Multidisciplinary Effort Leading to Effective Tuberculosis Community Outbreak Containment in Israel.

Tuberculosis (TB) is the second-most prevalent cause of mortality resulting from infectious diseases worldwide. It is caused by bacteria belonging to the Mycobacterium tuberculosis complex (MTBC). In Israel, TB incidence is low, acknowledged by the WHO as being in a pre-elimination phase. Most cases occur among immigrants from high TB incidence regions like the Horn of Africa and the former Soviet Union (FSU), with occasional outbreaks. The outbreak described in this report occurred between 2018 and 2024, increasing the incidence rate of TB in the region. Control of this outbreak posed challenges due to factors including a diverse population (including Ethiopian immigrants, Israeli-born citizens, and immigrants from other countries), economic and social barriers, and hesitancy to disclose information. The unique multidisciplinary team formed to address these challenges, involving the local TB clinic, district health ministry, health maintenance organization (HMO) infectious disease consultant, neighborhood clinic, and National Mycobacterium Reference Laboratory (NMRL), achieved effective treatment and containment. Whole genome sequencing (WGS) proved pivotal in unraveling patient connections during the outbreak. It pinpointed those patients overlooked in initial field investigations, established connections between patients across different health departments, and uncovered the existence of two distinct clusters with separate transmission chains within the same neighborhood. This study underscores collaborative efforts across sectors that successfully contained a challenging outbreak.

A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19.

Background: The coronavirus disease 2019 (COVID-19) outbreak has rapidly spread around the world, causing a global public health and economic crisis. A critical limitation in detecting COVID-19-related pneumonia is that it is often manifested as a "silent pneumonia", i.e. pulmonary auscultation that sounds "normal" using a standard stethoscope. Chest computed tomography is the gold standard for detecting COVID-19 pneumonia; however, radiation exposure, availability and cost preclude its utilisation as a screening tool for COVID-19 pneumonia. In this study we hypothesised that COVID-19 pneumonia, "silent" to the human ear using a standard stethoscope, is detectable using a full-spectrum auscultation device that contains a machine-learning analysis. Methods: Lung sound signals were acquired, using a novel full-spectrum (3-2000 Hz) stethoscope, from 164 COVID-19 pneumonia patients, 61 non-COVID-19 pneumonia patients and 141 healthy subjects. A machine-learning classifier was constructed and the data were classified into three groups: 1) normal lung sounds, 2) COVID-19 pneumonia and 3) non-COVID-19 pneumonia. Results: Standard auscultation found that 72% of the non-COVID-19 pneumonia patients had abnormal lung sounds compared with only 25% of the COVID-19 pneumonia patients. The classifier's sensitivity and specificity for the detection of COVID-19 pneumonia were 97% and 93%, respectively, when analysing the sound and infrasound data, and they were reduced to 93% and 80%, respectively, without the infrasound data (p<0.01 difference in receiver operating characteristic curves with and without infrasound). Conclusions: This study reveals that useful clinical information exists in the infrasound spectrum of COVID-19-related pneumonia and machine-learning analysis applied to the full spectrum of lung sounds is useful in its detection.