By November 30, 2021, approximately 130,781 COVID-19-associated deaths, one in six of all U.S. deaths from COVID-19, had occurred in California and New York.* COVID-19 vaccination protects against infection with SARS-CoV-2 (the virus that causes COVID-19), associated severe illness, and death (1,2); among those who survive, previous SARS-CoV-2 infection also confers protection against severe outcomes in the event of reinfection (3,4). The relative magnitude and duration of infection- and vaccine-derived protection, alone and together, can guide public health planning and epidemic forecasting. To examine the impact of primary COVID-19 vaccination and previous SARS-CoV-2 infection on COVID-19 incidence and hospitalization rates, statewide testing, surveillance, and COVID-19 immunization data from California and New York (which account for 18% of the U.S. population) were analyzed. Four cohorts of adults aged ≥18 years were considered: persons who were 1) unvaccinated with no previous laboratory-confirmed COVID-19 diagnosis, 2) vaccinated (14 days after completion of a primary COVID-19 vaccination series) with no previous COVID-19 diagnosis, 3) unvaccinated with a previous COVID-19 diagnosis, and 4) vaccinated with a previous COVID-19 diagnosis. Age-adjusted hazard rates of incident laboratory-confirmed COVID-19 cases in both states were compared among cohorts, and in California, hospitalizations during May 30-November 20, 2021, were also compared. During the study period, COVID-19 incidence in both states was highest among unvaccinated persons without a previous COVID-19 diagnosis compared with that among the other three groups. During the week beginning May 30, 2021, compared with COVID-19 case rates among unvaccinated persons without a previous COVID-19 diagnosis, COVID-19 case rates were 19.9-fold (California) and 18.4-fold (New York) lower among vaccinated persons without a previous diagnosis; 7.2-fold (California) and 9.9-fold lower (New York) among unvaccinated persons with a previous COVID-19 diagnosis; and 9.6-fold (California) and 8.5-fold lower (New York) among vaccinated persons with a previous COVID-19 diagnosis. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These relationships changed after the SARS-CoV-2 Delta variant became predominant (i.e., accounted for >50% of sequenced isolates) in late June and July. By the week beginning October 3, compared with COVID-19 cases rates among unvaccinated persons without a previous COVID-19 diagnosis, case rates among vaccinated persons without a previous COVID-19 diagnosis were 6.2-fold (California) and 4.5-fold (New York) lower; rates were substantially lower among both groups with previous COVID-19 diagnoses, including 29.0-fold (California) and 14.7-fold lower (New York) among unvaccinated persons with a previous diagnosis, and 32.5-fold (California) and 19.8-fold lower (New York) among vaccinated persons with a previous diagnosis of COVID-19. During the same period, compared with hospitalization rates among unvaccinated persons without a previous COVID-19 diagnosis, hospitalization rates in California followed a similar pattern. These results demonstrate that vaccination protects against COVID-19 and related hospitalization, and that surviving a previous infection protects against a reinfection and related hospitalization. Importantly, infection-derived protection was higher after the Delta variant became predominant, a time when vaccine-induced immunity for many persons declined because of immune evasion and immunologic waning (2,5,6). Similar cohort data accounting for booster doses needs to be assessed, as new variants, including Omicron, circulate. Although the epidemiology of COVID-19 might change with the emergence of new variants, vaccination remains the safest strategy to prevent SARS-CoV-2 infections and associated complications; all eligible persons should be up to date with COVID-19 vaccination. Additional recommendations for vaccine doses might be warranted in the future as the virus and immunity levels change.

Understanding tuberculosis (TB) transmission chains can help public health staff target their resources to prevent further transmission, but currently there are few tools to automate this process. We have developed the Logically Inferred Tuberculosis Transmission (LITT) algorithm to systematize the integration and analysis of whole-genome sequencing, clinical, and epidemiological data. Based on the work typically performed by hand during a cluster investigation, LITT identifies and ranks potential source cases for each case in a TB cluster. We evaluated LITT using a diverse dataset of 534 cases in 56 clusters (size range: 2-69 cases), which were investigated locally in three different U.S. jurisdictions. Investigators and LITT agreed on the most likely source case for 145 (80%) of 181 cases. By reviewing discrepancies, we found that many of the remaining differences resulted from errors in the dataset used for the LITT algorithm. In addition, we developed a graphical user interface, user's manual, and training resources to improve LITT accessibility for frontline staff. While LITT cannot replace thorough field investigation, the algorithm can help investigators systematically analyze and interpret complex data over the course of a TB cluster investigation. Code available at: https://github.com/CDCgov/TB_molecular_epidemiology/tree/1.0; https://zenodo.org/badge/latestdoi/166261171.

