affected millions of lives worldwide. On 29th April 2020, Malaysia reported more than 5,000 COVID-19 cases; the second highest in the Southeast Asian region after Singapore. Recently, a forecasting model was developed to measure and predict COVID-19 cases in Malaysia on daily basis for the next 10 days using previously-confirmed cases. A Recurrent Forecasting-Singular Spectrum Analysis (RF-SSA) is proposed by establishing L and ET parameters via several tests. The advantage of using this forecasting model is it would discriminate noise in a time series trend and produce significant forecasting results. The RF-SSA model assessment was based on the official COVID-19 data released by the World Health Organization (WHO) to predict daily confirmed cases between 30th April and 31st May, 2020. These results revealed that parameter L = 5 (T/20) for the RF-SSA model was indeed suitable for short-time series outbreak data, while the appropriate number of eigentriples was integral as it influenced the forecasting results. Evidently, the RF-SSA had over-forecasted the cases by 0.36%. This signifies the competence of RF-SSA in predicting the impending number of COVID-19 cases. Nonetheless, an enhanced RF-SSA algorithm should be developed for higher effectivity of capturing any extreme data changes.

Predicting COVID-19 cases using bidirectional LSTM on multivariate time series

To assist policymakers in making adequate decisions to stop the spread of the COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-means clustering algorithm. The cumulative case data of the clustered countries enriched with data related to the lockdown measures are fed to the bidirectional LSTM to train the forecasting model. We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar and the proposed model prediction from December 1st until December 31st, 2020. The quantitative evaluation shows that the proposed technique outperforms state-of-art forecasting approaches.

Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: A case study in the capitals of Brazil

In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.

An AI-based Analysis of the effect of COVID-19 Stringency Index on Infection rates: A case of India

Purpose: The impact of the COVID-19 pandemic has already been felt worldwide, disrupting the unremarkable life of individuals. Social consequences and viral transmission are challenges that must be resolved to effectively overcome the problems that occur throughout this pandemic. The COVID-19 infection data about India were represented using different statistical models. In this paper, the authors focus on the data collected between 1st January 2020 and 12th April 2021, try analyzing the different indexes related to India, and predict the number of infected people in the near future. Based on the infection rate, it is possible to classify a country as “fixed,” “evolving” and “exponential.” Based on the prediction, some recommendations are proposed to contain the outbreak of the disease. This will also help the government and policymakers to identify and analyze various risks associated with 'opening up' and 'shutting down' in response to the outbreak of the disease. With the help of these models, it is possible to predict the number of cases in the near future. Methodology: COVID-19 Stringency Index, Government Response Index, and Containment Health Index calculated, published, and updated real-time by a research group from Oxford University (https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker) on 21 mitigation and suppression measures employed by different countries were analyzed using a few mathematical models to find the relationship between Stringency Index and infection rates and forecast trends. A new model was proposed after analyzing a few mathematical models proposed by the researchers. Data analytics was also conducted using AI-based data analytics tools available online. The dataset was kept updated until the date April 20, 2021, was downloaded for this purpose. The appropriate values were extracted from the original dataset and used to construct a sub-dataset, which was then used for the analytics. An AI-based online Data Analytics tool provided by datapine was used to forecast trends. Findings/Result: It was observed that in India, as in other countries, there is a close association between Stringency Level and COVID-19 cases. The higher the degree of stringency, the lower the cases, and vice versa. The same can be said about the government's role and degree of containment &amp; health. Originality: In this paper, we analyzed various mathematical models for predicting the total number of COVID-19 cases and deaths due to COVID-19 in India. We also examined the relationship between total cases and the Government's Response Index, Containment &amp; Health Index, and Stringency Index indicators. The model we proposed to predict COVID-19 cases on a day-by-day basis had a 98 percent accuracy rate and a 2% error rate. Paper Type: Analytical. With prerecorded datasets obtained from online resources, and data analysis was conducted using mathematical models and AI-based analytical tools.

