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This report describes SARS-CoV-2 genomic surveillance conducted by the Department of Defense (DoD) Global Emerging Infections Surveillance Branch and the Next-Generation Sequencing and Bioinformatics Consortium (NGSBC) in response to the COVID-19 pandemic. Samples and sequence data were from SARS-CoV-2 infections occurring among Military Health System (MHS) beneficiaries from 1 March to 31 December 2020. There were 1,366 MHS samples sequenced from 10 countries, 36 U.S states or territories, and 5 Geographic Combatant Commands, representing approximately 2% of DoD cases in 2020. Genomes from these samples were compared with other public sequences; observed trends were similar to those of Centers for Disease Control and Prevention national surveillance in the U.S. with B.1, B.1.2, and other sub-lineages comprising the dominant variants of SARS-CoV-2. Sequence data were used to monitor transmission dynamics on U.S. Navy ships and at military training centers and installations. As new variants emerge, DoD medical and public health practitioners should maximize the use of genomic surveillance resources within DoD to inform force health protection measures.

Laboratory-based respiratory pathogen surveillance for SARS-CoV-2 and other respiratory pathogens was conducted in the 2020-2021 surveillance season among U.S. Military Health System (MHS) beneficiaries through the Department of Defense Global Respiratory Pathogen Surveillance Program (DoDGRPSP). Sentinel and participating sites submitted 96,660 specimens for clinical diagnostic testing. A total of 12,282 SARS-CoV-2 positive cases were identified, and 7,286 of the associated viruses were successfully sequenced. Two overlapping waves of SARS-CoV-2 activity were observed during the season. The B.1.1.7 (Alpha) lineage was dominant during February 2021 through May 2021. By July 2021, and continuing through the rest of the season, B.1.617.2/AY.x (Delta) lineage predominated and by September 2021 comprised 100% of identified SARS-CoV-2 lineages. The emergence of SARS-CoV-2 coincided with substantial reductions in the circulation of seasonal influenza viruses and most other non-SARS-CoV-2 respiratory pathogens. A total of 4,426 non-SARS-CoV-2 respiratory pathogens were identified, including 71 influenza. Of the 71 influenza positives, 64 were successfully sequenced. The majority of influenza strains sequenced belonged to influenza A(H3N2) clades 3C.2a1b.2a2. The most common non-SARSCoV-2 respiratory pathogen detected was rhinovirus/enterovirus (n=3,058).

The Department of Defense (DoD) Global Respiratory Pathogen Surveillance Program annually monitors the genetic diversity of influenza viruses circulating in DoD beneficiary populations. This program relies on a global network of partners across the DoD to submit respiratory specimens throughout the influenza season. In previous seasons, representative specimens for sequencing were chosen because of cost and time restrictions associated with reliance on Sanger-based sequencing technology. The effect of this specimen prioritization for sequencing has not been previously examined in the respiratory surveillance program. Here, specimen prioritization was simulated by iteratively subsetting sequencing data sets from 1 October 2013 through 15 March 2017 to determine how prioritizing affects common metrics of genetic diversity. Prioritization of specimens did not meaningfully affect calculations of average influenza genetic diversity within seasons or subtypes. Because of the high genetic diversity of influenza, prioritizing resulted in fewer unique viruses and less accurate measures of geographic relationships although it still provided relevant estimates. Given the advent of cost-effective next-generation sequencing approaches, all programs should carefully consider how best to prioritize influenza sequencing to recover meaningful information on the evolutionary dynamics of the virus.

Currently the National Center of Biotechnology Information (NCBI) assigns individual taxonomy identifiers to each distinct influenza virus isolate submitted to GenBank. To support this practice, individual flu isolates must be manually added to the NCBI taxonomy database and unique taxonomy identifiers generated. This added layer of manual processing is unique to influenza virus and prevents automatization of the flu sequence submission process. Here we outline a new NCBI policy that normalizes influenza virus taxonomy processing but maintains features supported by the previous approach. This change will reduce the amount of manual handling necessary for flu submissions and pave the way for increased automation of the submissions process. While this automation may disrupt some historic practices, it will better align influenza virus data processing with other viruses and ultimately lower the submission burden on data providers.

Aberrant topological properties of small-world human brain networks in patients with schizophrenia (SZ) have been documented in previous neuroimaging studies. Aberrant functional network connectivity (FNC, temporal relationships among independent component time courses) has also been found in SZ by a previous resting state functional magnetic resonance imaging (fMRI) study. However, no study has yet determined if topological properties of FNC are also altered in SZ. In this study, small-world network metrics of FNC during the resting state were examined in both healthy controls (HCs) and SZ subjects. FMRI data were obtained from 19 HCs and 19 SZ. Brain images were decomposed into independent components (ICs) by group independent component analysis (ICA). FNC maps were constructed via a partial correlation analysis of ICA time courses. A set of undirected graphs were built by thresholding the FNC maps and the small-world network metrics of these maps were evaluated. Our results demonstrated significantly altered topological properties of FNC in SZ relative to controls. In addition, topological measures of many ICs involving frontal, parietal, occipital and cerebellar areas were altered in SZ relative to controls. Specifically, topological measures of whole network and specific components in SZ were correlated with scores on the negative symptom scale of the Positive and Negative Symptom Scale (PANSS). These findings suggest that aberrant architecture of small-world brain topology in SZ consists of ICA temporally coherent brain networks.