Epidemic trend of COVID-19 in Taiwan, May to June 2021.
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ABSTRACT:
SUBMITTER: Lee WC
PROVIDER: S-EPMC8403659 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:In Italy, the first SARS-CoV-2 infections were diagnosed in Rome, Lazio region, at the end of January 2020, but sustained transmission occurred later, since the end of February. From 1 February to 12 April 2020, 17,164 nasopharyngeal swabs were tested by real time PCR for the presence of SARS-CoV-2 at the Laboratory of Virology of National Institute for Infectious Diseases "Lazzaro Spallanzani" (INMI) in Rome. In the same period, coincident with the winter peak of influenza and other respiratory illnesses, 847 samples were analyzed by multiplex PCR assay for the presence of common respiratory pathogens. In our study the time trend of SARS-CoV-2 and that of other respiratory pathogens in the same observation period were analysed. Overall, results obtained suggest that the spread of the pandemic SARS-CoV-2 virus did not substantially affect the time trend of other respiratory infections in our region, highlighting no significant difference in rates of SARS-CoV-2 infection in patients with or without other respiratory pathogens. Therefore, in the present scenario of COVID-19 pandemic, differential diagnosis resulting positive for common respiratory pathogen(s) should not exclude testing of SARS-CoV-2.
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Project description:(1) Background: During the COVID-19 outbreak in the Lazio region, a surge in emergency medical service (EMS) calls has been observed. The objective of present study is to investigate if there is any correlation between the variation in numbers of daily EMS calls, and the short-term evolution of the epidemic wave. (2) Methods: Data from the COVID-19 outbreak has been retrieved in order to draw the epidemic curve in the Lazio region. Data from EMS calls has been used in order to determine Excess of Calls (ExCa) in the 2020-2021 years, compared to the year 2019 (baseline). Multiple linear regression models have been run between ExCa and the first-order derivative (D') of the epidemic wave in time, each regression model anticipating the epidemic progression (up to 14 days), in order to probe a correlation between the variables. (3) Results: EMS calls variation from baseline is correlated with the slope of the curve of ICU admissions, with the most fitting value found at 7 days (R2 0.33, p < 0.001). (4) Conclusions: EMS calls deviation from baseline allows public health services to predict short-term epidemic trends in COVID-19 outbreaks, and can be used as validation of current data, or as an independent estimator of future trends.
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Project description:Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has gripped the entire world, almost paralysing the human race in its entirety. The virus rapidly transmits via human-to-human medium resulting in a massive increase of patients with COVID-19. In order to curb the spread of the disease, an immediate action of complete lockdown was implemented across the globe. India with a population of over 1.3 billion was not an exception and took the challenge to execute phase-wise lockdown, unlock and partial lockdown activities. In this study, we intend to summarise these different phases that the Government of India (GoI) imposed to fight against SARS-CoV-2 so that it can act as a reference guideline to help controlling future waves of COVID-19 and similar pandemic situations in India.
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Project description:To assess the current epidemic trend of COVID-19/SARS-CoV-2 in India, the epidemic dynamics of COVID-19 cases in India in terms of Case Fatality Rate (CFR), Case Recovery Rate (CRR) and Mortality rate (MR) COVID-19 have been evaluated during Lockdown-1. The analysis includes (i) epidemic curve of Covid-19 cases (ii) demographic analysis (iii) calculation of the CFR and CRR by different methods (iv) calculation of MR (v) Geo-temporal analysis (vi) epidemiological transmission factor (vii) evaluation of the effects and impact of infection, prevention and control in India. A total of 10,815 COVID-19 confirmed cases have been reported in 31 states/union territories as of April 14, 2020 with 9272 active cases (85.73%), 1190 cured/discharged (11%), and 353 deaths (3.23%). Among confirmed cases, most cases (59%) are aged 20-49 which is working age in India and 76% cases are reported for men. The median age of Indian COVID-19 patients found to be 39. As of April 14, the CFR per total cases in India is 3.32% and per closed cases is 23.27%. The CRR per total cases in India is 11.00% and per closed cases is 76.72%, which indicates that the recovery rate of COVID-19 is more than the fatality rate in India. The prevention and control measures taken by the state and central governments at all levels and measure of maintenance of social distancing by people have resulted in effective curbing in the COVID-19 transmission in India.
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Project description:ObjectivesIn Japan, several studies have reported no excess all-cause deaths (the difference between the observed and expected number of deaths) during the coronavirus disease 2019 (COVID-19) pandemic in 2020. This study aimed to estimate the weekly excess deaths in Japan's 47 prefectures for 2021 until June 27.Study designVital statistical data on deaths were obtained from the Ministry of Health, Labour and Welfare of Japan. For this analysis, we used data from January 2012 to June 2021.MethodsA quasi-Poisson regression was used to estimate the expected weekly number of deaths. Excess deaths were expressed as the range of differences between the observed and expected number of all-cause deaths and the 95% upper bound of the one-sided prediction interval.ResultsSince January 2021, excess deaths were observed for the first time in the week corresponding to April 12-18 and have continued through mid-June, with the highest excess percentage occurring in the week corresponding to May 31-June 6 (excess deaths: 1431-2587; excess percentage: 5.95-10.77%). Similarly, excess deaths were observed in consecutive weeks from April to June 2021 in 18 of 47 prefectures.ConclusionsFor the first time since February 2020, when the first COVID-19 death was reported in Japan, excess deaths possibly related to COVID-19 were observed in April 2021 in Japan, during the fourth wave. This may reflect the deaths of non-infected people owing to the disruption that the pandemic has caused.
