Decreased dengue cases attributable to the effect of COVID-19 in Guangzhou in 2020.
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ABSTRACT:
SUBMITTER: Jiang L
PROVIDER: S-EPMC8153456 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:During January 26-February 10, 2020, an outbreak of 2019 novel coronavirus disease in an air-conditioned restaurant in Guangzhou, China, involved 3 family clusters. The airflow direction was consistent with droplet transmission. To prevent the spread of the virus in restaurants, we recommend increasing the distance between tables and improving ventilation.
| S-EPMC7323555 | biostudies-literature
Project description:The SARS-CoV-2 Delta (B.1.617.2) variant is capable of infecting vaccinated persons. An open question remains as to whether deficiencies in specific vaccine-elicited immune responses result in susceptibility to vaccine breakthrough infection. We investigated 55 vaccine breakthrough infection cases (mostly Delta) in Singapore, comparing them against 86 vaccinated close contacts who did not contract infection. Vaccine breakthrough cases showed lower memory B cell frequencies against SARS-CoV-2 receptor binding domain (RBD). Compared to plasma antibodies, antibodies secreted by memory B cells retained a higher fraction of neutralizing properties against the Delta variant. Inflammatory cytokines including IL-1β and TNF were lower in vaccine breakthrough infections than primary infection of similar disease severity, underscoring the usefulness of vaccination in preventing inflammation. This report highlights the importance of memory B cells against vaccine breakthrough, and suggests that lower memory B cell levels may be a correlate of risk for Delta vaccine breakthrough infection.
| S-SCDT-EMM-2021-15227 | biostudies-other
Project description:IntroductionDengue is endemic in more than 100 countries, mainly in tropical and subtropical regions, and the incidence has increased 30-fold in the past 50 years. The situation of dengue in China has become more and more severe, with an unprecedented dengue outbreak hitting south China in 2014. Building a dengue early warning system is therefore urgent and necessary for timely and effective response.Methodology and principal findingsIn the study we developed a time series Poisson multivariate regression model using imported dengue cases, local minimum temperature and accumulative precipitation to predict the dengue occurrence in four districts of Guangzhou, China. The time series data were decomposed into seasonal, trend and remainder components using a seasonal-trend decomposition procedure based on loess (STL). The time lag of climatic factors included in the model was chosen based on Spearman correlation analysis. Autocorrelation, seasonality and long-term trend were controlled in the model. A best model was selected and validated using Generalized Cross Validation (GCV) score and residual test. The data from March 2006 to December 2012 were used to develop the model while the data from January 2013 to September 2014 were employed to validate the model. Time series Poisson model showed that imported cases in the previous month, minimum temperature in the previous month and accumulative precipitation with three month lags could project the dengue outbreaks occurred in 2013 and 2014 after controlling the autocorrelation, seasonality and long-term trend.ConclusionsTogether with the sole transmission vector Aedes albopictus, imported cases, monthly minimum temperature and monthly accumulative precipitation may be used to develop a low-cost effective early warning system.
| S-EPMC4447292 | biostudies-literature
Project description:ObjectivesIn 2020, Croatia reported the first increase in the unemployment rate after six consecutive years of reduction in the number of unemployed persons. Unemployment is associated with an increase in morbidity and mortality among unemployed persons. We estimated the number of potential excess deaths that could be associated with an increase in unemployment seen after the beginning of the COVID-19 pandemic in 2020.Study designThis was a cross-sectional analytic study.MethodsWe used previously published meta-analyzed hazard ratios for the unemployment-mortality association and unemployment and mortality data from the Croatian Bureau of Statistics to estimate 1-year age-standardized deaths potentially attributable to COVID-19-related unemployment for persons aged 20-64 in Croatia.ResultsIn January 2021, we observed a 19% increase in unemployment among persons aged 20-64 years compared with February 2020 (prepandemic). This increase in unemployment could lead to 23 excess deaths among newly unemployed persons. This would constitute a 42% increase in the number of deaths and 29% of all deaths among this group. Deaths were disproportionately higher among men and those aged >40 years.ConclusionsTo mitigate the negative impact of COVID-19-related unemployment on population health, interventions that will reduce the further spread of SARS-CoV-2 and policies that will ensure economic recovery and reduction of unemployment are needed. Job skills training and provision of legal and welfare advice programs for unemployed persons should be integrated with health interventions.
| S-EPMC9042730 | biostudies-literature
Project description:BackgroundTimely monitoring of COVID-19 impact on mortality is critical for rapid risk assessment and public health action.AimBuilding upon well-established models to estimate influenza-related mortality, we propose a new statistical Attributable Mortality Model (AttMOMO), which estimates mortality attributable to one or more pathogens simultaneously (e.g. SARS-CoV-2 and seasonal influenza viruses), while adjusting for seasonality and excess temperatures.MethodsData from Nationwide Danish registers from 2014-week(W)W27 to 2020-W22 were used to exemplify utilities of the model, and to estimate COVID-19 and influenza attributable mortality from 2019-W40 to 2020-W20.ResultsSARS-CoV-2 was registered in Denmark from 2020-W09. Mortality attributable to COVID-19 in Denmark increased steeply, and peaked in 2020-W14. As preventive measures and national lockdown were implemented from 2020-W12, the attributable mortality started declining within a few weeks. Mortality attributable to COVID-19 from 2020-W09 to 2020-W20 was estimated to 16.2 (95% confidence interval (CI): 12.0 to 20.4) per 100,000 person-years. The 2019/20 influenza season was mild with few deaths attributable to influenza, 3.2 (95% CI: 1.1 to 5.4) per 100,000 person-years.ConclusionAttMOMO estimates mortality attributable to several pathogens simultaneously, providing a fuller picture of mortality by COVID-19 during the pandemic in the context of other seasonal diseases and mortality patterns. Using Danish data, we show that the model accurately estimates mortality attributable to COVID-19 and influenza, respectively. We propose using standardised indicators for pathogen circulation in the population, to make estimates comparable between countries and applicable for timely monitoring.
