In New York State during winter 2004, there was a high incidence of influenza-like illness that tested negative both for influenza virus, by molecular methods, and for other respiratory viruses, by virus culture. Concern that a novel pathogen might be implicated led us to implement a new multiplex diagnostic tool. MassTag polymerase chain reaction resolved 26 of 79 previously negative samples, revealing the presence of rhinoviruses in a large proportion of samples, half of which belonged to a previously uncharacterized genetic clade. In some instances, knowledge of the detected viral and/or bacterial (co)infection could have altered clinical management.

BackgroundThe United States' COVID-19 epidemic has grown extensively since February 2020, with substantial associated hospitalizations and mortality; New York State (NYS) has emerged as the national epicenter. We report on the extent of testing and test results during the month of March in NYS, along with risk factors, outcomes, and household prevalence among initial cases subject to indepth investigations. MethodsSpecimen collection for COVID-19 testing was conducted in healthcare settings, community-based collection sites, and by home testing teams. Information on demographics, risk factors, and hospital outcomes of cases was obtained through epidemiological investigations and an electronic medical records match, and summarized descriptively. Active testing of initial case's households enabled estimation of household prevalence. ResultsDuring March In NYS, outside of New York City, a total of 47,326 persons tested positive for SARS-CoV-2, out of 141,495 tests (33% test-positive), with the highest number of cases located in the metropolitan region counties. Among 229 initial cases diagnosed through March 12, by March 30 13% were hospitalized and 2% died. Testing conducted among 498 members of these case's households found prevalent infection among 57%; excluding first-reported cases 38%. In these homes, we found a significant age gradient in prevalence, from 23% among those <5 years to 68% among those ≥65 years (p<.0001). ConclusionsNew York State faced a substantial and increasing COVID-19 outbreak during March 2020. The earliest cases had high levels of infection in their households and by the end of the month, the risks of hospitalization and death were high.

BackgroundEffective influenza surveillance requires new methods capable of rapid and inexpensive genomic analysis of evolving viral species for pandemic preparedness, to understand the evolution of circulating viral species, and for vaccine strain selection. We have developed one such approach based on previously described broad-range reverse transcription PCR/electrospray ionization mass spectrometry (RT-PCR/ESI-MS) technology.Methods and Principal FindingsAnalysis of base compositions of RT-PCR amplicons from influenza core gene segments (PB1, PB2, PA, M, NS, NP) are used to provide sub-species identification and infer influenza virus H and N subtypes. Using this approach, we detected and correctly identified 92 mammalian and avian influenza isolates, representing 30 different H and N types, including 29 avian H5N1 isolates. Further, direct analysis of 656 human clinical respiratory specimens collected over a seven-year period (1999–2006) showed correct identification of the viral species and subtypes with >97% sensitivity and specificity. Base composition derived clusters inferred from this analysis showed 100% concordance to previously established clades. Ongoing surveillance of samples from the recent influenza virus seasons (2005–2006) showed evidence for emergence and establishment of new genotypes of circulating H3N2 strains worldwide. Mixed viral quasispecies were found in approximately 1% of these recent samples providing a view into viral evolution.Conclusion/SignificanceThus, rapid RT-PCR/ESI-MS analysis can be used to simultaneously identify all species of influenza viruses with clade-level resolution, identify mixed viral populations and monitor global spread and emergence of novel viral genotypes. This high-throughput method promises to become an integral component of influenza surveillance.