A Comparative Study on Covid-19 Cases in Top 10 States/UTs of India in Using Machine Learning Models

Coronavirus is a dangerous sickness came from a new virus. It has been assumed as an overall pandemic and a very hard circumstance to control the COVID-19 epidemic in India and global and so needed some severe actions to control its rate of increment. This disease causes for cold, dry cough, high fever, sore throat and serious breathing problems. This paper presents analysis of confirmed, cured and deaths cases, age and gender based cases in top 10 States/UTs of India. We analyzed various trends and patterns from various state/UTs units, MHFW of India data sources (up to 16th November 2020). Now a day’s plentiful models are proposed to predict covid-19 cases in India and world countries. The novel COVID-19 datasets are taken from Kaggle and GitHub repositories to analyze the epidemiological cases of the disease in top 10 states/UTs of India. We used various machine learning algorithms like Linear Regression, KNN Regressor, LASSO Regression, Elasticnet Regression and Decision Tree regressor to analyze the number of novel Coronavirus (COVID-19) reported cases in top 10 states/UTs of India. The model analyzes datasets containing the COVID-19 cases (confirmed, cured and death cases) up to 24th November, 2020 using ML models. From the results it is proven that Decision Tree and KNN regressor performs best in analyzing the number of confirmed cases and number of death cases. But for number of cured cases LASSO and linear regression models give the best accuracy results. Unfortunately, Elastic net produced poor accuracy results due to some changes in original datasets. Especially, this work analyzes the calculations based on the exactness rate on a test dataset.

A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients.

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier’s accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy.

Nonlinear Neural Network Based Forecasting Model for Predicting COVID-19 Cases.

The recent COVID-19 outbreak has severely affected people around the world. There is a need of an efficient decision making tool to improve awareness about the spread of COVID-19 infections among the common public. An accurate and reliable neural network based tool for predicting confirmed, recovered and death cases of COVID-19 can be very helpful to the health consultants for taking appropriate actions to control the outbreak. This paper proposes a novel Nonlinear Autoregressive (NAR) Neural Network Time Series (NAR-NNTS) model for forecasting COVID-19 cases. This NAR-NNTS model is trained with Scaled Conjugate Gradient (SCG), Levenberg Marquardt (LM) and Bayesian Regularization (BR) training algorithms. The performance of the proposed model has been compared by using Root Mean Square Error (RMSE), Mean Square Error (MSE) and correlation co-efficient i.e. R-value. The results show that NAR-NNTS model trained with LM training algorithm performs better than other models for COVID-19 epidemiological data prediction.

Sectoral Nonlinear Causality Between Stock Market Volatility and the COVID-19 Pandemic: Evidence From India

This paper examines the linear and nonlinear relationship between daily confirmed COVID-19 cases and sectoral stock market volatility in India. The linear Granger causality test reveals bidirectional causality. Further, we observe that bidirectional nonlinear Granger causality exists between stock market volatility and COVID-19. This implies that the historical and lagged information can have a significant role in predicting COVID-19 cases and the stock market.

Study of Coronavirus Disease (COVID-19) Outbreak in India

Background: COVID-19 is an infectious disease caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). It has been recognized as a pandemic by the World Health Organization (WHO) on 11th March, 2020. Objective: To identify various factors that can increase coronavirus spread in India and predict COVID-19 cases up to 27th December, 2020, minimum and maximum number of deaths due to COVID-19 in India. Methods: This work predicts COVID-19 cases, the minimum and the maximum number of deaths due to COVID-19 in India based on the infection rate and suspected cases. Results: Our result shows that the number of COVID-19 cases will increase exponentially in India to approximately 859421415 cases by 27th December 2020. Conclusion: The spread of COVID-19 in India depends on a lot of factors such as religious congregation, social contact structure, low testing rates, identification of COVID-19 suspects, measures such as lockdown and sealing of hot stop, etc. taken by the Indian government. In India, lockdown proved to be a good decision.

A comparative study of time series models in predicting COVID-19 cases

Objective: To compare the performances of different time series models in predicting COVID-19 in different countries. Methods: We collected the daily confirmed case numbers of COVID-19 in the USA, India, and Brazil from April 1 to September 30, 2020, and then constructed an autoregressive integrated moving average (ARIMA) model and a recurrent neural network (RNN) model, respectively. We applied the mean absolute percentage error (MAPE) and root mean square error (RMSE) to compare the performances of the two models in predicting the case numbers from September 21 to September 30, 2020. Results: For the ARIMA models applied in the USA, India, and Brazil, the MAPEs were 13.18%, 9.18%, and 17.30%, respectively, and the RMSEs were 6 542.32, 8 069.50, and 3 954.59, respectively. For the RNN models applied in the USA, India, and Brazil, the MAPEs were 15.27%, 7.23% and 26.02%, respectively, and the RMSEs were 6 877.71, 6 457.07, and 5 950.88, respectively. Conclusions: The performance of the prediction models varied with country. The ARIMA model had a better prediction performance for COVID-19 in the USA and Brazil, while the RNN model was more suitable in India.

