Mobility, exposure, and epidemiological timelines of COVID-19 infections in China outside Hubei province

Data challenges for international health emergencies: lessons learned from ten international COVID-19 driver projects

The COVID-19 pandemic highlighted the importance of international data sharing and access to improve health outcomes for all. The International COVID-19 Data Alliance (ICODA) programme enabled 12 exemplar or driver projects to use existing health-related data to address major research questions relating to the pandemic, and developed data science approaches that helped each research team to overcome challenges, accelerate the data research cycle, and produce rapid insights and outputs. These approaches also sought to address inequity in data access and use, test approaches to ethical health data use, and make summary datasets and outputs accessible to a wider group of researchers. This Health Policy paper focuses on the challenges and lessons learned from ten of the ICODA driver projects, involving researchers from 19 countries and a range of health-related datasets. The ICODA programme reviewed the time taken for each project to complete stages of the health data research cycle and identified common challenges in areas such as data sharing agreements and data curation. Solutions included provision of standard data sharing templates, additional data curation expertise at an early stage, and a trusted research environment that facilitated data sharing across national boundaries and reduced risk. These approaches enabled the driver projects to rapidly produce research outputs, including publications, shared code, dashboards, and innovative resources, which can all be accessed and used by other research teams to address global health challenges.

Mobility networks in Greater Mexico City

Based on more than 11 billion geolocated cell phone records from 33 million different devices, daily mobility networks were constructed over a 15-month period for Greater Mexico City, one of the largest and most diverse metropolitan areas globally. The time frame considered spans the entire year of 2020 and the first three months of 2021, enabling the analysis of population movement dynamics before, during, and after the COVID-19 health contingency. The nodes within the 456 networks represent the basic statistical geographic areas (AGEBs) established by the National Institute of Statistics, Geography, and Informatics (INEGI) in Mexico. This framework facilitates the integration of mobility data with numerous indicators provided by INEGI. Edges connecting these nodes represent movement between AGEBs, with edge weights indicating the volume of trips from one AGEB to another. This extensive dataset allows researchers to uncover travel patterns, cross-reference data with socio-economic indicators, and conduct segregation studies, among other potential analyses.

COVID-19 spreading patterns in family clusters reveal gender roles in China

Understanding different gender roles forms part of the efforts to reduce gender inequality. This paper analyses COVID-19 family clusters outside Hubei Province in mainland China during the 2020 outbreak, revealing significant differences in spreading patterns across gender and family roles. Results show that men are more likely to be the imported cases of a family cluster, and women are more likely to be infected within the family. This finding provides new supportive evidence of the 'men as breadwinner and women as homemaker' (MBWH) gender roles in China. Further analyses reveal that the MBWH pattern is stronger in eastern than in western China, stronger for younger than for elder people. This paper offers not only valuable references for formulating gender-differentiated epidemic prevention policies but also an exemplification for studying group differences in similar scenarios.

Protocol for the automatic extraction of epidemiological information via a pre-trained language model

The lack of systems to automatically extract epidemiological fields from open-access COVID-19 cases restricts the timeliness of formulating prevention measures. Here we present a protocol for using CCIE, a COVID-19 Cases Information Extraction system based on the pre-trained language model.1 We describe steps for preparing supervised training data and executing python scripts for named entity recognition and text category classification. We then detail the use of machine evaluation and manual validation to illustrate the effectiveness of CCIE. For complete details on the use and execution of this protocol, please refer to Wang et al.2.

Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19

The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics.

Epidemiologic information discovery from open-access COVID-19 case reports via pretrained language model

Although open-access data are increasingly common and useful to epidemiological research, the curation of such datasets is resource-intensive and time-consuming. Despite the existence of a major source of COVID-19 data, the regularly disclosed case reports were often written in natural language with an unstructured format. Here, we propose a computational framework that can automatically extract epidemiological information from open-access COVID-19 case reports. We develop this framework by coupling a language model developed using deep neural networks with training samples compiled using an optimized data annotation strategy. When applied to the COVID-19 case reports collected from mainland China, our framework outperforms all other state-of-the-art deep learning models. The information extracted from our approach is highly consistent with that obtained from the gold-standard manual coding, with a matching rate of 80%. To disseminate our algorithm, we provide an open-access online platform that is able to estimate key epidemiological statistics in real time, with much less effort for data curation.

Assessing the spread risk of COVID-19 associated with multi-mode transportation networks in China

The spatial spread of COVID-19 during early 2020 in China was primarily driven by outbound travelers leaving the epicenter, Wuhan, Hubei province. Existing studies focus on the influence of aggregated out-bound population flows originating from Wuhan; however, the impacts of different modes of transportation and the network structure of transportation systems on the early spread of COVID-19 in China are not well understood. Here, we assess the roles of the road, railway, and air transportation networks in driving the spatial spread of COVID-19 in China. We find that the short-range spread within Hubei province was dominated by ground traffic, notably, the railway transportation. In contrast, long-range spread to cities in other provinces was mediated by multiple factors, including a higher risk of case importation associated with air transportation and a larger outbreak size in hub cities located at the center of transportation networks. We further show that, although the dissemination of SARS-CoV-2 across countries and continents is determined by the worldwide air transportation network, the early geographic dispersal of COVID-19 within China is better predicted by the railway traffic. Given the recent emergence of multiple more transmissible variants of SARS-CoV-2, our findings can support a better assessment of the spread risk of those variants and improve future pandemic preparedness and responses.

Linking genomic and epidemiologic information to advance the study of COVID-19

The outbreak of Coronavirus Disease 2019 (COVID-19) at the end of 2019 turned into a global pandemic. To help analyze the spread and evolution of the virus, we collated and analyzed data related to the viral genome, sequence variations, and locations in temporal and spatial distribution from GISAID. Information from the Wikipedia web page and published research papers were categorized and mined to extract epidemiological data, which was then integrated with the public dataset. Genomic and epidemiological data were matched with public information, and the data quality was verified by manual curation. Finally, an online database centered on virus genomic information and epidemiological data can be freely accessible at https://www.biosino.org/kgcov/, which is helpful to identify relevant knowledge and devising epidemic prevention and control policies in collaboration with disease control personnel.

Measurement(s)	Viral Epidemiology • genetic sequence variation analysis
Technology Type(s)	digital curation • Bioinformatics
Sample Characteristic - Organism	Severe acute respiratory syndrome coronavirus 2

Electricity-consumption data reveals the economic impact and industry recovery during the pandemic

Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises' production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In this paper, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities.