No abstract

No abstract

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.

Short-term forecasts of traditional streams from public health reporting (such as cases, hospitalizations, and deaths) are a key input to public health decision-making during a pandemic. Since early 2020, our research group has worked with data partners to collect, curate, and make publicly available numerous real-time COVID-19 indicators, providing multiple views of pandemic activity in the United States. This paper studies the utility of five such indicators—derived from deidentified medical insurance claims, self-reported symptoms from online surveys, and COVID-related Google search activity—from a forecasting perspective. For each indicator, we ask whether its inclusion in an autoregressive (AR) model leads to improved predictive accuracy relative to the same model excluding it. Such an AR model, without external features, is already competitive with many top COVID-19 forecasting models in use today. Our analysis reveals that 1) inclusion of each of these five indicators improves on the overall predictive accuracy of the AR model; 2) predictive gains are in general most pronounced during times in which COVID cases are trending in “flat” or “down” directions; and 3) one indicator, based on Google searches, seems to be particularly helpful during “up” trends.

Purpose-The purpose of this paper is to describe the policy and trends in rural education in China over the past 40 years; and also discuss a number of challenges that are faced by China's rural school system. Design/methodology/approach-The authors use secondary data on policies and trends over the past 40 years for preschool, primary/junior high school, and high school. Findings-The trends over the past 40 years in all areas of rural schooling have been continually upward and strong. While only a low share of rural children attended preschool in the 1980s, by 2014 more than 90 percent of rural children were attending. The biggest achievement in compulsory education is that the rise in the number of primary students that finish grade 6 and matriculate to junior high school. There also was a steep rise of those going to and completing high school. While the successes in upscaling rural education are absolutely unprecedented, there are still challenges. Research limitations/implications-This is descriptive analysis and there is not causal link established between policies and rural schooling outcomes. Practical implications-The authors illustrate one of the most rapid rises of rural education in history and match the achievements up with the policy efforts of the government. The authors also explore policy priorities that will be needed in the coming years to raise the quality of schooling. Originality/value-This is the first paper that documents both the policies and the empirical trends of the success that China has created in building rural education from preschool to high school during the first 40 years of reform (1978-2018). The paper also documentsdrawing on the literature and the own researchthe achievements and challenges that China still face in the coming years, including issues of gender, urbanization, early childhood education and health and nutrition of students.