Analyzed COVID-19 Data Research Articles

Regional geographies and public health lessons of the COVID-19 pandemic in the Arctic.

This study examines the COVID-19 pandemic's spatiotemporal dynamics in 52 sub-regions in eight Arctic states. This study further investigates the potential impact of early vaccination coverage on subsequent COVID-19 outcomes within these regions, potentially revealing public health insights of global significance. We assessed the outcomes of the COVID-19 pandemic in Arctic sub-regions using three key epidemiological variables: confirmed cases, confirmed deaths, and case fatality ratio (CFR), along with vaccination rates to evaluate the effectiveness of the early vaccination campaign on the later dynamics of COVID-19 outcomes in these regions. From February 2020 to February 2023, the Arctic experienced five distinct waves of COVID-19 infections and fatalities. However, most Arctic regions consistently maintained Case Fatality Ratios (CFRs) below their respective national levels throughout these waves. Further, the regression analysis indicated that the impact of initial vaccination coverage on subsequent cumulative mortality rates and Case Fatality Ratio (CFR) was inverse and statistically significant. A common trend was the delayed onset of the pandemic in the Arctic due to its remoteness. A few regions, including Greenland, Iceland, the Faroe Islands, Northern Canada, Finland, and Norway, experienced isolated spikes in cases at the beginning of the pandemic with minimal or no fatalities. In contrast, Alaska, Northern Sweden, and Russia had generally high death rates, with surges in cases and fatalities. Analyzing COVID-19 data from 52 Arctic subregions shows significant spatial and temporal variations in the pandemic's severity. Greenland, Iceland, the Faroe Islands, Northern Canada, Finland, and Norway exemplify successful pandemic management models characterized by low cases and deaths. These outcomes can be attributed to successful vaccination campaigns, and proactive public health initiatives along the delayed onset of the pandemic, which reduced the impact of COVID-19, given structural and population vulnerabilities. Thus, the Arctic experience of COVID-19 informs preparedness for future pandemic-like public health emergencies in remote regions and marginalized communities worldwide that share similar contexts.

Pelajaran dari Aktivisme Digital KawalCOVID19 dalam Melawan Infodemic pada Masa Pandemi COVID-19 di Indonesia

Infodemic and inadequate government public communications have encouraged the birth of digital activism KawalCOVID19 as a source of information about COVID-19. This article aims to identify practices, challenges, and forms of KawalCOVID19 digital activism in fighting the infodemic in Indonesia. This study uses the concept of infodemic, digital activism, and New Social Movement theory with a single case study qualitative method. This study found that KawalCOVID19 fights the infodemic with strategies for collecting, verifying, and analyzing COVID-19 data at the regional and central levels; providing informational, educational, and verification content about COVID-19; disseminating information on the website and social media to the public, and advocating government policy; and collaborating to extend the reach. KawalCOVID19 faces challenges from technological, interactional, personal, and policy perspectives. KawalCOVID19 is a form of data activism in the category of digital gladiatorial activities. Furthermore, digital activism continues to grow and presents opportunities for theoretical and empirical studies with different contexts and different activism actors and also analysis of the reception of activism.

Coronavirus pandemic in the South Asia region: Health policy and economy trade-off

The South Asian Association for Regional Cooperation (SAARC) covers Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. We conducted a comparative analysis of the trade-off between the health policies for the prevention of COVID-19 spread and the impact of these policies on the economies and livelihoods of the South Asia populations. We analyzed COVID-19 data on epidemiology, public health and health policy, health system capacity, and macroeconomic indicators from January 2020 to March 2021 to determine temporal trends by conducting joinpoint regression analysis using average weekly percent change (AWPC). Bangladesh had the highest statistically significant AWPC for new COVID-19 cases (17.0; 95% CI = 7.7-27.1, P < 0.001), followed by the Maldives (12.9; 95% CI = 5.3-21.0, P < 0.001) and India (10.0; 95% CI = 8.4-11.5, P < 0.001). The AWPC for COVID-19 deaths was significant for India (6.5; 95% CI = 4.3-8.9, P < 0.001) and Bangladesh (6.1; 95% CI = 3.7-8.5, P < 0.001). Nepal (55.79%), and India (34.91%) had the second- and third-highest increase in unemployment, while Afghanistan (6.83%) and Pakistan (16.83%) had the lowest. The rate of change of real GDP had the highest decrease for Maldives (557.51%), and India (297.03%); Pakistan (46.46%) and Bangladesh (70.80%), however, had the lowest decrease. The government response stringency index for Pakistan had a see-saw pattern with a sharp decline followed by an increase in the government health policy restrictions that approximated the test-positivity trend. Unlike developed economies, the South Asian developing countries experienced a trade-off between health policy and their economies during the COVID-19 pandemic. South Asian countries (Nepal and India), with extended periods of lockdowns and a mismatch between temporal trends of government response stringency index and the test-positivity or disease incidence, had higher adverse economic effects, unemployment, and burden of COVID-19. Pakistan demonstrated targeted lockdowns with a rapid see-saw pattern of government health policy response that approximated the test-positivity trend and resulted in lesser adverse economic effects, unemployment, and burden of COVID-19.

Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia

Analyzing Covid-19 data has been conducted in many types of research, but research on classifying each case from Covid-19 data in all provinces in Indonesia has yet to be available. This study uses two clustering algorithms, namely K-Means and K-Medoids, to classify positive cases recovered and died in the Covid-19 data into three clusters, namely low, medium and high. The research data is Covid-19 case data in all provinces in Indonesia from 2020 to 2021. In the clustering calculations, the three distance methods used in this study are the Chebyshev Distance, Manhattan Distance, and Euclidean Distance. Based on the Silhouette Coefficient test results for the three distance calculation methods, it was found that Manhattan Distance is the best distance calculation method for K-Means and K-Medoids. Furthermore, the results of testing the Sum Squared Error (SSE), Silhouette Coefficient (SC) and Davies Index Bouldin (DBI) methods for the resulting clusters show that the value generated by the K-Means algorithm is higher in the SC and DBI methods. This result is evidenced by the SC value of 0.838; 0.838; and 0.925 in positive cases, recovered and died. While the DBI value is 0.305 for positive cases, 0.295 for recovered cases and 1.569 for dead cases. Based on these values, it proves that K-Means is superior in grouping and placing clusters compared to K-Medoids.

Analyzing COVID-19 data in the Canadian province of Manitoba: A new approach

The basic homogeneous SEIR (susceptible–exposed–infected–removed) model is a commonly used compartmental model for analysing infectious diseases such as influenza and COVID-19. However, in the homogeneous SEIR model, it is assumed that the population of study is homogeneous and, one cannot incorporate individual-level information (e.g., location of infected people, distance between susceptible and infected individuals, vaccination status) which may be important in predicting new disease cases. Recently, a geographically-dependent individual-level model (GD-ILM) within an SEIR framework was developed for when both regional and individual-level spatial data are available. In this paper, we propose to use an SEIR GD-ILM for each health region of Manitoba (central Canadian province) population to analyse the COVID-19 data. As different health regions of the population under study may act differently, we assume that each health region has its own corresponding parameters determined by a homogeneous SEIR model (such as contact rate, latent period, infectious period). A Monte Carlo Expectation Conditional Maximization (MCECM) algorithm is used for inference. Using estimated parameters we predict the infection rate at each health region of Manitoba over time to identify highly risk local geographical areas. Performance of the proposed approach is also evaluated through simulation studies.

COVID-19 Outbreak Related to PM10, PM2.5, Air Temperature and Relative Humidity in Ahvaz, Iran

AbstractIn this study, we assessed several points related to the incidence of COVID-19 between March 2020 and March 2021 in the Petroleum Hospital of Ahvaz (Iran) by analyzing COVID-19 data from patients referred to the hospital. We found that 57.5% of infected referrals were male, 61.7% of deaths by COVID-19 occurred in subjects over 65 years of age, and only 2.4% of deaths occurred in younger subjects (&lt; 30 years old). Analysis showed that mean PM10 and PM2.5 concentrations were correlated to the incidence of COVID-19 (r = 0.547, P &lt; 0.05, and r = 0.609, P &lt; 0.05, respectively) and positive chest CT scans (r = 0.597, P &lt; 0.05, and r = 0.541, P &lt; 0.05 respectively). We observed that a high daily air temperature (30–51 °C) and a high relative humidity (60–97%) led to a significant reduction in the daily incidence of COVID-19. The highest number of positive chest CT scans were obtained in June 2020 and March 2021 for daily air temperature ranging from 38 °C and 49 °C and 11 °C and 15 °C, respectively. A negative correlation was detected between COVID-19 cases and air temperature (r = − 0.320, P &lt; 0.05) and relative humidity (r = − 0.384, P &lt; 0.05). In Ahvaz, a daily air temperature of 10–28 °C and relative humidity of 19–40% are suitable for the spread of coronavirus. The highest correlation with the number of COVID-19 cases was found at lag3 (r = 0.42) and at lag0 with a positive chest CT scan (r = 0.56). For air temperature and relative humidity, the highest correlations were found at day 0 (lag0). During lockdown (22 March to 21 April 2020), a reduction was observed for PM10 (29.6%), PM2.5 (36.9%) and the Air Quality Index (33.3%) when compared to the previous month. During the pandemic period (2020–2021), the annual mean concentrations of PM10 (27.3%) and PM2.5 (17.8%) were reduced compared to the 2015–2019 period.

Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data.

Tracking the progress of an infectious disease is critical during a pandemic. However, the incubation period, diagnosis, and treatment most often cause uncertainties in the reporting of both cases and deaths, leading in turn to unreliable death rates. Moreover, even if the reported counts were accurate, the "crude" estimates of death rates which simply divide country-wise reported deaths by case numbers may still be poor or even non-computable in the presence of small (or zero) counts. We present a novel methodological contribution which describes the problem of analyzing COVID-19 data by two nested Poisson models: (i) an "upper model" for the cases infected by COVID-19 with an offset of population size, and (ii) a "lower" model for deaths of COVID-19 with the cases infected by COVID-19 as an offset, each equipped with their own random effect. This approach generates robustness in both the numerator as well as the denominator of the estimated death rates to the presence of small or zero counts, by "borrowing" information from other countries in the overall dataset, and guarantees positivity of both the numerator and denominator. The estimation will be carried out through non-parametric maximum likelihood which approximates the random effect distribution through a discrete mixture. An added advantage of this approach is that it allows for the detection of latent subpopulations or subgroups of countries sharing similar behavior in terms of their death rates.

Undergraduate Research on Physics-Informed Graph Attention Networks for COVID-19 Prediction

The COVID-19 pandemic has significantly impacted most countries in the world. Analyzing COVID-19 data from these countries together is a prominent challenge. Under the sponsorship of NSF REU, this paper describes our experience with a ten-week project that aims to guide an REU scholar to develop a physics-guided graph attention network to predict the global COVID- 19 Pandemics. We mainly presented the preparation, implementation, and dissemination of the addressed project. The COVID-19 situation in a country could be dramatically different from that of others, which suggests that COVID-19 pandemic data are generated based on different mechanisms, making COVID-19 data in different countries follow different probability distributions. Learning more than one hundred underlying probability distributions for countries in the world from large scale COVID- 19 data is beyond a single machine learning model. To address this challenge, we proposed two team-learning frameworks for predicting the COVID-19 pandemic trends: peer learning and layered ensemble learning framework. This addressed framework assigns an adaptive physics-guided graph attention network (GAT) to each learning agent. All the learning agents are fabricated in a hierarchical architecture, which enables agents to collaborate with each other in peer-to-peer and cross-layer way. This layered architecture shares the burden of large-scale data processing on machine learning models of all units. Experiments are run to verify the effectiveness of our approaches. The results indicate the proposed ensemble outperforms baseline methods. Besides being documented on GitHub, this work has resulted in two journal papers.

Reducing COVID-19 Racial Disparities: Why Some Counties Make Data-driven Decisions and Others Do Not?

Despite the progress in understanding the theoretical underpinnings behind governments’ use of performance information, there is limited understanding of whether governments use performance data to reduce racial and ethnic inequalities. This study uses the COVID-19 pandemic as a case study to examine what actions local governments have taken to remedy the disproportionally negative impact of the COVID-19 pandemic on racial and ethnic minorities. Specifically, the study examines the antecedents of the use of disaggregated performance data and studies the influence of the organizational culture and organizational learning on the use of disaggregated data in decision-making. An online survey was administered to 295 counties in the U.S., and the results indicate that attention dedicated to discussing and analyzing COVID-19 data and developmental organizational culture are positively associated with making data-driven decisions. Contrary to the widespread expectations, the percentage of minority population and prevalence of COVID-19 cases do not result in greater efforts to assist minorities in dealing with the pandemic.

COVID-19 data driven planning: The SouthEast Texas approach.