| S-EPMC7908066 | biostudies-literature
Project description:We report a dengue outbreak in Key Largo, Florida, USA, from February through August 2020, during the COVID-19 pandemic. Successful community engagement resulted in 61% of case-patients self-reporting. We also describe COVID-19 pandemic effects on the dengue outbreak investigation and the need to increase clinician awareness of dengue testing recommendations.
| S-EPMC10370846 | biostudies-literature
Project description:In 2019-2020, dengue virus (DENV) type 4 emerged to cause the largest DENV outbreak in Paraguay's history. This study sought to characterize dengue relative to other acute illness cases and use phylogenetic analysis to understand the outbreak's origin. Individuals with an acute illness (≤7 days) were enrolled and tested for DENV nonstructural protein 1 (NS1) and viral RNA by real-time RT-PCR. Near-complete genome sequences were obtained from 62 DENV-4 positive samples. From January 2019 to March 2020, 799 participants were enrolled: 253 dengue (14 severe dengue, 5.5%) and 546 other acute illness cases. DENV-4 was detected in 238 dengue cases (94.1%). NS1 detection by rapid test was 52.5% sensitive (53/101) and 96.5% specific (387/401) for dengue compared to rRT-PCR. DENV-4 sequences were grouped into two clades within genotype II. No clustering was observed based on dengue severity, location, or date. Sequences obtained here were most closely related to 2018 DENV-4 sequences from Paraguay, followed by a 2013 sequence from southern Brazil. DENV-4 can result in large outbreaks, including severe cases, and is poorly detected with available rapid diagnostics. Outbreak strains seem to have been circulating in Paraguay and Brazil prior to 2018, highlighting the importance of sustained DENV genomic surveillance.
| S-EPMC10892180 | biostudies-literature
Project description:Dengue is the fastest spreading mosquito-transmitted disease in the world. In China, Guangzhou City is believed to be the most important epicenter of dengue outbreaks although the transmission patterns are still poorly understood. We developed an autoregressive integrated moving average model incorporating external regressors to examine the association between the monthly number of locally acquired dengue infections and imported cases, mosquito densities, temperature and precipitation in Guangzhou. In multivariate analysis, imported cases and minimum temperature (both at lag 0) were both associated with the number of locally acquired infections (P < 0.05). This multivariate model performed best, featuring the lowest fitting root mean squared error (RMSE) (0.7520), AIC (393.7854) and test RMSE (0.6445), as well as the best effect in model validation for testing outbreak with a sensitivity of 1.0000, a specificity of 0.7368 and a consistency rate of 0.7917. Our findings suggest that imported cases and minimum temperature are two key determinants of dengue local transmission in Guangzhou. The modelling method can be used to predict dengue transmission in non-endemic countries and to inform dengue prevention and control strategies.
| S-EPMC9134281 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) is currently the biggest public health problem worldwide. Intensive international travel and tourism have greatly contributed to its rapid global spreading. This study is the first comprehensive analysis of the epidemiological characteristics and clinical outcomes of the travel-associated COVID-19 cases in Vojvodina, Serbia, from March 6 to December 31, 2020 and it concerns permanent residents of Serbia. A cross-sectional study was conducted using data from the dedicated surveillance database of the Institute of Public Health of Vojvodina. Overall, 650 travel-associated COVID-19 cases were recorded in Vojvodina during the observed period, mainly imported from Bosnia and Herzegovina, followed by Austria and Germany (N = 195, 30%; N = 79, 12.15% and N = 75, 11.54%, respectively). The majority of cases were in the working-age groups, 18-44 and 45-64 years (56.46% and 34.15%, respectively). Overall, 54 (8.31%) patients developed pneumonia. In comparison to patients without pneumonia, those with pneumonia were older (mean age = 55.39 years vs. 41.34 years, p<0.01) and had a higher percentage of comorbidities (57.41% vs. 16.61%). Men were more likely to develop pneumonia than women (OR = 2.22; 95% CI: 1.14-4.30, p = 0.02), as well as those in retired-age group (OR = 4.11; 95% CI: 2.0-8.46, p<0.01). Obesity (OR = 14.40; 95% CI: 3.8-54.6, p<0.01), diabetes (OR = 9.82; 95% CI: 3.15-30.61, p<0.01) and hypertension (OR = 7.99; 95% CI: 3.98-16.02, p<0.01) were the most prominent main comorbidities as predictors of pneumonia. Our results represent general epidemiological and clinical dynamics of COVID-19 disease in Vojvodina. Also, they provide evidence that the predictors of pneumonia were: increasing age, male sex, having underlying comorbidities, an increasing number of days from the return to laboratory confirmation of COVID-19 (OR = 1.08, 95% CI: 1.03-1.12, p<0.01), as well as an increasing number of days from symptoms onset to diagnosis (OR = 1.14, 95% CI: 1.07-1.21, p<0.01), while anosmia and ageusia were protective factors for developing it (OR = 0.31, 95% CI: 0.12-0.79, p = 0.01).
| S-EPMC8699980 | biostudies-literature
Project description: Not available
| S-EPMC8393009 | biostudies-literature