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Project description:IntroductionVaccine hesitancy presents a challenge to COVID-19 control efforts. To identify beliefs associated with delayed vaccine uptake, we developed and implemented a vaccine hesitancy survey for the COVID-19 Community Research Partnership.MethodsIn June 2021, we assessed attitudes and beliefs associated with COVID-19 vaccination using an online survey. Self-reported vaccination data were requested daily through October 2021. We compared responses between vaccinated and unvaccinated respondents using absolute standardized mean differences (ASMD). We assessed validity and reliability using exploratory factor analysis and identified latent factors associated with a subset of survey items. Cox proportional hazards models and mediation analyses assessed predictors of subsequent vaccination among those initially unvaccinated.ResultsIn June 2021, 29,522 vaccinated and 1,272 unvaccinated participants completed surveys. Among those unvaccinated in June 2021, 559 (43.9 %) became vaccinated by October 31, 2021. In June, unvaccinated participants were less likely to feel "very concerned" about getting COVID-19 than vaccinated participants (10.6 % vs. 43.3 %, ASMD 0.792). Among those initially unvaccinated, greater intent to become vaccinated was associated with getting vaccinated and shorter time to vaccination. However, even among participants who reported no intention to become vaccinated, 28.5 % reported vaccination before study end. Two latent factors predicted subsequent vaccination-being 'more receptive' was derived from motivation to protect one's own or others' health and resume usual activities; being 'less receptive' was derived from concerns about COVID-19 vaccines. In a Cox model, both factors were partially mediated by vaccination intention.ConclusionThis study characterizes vaccine hesitant individuals and identifies predictors of eventual COVID-19 vaccination through October 31, 2021. Even individuals with no intention to be vaccinated can shift to vaccine uptake. Our data suggest factors of perceived severity of COVID-19 disease, vaccine safety, and trust in the vaccine development process are predictive of vaccination and may be important opportunities for ongoing interventions.
| S-EPMC10063571 | biostudies-literature
Project description:The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that solely applied traditional epidemic models or machine learning models were subject to underfitting or overfitting problems. We propose a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. We used the model to predict the long-term reported cumulative numbers of COVID-19 cases in China from January 27, 2020. We evaluated our model on officially reported confirmed cases from three different regions in China, and the results proved the effectiveness of our model in terms of simulating and predicting the trend of the COVID-19 outbreak. In China-Excluding-Hubei area within 7 days after the first public report, our model successfully and accurately predicted the long trend up to 40 days and the exact date of the outbreak peak. The predicted cumulative number (12,506) by March 10, 2020, was only 3·8% different from the actual number (13,005). The parameters obtained by our model proved the effectiveness of prevention and intervention strategies on epidemic control in China. The prediction results for five other countries suggested the external validity of our model. The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of the COVID-19 outbreak. The model parameters also provided insights into the analysis of COVID-19 transmission and the effectiveness of interventions in China.
| S-EPMC7713358 | biostudies-literature
Project description:Observational studies are needed to demonstrate real-world vaccine effectiveness (VE) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outcomes. Our objective was to conduct a review of published SARS-CoV-2 VE articles, supplemented by preprints, during the first 6 months of COVID-19 vaccine availability. This review compares the effectiveness of completing the primary COVID-19 vaccination series against multiple SARS-CoV-2 disease presentations and disease severity outcomes in three population groups (general population, frontline workers, and older adults). Four hundred and seventy-one published articles and 47 preprints were identified. After title and abstract screening and full article review, 50 studies (28 published articles, 22 preprints) were included. VE results were reported for five COVID-19 vaccines and four combinations of COVID-19 vaccines. VE results for BNT162b2 were reported in 70.6% of all studies. Seventeen studies reported variant specific VE estimates; Alpha was the most common. This comprehensive review demonstrates that COVID-19 vaccination is an important tool for preventing COVID-19 morbidity and mortality among fully vaccinated persons aged 16 years and older and serves as an important baseline from which to follow future trends in COVID-19 evolution and effectiveness of new and updated vaccines.
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Project description: Not available
| S-EPMC8499767 | biostudies-literature
Project description:IntroductionRecent studies have indicated the coronavirus disease 2019 (COVID-19) pandemic has disrupted routine vaccinations. This study describes the prevalence and characteristics of children and adolescents experiencing disrupted routine vaccination and other medical visits in the United States between January and June 2021.MethodsThe National Immunization Surveys were the source of data for this cross-sectional analysis (n= 86,893). Parents/guardians of children aged 6 months through 17 years were identified through random digit dialing of cellular phone numbers and interviewed. Disrupted visits were assessed by asking, "In the last two months, was a medical check-up, well child visit, or vaccination appointment for the child delayed, missed, or not scheduled for any reason?" Respondents answering yes were asked "Was it because of COVID-19?" Sociodemographic characteristics of children/adolescents with (1) COVID-19-related missed visits and (2) non-COVID-19-related missed visits were examined. Statistical differences within demographic subgroups were determined using t-tests, with p<0.05 considered statistically significant. Linear regression models were used to examine trends in disrupted visits over time.ResultsAn estimated 7.9% of children/adolescents had a missed visit attributed to COVID-19; 5.2% had a missed visit that was not COVID-19-related. Among children/adolescents with a COVID-19-related missed visit, a higher percentage were of minority race or ethnicity, lived below the poverty level, had a mother without a college degree, and lived in the western United States. There was a significant decline in COVID-19-related missed visits over time.ConclusionCOVID-19 disrupted routine vaccination or other medical visits inequitably. Catch-up immunizations are essential for achieving adequate vaccination of all children/adolescents.
| S-EPMC10247587 | biostudies-literature