Improved Barnacle Mating Optimizer-Based Least Square Support Vector Machine to Predict COVID-19 Confirmed Cases with Total Vaccination

Every country must have an accurate and efficient forecasting model to avoid and manage the epidemic. This paper suggests an upgrade to one of the evolutionary algorithms inspired by nature, the Barnacle Mating Optimizer (BMO). First, the exploration phase of the original BMO is enhanced by enforcing and replacing the sperm cast equation through Levy flight. Then, the Least Square Support Vector Machine (LSSVM) is partnered with the improved BMO (IBMO). This hybrid approach, IBMO-LSSVM, has been deployed effectively for time-series forecasting to enhance the RBF kernel-based LSSVM model since vaccination started against COVID-19 in Malaysia. In comparison to other well-known algorithms, our outcomes are superior. In addition, the IBMO is assessed on 19 conventional benchmarks and the IEEE Congress of Evolutionary Computation Benchmark Test Functions (CECC06, 2019 Competition). In most cases, IBMO outputs are better than comparison algorithms. However, in other circumstances, the outcomes are comparable.

A Hybrid Method for Analyzing the Situation Based on Cumulative Fully Vaccinated and Confirmed Cases of Covid-19 in Malaysia

SARS-CoV-2 is an infection that affects several organs and has a wide range of symptoms in addition to producing severe acute respiratory syndrome. Millions of individuals were infected when it first started because of how quickly it travelled from its starting location to nearby countries. Anticipating positive Covid-19 incidences is required in order to better understand future risk and take the proper preventative and precautionary measures. As a result, it is critical to create mathematical models that are durable and have as few prediction errors as possible. This study suggests a unique hybrid strategy for examining the status of Covid-19 confirmed patients in conjunction with complete vaccination. First, the selective opposition technique is initially included into the Grey Wolf Optimizer (GWO) in this study to improve the exploration and exploitation capacity for the given challenge. Second, to execute the prediction task with the optimized hyper-parameter values, the Least Squares Support Vector Machines (LSSVM) method is integrated with Selective Opposition based GWO as an objective function. The data source includes daily occurrences of confirmed cases in Malaysia from February 24, 2021 to July 27, 2022. Based on the experimental results, this paper shows that SOGWO-LSSVM outperforms a few other hybrid techniques with ideally adjusted parameters. © 2022 IEEE.

Association of Vaccine Pass Policy and Omicron Incidence with COVID vaccine uptakes in Hong Kong.

We observed the association of vaccine coverage with the implementation of the Vaccine Pass policy and the intensity of the Omicron pandemic. Vaccine policy and transparent information dissemination are indispensable interventions promoting vaccination uptake.

Effect of vaccination patterns and vaccination rates on the spread and mortality of COVID-19 pandemic.

Objectives: Acquiring herd immunity through vaccination is the best way to curb the COVID-19 infection. Many countries have attempted to reach the herd immunity threshold as early as possible since the commencement of vaccination at the end of 2020. The purpose of this study is to (1) examine whether the pattern of vaccination rates affects the spread of COVID-19 and the consequent mortality and (2) investigate the level of cumulative vaccination rates that can begin to have an impact on reducing the spread and mortality of the pandemic. Methods: This study selected 33 countries with higher vaccination rates as its sample set, classifying them into three groups as per vaccination patterns. Results: The results showed that vaccination patterns have a significant impact on reducing spread and mortality. The full-speed vaccination pattern showed greater improvement in the spread of the COVID-19 pandemic than the other two patterns, while the striving vaccination pattern improved the most in terms of mortality. Secondly, the spread and mortality of the COVID pandemic started to significantly decline when the average cumulative vaccination rate reached 29.06 doses per 100 people and 7.88 doses per 100 people, respectively. Conclusion: The study highlights the important role of vaccination patterns and the VTMR in reducing the epidemic spread and mortality.

COVID-19: A Never Seen Pandemic

People have long been affected by epidemics and pandemics of communicable illnesses. The outbreaks have been around for thousands of years. Even in our modern day, epidemics have ravaged civilization till it leads people to despair. In the meanwhile, viruses have always offered huge difficulties that have ignited horrific epidemics and pandemics. A pandemic is the widespread spread of a new sickness. Viral respiratory diseases, such as those caused by a novel influenza virus or the coronavirus COVID-19, are the most likely to evolve into a pandemic. A pandemic is not the same as an epidemic. In an epidemic, many more cases of a health condition occur than would typically develop in a community or area, however the ailment does not move outside. The World Health Organization (WHO) is responsible for declaring when a worldwide epidemic is underway. The WHO achieves this by monitoring outbreaks of a disease and receiving advice from worldwide health experts. This paper depicts the impact of COVID-19 in globally in various fields and also visualize the current status of this pandemic. © 2022 University of Stockholm. All rights reserved.

