Real-time estimates of the true size and trajectory of local COVID-19 epidemics are key metrics to guide policy responses. We developed a Bayesian nowcasting approach that explicitly accounts for reporting delays and secular changes in case ascertainment to generate real-time estimates of COVID-19 epidemiology on the basis of reported cases and deaths. Using this approach, we estimate time trends in infections, symptomatic cases, and deaths for all 50 US states and the District of Columbia from early-March through June 11, 2020. At the beginning of June, our best estimates of the effective reproduction number (Rt) are close to 1 in most states, indicating a stabilization of incidence, but there is considerable variability in the level of incidence and the estimated proportion of the population that has already been infected.

Background The incidence of multidrug-resistant tuberculosis (MDR-TB) remains critically high in countries of the former Soviet Union, where >20% of new cases and >50% of previously treated cases have resistance to rifampin and isoniazid. Transmission of resistant strains, as opposed to resistance selected through inadequate treatment of drug-susceptible tuberculosis (TB), is the main driver of incident MDR-TB in these countries. Methods and findings We conducted a prospective, genomic analysis of all culture-positive TB cases diagnosed in 2018 and 2019 in the Republic of Moldova. We used phylogenetic methods to identify putative transmission clusters; spatial and demographic data were analyzed to further describe local transmission of Mycobacterium tuberculosis. Of 2,236 participants, 779 (36%) had MDR-TB, of whom 386 (50%) had never been treated previously for TB. Moreover, 92% of multidrug-resistant (MDR) M. tuberculosis strains belonged to putative transmission clusters. Phylogenetic reconstruction identified 3 large clades that were comprised nearly uniformly of MDR-TB: 2 of these clades were of Beijing lineage, and 1 of Ural lineage, and each had additional distinct clade-specific second-line drug resistance mutations and geographic distributions. Spatial and temporal proximity between pairs of cases within a cluster was associated with greater genomic similarity. Our study lasted for only 2 years, a relatively short duration compared with the natural history of TB, and, thus, the ability to infer the full extent of transmission is limited. Conclusions The MDR-TB epidemic in Moldova is associated with the local transmission of multiple M. tuberculosis strains, including distinct clades of highly drug-resistant M. tuberculosis with varying geographic distributions and drug resistance profiles. This study demonstrates the role of comprehensive genomic surveillance for understanding the transmission of M. tuberculosis and highlights the urgency of interventions to interrupt transmission of highly drug-resistant M. tuberculosis.

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

Background Limitations in the sensitivity and accessibility of diagnostic tools for childhood tuberculosis contribute to the substantial gap between estimated cases and cases notified to national tuberculosis programs. Thus, tools to make accurate and rapid clinical diagnoses are necessary to initiate more children on antituberculosis treatment. Methods We analyzed data from a prospective cohort of children <13 years being routinely evaluated for pulmonary tuberculosis in Cape Town, South Africa from March 2012 to November 2017. We developed a regression model to describe the contributions of baseline clinical evaluation to the diagnosis of tuberculosis using standardized, retrospective case definitions. We included results from baseline chest radiography and Xpert MTB/RIF to the model to develop an algorithm with at least 90% sensitivity in predicting tuberculosis. Results Data from 478 children being evaluated for pulmonary tuberculosis were analyzed (median age: 16.2 months, interquartile range: 9.8-30.9); 242 (50.6%) were retrospectively classified with tuberculosis, of which 104 (43.0%) were bacteriologically-confirmed. The area under the receiver operating characteristic curve for the final model was 0.87. Clinical evidence identified 71.4% of all tuberculosis cases in this cohort, and inclusion of baseline chest radiography results increased the proportion to 89.3%. The algorithm was 90.1% sensitive and 52.1% specific, and maintained a sensitivity of above 90% among children <2 years or with low weight-for-age. Conclusions Clinical evidence alone was sufficient to make most clinical antituberculosis treatment decisions. The use of evidence-based algorithms may improve decentralized, rapid treatment-initiation, reducing the global burden of childhood mortality.

Introduction The chest radiograph (CR) remains a key tool in the diagnosis of pediatric tuberculosis (TB). In children with presumptive intrathoracic TB, we aimed to identify CR features which had high specificity for, and were strongly associated with, bacteriologically confirmed TB. Methods We analyzed CR data from children with presumptive intrathoracic TB prospectively enrolled in a cohort study in a high-TB burden setting and who were classified using standard clinical case definitions as confirmed, unconfirmed or unlikely TB. We report the CR features and inter-reader agreement between expert readers who interpreted the CRs. We calculated the sensitivity and specificity of the CR features with at least moderate inter-reader agreement and analyzed the relationship between these CR features and the classification of TB in a multivariable regression model. Results Of features with at least moderate inter-reader agreement, enlargement of perihilar and/or paratracheal lymph nodes, bronchial deviation/compression, cavities, expansile pneumonia and pleural effusion had a specificity of >90% for confirmed TB, compared to unlikely TB. Enlargement of perihilar (adjusted odds ratio [aOR]: 6.6; 95% confidence interval [CI] 3.80-11.72) and/or paratracheal lymph nodes (aOR: 5.14; 95%CI: 2.25-12.58), bronchial deviation/compression (aOR: 6.22; 95%CI: 2.70-15.69), pleural effusion (aOR: 2.27; 95%CI: 1.04-4.78) and cavities (aOR: 7.45; 95%CI: 3.38-17.45) were associated with confirmed TB in the multivariate regression model while alveolar opacification (aOR: 1.16; 95%CI: 0.76-1.77) and expansile pneumonia (aOR: 4.16; 95%CI: 0.93-22.34) were not. Conclusions In children investigated for intrathoracic TB enlargement of perihilar or paratracheal lymph nodes, bronchial compression/deviation, pleural effusion, or cavities on CR strongly support the diagnosis.