OBJECTIVES: Persons experiencing homelessness (PEH) are disproportionately affected by tuberculosis (TB). We estimate area-specific rates of TB among PEH and characterize the extent to which available data support recent transmission as an explanation of high TB incidence. METHODS: We estimated TB incidence among PEH using National Tuberculosis Surveillance System data and population estimates for the US Department of Housing and Urban Development's Continuums of Care areas. For areas with TB incidence higher than the national average among PEH, we estimated recent transmission using genotyping and a plausible source-case method. For cases with ≥1 plausible source case, we assessed with TB program partners whether available whole-genome sequencing and local epidemiologic data were consistent with recent transmission. RESULTS: During 2011-2016, 3164 TB patients reported experiencing homelessness. National incidence was 36 cases/100,000 PEH. Incidence estimates varied among 21 areas with ≥10,000 PEH (9-150 cases/100,000 PEH); 9 areas had higher than average incidence. Of the 2349 cases with Mycobacterium tuberculosis genotyping results, 874 (37%) had ≥1 plausible source identified. In the 9 areas, 23%-82% of cases had ≥1 plausible source. Of cases with ≥1 plausible source, 63% were consistent and 7% were inconsistent with recent transmission; 29% were inconclusive. CONCLUSIONS: Disparities in TB incidence for PEH persist; estimates of TB incidence and recent transmission vary by area. With a better understanding of the TB risk among PEH in their jurisdictions and the role of recent transmission as a driver, programs can make more informed decisions about prioritizing TB prevention strategies.

On December 14, 2020, the United Kingdom reported a SARS-CoV-2 variant of concern (VOC), lineage B.1.1.7, also referred to as VOC 202012/01 or 20I/501Y.V1.* The B.1.1.7 variant is estimated to have emerged in September 2020 and has quickly become the dominant circulating SARS-CoV-2 variant in England (1). B.1.1.7 has been detected in over 30 countries, including the United States. As of January 13, 2021, approximately 76 cases of B.1.1.7 have been detected in 12 U.S. states.(†) Multiple lines of evidence indicate that B.1.1.7 is more efficiently transmitted than are other SARS-CoV-2 variants (1-3). The modeled trajectory of this variant in the U.S. exhibits rapid growth in early 2021, becoming the predominant variant in March. Increased SARS-CoV-2 transmission might threaten strained health care resources, require extended and more rigorous implementation of public health strategies (4), and increase the percentage of population immunity required for pandemic control. Taking measures to reduce transmission now can lessen the potential impact of B.1.1.7 and allow critical time to increase vaccination coverage. Collectively, enhanced genomic surveillance combined with continued compliance with effective public health measures, including vaccination, physical distancing, use of masks, hand hygiene, and isolation and quarantine, will be essential to limiting the spread of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). Strategic testing of persons without symptoms but at higher risk of infection, such as those exposed to SARS-CoV-2 or who have frequent unavoidable contact with the public, provides another opportunity to limit ongoing spread.

BACKGROUND: In the United States, laboratory confirmed coronavirus disease 2019 (COVID-19) is nationally notifiable. However, reported case counts are recognized to be less than the true number of cases because detection and reporting are incomplete and can vary by disease severity, geography, and over time. METHODS: To estimate the cumulative incidence SARS-CoV-2 infections, symptomatic illnesses, and hospitalizations, we adapted a simple probabilistic multiplier model. Laboratory-confirmed case counts that were reported nationally were adjusted for sources of under-detection based on testing practices in inpatient and outpatient settings and assay sensitivity. RESULTS: We estimated that through the end of September, 1 of every 2.5 (95% Uncertainty Interval (UI): 2.0-3.1) hospitalized infections and 1 of every 7.1 (95% UI: 5.8-9.0) non-hospitalized illnesses may have been nationally reported. Applying these multipliers to reported SARS-CoV-2 cases along with data on the prevalence of asymptomatic infection from published systematic reviews, we estimate that 2.4 million hospitalizations, 44.8 million symptomatic illnesses, and 52.9 million total infections may have occurred in the U.S. population from February 27-September 30, 2020. CONCLUSIONS: These preliminary estimates help demonstrate the societal and healthcare burdens of the COVID-19 pandemic and can help inform resource allocation and mitigation planning.

