The shock, the coping, the resilience: smartphone application use reveals Covid-19 lockdown effects on human behaviors.

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.

Post-marketing safety concerns with nirmatrelvir: A disproportionality analysis of spontaneous reports submitted to the FDA Adverse Event Reporting System.

AIMS: Nirmatrelvir is an antiviral drug with a novel mechanism of action, targeting the 3-CL protease, and is used in the treatment of COVID-19. However, the potential side effects have not yet been fully studied. The aim of this study was to identify potential safety signals of nirmatrelvir by analysing post-marketing safety data based on the largest publicly available worldwide pharmacovigilance database. METHODS: We analysed nirmatrelvir adverse events to identify and characterize relevant safety signals based on the FDA Adverse Event Reporting System database in 2022. The case/non-case approach was used to estimate the reporting odds ratio (ROR) and information component (IC) with relevant confidence intervals (95% CI) for adverse events (AEs) that numbered 4 or more. RESULTS: A total of 26 846 cases were included. Disease recurrence (ROR [95% CI] = 413.2 [395.6-431.59]), dysgeusia (ROR [95% CI] = 110.84 [106.04-115.85]), anosmia (ROR [95% CI] = 15.21 [12.76-18.11]), ageusia (ROR [95% CI] = 9.80 [8.50-11.3]) and urticaria (ROR [95% CI] = 1.91 [1.69-2.17]) were the main safety signals. In addition, abdominal pain upper and skin toxicity were two specific safety signals of nirmatrelvir. In the pregnant population, there was a significant increased ROR for life-threatening conditions (ROR [95% CI] = 8.00 [1.77-36.20]). CONCLUSIONS: Our study identified that the main and specific safety signals of nirmatrelvir were disease recurrence, dysgeusia, abdominal pain upper and skin toxicity. Clinicians and pharmacists should be vigilant of these AEs, although differentiating between COVID-19 symptoms and AEs can be challenging. Notably, a potential safety concern of nirmatrelvir should be a warning based on a small number of events in the pregnant population. However, the available data are insufficient, and further continued pharmacovigilance and surveillance is needed to fully understand this issue.

Time series clustering of COVID-19 pandemic-related data

The COVID-19 pandemic continues to impact daily life worldwide. It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic. Here, we aim to demonstrate that it is feasible to analyze the patterns of the pandemic using a time-series clustering approach. In this work, we use dynamic time warping distance and hierarchical clustering to cluster time series of daily new cases and deaths from different countries into four patterns. It is found that geographic factors have a large but not decisive influence on the pattern of pandemic development. Moreover, the age structure of the population may also influence the formation of cluster patterns. Our proven valid method may provide a different but very useful perspective for other scholars and researchers.

The Characteristics of Light-Duty Passenger Vehicle Mileage and Impact Analysis in China from a Big Data Perspective

Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to analyze the activity level characteristics of the light-duty passenger vehicle fleet with the highest ownership proportion. We found that the annual mileage of vehicles does not decay significantly with the increase in vehicle age, and the mileage of vehicles is relatively low in the first few years due to the run-in period, among other reasons. This study indicated that the average mileage of the private passenger car fleet is 10,300 km/yr and that of the taxi fleet was 80,000 km/yr in China in 2019, and the annual mileage dropped by 22% in 2020 due to the pandemic. Based on the vehicle mileage characteristics, the emission inventory of major pollutants from light-duty passenger vehicles in China for 2010–2020 was able to be updated, which will provide important data support for more accurate environmental and climate benefit assessments in the future. [ FROM AUTHOR]

The effect of COVID-19 restrictions on atmospheric new particle formation in Beijing

During the COVID-19 lockdown, the dramatic reduction of anthropogenic emissions provided a unique opportunity to investigate the effects of reduced anthropogenic activity and primary emissions on atmospheric chemical processes and the consequent formation of secondary pollutants. Here, we utilize comprehensive observations to examine the response of atmospheric new particle formation (NPF) to the changes in the atmospheric chemical cocktail. We find that the main clustering process was unaffected by the drastically reduced traffic emissions, and the formation rate of 1.5 nm particles remained unaltered. However, particle survival probability was enhanced due to an increased particle growth rate (GR) during the lockdown period, explaining the enhanced NPF activity in earlier studies. For GR at 1.5–3 nm, sulfuric acid (SA) was the main contributor at high temperatures, whilst there were unaccounted contributing vapors at low temperatures. For GR at 3–7 and 7–15 nm, oxygenated organic molecules (OOMs) played a major role. Surprisingly, OOM composition and volatility were insensitive to the large change of atmospheric NOx concentration;instead the associated high particle growth rates and high OOM concentration during the lockdown period were mostly caused by the enhanced atmospheric oxidative capacity. Overall, our findings suggest a limited role of traffic emissions in NPF.

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.

Rethinking Academic Conferences in the Age of Pandemic

The year 2020 witnessed the havoc wreaked by the coronavirus disease COVID-19 due to its onset in late 2019. The COVID-19 pandemic is the cruelest public health crisis humankind has ever seen. The COVID-19 pandemic profoundly affected every walk of life, and academic research has been no exception. Academic conferences are an indispensable component of research. Note that the pandemic together with its variants ravaged the globe in 2020, while their recurrences yet have a deep shadow across 2021 and 2022 with uncertainties for the near future. Under the sway of the pandemic, many conferences are conducted in virtual mode to mitigate the propagation of the virus. It is no surprise that academic conferences charge the attendees for registration fees with the amount varying by countries and disciplines. Here, we collect the registration fee information for conferences held in 2019, 2020 and 2021. Note that virtual conferences barely cater to attendees except by providing online platforms. However, we discover that most of the virtual conferences held in 2020 and 2021 still charged high registration fees compared to those in 2019, while the remaining conferences only applied small discounts. In light of the current situation of the pandemic as well as uncertainties in the future, virtual conferences could be a common form of academic activity. Considering the sluggish global economy at well as other potential issues, here, we advocate that going virtual should always be an option for academic conferences in the future. We also suggest that virtual conferences should charge less and the expenditure of the fees should be open to the public.