This coautoethnographic case study used the Open-Source Public Health Intelligence process to explore and share the South East Texas Regional Advisory Councils' (SETRAC) experience in collecting, processing, disseminating/visualizing, and analyzing COVID-19 data during the pandemic in the largest national medical setting in the United States. Specifically, it details the production of Business Intelligence reports powered by PowerBI both with general publics and with Regional Healthcare Preparedness Program (HPP) Coalition Coordinators, County Judges and City Mayors, Texas Department of State Health Services (DSHS) executive leadership, the Offices of the Texas Governor, and the Federal Pandemic Task Force led by the US Vice President, in order to provide a foundation for situational awareness, inter-regional collaboration, allocation of scare resources, and local, regional, and state policy decisions. It highlights best practices in risk and crisis communications during the COVID-19 response, underscores cross-sector collaboration and standardization of data collection for effective planning and response, discusses pervasive data revealed during the analysis, and evaluates collaborative and feedback processes that have implications for the Health Care System and Homeland Security Enterprise information sharing.

Improved log-Gaussian approximation for over-dispersed Poisson regression: Application to spatial analysis of COVID-19.

In the era of open data, Poisson and other count regression models are increasingly important. Still, conventional Poisson regression has remaining issues in terms of identifiability and computational efficiency. Especially, due to an identification problem, Poisson regression can be unstable for small samples with many zeros. Provided this, we develop a closed-form inference for an over-dispersed Poisson regression including Poisson additive mixed models. The approach is derived via mode-based log-Gaussian approximation. The resulting method is fast, practical, and free from the identification problem. Monte Carlo experiments demonstrate that the estimation error of the proposed method is a considerably smaller estimation error than the closed-form alternatives and as small as the usual Poisson regressions. For counts with many zeros, our approximation has better estimation accuracy than conventional Poisson regression. We obtained similar results in the case of Poisson additive mixed modeling considering spatial or group effects. The developed method was applied for analyzing COVID-19 data in Japan. This result suggests that influences of pedestrian density, age, and other factors on the number of cases change over periods.

Unified statistical inference for a nonlinear dynamic functional/longitudinal data model

Studying massive functional/longitudinal data, we adopt a flexible nonlinear dynamic regression method named the Semi-Varying Coefficient Additive Model, in which the response can be a functional/longitudinal variable, and the explanatory variables can be a mixture of functional/longitudinal and scalar variables. With the aid of an initial B-spline approximation, a local linear smoothing is proposed to estimate the unknown functional effects in the model. Existing methods of statistical inference for sparse data and dense data are significantly different. We therefore develop the asymptotic theories of the resultant pilot estimation based local linear estimators (PEBLLE) on a unified framework of sparse, dense and ultra-dense cases of data. Remarkably, we obtain the oracle properties as if other functions were known in advance. Extensive Monte Carlo simulation studies investigating the finite sample performance of the proposed methodologies confirm our asymptotic results. We further illustrate our methodologies by analyzing COVID-19 data from China.

A Propensity-Matched Analysis of COVID-19 in Kidney Transplant Recipients Compared With Non-Kidney Transplant Patients: A Single-Center Report From India.

Comparisons of COVID-19 incidence between kidney transplant recipients and patients who did not receive kidney transplant are underexplored in various geographic regions. This Indian, single-center, retrospective study analyzed COVID-19 data of patients hospitalized between May 12, 2020, and January 11, 2021. A propensity matching score was used to compare outcomes between the 2 groups. We also used multivariable Cox proportional hazard analyses to assess association of kidney transplantation with mortality. Of the 1627 COVID-19 cases, 179 were kidney transplant recipients and 1448 were not kidney transplant patients (control group). Ofthe 436 reported in-hospital deaths, 20 (11.1%) were in the kidney transplant group and 416 (28.7%) were in the control group. Propensity matching identified 98 kidney transplantrecipients and167 controlpatients. InKaplanMeier survival plots for these patients, there was no statistical difference in mortality (log-rank, Mantel Cox test; P = .07) or severity (log-rank, Mantel Cox test; P = .07) with regard to COVID-19. In Cox analysis, age groups from 61 to 70 years (hazard ratio = 1.5; 95% CI, 1.0-2.2; P = .04), 71 to 80 years (hazard ratio = 1.64; 95% CI, 1.0-2.5; P = .02), and >80 years (hazard ratio = 1.91; 95% CI, 1.1-3.1; P = .01)were associatedwith statistically significant greater mortality.Having a kidney transplant (hazard ratio = 0.43; 95% CI, 0.3-0.7; P = 0.001) was not associated with mortality. In our analysis, age was the most important predictor of mortality. Kidney transplant status was not found to have an independent association with mortality and severity.