As of April 9, 2020, the coronavirus disease 2019 (COVID-19) pandemic had resulted in 1,521,252 cases and 92,798 deaths worldwide, including 459,165 cases and 16,570 deaths in the United States (1,2). Health care personnel (HCP) are essential workers defined as paid and unpaid persons serving in health care settings who have the potential for direct or indirect exposure to patients or infectious materials (3). During February 12-April 9, among 315,531 COVID-19 cases reported to CDC using a standardized form, 49,370 (16%) included data on whether the patient was a health care worker in the United States; including 9,282 (19%) who were identified as HCP. Among HCP patients with data available, the median age was 42 years (interquartile range [IQR] = 32-54 years), 6,603 (73%) were female, and 1,779 (38%) reported at least one underlying health condition. Among HCP patients with data on health care, household, and community exposures, 780 (55%) reported contact with a COVID-19 patient only in health care settings. Although 4,336 (92%) HCP patients reported having at least one symptom among fever, cough, or shortness of breath, the remaining 8% did not report any of these symptoms. Most HCP with COVID-19 (6,760, 90%) were not hospitalized; however, severe outcomes, including 27 deaths, occurred across all age groups; deaths most frequently occurred in HCP aged ≥65 years. These preliminary findings highlight that whether HCP acquire infection at work or in the community, it is necessary to protect the health and safety of this essential national workforce.

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.

Measuring tuberculosis transmission is exceedingly difficult, given the remarkable variability in the timing of clinical disease after Mycobacterium tuberculosis infection; incident disease can result from either a recent (ie, weeks to months) or a remote (ie, several years to decades) infection event. Although we cannot identify with certainty the timing and location of tuberculosis transmission for individuals, approaches for estimating the individual probability of recent transmission and for estimating the fraction of tuberculosis cases due to recent transmission in populations have been developed. Data used to estimate the probable burden of recent transmission include tuberculosis case notifications in young children and trends in tuberculin skin test and interferon gamma-release assays. More recently, M. tuberculosis whole-genome sequencing has been used to estimate population levels of recent transmission, identify the distribution of specific strains within communities, and decipher chains of transmission among culture-positive tuberculosis cases. The factors that drive the transmission of tuberculosis in communities depend on the burden of prevalent tuberculosis; the ways in which individuals live, work, and interact (eg, congregate settings); and the capacity of healthcare and public health systems to identify and effectively treat individuals with infectious forms of tuberculosis. Here we provide an overview of these factors, describe tools for measurement of ongoing transmission, and highlight knowledge gaps that must be addressed.

BACKGROUND: Using genotyping techniques that have differentiated Mycobacterium bovis from M. tuberculosis since 2005, we review the epidemiology of human TB caused by M. bovis in the United States and validate previous findings nationally. METHODS: All TB cases with a genotyped M. tuberculosis complex isolate reported during 2006-2013 in the United States were eligible for analysis. We used binomial regression to identify characteristics independently associated with M. bovis disease using adjusted prevalence ratios (aPRs) and corresponding 95% confidence intervals (95%CI). RESULTS: During 2006-2013, the annual percentages of TB cases attributable to M. bovis remained consistent nationally (range: 1.3-1.6%) among all TB cases (n=59,273). Compared with adults 25-44 years of age, infants 0-4 years (aPR 1.9, 95% CI 1.4-2.8) and children 5-14 years (aPR 4.0, 95% CI 3.1-5.3) had higher prevalences of M. bovis disease. Patients who were foreign-born (aPR 1.4, 95% CI 1.2-1.7), Hispanic (aPR 3.9, 95% CI 3.0-5.0), female (aPR 1.4, 95% CI 1.3-1.6),), and resided in U.S.-Mexico border counties (aPR 2.0, 95% CI 1.7-2.4) also had higher M. bovis prevalences. Exclusively extrapulmonary disease (aPR 3.7, 95% CI 3.3-4.2) or disease that was both pulmonary and extrapulmonary (aPR 2.4, 95% CI 2.1-2.9) were associated with a higher prevalence of M. bovis disease CONCLUSIONS: Children, foreign-born persons, Hispanics, and women are disproportionately affected by M. bovis, which was independently associated with extrapulmonary disease. Targeted prevention efforts aimed at Hispanic mothers and caregivers are warranted.

