Search

Norovirus is a leading cause of acute gastroenteritis and imposes a substantial disease burden. In California, USA, norovirus surveillance is limited. We evaluated correlations between wastewater norovirus concentrations and available public health surveillance data. Wastewater surveillance for norovirus genotype GII in California provided timely, localized, and actionable data for public health authorities.

We detected a novel GII.4 variant with an amino acid insertion at the start of epitope A in viral protein 1 of noroviruses from the United States, Gabon, South Africa, and the United Kingdom collected during 2017–2022. Early identification of GII.4 variants is crucial for assessing pandemic potential and informing vaccine development.

We report 5 clustered acute gastroenteritis outbreaks in long-term care facilities in Utah, USA, that were linked to healthcare employees working at multiple facilities. Four outbreaks were caused by norovirus genotype GIX. We recommend continued norovirus surveillance and genotyping to determine contributions of this genotype to norovirus outbreaks.

IntroductionThe SARS-CoV-2 pandemic represented a formidable scientific and technological challenge to public health due to its rapid spread and evolution. To meet these challenges and to characterize the virus over time, the State of California established the California SARS-CoV-2 Whole Genome Sequencing (WGS) Initiative, or “California COVIDNet”. This initiative constituted an unprecedented multi-sector collaborative effort to achieve large-scale genomic surveillance of SARS-CoV-2 across California to monitor the spread of variants within the state, to detect new and emerging variants, and to characterize outbreaks in congregate, workplace, and other settings.MethodsCalifornia COVIDNet consists of 50 laboratory partners that include public health laboratories, private clinical diagnostic laboratories, and academic sequencing facilities as well as expert advisors, scientists, consultants, and contractors. Data management, sample sourcing and processing, and computational infrastructure were major challenges that had to be resolved in the midst of the pandemic chaos in order to conduct SARS-CoV-2 genomic surveillance. Data management, storage, and analytics needs were addressed with both conventional database applications and newer cloud-based data solutions, which also fulfilled computational requirements.ResultsRepresentative and randomly selected samples were sourced from state-sponsored community testing sites. Since March of 2021, California COVIDNet partners have contributed more than 450,000 SARS-CoV-2 genomes sequenced from remnant samples from both molecular and antigen tests. Combined with genomes from CDC-contracted WGS labs, there are currently nearly 800,000 genomes from all 61 local health jurisdictions (LHJs) in California in the COVIDNet sequence database. More than 5% of all reported positive tests in the state have been sequenced, with similar rates of sequencing across 5 major geographic regions in the state.DiscussionImplementation of California COVIDNet revealed challenges and limitations in the public health system. These were overcome by engaging in novel partnerships that established a successful genomic surveillance program which provided valuable data to inform the COVID-19 public health response in California. Significantly, California COVIDNet has provided a foundational data framework and computational infrastructure needed to respond to future public health crises.

Noroviruses are a leading cause of acute gastroenteritis (AGE) among adults and children worldwide. NoroSurv is a global network for norovirus strain surveillance among children

During October 2010–July 2011, 1.0% of pandemic (H1N1) 2009 viruses in the United States were oseltamivir resistant, compared with 0.5% during the 2009–10 influenza season. Of resistant viruses from 2010–11 and 2009–10, 26% and 89%, respectively, were from persons exposed to oseltamivir before specimen collection. Findings suggest limited community transmission of oseltamivir-resistant virus.

During April 2009–June 2010, thirty-seven (0.5%) of 6,740 pandemic (H1N1) 2009 viruses submitted to a US surveillance system were oseltamivir resistant. Most patients with oseltamivir-resistant infections were severely immunocompromised (76%) and had received oseltamivir before specimen collection (89%). No evidence was found for community circulation of resistant viruses; only 4 (unlinked) patients had no oseltamivir exposure.

Ant colony optimization (ACO) is often used to solve optimization problems, such as traveling salesman problem (TSP). When it is applied to TSP, its runtime is proportional to the squared size of problem N so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size N becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.