County-Level Social Determinants of Health and COVID-19 in Nursing Homes, United States, June 1, 2020-January 31, 2021.

Nursing homes are a primary setting of COVID-19 transmission and death, but research has primarily focused only on factors within nursing homes. We investigated the relationship between US nursing home-associated COVID-19 infection rates and county-level and nursing home attributes. We constructed panel data from the Centers for Medicare & Medicaid Services (CMS) minimum dataset, CMS nursing home data, 2010 US Census data, 5-year (2012-2016) American Community Survey estimates, and county COVID-19 infection rates. We analyzed COVID-19 data from June 1, 2020, through January 31, 2021, during 7 five-week periods. We used a maximum likelihood estimator, including an autoregressive term, to estimate effects and changes over time. We performed 3 model forms (basic, partial, and full) for analysis. Nursing homes with nursing (0.005) and staff (0.002) shortages had high COVID-19 infection rates, and locally owned (-0.007) or state-owned (-0.025) and nonprofit (-0.011) agencies had lower COVID-19 infection rates than privately owned agencies. County-level COVID-19 infection rates corresponded with COVID-19 infection rates in nursing homes. Racial and ethnic minority groups had high nursing home-associated COVID-19 infection rates early in the study. High median annual personal income (-0.002) at the county level correlated with lower nursing home-associated COVID-19 infection rates. Communities with low rates of nursing home infections had access to more resources (eg, financial resources, staffing) and likely had better mitigation efforts in place earlier in the pandemic than nursing homes that had access to few resources and poor mitigation efforts. Future research should address the social and structural determinants of health that are leaving racial and ethnic minority populations and institutions such as nursing homes vulnerable during times of crises.

COVID-19 Dashboard for the Lisbon Region - an advisory tool for policy

Issue/problemAt the time of arrival of the COVID-19 pandemic to Portugal, the Public Health Department of Lisbon and Tagus Valley Regional Health Administration was confronted with its obsolete information system, which could not provide the information that Public Health Authorities needed to bring the pandemic under control. In order to tackle this issue, our goal was to aggregate all the relevant epidemiological data for the region in an accessible and user-friendly dashboard.MethodsStarting from March 2020 we created a dashboard, which compiled and analyzed COVID-19 data, such as: confirmed and active cases, deaths, hospitalizations (including ICU beds), high/low risk contacts, mandatory confinements, tests, and clusters. This dashboard had anonymized data and was updated daily as well as constantly upgraded by taking into consideration new incoming data and feedback provided by users, serving its purpose as an advisory tool for policy.ResultsThe dashboard was available to Local, Regional, and National Public Health Authorities and made possible to monitor trends and predict epidemiological changes that, prior to its existence, were dispersed in several datasets. Most importantly, it allowed for a better allocation of human resources for contact tracing and case management activities, to easily identify basic social needs for the most vulnerable citizens. It also provided for policy measures adjusted to the smallest administrative division in Portugal, the parish, that ultimately allowed for a better epidemiological control in Lisbon Metropolitan Area in 2020.LessonsThe regional dashboard is far from perfect, but it highlights the constant need for robust information and epidemiological surveillance systems, serving both National, Regional and Local Public Health Authorities. It appeared out of necessity during a pandemic and it proved once more the importance of data analytics for guiding public health action based on evidence.Key messages Data analytics can serve both as an advisory tool for policy as well as to guide public health interventions.The COVID-19 Dashboard for the Lisbon Region aggregates relevant epidemiological data that helped Public Health authorities better understand and control the Pandemic in the region.

Interpreting airborne pandemics spreading using fractal kinetics’ principles

Introduction The reaction between susceptible and infected subjects has been studied under the well-mixed hypothesis for almost a century. Here, we present a consistent analysis for a not well-mixed system using fractal kinetics’ principles. Methods We analyzed COVID-19 data to get insights on the disease spreading in absence/presence of preventive measures. We derived a three-parameter model and show that the “fractal” exponent h of time larger than unity can capture the impact of preventive measures affecting population mobility. Results The h=1 case, which is a power of time model, accurately describes the situation without such measures in line with a herd immunity policy. The pandemic spread in four model countries (France, Greece, Italy and Spain) for the first 10 months has gone through four stages: stages 1 and 3 with limited to no measures, stages 2 and 4 with varying lockdown conditions. For each stage and country two or three model parameters have been determined using appropriate fitting procedures. The fractal kinetics model was found to be more akin to real life. Conclusion Model predictions and their implications lead to the conclusion that the fractal kinetics model can be used as a prototype for the analysis of all contagious airborne pandemics.