Statistical investigation on association between the cumulative number of fully vaccinated people in the U.S. and the daily Covid-19 confirmation cases

Background: limited research about Covid-19 has been done on investigating the relationship between the number of vaccinated people and confirmed cases. We investigate the hypothesis, that the number of confirmed cases would negatively correlate with the number of people fully vaccinated. Methods: The data we chose to analyze is the number of Covid-19 confirmed cases versus the cumulative number of vaccinated people in the U.S. The data is collected from CDC's official website. The data was updated daily from 13 December 2020 (The start date when the Covid-19 vaccine was first available in the U.S.) till the date we collected it, 16 June 2021. Conclusion: our study indicates that the number of Covid-19 confirmed cases decreases as the number of fully vaccinated people increases. The results of this study will provide reasonable suggestions to people who are currently uncertain about the safety and effectiveness of the vaccines and convey profound cosmopolitan implications on other countries to contain the Covid-19 outbreak. © COPYRIGHT SPIE.

DOES THE COVID-19 DISCRIMINATE BY GENDER? CROATIAN AND SLOVENIAN CASE

The paper aim is to investigate whether males or females are more likely to get infected by the COVID-19 disease. Due to the fact that the COVID-19 disease is a new disease about which a lot of things are not well known yet, in the analysis daily data from the one-year period from March 1, 2020 to February 28, 2021 are used. The comparison of total confirmed COVID-19 cases according to gender is conducted for Croatia and Slovenia. In addition, the comparison is conducted by taking into account age groups as well. © 2021 Samo Drobne – Lidija Zadnik Stirn – Mirjana Kljajić Borštnar – Janez Povh – Janez Žerovnik

A Global Survey of Technological Resources and Datasets on COVID-19

—The application and successful utilization of technological resources in developing solutions to health, safety, and economic issues caused by COVID-19 indicate the importance of technology in curbing COVID-19. Also, the medical field has had to race against tie to develop and distribute the COVID-19 vaccine. This endeavour became successful with the vaccines created and approved in less than a year, a feat in medical history. Currently, much work is being done on data collection, where all significant factors impacting the disease are recorded. These factors include confirmed cases, death rates, vaccine rates, hospitalization data, and geographic regions affected by the pandemic. Continued research and use of technological resources are highly recommendable—the paper surveys list of packages, applications and datasets used to analyse COVID-19 © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Effect of mutation and vaccination on spread, severity, and mortality of COVID-19 disease.

Coronavirus disease 2019 (COVID-19) has had different waves within the same country. The spread rate and severity showed different properties within the COVID-19 different waves. The present work aims to compare the spread and the severity of the different waves using the available data of confirmed COVID-19 cases and death cases. Real-data sets collected from the Johns Hopkins University Center for Systems Science were used to perform a comparative study between COVID-19 different waves in 12 countries with the highest total performed tests for severe acute respiratory syndrome coronavirus 2 detection in the world (Italy, Brazil, Japan, Germany, Spain, India, USA, UAE, Poland, Colombia, Turkey, and Switzerland). The total number of confirmed cases and death cases in different waves of COVID-19 were compared to that of the previous one for equivalent periods. The total number of death cases in each wave was presented as a percentage of the total number of confirmed cases for the same periods. In all the selected 12 countries, Wave 2 had a much higher number of confirmed cases than that in Wave 1. However, the death cases increase was not comparable with that of the confirmed cases to the extent that some countries had lower death cases than in Wave 1, UAE, and Spain. The death cases as a percentage of the total number of confirmed cases in Wave 1 were much higher than that in Wave 2. Some countries have had Waves 3 and 4. Waves 3 and 4 have had lower confirmed cases than Wave 2, however, the death cases were variable in different countries. The death cases in Waves 3 and 4 were similar to or higher than Wave 2 in most countries. Wave 2 of COVID-19 had a much higher spread rate but much lower severity resulting in a lower death rate in Wave 2 compared with that of the first wave. Waves 3 and 4 have had lower confirmed cases than Wave 2; that could be due to the presence of appropriate treatment and vaccination. However, that was not reflected in the death cases, which were similar to or higher than Wave 2 in most countries. Further studies are needed to explain these findings.

Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development.

The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.

Effect of Socioeconomic and Ethnic Characteristics on COVID-19 Infection: the Case of the Ultra-Orthodox and the Arab Communities in Israel.

