Whole genome sequencing is replacing traditional laboratory surveillance methods as the primary tool to track and characterize clusters and outbreaks of the foodborne and zoonotic pathogen Salmonella enterica (S. enterica). In this study, 438 S. enterica isolates representing 35 serovars and 13 broad vehicle categories from one hundred epidemiologically confirmed outbreaks were evaluated for genetic variation to develop epidemiologically relevant interpretation guidelines for Salmonella disease cluster detection. The Illumina sequences were analyzed by core genome multi-locus sequence typing (cgMLST) and screened for antimicrobial resistance (AR) determinants and plasmids. Ninety-three of the one hundred outbreaks exhibited a close allele range (less than 10 allele differences with a subset closer than 5). The remaining seven outbreaks showed increased variation, of which three were considered polyclonal. A total of 16 and 28 outbreaks, respectively, showed variations in the AR and plasmid profiles. The serovars Newport and I 4,[5],12:i:-, as well as the zoonotic and poultry product vehicles, were overrepresented among the outbreaks, showing increased variation. A close allele range in cgMLST profiles can be considered a reliable proxy for epidemiological relatedness for the vast majority of S. enterica outbreak investigations. Variations associated with mobile elements happen relatively frequently during outbreaks and could be reflective of changing selective pressures.

On February 20, 2019, PulseNet, the molecular subtyping network for foodborne disease surveillance, identified six Shiga toxin–producing Escherichia coli (STEC) O26:H11 infections with the same pulsed-field gel electrophoresis (PFGE) pattern combination. This PFGE pattern combination matched that of infections from a July 2018 outbreak that was associated with ground beef. In response, CDC initiated an investigation with federal, state, and local partners to identify the outbreak source and implement prevention measures. | | CDC defined a case as STEC O26 infection with an isolate matching the outbreak strain by PFGE or related by core genome multilocus sequence typing scheme (cgMLST), with dates of illness onset during December 11, 2018–May 21, 2019. Investigators initially hypothesized that ground beef was the outbreak cause because of the PFGE match to the July 2018 outbreak and because in early interviews, patients commonly reported eating ground beef and leafy greens. Investigators used cgMLST to compare the genetic sequences of isolates from both outbreaks and determined that they fell into separate genetic clades (differing by 6–11 alleles), suggesting that something other than ground beef caused the illness in 2019. CDC noted that one patient consumed raw cookie dough and that most patients were young adult females, similar to demographic distributions of past flour-associated STEC outbreaks (1–3). Investigators developed a supplemental questionnaire focusing on beef, leafy greens, and flour exposures.

Community transmission of coronavirus disease 2019 (COVID-19) was first detected in the United States in February 2020. By mid-March, all 50 states, the District of Columbia (DC), New York City (NYC), and four U.S. territories had reported cases of COVID-19. This report describes the geographic distribution of laboratory-confirmed COVID-19 cases and related deaths reported by each U.S. state, each territory and freely associated state,* DC, and NYC during February 12-April 7, 2020, and estimates cumulative incidence for each jurisdiction. In addition, it projects the jurisdiction-level trajectory of this pandemic by estimating case doubling times on April 7 and changes in cumulative incidence during the most recent 7-day period (March 31-April 7). As of April 7, 2020, a total of 395,926 cases of COVID-19, including 12,757 related deaths, were reported in the United States. Cumulative COVID-19 incidence varied substantially by jurisdiction, ranging from 20.6 cases per 100,000 in Minnesota to 915.3 in NYC. On April 7, national case doubling time was approximately 6.5 days, although this ranged from 5.5 to 8.0 days in the 10 jurisdictions reporting the most cases. Absolute change in cumulative incidence during March 31-April 7 also varied widely, ranging from an increase of 8.3 cases per 100,000 in Minnesota to 418.0 in NYC. Geographic differences in numbers of COVID-19 cases and deaths, cumulative incidence, and changes in incidence likely reflect a combination of jurisdiction-specific epidemiologic and population-level factors, including 1) the timing of COVID-19 introductions; 2) population density; 3) age distribution and prevalence of underlying medical conditions among COVID-19 patients (1-3); 4) the timing and extent of community mitigation measures; 5) diagnostic testing capacity; and 6) public health reporting practices. Monitoring jurisdiction-level numbers of COVID-19 cases, deaths, and changes in incidence is critical for understanding community risk and making decisions about community mitigation, including social distancing, and strategic health care resource allocation.