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Background The initial focus of the US public health response to COVID-19 was the implementation of numerous social distancing policies. While COVID-19 was the impetus for imposing these policies, it is not the only respiratory disease affected by their implementation. This study aimed to assess the impact of social distancing policies on non-SARS-CoV-2 respiratory pathogens typically circulating across multiple US states. Methods Linear mixed-effect models were implemented to explore the effects of five social distancing policies on non-SARS-CoV-2 respiratory pathogens across nine states from January 1 through May 1, 2020. The observed 2020 pathogen detection rates were compared week-by-week to historical rates to determine when the detection rates were different. Results Model results indicate that several social distancing policies were associated with a reduction in total detection rate, by nearly 15%. Policies were associated with decreases in pathogen circulation of human rhinovirus/enterovirus and human metapneumovirus, as well as influenza A, which typically decrease after winter. Parainfluenza viruses failed to circulate at historical levels during the spring. Total detection rate in April 2020 was 35% less than historical average. Many of the pathogens driving this difference fell below historical detection rate ranges within two weeks of initial policy implementation. Conclusion This analysis investigated the effect of multiple social distancing policies implemented to reduce transmission of SARS-CoV-2 on non-SARS-CoV-2 respiratory pathogens. These findings suggest that social distancing policies may be used as an impactful public health tool to reduce communicable respiratory illness.

Acute gastrointestinal infection (AGI) represents a significant public health concern. To control and treat AGI, it is critical to quickly and accurately identify its causes. The use of novel multiplex molecular assays for pathogen detection and identification provides a unique opportunity to improve pathogen detection, and better understand risk factors and burden associated with AGI in the community. In this study, de-identified results from BioFire® FilmArray® Gastrointestinal (GI) Panel were obtained from January 01, 2016 to October 31, 2018 through BioFire® Syndromic Trends (Trend), a cloud database. Data was analyzed to describe the occurrence of pathogens causing AGI across United States sites and the relative rankings of pathogens monitored by FoodNet, a CDC surveillance system were compared. During the period of the study, the number of tests performed increased 10-fold and overall, 42.6% were positive for one or more pathogens. Seventy percent of the detections were bacteria, 25% viruses, and 4% parasites. Clostridium difficile, enteropathogenic Escherichia coli (EPEC) and norovirus were the most frequently detected pathogens. Seasonality was observed for several pathogens including astrovirus, rotavirus, and norovirus, EPEC, and Campylobacter. The co-detection rate was 10.2%. Enterotoxigenic E. coli (ETEC), Plesiomonas shigelloides, enteroaggregative E. coli (EAEC), and Entamoeba histolytica were detected with another pathogen over 60% of the time, while less than 30% of C. difficile and Cyclospora cayetanensis were detected with another pathogen. Positive correlations among co-detections were found between Shigella/Enteroinvasive E. coli with E. histolytica, and ETEC with EAEC. Overall, the relative ranking of detections for the eight GI pathogens monitored by FoodNet and BioFire Trend were similar for five of them. AGI data from BioFire Trend is available in near real-time and represents a rich data source for the study of disease burden and GI pathogen circulation in the community, especially for those pathogens not often targeted by surveillance.

Background Antimicrobial resistance (AMR) surveillance is critical in informing strategies for infection control in slowing the spread of resistant organisms and for antimicrobial stewardship in the care of patients. However, significant challenges exist in timely and comprehensive AMR surveillance. Methods Using BioFire Pneumonia and Blood Culture 2 Panels data from BioFire Syndromic Trends (Trend), a cloud-based population surveillance network, we described the detection rate of AMR among a US cohort. Data was included from 2019-2021 for gram-positive and -negative organisms and their related AMR genomic resistant determinants as well as for detections of Candida auris. Regional and between panel AMR detection rate differences were compared. Additionally, AMR codetections and detection rate per organism were evaluated for gram-negative organisms. Results A total of 26,912 tests were performed, primarily in the Midwest. Overall, AMR detection rate was highest in the South and more common for respiratory specimens than blood. MRSA and VRE detection rates were 34.9% and 15.9%, respectively, while AMR for gram-negative organisms was lower with 7.0% CTX-M and 2.9% carbapenemases. Additionally, 10 mcr-1 and four C. auris detections were observed. For gram-negative organisms, K. pneumoniae and E. coli were most likely to be detected with an AMR gene, and of gram-negative organisms, K. pneumoniae was most often associated with two or more AMR genes. Conclusion Our study provides important in-depth evaluation of the epidemiology of AMR among respiratory and blood specimens for gram-positive and -negative organism in the United States. The Trend surveillance network allows for near-real time surveillance of AMR.

Known genetic variation for a pathogen, in conjunction with post-PCR melting curve analysis, can be leveraged to provide increased taxonomic detail for pathogen identification in commercial molecular diagnostic tests. The increased taxonomic detail may be used by clinicians and public health decision makers to observe circulation patterns, monitor for outbreaks, and inform local testing practices. We propose a method for expanding the taxonomic resolution of PCR diagnostic systems by incorporating a priori knowledge of the assay design and publicly available sequence information into a genotyping classification model. For multiplexed PCR systems, this framework is generalized to incorporate information from multiple assays that react with different gene targets of the same pathogen to increase classification accuracy. To illustrate the method, a hierarchical classification model is developed for the BioFire® Respiratory 2.1 Panel and the BioFire® Respiratory 2 Panel (collectively the BioFire Respiratory Panels – highly multiplexed PCR diagnostic tests) to predict the species of human adenovirus (HAdV) from Adenovirus Detected test results. Performance of the classification model was characterized via a 10-fold cross-validation on a labeled dataset and exhibited 95% ± 4% accuracy. The model was then applied to the BioFire® Syndromic Trends dataset, which contains deidentified patient test data from BioFire Respiratory Panels collected at over 100 sites across the globe since 2015. Adenovirus Detected test results in BioFire Syndromic Trends were classified to produce predicted prevalence of each HAdV species within the United States from 2018 through 2021. These results show a marked change in both the predicted prevalence for HAdV and the species makeup with the onset of the COVID-19 pandemic. In particular, HAdV-B decreased from a pre-pandemic predicted prevalence of up to 40% to less than 5% in 2021, while HAdV-A and HAdV-F species both increased in predicted prevalence.