BACKGROUND: During infectious disease outbreaks, the weakest communities are more vulnerable to infection and its deleterious effects. In Israel, the Arab and Ultra-Orthodox Jewish communities have unique demographic and cultural characteristics that place them at higher risk of infection. OBJECTIVE: To examine socioeconomic and ethnic differences in rates of COVID-19 testing, confirmed cases and deaths, and to analyze patterns of transmission in ethnically diverse communities. METHODS: A cross-sectional ecologic study design was used. Consecutive data on rates of COVID-19 diagnostic testing, lab-confirmed cases, and deaths collected from March 31 through May 1, 2020, in 174 localities across Israel (84% of the population) were analyzed by socioeconomic ranking and ethnicity. RESULTS: Tests were performed on 331,594 individuals (4.29% of the total population). Of those, 14,865 individuals (4.48%) were positive for COVID-19 and 203 died (1.37% of confirmed cases). Testing rate was 26% higher in the lowest SE category compared with the highest. The risk of testing positive was 2.16 times higher in the lowest socioeconomic category, compared with the highest. The proportion of confirmed cases was 4.96 times higher in the Jewish compared with the Arab population. The rate of confirmed cases in 2 Ultra-Orthodox localities increased relatively early and quickly. Other Jewish and Arab localities showed consistently low rates of confirmed COVID-19 cases, regardless of socioeconomic ranking. CONCLUSIONS: Culturally different communities reacted differently to the COVID-19 outbreak and to government measures, resulting in different outcomes. Socioeconomic and ethnic variables cannot fully explain communities' reaction to the pandemic. Our findings stress the need for a culturally adapted approach for dealing with health crises.

COVID-19 caseness: An epidemiologic perspective.

The concept of caseness in the COVID-19 virus is important for early case finding and reporting. These are essential steps for prevention and control. This review defines and differentiates between types of cases and specifies the elements of each case definition in general with their application to COVID-19, where appropriate. These terms and their application are useful for the surveillance team, epidemiologists, clinicians, policy makers as well as the public in general.

Estimation of global case fatality rate of coronavirus disease 2019 (COVID-19) using meta-analyses: Comparison between calendar date and days since the outbreak of the first confirmed case.

OBJECTIVE: Since the outbreak of the coronavirus disease 2019 (COVID-19) in December of 2019 in China, estimating the pandemic's case fatality rate (CFR) has been the focus and interest of many stakeholders. In this manuscript, we prove that the method of using the cumulative CFR is static and does not reflect the trend according to the daily change per unit of time. METHODS: A proportion meta-analysis was carried out on the CFR in every country reporting COVID-19 cases. Based on these results, we performed a meta-analysis for a global COVID-19 CFR. Each analysis was performed using two different calculations of CFR: according to the calendar date and according to the days since the outbreak of the first confirmed case. We thus explored an innovative and original calculation of CFR, concurrently based on the date of the first confirmed case as well as on a daily basis. RESULTS: For the first time, we showed that using meta-analyses according to the calendar date and days since the outbreak of the first confirmed case, were different. CONCLUSION: We propose that a CFR according to days since the outbreak of the first confirmed case might be a better predictor of the current CFR of COVID-19 and its kinetics.

A suspected case of COVID-19 turned into a confirmed case: a case report.

COVID-19 (coronavirus disease 2019) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and discovered in 2019. The clinical manifestations include fever, coughing, difficulty in breathing and even death from multiple organ failure. Nucleic acid test is the golden standard method for confirmation of infection. According to the Chinese 'Seventh Edition of the COVID-19 Diagnosis and Treatment Protocol', suspected patients with negative nucleic acid tests from two consecutive airway specimens can be excluded from diagnosis and released from quarantine. The current report describes a suspected COVID-19 case that had a history of close contact with a COVID-19 patient. The diagnosis was confirmed after the SARS-CoV-2 nucleic acid was detected after four sputum sample tests (sampling interval of at least 24 h).

Modeling the Number of Confirmed Cases and Deaths from the COVID-19 Pandemic in the UK and Forecasting from April 15 to May 30, 2020.

OBJECTIVE: The UK is one of the epicenters of coronavirus disease (COVID-19) in the world. As of April 14, there have been 93 873 confirmed patients of COVID-19 in the UK and 12 107 deaths with confirmed infection. On April 14, it was reported that COVID-19 was the cause of more than half of the deaths in London. METHODS: The present paper addresses the modeling and forecasting of the outbreak of COVID-19 in the UK. This modeling must be accomplished through a 2-part time series model to study the number of confirmed cases and deaths. The period we aimed at a forecast was 46 days from April 15 to May 30, 2020. All the computations and simulations were conducted on Matlab R2015b, and the average curves and confidence intervals were calculated based on 100 simulations of the fitted models. RESULTS: According to the obtained model, we expect that the cumulative number of confirmed cases will reach 282 000 with an 80% confidence interval (242 000 to 316 500) on May 30, from 93 873 on April 14. In addition, it is expected that, over this period, the number of daily new confirmed cases will fall to the interval 1330 to 6450 with the probability of 0.80 by the point estimation around 3100. Regarding death, our model establishes that the real case fatality rate of the pandemic in the UK approaches 11% (80% confidence interval: 8%-15%). Accordingly, we forecast that the total death in the UK will rise to 35 000 (28 000-50 000 with the probability of 80%). CONCLUSIONS: The drawback of this study is the shortage of observations. Also, to conduct a more exact study, it is possible to take the number of the tests into account as an explanatory variable besides time.