scholarly journals Estimation of COVID-19 outbreak size in Harbin, China

2021 ◽  
Author(s):  
Haitao Song ◽  
Zhongwei Jia ◽  
Zhen Jin ◽  
Shengqiang Liu
Keyword(s):  
Outbreak Size

scholarly journals Restaurant Policies and Practices Related to Norovirus Outbreak Size and Duration

2020 ◽  
Vol 83 (9) ◽  
pp. 1607-1618
Author(s):  
E. RICKAMER HOOVER ◽  
NICOLE HEDEEN ◽  
AMY FREELAND ◽  
ANITA KAMBHAMPATI ◽  
DANIEL DEWEY-MATTIA ◽  
...  
Keyword(s):  
United States ◽  
Food Safety ◽  
The United States ◽  
Safety Training ◽  
Foodborne Illness ◽  
Surveillance Systems ◽  
Safety Certification ◽  
Food Safety Training ◽  
Outbreak Size ◽  
Policies And Practices

ABSTRACT Norovirus is the leading cause of foodborne illness outbreaks in the United States, and restaurants are the most common setting of foodborne norovirus outbreaks. Therefore, prevention and control of restaurant-related foodborne norovirus outbreaks is critical to lowering the burden of foodborne illness in the United States. Data for 124 norovirus outbreaks and outbreak restaurants were obtained from Centers for Disease Control and Prevention surveillance systems and analyzed to identify relationships between restaurant characteristics and outbreak size and duration. Findings showed that restaurant characteristics, policies, and practices were linked with both outbreak size and outbreak duration. Compared with their counterparts, restaurants that had smaller outbreaks had the following characteristics: managers received food safety certification, managers and workers received food safety training, food workers wore gloves, and restaurants had cleaning policies. In addition, restaurants that provided food safety training to managers, served food items requiring less complex food preparation, and had fewer managers had shorter outbreaks compared with their counterparts. These findings suggest that restaurant characteristics play a role in norovirus outbreak prevention and intervention; therefore, implementing food safety training, policies, and practices likely reduces norovirus transmission, leading to smaller or shorter outbreaks. HIGHLIGHTS

scholarly journals SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study

2021 ◽  
Vol 8 (3) ◽  
Author(s):  
Qimin Huang ◽  
Anirban Mondal ◽  
Xiaobing Jiang ◽  
Mary Ann Horn ◽  
Fei Fan ◽  
...  
Keyword(s):  
Healthcare Workers ◽  
Hospital Setting ◽  
Credible Interval ◽  
Hospital Unit ◽  
Early Implementation ◽  
Using Data ◽  
And Control ◽  
The Impact ◽  
Pharmaceutical Interventions ◽  
Outbreak Size

Development of strategies for mitigating the severity of COVID-19 is now a top public health priority. We sought to assess strategies for mitigating the COVID-19 outbreak in a hospital setting via the use of non-pharmaceutical interventions. We developed an individual-based model for COVID-19 transmission in a hospital setting. We calibrated the model using data of a COVID-19 outbreak in a hospital unit in Wuhan. The calibrated model was used to simulate different intervention scenarios and estimate the impact of different interventions on outbreak size and workday loss. The use of high-efficacy facial masks was shown to be able to reduce infection cases and workday loss by 80% (90% credible interval (CrI): 73.1–85.7%) and 87% (CrI: 80.0–92.5%), respectively. The use of social distancing alone, through reduced contacts between healthcare workers, had a marginal impact on the outbreak. Our results also indicated that a quarantine policy should be coupled with other interventions to achieve its effect. The effectiveness of all these interventions was shown to increase with their early implementation. Our analysis shows that a COVID-19 outbreak in a hospital's non-COVID-19 unit can be controlled or mitigated by the use of existing non-pharmaceutical measures.

