Environmental indicators for human norovirus outbreaks.

Norovirus is the most common cause of outbreaks of non-bacterial gastroenteritis in human. While the winter seasonality of norovirus outbreaks has been widely reported, the association between norovirus outbreak epidemics and environmental factors remains not fully understood. This literature review is intended to improve understanding of environmental factors governing norovirus outbreaks and how the factors affect norovirus transmission. To that end, a large number of studies (67) from countries around the world were critically reviewed and discussed. Results of the literature review show that temperature, humidity, and rainfall are the most important environmental variables governing the norovirus epidemic cycle. It was found that low temperature between -6.6 and 20 °C, relative humidity between 10 and 66 %, and rainfall from 1 day to 3 months before an outbreak are effective ranges of the environmental factors, which favor the prevalence of norovirus. Some other environmental factors might have an association with the cycle of norovirus epidemics. However, further investigations are needed to understand effects of the other factors on norovirus incidence. The findings of this literature review improve our understanding of the relationship between norovirus outbreaks and environmental factors and provide the direction for future research on norovirus outbreaks.

Restaurant Policies and Practices Related to Norovirus Outbreak Size and Duration.

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.

Development of genetic programming-based model for predicting oyster norovirus outbreak risks.

Oyster norovirus outbreaks pose increasing risks to human health and seafood industry worldwide but exact causes of the outbreaks are rarely identified, making it highly unlikely to reduce the risks. This paper presents a genetic programming (GP) based approach to identifying the primary cause of oyster norovirus outbreaks and predicting oyster norovirus outbreaks in order to reduce the risks. In terms of the primary cause, it was found that oyster norovirus outbreaks were controlled by cumulative effects of antecedent environmental conditions characterized by low solar radiation, low water temperature, low gage height (the height of water above a gage datum), low salinity, heavy rainfall, and strong offshore wind. The six environmental variables were determined by using Random Forest (RF) and Binary Logistic Regression (BLR) methods within the framework of the GP approach. In terms of predicting norovirus outbreaks, a risk-based GP model was developed using the six environmental variables and various combinations of the variables with different time lags. The results of local and global sensitivity analyses showed that gage height, temperature, and solar radiation were by far the three most important environmental predictors for oyster norovirus outbreaks, though other variables were also important. Specifically, very low temperature and gage height significantly increased the risk of norovirus outbreaks while high solar radiation markedly reduced the risk, suggesting that low temperature and gage height were associated with the norovirus source while solar radiation was the primary sink of norovirus. The GP model was utilized to hindcast daily risks of oyster norovirus outbreaks along the Northern Gulf of Mexico coast. The daily hindcasting results indicated that the GP model was capable of hindcasting all historical oyster norovirus outbreaks from January 2002 to June 2014 in the Gulf of Mexico with only two false positive outbreaks for the 12.5-year period. The performance of the GP model was characterized with the area under the Receiver Operating Characteristic curve of 0.86, the true positive rate (sensitivity) of 78.53% and the true negative rate (specificity) of 88.82%, respectively, demonstrating the efficacy of the GP model. The findings and results offered new insights into the oyster norovirus outbreaks in terms of source, sink, cause, and predictors. The GP model provided an efficient and effective tool for predicting potential oyster norovirus outbreaks and implementing management interventions to prevent or at least reduce norovirus risks to both the human health and the seafood industry.

Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast.

This paper presents an artificial intelligence-based model, called ANN-2Day model, for forecasting, managing and ultimately eliminating the growing risk of oyster norovirus outbreaks. The ANN-2Day model was developed using Artificial Neural Network (ANN) Toolbox in MATLAB Program and 15-years of epidemiological and environmental data for six independent environmental predictors including water temperature, solar radiation, gage height, salinity, wind, and rainfall. It was found that oyster norovirus outbreaks can be forecasted with two-day lead time using the ANN-2Day model and daily data of the six environmental predictors. Forecasting results of the ANN-2Day model indicated that the model was capable of reproducing 19years of historical oyster norovirus outbreaks along the Northern Gulf of Mexico coast with the positive predictive value of 76.82%, the negative predictive value of 100.00%, the sensitivity of 100.00%, the specificity of 99.84%, and the overall accuracy of 99.83%, respectively, demonstrating the efficacy of the ANN-2Day model in predicting the risk of norovirus outbreaks to human health. The 2-day lead time enables public health agencies and oyster harvesters to plan for management interventions and thus makes it possible to achieve a paradigm shift of their daily management and operation from primarily reacting to epidemic incidents of norovirus infection after they have occurred to eliminating (or at least reducing) the risk of costly incidents.

Environmental indicators of oyster norovirus outbreaks in coastal waters.

This paper presents an artificial intelligence-based approach to identifying environmental indicators of oyster norovirus outbreaks in coastal waters. It was found that oyster norovirus outbreaks are generally linked to the extreme combination of antecedent environmental conditions characterized by low water temperature, low solar radiation, low gage height, low salinity, strong wind, and heavy precipitation. Among the six environmental indicators, the most important three indicators, including water temperature, solar radiation and gage height, are capable of explaining 77.7% of model-predicted oyster norovirus outbreaks while the extremely low temperature alone may explain 37.2% of oyster norovirus outbreaks. It is, therefore, recommended that water temperature in oyster harvesting areas be monitored in the cold season and particularly the extremely low temperature during a low gage height be used as the primary indicator of oyster norovirus outbreaks. The findings are of profound significance to reducing the public health risk of norovirus outbreaks associated with consumption of oysters.

Temporary suspension of visiting during norovirus outbreaks in NHS Boards and the independent care home sector in Scotland: a cross-sectional survey of practice.

BACKGROUND: Norovirus outbreaks cause significant patient distress and adversely affect healthcare service delivery. Measures to manage outbreaks include controlling patient/staff movement and advising visitors of the risks of infection; temporary suspension of visiting (TSV) is advocated by some. Factors influencing the use of TSV have not previously been reported. AIM: To describe current practice in Scotland regarding TSV during norovirus outbreaks. METHODS: Cross-sectional survey of Scottish Health Boards [National Health Service (NHS)] Infection Prevention Leads (N = 22) and independent care home (ICH) managers (N = 107). FINDINGS: TSV practice is inconsistent across care settings. NHS findings: although 86.4% reported a recent norovirus outbreak, only 36.4% reported having criteria in place to guide TSV decisions and only 57.9% of those who had an outbreak implemented TSV. Conversely, 77.6% ICH respondents do have TSV criteria in place; 70.1% who had previously experienced an outbreak all reported that they would normally close to visitors. The majority of both NHS (81.8%) and ICH (84.2%) respondents reported making exceptions to TSV for individual cases. Despite variation in practice, 75% NHS and 81.8% ICH respondents agreed that TSV was helpful in controlling outbreaks. Factors influencing TSV implementation decisions included use of judgement in individual cases, perceived lack of evidence for the role of visitors in transmission, and belief in patients' rights to have visitors. CONCLUSION: Implementation of TSV in Scotland is inconsistent, with variation in the use of criteria, personal beliefs, and professional judgements. Further research on the role of visitors in transmission and service-user acceptability of TSV is required for policy development.