Listeria monocytogenes(Lm) causes severe foodborne illness (listeriosis). Previous molecular subtyping methods, such as pulsed-field gel electrophoresis (PFGE), were critical in detecting outbreaks that led to food safety improvements and declining incidence, but PFGE provides limited genetic resolution. A multiagency collaboration began performing real-time, whole-genome sequencing (WGS) on all U.S.Lmisolates from patients, food, and the environment in September 2013, posting sequencing data into a public repository. Compared with the year before the project began, WGS, combined with epidemiologic and product trace-back data, detected more listeriosis clusters and solved more outbreaks (2 outbreaks in pre-WGS year, 5 in WGS year 1, and 9 in year 2). Whole-genome multilocus sequence typing and single nucleotide polymorphism analyses provided equivalent phylogenetic relationships relevant to investigations; results were most useful when interpreted in context of epidemiological data. WGS has transformed listeriosis outbreak surveillance and is being implemented for other foodborne pathogens.

We used whole genome sequencing to determine evolutionary relationships among 20 outbreak-associated clinical isolates of Listeria monocytogenes serotypes 1/2a and 1/2b. Isolates from six of eleven outbreaks fell outside of the clonal groups or 'epidemic clones' that have been previously associated with outbreaks, suggesting that epidemic potential may be widespread in L. monocytogenes and is not limited to the recognized epidemic clones. Pairwise comparisons between epidemiologically-related isolates within clonal complexes showed that genome-level variation differed by two orders of magnitude between different comparisons, and the distribution of point mutations (core versus accessory genome) also varied. In addition, genetic divergence between one closely related pair of isolates from a single outbreak was driven primarily by changes in phage regions. The evolutionary analysis showed the changes could be attributed to horizontal gene transfer; members of the diverse bacterial community found in the production facility could have served as the source of novel genetic material at some point in the production chain. The results raise the question of how to best utilize information contained within the accessory genome in outbreak investigations. The full magnitude and complexity of genetic changes revealed by genome sequencing could not be discerned from traditional subtyping methods and the results demonstrate the challenges of interpreting genetic variation among isolates recovered from a single outbreak. Epidemiological information remains critical for proper interpretation of nucleotide and structural diversity among isolates recovered during outbreaks, and will remain so until we understand more about how various population histories influence genetic variation.

A multistate listeriosis outbreak associated with cantaloupe consumption was reported in the United States in September, 2011. The outbreak investigation recorded a total of 146 invasive illnesses, 30 deaths and one miscarriage. Subtyping of the outbreak associated clinical, food and environmental isolates revealed two serotypes (1/2a and 1/2b) and four pulsed-field gel electrophoresis two-enzyme pattern combinations I, II, III, and IV, including one rarely seen before this outbreak. A DNA-microarray, Listeria GeneChip(R), developed by FDA from 24 Listeria monocytogenes genome sequences, was used to further characterize a representative sample of the outbreak isolates. The microarray data (in the form of present or absent calls of specific DNA sequences) separated the isolates into two distinct groups as per their serotypes. The gene content of the outbreak-associated isolates was distinct from that of the previously-reported outbreak strains belonging to the same serotypes. Although the 1/2b outbreak associated isolates are closely related to each other, the 1/2a isolates could be further divided into two distinct genomic groups, one represented by pattern combination I strains and the other represented by highly similar pattern combinations III and IV strains. Gene content analysis of these groups revealed unique genomic sequences associated with these two 1/2a genovars. This work underscores the utility of multiple approaches, such as serotyping, PFGE and DNA microarray analysis to characterize the composition of complex polyclonal listeriosis outbreaks.