The world is not on track to achieve the goals for immunization coverage and equity described by the World Health Organization in the Global Vaccine Action Plan. In India, only 62 percent of children had received a full course of basic vaccines in 2016.

Despite improvement in health outcomes over the past few decades, China still experiences striking rural-urban health inequalities. There is limited research on the rural-urban differences in health system performance in China.

Epidemics pose a growing threat. Our cities are increasingly densely populated, we are more connected than ever before, and in recent years we have witnessed successive waves of new (severe acute respiratory syndrome [SARS], Zika virus, Ebola virus, and now coronavirus disease 2019 [COVID-19]) and old (influenza) infectious disease threats causing global pandemics. Urgent investment in surveillance systems and global partnerships are needed to prepare for the pandemics that will continue to emerge in the coming decades. There has been discussion of the promise of integrating sophisticated epidemiological models and new big data streams—for example, from mobile phones, satellites, or social media—at various stages of the public health response, particularly in the context of epidemic forecasting and decision making. These new data streams provide important, real-time information about travel patterns that spread disease and spatial shifts in populations at risk, which until recently have been very difficult to quantify on timescales relevant to a fast-moving epidemic. With growing mobility and increasing global connectivity, this information will be key to planning surveillance and containment strategies.

Point-of-care testing (POCT) assays for chlamydia are being developed. Their potential impact on the burden of chlamydial infection in the United States, in light of suboptimal screening coverage, remains unclear.

Respondent-driven sampling (RDS) is widely used for collecting data on hard-to-reach populations, including information about the structure of the networks connecting the individuals. Characterizing network features can be important for designing and evaluating health programs, particularly those that involve infectious disease transmission. While the validity of population proportions estimated from RDS-based datasets has been well studied, little is known about potential biases in inference about network structure from RDS. We developed a mathematical and statistical platform to simulate network structures with exponential random graph models, and to mimic the data generation mechanisms produced by RDS. We used this framework to characterize biases in three important network statistics – density/mean degree, homophily, and transitivity. Generalized linear models were used to predict the network statistics of the original network from the network statistics of the sample network and observable sample design features. We found that RDS may introduce significant biases in the estimation of density/mean degree and transitivity, and may exaggerate homophily when preferential recruitment occurs. Adjustments to network-generating statistics derived from the prediction models could substantially improve validity of simulated networks in terms of density, and could reduce bias in replicating mean degree, homophily, and transitivity from the original network.

Automated genotyping of drug-resistant Mycobacterium tuberculosis (MTB) directly from sputum is challenging for three primary reasons. First, the sample matrix, sputum, is highly viscous and heterogeneous, posing a challenge for sample processing. Second, acid-fast MTB bacilli are difficult to lyse. And third, there are hundreds of MTB mutations that confer drug resistance. An additional constraint is that MTB is most prevalent where test affordability is paramount.