SIRVD-DL: A COVID-19 deep learning prediction model based on time-dependent SIRVD

COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the most interesting and widely used. However, only using artificial intelligence methods for prediction cannot capture the time change pattern of the transmission of infectious diseases. To solve this problem, this paper proposes a COVID-19 prediction model based on time-dependent SIRVD by using deep learning. This model combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the mathematical model of infectious diseases by fusing deep learning models such as LSTM and other time prediction methods. In the current situation of mass vaccination, we analyzed COVID-19 data from January 15, 2021, to May 27, 2021 in seven countries – India, Argentina, Brazil, South Korea, Russia, the United Kingdom, France, Germany, and Italy. The experimental results show that the prediction model not only has a 50% improvement in single-day predictions compared to pure deep learning methods, but also can be adapted to short- and medium-term predictions, which makes the overall prediction more interpretable and robust.

Interpreting airborne pandemics spreading using fractal kinetics'principles.

Introduction The reaction between susceptible and infected subjects has been studied under the well-mixed hypothesis for almost a century. Here, we present a consistent analysis for a not well-mixed system using fractal kinetics'principles. Methods We analyzed COVID-19 data to get insights on the disease spreading in absence/presence of preventive measures.We deriveda three-parameter model and show that the "fractal" exponenthof time larger than unity can capture the impact of preventive measures affecting population mobility. Results Theh=1 case, which is a power of time model, accurately describes the situation without such measures in line with a herd immunity policy.The pandemic spread in four model countries (France, Greece, Italy and Spain) for the first 10 months has gone throughfourstages: stages 1 and 3 with limited to no measures, stages 2 and 4 with varying lockdown conditions. Foreach stage and country two or three model parameters have been determined using appropriate fitting procedures. The fractal kinetics model was found to be more akin to real life. Conclusion Model predictions and their implications lead to the conclusion that the fractal kinetics model can be used as a prototype for the analysis of all contagious airborne pandemics.

Forecasting the COVID-19 Pandemic in Saudi Arabia Using a Modified Singular Spectrum Analysis Approach: Model Development and Data Analysis

BackgroundInfectious disease is one of the main issues that threatens human health worldwide. The 2019 outbreak of the new coronavirus SARS-CoV-2, which causes the disease COVID-19, has become a serious global pandemic. Many attempts have been made to forecast the spread of the disease using various methods, including time series models. Among the attempts to model the pandemic, to the best of our knowledge, no studies have used the singular spectrum analysis (SSA) technique to forecast confirmed cases.ObjectiveThe primary objective of this paper is to construct a reliable, robust, and interpretable model for describing, decomposing, and forecasting the number of confirmed cases of COVID-19 and predicting the peak of the pandemic in Saudi Arabia.MethodsA modified singular spectrum analysis (SSA) approach was applied for the analysis of the COVID-19 pandemic in Saudi Arabia. We proposed this approach and developed it in our previous studies regarding the separability and grouping steps in SSA, which play important roles in reconstruction and forecasting. The modified SSA approach mainly enables us to identify the number of interpretable components required for separability, signal extraction, and noise reduction. The approach was examined using different levels of simulated and real data with different structures and signal-to-noise ratios. In this study, we examined the capability of the approach to analyze COVID-19 data. We then used vector SSA to predict new data points and the peak of the pandemic in Saudi Arabia.ResultsIn the first stage, the confirmed daily cases on the first 42 days (March 02 to April 12, 2020) were used and analyzed to identify the value of the number of required eigenvalues (r) for separability between noise and signal. After obtaining the value of r, which was 2, and extracting the signals, vector SSA was used to predict and determine the pandemic peak. In the second stage, we updated the data and included 81 daily case values. We used the same window length and number of eigenvalues for reconstruction and forecasting of the points 90 days ahead. The results of both forecasting scenarios indicated that the peak would occur around the end of May or June 2020 and that the crisis would end between the end of June and the middle of August 2020, with a total number of infected people of approximately 330,000.ConclusionsOur results confirm the impressive performance of modified SSA in analyzing COVID-19 data and selecting the value of r for identifying the signal subspace from a noisy time series and then making a reliable prediction of daily confirmed cases using the vector SSA method.

Applications of Big Data Analytics to Control COVID-19 Pandemic.

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.