Evaluation of the Impact of the Source (Patient Versus Staff) on Nosocomial Norovirus Outbreak Severity

2005 ◽  
Vol 26 (3) ◽  
pp. 268-272 ◽  
Author(s):  
Frauke Mattner ◽  
Lutz Mattner ◽  
Hans Ulrich Borck ◽  
Petra Gastmeier
Keyword(s):  
Odds Ratio ◽  
Index Case ◽  
Infection Risk ◽  
Prevention Measures ◽  
Detectable Difference ◽  
Outbreak Investigations ◽  
The Impact ◽  
Index Cases ◽  
Case Classification ◽  
Outbreak Size

AbstractObjective:To study the dependence of infection risk and outbreak size on the type of index case (ie, patient or staff).Methods:Nosocomial outbreaks were reviewed and categorized into those started by patients and those started by staff. Infection risks and outbreak sizes were evaluated taking into account the index case category.Results:Of the 30 nosocomial outbreaks of norovirus with person-to-person transmission, 20 (67%) involved patients as the index cases. Patient-indexed outbreaks affected significantly more patients than did staff-indexed outbreaks (difference in means, 16.25; 95% confidence interval [CI95], 5.1 to 27.0). For the numbers of affected staff, no dependence on the index case category was detectable (difference in means, -1.05; CI95, -9.0 to 6.9). For patients exposed during patient-indexed outbreaks, the risk of acquiring a norovirus infection was approximately 4.8 times as high as the corresponding risk for patients exposed during staff-indexed outbreaks (odds ratio [OR], 4.79; CI95,1.82 to 8.28). The infection risk for exposed staff during patient-indexed outbreaks was approximately 1.5 times as high as the corresponding risk during staff-indexed outbreaks (OR, 1.51; CI95, 0.92 to 2.49).Conclusions:Patient-indexed norovirus outbreaks generally affect more patients than do staff-indexed outbreaks. Staff appear to be similarly affected by both outbreak index category groups. This study demonstrates the importance of obtaining complete outbreak data, including the index case classification as staff or patient, during norovirus outbreak investigations. Such information may be useful for further targeting prevention measures.

scholarly journals Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America

Epidemics ◽  
2017 ◽  
Vol 21 ◽  
pp. 63-79 ◽  
Author(s):  
Deborah P. Shutt ◽  
Carrie A. Manore ◽  
Stephen Pankavich ◽  
Aaron T. Porter ◽  
Sara Y. Del Valle
Keyword(s):  
Central America ◽  
Reproductive Number ◽  
Reporting Rates ◽  
Outbreak Size

scholarly journals Real-time projections of Ebola outbreak size and duration with and without vaccine use in Équateur, Democratic Republic of Congo, as of May 27, 2018

2018 ◽  
Author(s):  
J. Daniel Kelly ◽  
Lee Worden ◽  
Rae Wannier ◽  
Nicole A. Hoff ◽  
Patrick Mukadi ◽  
...  
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Vaccination Coverage ◽  
Ebola Virus ◽  
Democratic Republic ◽  
Democratic Republic Of Congo ◽  
Credible Interval ◽  
Transmission Model ◽  
Republic Of Congo ◽  
Outbreak Size

AbstractBackgroundAs of May 27, 2018, 54 cases of Ebola virus disease (EVD) were reported in Équateur Province, Democratic Republic of Congo. We used reported case counts and time series from prior outbreaks to estimate the current outbreak size and duration with and without vaccine use.MethodsWe modeled Ebola virus transmission using a stochastic branching process model with a negative binomial distribution, using both estimates of reproduction number R declining from supercritical to subcritical derived from past Ebola outbreaks, as well as a particle filtering method to generate a probabilistic projection of the future course of the outbreak conditioned on its reported trajectory to date; modeled using 0%, 44%, and 62% estimates of vaccination coverage. Additionally, we used the time series for 18 prior Ebola outbreaks from 1976 to 2016 to parameterize a regression model predicting the outbreak size from the number of observed cases from April 4 to May 27.ResultsWith the stochastic transmission model, we projected a median outbreak size of 78 EVD cases (95% credible interval: 52, 125.4), 86 cases (95% credible interval: 53, 174.3), and 91 cases (95% credible interval: 52, 843.5), using 62%, 44%, and 0% estimates of vaccination coverage. With the regression model, we estimated a median size of 85.0 cases (95% prediction interval: 53.5, 216.6).ConclusionsThis outbreak has the potential to be the largest outbreak in DRC since 2007. Vaccines are projected to limit outbreak size and duration but are only part of prevention, control, and care strategies.

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