Intelligent Software-Aided Contact Tracing Framework: Towards Real-Time Model-Driven Prediction of Covid-19 Cases in Nigeria

As many countries around the world are trying to live with the deadly coronavirus by adhering to the safety measures put in place by their government as regulated by World Health Organization (WHO), it becomes very vital to continuously trace patients with COVID-19 symptoms for isolation, quarantine and treatment. In this work, an intelligent software-aided contact tracing for real-time model-driven prediction of COVID-19 cases is proposed utilizing COVID-19 dataset from kaggle.com. The dataset is preprocessed using One-Hot encoding and Principal Component Analysis. Isolation Forest algorithm is used to train and predict COVID-19 cases. The performance of the model is evaluated using Accuracy, Precision, Recall and F1-Score. The intelligent software-aided contact tracing framework has four layers: symptoms, modeling/prediction, cloud storage/contact routing and contact tracers. The contact tracing system is an android application that receives symptom values, predict it and automatically send the prediction result together with user’s contact and location details to the closest contact tracer via the Firebase real-time database. The closest contact tracer is determined by employing a dynamic routing algorithm (contact routing algorithm) that uses Open Shortest Path First (OSPF) protocol to compute the distance between two geographic locations (user and contact tracer) and chooses a contact tracer with shortest distance to the patient utilizing a unicast routing technique (routing a patient to a contact tracer in a one-to-one relationship). The predictive model along with the android application for software-aided contact tracing is implemented using the python, and Java programming language on Pycharm and Android Studio IDE respectively. This Framework is capable of predicting COVID-19 patients, notifying contact tracers of positive cases for proper follow-up which can subsequently curtail the spread of the virus.

Forecasting COVID-19 Confirmed Cases in Ghana: A Model Selection Approach

This study seeks to determine an appropriate statistical technique for forecasting the cumulated confirm cases of Coronavirus in Ghana. Cumulated daily data spanning from March 12, 2020, to August 04, 2020, was retrieved from the Center for Systems Science and Engineering at Johns Hopkins University. Four statistical forecasting techniques: Autoregressive Integrated Moving Average, Artificial Neural Network, Exponential smoothing and Autoregressive Fractional Integrated Moving Average were fitted to the COVID-19 series. Their respective forecast accuracy measures were compared to select the appropriate technique for forecasting the COVID-19 cases. Our findings revealed that the ARFIMA technique was a suitable statistical model for predicting COVID-19 cases in Ghana. The "best" model for forecasting is ARFIMA (2, 0.49, 4) which passed all the needed diagnostic tests. An unequal weight was estimated to derive a combined model for all four forecasting techniques. A 149-cumulated daily forecast from the "best" model and the combined model revealed that the number of confirmed COVID-19 cases would increase slightly until the end of this year.

Surface swabbing could predict COVID-19 cases

People infected with SARS-CoV-2 can leave the virus on surfaces they touch. Researchers have now shown that while the risk of picking up a SARS-CoV-2 infection from an individual contact point is v...

Forecasting of COVID-19 cases using deep learning models: Is it reliable and practically significant?

The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global economic growth and henceforth, all of society since neither a curing drug nor a preventing vaccine is discovered. The spread of COVID-19 is increasing day by day, imposing human lives and economy at risk. Due to the increased enormity of the number of COVID-19 cases, the role of Artificial Intelligence (AI) is imperative in the current scenario. AI would be a powerful tool to fight against this pandemic outbreak by predicting the number of cases in advance. Deep learning-based time series techniques are considered to predict world-wide COVID-19 cases in advance for short-term and medium-term dependencies with adaptive learning. Initially, the data pre-processing and feature extraction is made with the real world COVID-19 dataset. Subsequently, the prediction of cumulative confirmed, death and recovered global cases are modelled with Auto-Regressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (SLSTM) and Prophet approaches. For long-term forecasting of COVID-19 cases, multivariate LSTM models is employed. The performance metrics are computed for all the models and the prediction results are subjected to comparative analysis to identify the most reliable model. From the results, it is evident that the Stacked LSTM algorithm yields higher accuracy with an error of less than 2% as compared to the other considered algorithms for the studied performance metrics. Country-specific analysis and city-specific analysis of COVID-19 cases for India and Chennai, respectively, are predicted and analyzed in detail. Also, statistical hypothesis analysis and correlation analysis are done on the COVID-19 datasets by including the features like temperature, rainfall, population, total infected cases, area and population density during the months of May, June, July and August to find out the best suitable model. Further, practical significance of predicting COVID-19 cases is elucidated in terms of assessing pandemic characteristics, scenario planning, optimization of models and supporting Sustainable Development Goals (SDGs).