Undetected cases: The virus causing coronavirus disease 2019 (COVID-19) has now become pandemic. How has it managed to spread from China to all around the world within 3 to 4 months? Li et al. used multiple sources to infer the proportion of early infections that went undetected and their contribution to virus spread. The researchers combined data from Tencent, one of the world's largest social media and technology companies, with a networked dynamic metapopulation model and Bayesian inference to analyze early spread within China. They estimate that ∼86% of cases were undocumented before travel restrictions were put in place. Before travel restriction and personal isolation were implemented, the transmission rate of undocumented infections was a little more than half that of the known cases. However, because of their greater numbers, undocumented infections were the source for ∼80% of the documented cases. Immediately after travel restrictions were imposed, ∼65% of cases were documented. These findings help to explain the lightning-fast spread of this virus around the world. Science , this issue p. 489

The most effective interventions: By 23 January 2020, China had imposed a national emergency response to restrict travel and impose social distancing measures on its populace in an attempt to inhibit the transmission of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2). However, which measures were most effective is uncertain. Tian et al. performed a quantitative analysis of the impact of control measures between 31 December 2019 and 19 February 2020, which encompasses the Lunar New Year period when millions of people traveled across China for family visits. Travel restrictions in and out of Wuhan were too late to prevent the spread of the virus to 262 cities within 28 days. However, the epidemic peaked in Hubei province on 4 February 2020, indicating that measures such as closing citywide public transport and entertainment venues and banning public gatherings combined to avert hundreds of thousands of cases of infection. It is unlikely that this decline happened because the supply of susceptible people was exhausted, so relaxing control measures could lead to a resurgence. Science , this issue p. 638

Fine particulate matter with aerodynamic diameters ≤2.5 µm (PM_2.5) has adverse effects on human health and the atmospheric environment. The estimation of surface PM_2.5 concentrations has made intensive use of satellite-derived aerosol products. However, it has been a great challenge to obtain high-quality and high-resolution PM_2.5 data from both ground and satellite observations, which is essential to monitor air pollution over small-scale areas such as metropolitan regions. Here, the space–time extremely randomized trees (STET) model was enhanced by integrating updated spatiotemporal information and additional auxiliary data to improve the spatial resolution and overall accuracy of PM_2.5 estimates across China. To this end, the newly released Moderate Resolution Imaging Spectroradiometer Multi-Angle Implementation of Atmospheric Correction AOD product, along with meteorological, topographical and land-use data and pollution emissions, was input to the STET model, and daily 1 km PM_2.5 maps for 2018 covering mainland China were produced. The STET model performed well, with a high out-of-sample (out-of-station) cross-validation coefficient of determination (R²) of 0.89 (0.88), a low root-mean-square error of 10.33 (10.93) µg m⁻³, a small mean absolute error of 6.69 (7.15) µg m⁻³ and a small mean relative error of 21.28 % (23.69 %). In particular, the model captured well the PM_2.5 concentrations at both regional and individual site scales. The North China Plain, the Sichuan Basin and Xinjiang Province always featured high PM_2.5 pollution levels, especially in winter. The STET model outperformed most models presented in previous related studies, with a strong predictive power (e.g., monthly R²=0.80), which can be used to estimate historical PM_2.5 records. More importantly, this study provides a new approach for obtaining high-resolution and high-quality PM_2.5 dataset across mainland China (i.e., ChinaHighPM_2.5), important for air pollution studies focused on urban areas.