ABSTRACT
Human mobility restriction policies have been widely used to contain the coronavirus disease-19 (COVID-19). However, a critical question is how these policies affect individuals' behavioral and psychological well-being during and after confinement periods. Here, we analyze China's five most stringent city-level lockdowns in 2021, treating them as natural experiments that allow for examining behavioral changes in millions of people through smartphone application use. We made three fundamental observations. First, the use of physical and economic activity-related apps experienced a steep decline, yet apps that provide daily necessities maintained normal usage. Second, apps that fulfilled lower-level human needs, such as working, socializing, information seeking, and entertainment, saw an immediate and substantial increase in screen time. Those that satisfied higher-level needs, such as education, only attracted delayed attention. Third, human behaviors demonstrated resilience as most routines resumed after the lockdowns were lifted. Nonetheless, long-term lifestyle changes were observed, as significant numbers of people chose to continue working and learning online, becoming "digital residents." This study also demonstrates the capability of smartphone screen time analytics in the study of human behaviors. Supplementary Information: The online version contains supplementary material available at 10.1140/epjds/s13688-023-00391-9.
ABSTRACT
We analysed the effectiveness of various non-pharmaceutical interventions in containing the 2022 Omicron outbreak in China. The results show that the Rapid Antigen Test contributed to containing the outbreak, reducing the reproduction number by 0.788 (95% CI:-0.306, 1.880) in studied cities.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
Precision mitigation of COVID-19 is in pressing need for postpandemic time with the absence of pharmaceutical interventions. In this study, the effectiveness and cost of digital contact tracing (DCT) technology-based on-campus mitigation strategy are studied through epidemic simulations using high-resolution empirical contact networks of teachers and students. Compared with traditional class, grade, and school closure strategies, the DCT-based strategy offers a practical yet much more efficient way of mitigating COVID-19 spreading in the crowded campus. Specifically, the strategy based on DCT can achieve the same level of disease control as rigid school suspensions but with significantly fewer students quarantined. We further explore the necessary conditions to ensure the effectiveness of DCT-based strategy and auxiliary strategies to enhance mitigation effectiveness and make the following recommendation: social distancing should be implemented along with DCT, the adoption rate of DCT devices should be assured, and swift virus tests should be carried out to discover asymptomatic infections and stop their subsequent transmissions. We also argue that primary schools have higher disease transmission risks than high schools and, thereby, should be alerted when considering reopenings.
ABSTRACT
The 2019 coronavirus disease (COVID-19) is pseudonymously linked to more than 100 million cases in the world as of January 2021. High-quality data are needed but lacking in the understanding of and fighting against COVID-19. We provide a complete and updating hand-coded line-list dataset containing detailed information of the cases in China and outside the epicenter in Hubei province. The data are extracted from public disclosures by local health authorities, starting from January 19. This dataset contains a very rich set of features for the characterization of COVID-19's epidemiological properties, including individual cases' demographic information, travel history, potential virus exposure scenario, contacts with known infections, and timelines of symptom onset, quarantine, infection confirmation, and hospitalization. These cases can be considered the baseline COVID-19 transmissibility under extreme mitigation measures, and therefore, a reference for comparative scientific investigation and public policymaking.
Subject(s)
COVID-19/epidemiology , COVID-19/diagnosis , COVID-19/transmission , China/epidemiology , Contact Tracing , Demography , Hospitalization , Humans , Quarantine , TravelABSTRACT
BACKGROUND: Knowledge on the epidemiological features and transmission patterns of novel coronavirus disease (COVID-19) is accumulating. Detailed line-list data with household settings can advance the understanding of COVID-19 transmission dynamics. METHODS: A unique database with detailed demographic characteristics, travel history, social relationships, and epidemiological timelines for 1407 transmission pairs that formed 643 transmission clusters in mainland China was reconstructed from 9120 COVID-19 confirmed cases reported during 15 January-29 February 2020. Statistical model fittings were used to identify the superspreading events and estimate serial interval distributions. Age- and sex-stratified hazards of infection were estimated for household vs nonhousehold transmissions. RESULTS: There were 34 primary cases identified as superspreaders, with 5 superspreading events occurred within households. Mean and standard deviation of serial intervals were estimated as 5.0 (95% credible interval [CrI], 4.4-5.5) days and 5.2 (95% CrI, 4.9-5.7) days for household transmissions and 5.2 (95% CrI, 4.6-5.8) and 5.3 (95% CrI, 4.9-5.7) days for nonhousehold transmissions, respectively. The hazard of being infected outside of households is higher for people aged 18-64 years, whereas hazard of being infected within households is higher for young and old people. CONCLUSIONS: Nonnegligible frequency of superspreading events, short serial intervals, and a higher risk of being infected outside of households for male people of working age indicate a significant barrier to the identification and management of COVID-19 cases, which requires enhanced nonpharmaceutical interventions to mitigate this pandemic.