ABSTRACT
Background Tuberculosis is a leading infectious cause of death worldwide. Novel vaccines will be required to reach global targets and reverse setbacks from the COVID-19 pandemic. We estimated the impact of novel tuberculosis vaccines in low- and middle-income countries (LMICs), under alternative delivery scenarios. Methods We calibrated a tuberculosis model to 105 LMICs (93% of global incidence). Vaccine scenarios were implemented as Basecase routine vaccination of 9-year-olds and a one-time vaccination campaign for ages ≥10 with country-specific introduction between 2028–2047 and 5-year scale-up to target coverage; Accelerated Scale-up as Basecase , but all countries introducing in 2025 with instant scale-up; and Routine Only as Basecase , but routine vaccination only. Vaccines protected against disease for 10-years, with 50% efficacy. Findings The Basecase scenario reduced tuberculosis incidence (19.5% [95% uncertainty range=18.3– 21.6%]) and mortality (20.6% [19.2–23.4%]) rates in 2050 and prevented 3.6 (3.3–3.9) million deaths before 2050, including 1.6 million in the WHO South-East Asian region. The Accelerated Scale-up scenario reduced tuberculosis incidence (25.2% [23.9–27.5%]), mortality (26.7% [25.2–29.9%]), and prevented 7.9 (7.3–8.5) million deaths. The Routine Only scenario reduced tuberculosis incidence (9.9% [9.0–11.6%]), mortality (9.9% [8.9–12.3%]), and prevented 1.1 (0.9–1.2) million deaths. Interpretations Novel tuberculosis vaccines could have substantial impact, which will vary depending on delivery strategy. Including a campaign will be crucial for rapid impact. Accelerated introduction, similar to the pace of COVID-19 vaccines, could approximately double the lives saved before 2050. Investment is required to support vaccine development, manufacturing, prompt introduction and scale-up. Funding WHO (2020/985800-0)
Subject(s)
COVID-19 , TuberculosisABSTRACT
Global efforts to prevent the spread of the SARS-COV-2 pandemic in early 2020 focused on non-pharmaceutical interventions like social distancing; policies that aim to reduce transmission by changing mixing patterns between people. As countries have implemented these interventions, aggregated location data from mobile phones have become an important source of real-time information about human mobility and behavioral changes on a population level. Human activity measured using mobile phones reflects the aggregate behavior of a subset of people, and although metrics of mobility are related to contact patterns between people that spread the coronavirus, they do not provide a direct measure. In this study, we use results from a nowcasting approach from 1,396 counties across the US between January 22nd, 2020 and July 9th, 2020 to determine the effective reproductive number (R(t)) along an urban/rural gradient. For each county, we compare the time series of R(t) values with mobility proxies from mobile phone data from Camber Systems, an aggregator of mobility data from various providers in the United States. We show that the reproduction number is most strongly associated with mobility proxies for change in the travel into counties compared to baseline, but that the relationship weakens considerably after the initial 15 weeks of the epidemic, consistent with the emergence of a more complex ecosystem of local policies and behaviors including masking. Importantly, we highlight potential issues in the data generation process, representativeness and equity of access which must be addressed to allow for general use of these data in public health.