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: During 2010-2018, the Arkansas Department of Health reported 21 genotype-matched cases of tuberculosis (TB) among residents of a rural county in Arkansas with a low incidence of TB and in nearby counties. The Arkansas Department of Health and the Centers for Disease Control and Prevention investigated to determine the extent of TB transmission and provide recommendations for TB control. METHODS: We reviewed medical and public health records, interviewed patients, and reviewed patients' social media posts to describe patient characteristics, identify epidemiologic links, and establish likely chains of transmission. RESULTS: We identified 21 cases; 11 reported during 2010-2013 and 10 during 2016-2018. All case patients were US-born non-Hispanic Black people. Eighteen case patients had the outbreak genotype, and 3 clinically diagnosed (non-culture-confirmed) case patients had epidemiologic links to patients with the outbreak genotype. Social media reviews revealed epidemiologic links among 10 case patients not previously disclosed during interviews. Eight case patients (38%) had ≥1 health care visit during their infectious period, and 7 patients had estimated infectious periods of >12 months. CONCLUSIONS: Delayed diagnoses and prolonged infectiousness led to TB transmission in this rural community. TB education and awareness is critical to reducing transmission, morbidity, and mortality, especially in areas where health care providers have limited TB experience. Use of social media can help elucidate people at risk, especially when traditional TB investigation techniques are insufficient.

INTRODUCTION: Pediatric tuberculosis (TB) cases are sentinel events for Mycobacterium tuberculosis transmission in communities because children, by definition, must have been infected relatively recently. However, these events are not consistently identified by genotype-dependent surveillance alerting methods because many pediatric TB cases are not culture-positive, a prerequisite for genotyping. METHODS: We developed 3 potential indicators of ongoing TB transmission based on identifying counties in the United States with relatively high pediatric (aged <15 years) TB incidence: (1) a case proportion indicator: an above-average proportion of pediatric TB cases among all TB cases; (2) a case rate indicator: an above-average pediatric TB case rate; and (3) a statistical model indicator: a statistical model based on a significant increase in pediatric TB cases from the previous 8-quarter moving average. RESULTS: Of the 249 US counties reporting ≥2 pediatric TB cases during 2009-2017, 240 and 249 counties were identified by the case proportion and case rate indicators, respectively. The statistical model indicator identified 40 counties with a significant increase in the number of pediatric TB cases. We compared results from the 3 indicators with an independently generated list of 91 likely transmission events involving ≥2 pediatric cases (ie, known TB outbreaks or case clusters with reported epidemiologic links). All counties with likely transmission events involving multiple pediatric cases were identified by ≥1 indicator; 23 were identified by all 3 indicators. PRACTICE IMPLICATIONS: This retrospective analysis demonstrates the feasibility of using routine TB surveillance data to identify counties where ongoing TB transmission might be occurring, even in the absence of available genotyping data.

OBJECTIVES: In late June 2020, a large outbreak of coronavirus disease 2019 (COVID-19) occurred at a sleep-away youth camp in Georgia, affecting primarily persons </=21 years. We conducted a retrospective cohort study among campers and staff (attendees) to determine the extent of the outbreak and assess factors contributing to transmission. METHODS: Attendees were interviewed to ascertain demographic characteristics, known exposures to COVID-19 and community exposures, and mitigation measures before, during, and after attending camp. COVID-19 case status was determined for all camp attendees on the basis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test results and reported symptoms. We calculated attack rates and instantaneous reproduction numbers and sequenced SARS-CoV-2 viral genomes from the outbreak. RESULTS: Among 627 attendees, the median age was 15 years (interquartile range: 12-16 years); 56% (351 of 627) of attendees were female. The attack rate was 56% (351 of 627) among all attendees. On the basis of date of illness onset or first positive test result on a specimen collected, 12 case patients were infected before arriving at camp and 339 case patients were camp associated. Among 288 case patients with available symptom information, 45 (16%) were asymptomatic. Despite cohorting, 50% of attendees reported direct contact with people outside their cabin cohort. On the first day of camp session, the instantaneous reproduction number was 10. Viral genomic diversity was low. CONCLUSIONS: Few introductions of SARS-CoV-2 into a youth congregate setting resulted in a large outbreak. Testing strategies should be combined with prearrival quarantine, routine symptom monitoring with appropriate isolation and quarantine, cohorting, social distancing, mask wearing, and enhanced disinfection and hand hygiene. Promotion of mitigation measures among younger populations is needed.

Limited data are available about transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), among youths. During June 17-20, an overnight camp in Georgia (camp A) held orientation for 138 trainees and 120 staff members; staff members remained for the first camp session, scheduled during June 21-27, and were joined by 363 campers and three senior staff members on June 21. Camp A adhered to the measures in Georgia's Executive Order* that allowed overnight camps to operate beginning on May 31, including requiring all trainees, staff members, and campers to provide documentation of a negative viral SARS-CoV-2 test ≤12 days before arriving. Camp A adopted most(†) components of CDC's Suggestions for Youth and Summer Camps(§) to minimize the risk for SARS-CoV-2 introduction and transmission. Measures not implemented were cloth masks for campers and opening windows and doors for increased ventilation in buildings. Cloth masks were required for staff members. Camp attendees were cohorted by cabin and engaged in a variety of indoor and outdoor activities, including daily vigorous singing and cheering. On June 23, a teenage staff member left camp A after developing chills the previous evening. The staff member was tested and reported a positive test result for SARS-CoV-2 the following day (June 24). Camp A officials began sending campers home on June 24 and closed the camp on June 27. On June 25, the Georgia Department of Public Health (DPH) was notified and initiated an investigation. DPH recommended that all attendees be tested and self-quarantine, and isolate if they had a positive test result.

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.