[Concentration Variation and Source Analysis of Metal Elements in PM2.5 During COVID-19 Control in Suzhou].

To study the variation in concentration and source analysis of metal elements during COVID-19 control in Suzhou, a multi-metal online monitor was used to determine hourly online data of 14 metal elements from December 1, 2019 to March 31, 2020. This study analyzed variation in concentration and source analysis of metal elements using a PMF model before, during, and after shutdown during COVID-19 control. The results showed that the concentrations of Cr, Mn, Zn, and Fe during shutdown decreased the most, by 87.6%, 85.6%, 78.3%, and 72.2%, respectively, compared with those before shutdown. The concentrations of Mn, Cr, Zn, and Fe after shutdown increased the most, by 227.0%, 215.4%, 147.4%, and 113.4%, respectively, compared with those of the previous stage. The diurnal variation in K differed at three stages. Zn showed a single peak shape at three stages, but the peak width and peak time were different. Unlike the concentrations, the diurnal variations in Fe, Mn, Pb, Se, and Hg were not significantly changed. The daily variation characteristics of Ca, Ba, Cu, As, Cr, and Ni during and after shutdown were significantly different from those before shutdown. The results of source analysis by the PMF model showed that metal elements mainly came from dust, motor vehicle, coal burning, industrial smelting, and mixed-combustion sources. Among them, the concentration of industrial smelting sources changed greatly, with the concentration decreasing by 89.0% during shutdown and increasing by 358.0% after shutdown.

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.

The new CORSIA baseline has limited motivation to promote the green recovery of global aviation.

The Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) is the first programme to tackle carbon dioxide (CO2) emissions from a single industry at the global level, to realize the carbon-neutral growth of international flights from 2020 onwards. However, the COVID-19 pandemic has caused a drastic decline in the global aviation industry. The International Civil Aviation Organization (ICAO) has adjusted the CORSIA by removing 2020 emissions from the baseline, which now will only be based on 2019 emissions. We estimate that the total carbon dioxide (CO2) emissions from global international flights decreased by 70 % from February to July 2020 compared to those in 2019. Our analysis suggests that the annual CO2 emissions from international flights during the pilot stage of CORSIA (2021-2023) will be far below the revised baseline even if the global aviation industry could embrace an optimistic recovery. The major airline companies will have very limited motivations due to the CORSIA scheme to implement mitigation actions proactively. Therefore, more progressive actions are needed to align the industry recovery of global aviation and climate change mitigation during the post-COVID-19 period.

RENET2: high-performance full-text gene-disease relation extraction with iterative training data expansion.

Relation extraction (RE) is a fundamental task for extracting gene-disease associations from biomedical text. Many state-of-the-art tools have limited capacity, as they can extract gene-disease associations only from single sentences or abstract texts. A few studies have explored extracting gene-disease associations from full-text articles, but there exists a large room for improvements. In this work, we propose RENET2, a deep learning-based RE method, which implements Section Filtering and ambiguous relations modeling to extract gene-disease associations from full-text articles. We designed a novel iterative training data expansion strategy to build an annotated full-text dataset to resolve the scarcity of labels on full-text articles. In our experiments, RENET2 achieved an F1-score of 72.13% for extracting gene-disease associations from an annotated full-text dataset, which was 27.22, 30.30, 29.24 and 23.87% higher than BeFree, DTMiner, BioBERT and RENET, respectively. We applied RENET2 to (i) ∼1.89M full-text articles from PubMed Central and found ∼3.72M gene-disease associations; and (ii) the LitCovid articles and ranked the top 15 proteins associated with COVID-19, supported by recent articles. RENET2 is an efficient and accurate method for full-text gene-disease association extraction. The source-code, manually curated abstract/full-text training data, and results of RENET2 are available at GitHub.

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model.

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 µm by -30.1% and -17.5%, respectively, but a 5.7% increase in O3 Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

Mitigating COVID-19 Transmission in Schools With Digital Contact Tracing.

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.

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

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.

Reconstruction of Transmission Pairs for Novel Coronavirus Disease 2019 (COVID-19) in Mainland China: Estimation of Superspreading Events, Serial Interval, and Hazard of Infection.

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.

Low dispersion in the infectiousness of COVID-19 cases implies difficulty in control.

The individual infectiousness of coronavirus disease 2019 (COVID-19), quantified by the number of secondary cases of a typical index case, is conventionally modelled by a negative-binomial (NB) distribution. Based on patient data of 9120 confirmed cases in China, we calculated the variation of the individual infectiousness, i.e., the dispersion parameter k of the NB distribution, at 0.70 (95% confidence interval: 0.59, 0.98). This suggests that the dispersion in the individual infectiousness is probably low, thus COVID-19 infection is relatively easy to sustain in the population and more challenging to control. Instead of focusing on the much fewer super spreading events, we also need to focus on almost every case to effectively reduce transmission.

Household transmissions of SARS-CoV-2 in the time of unprecedented travel lockdown in China.

QUESTION: What are the characteristics of household and social transmissions of COVID-19 areas outside of epidemic centers? FINDINGS: Based on 1,407 COVID-19 reported infection events in China outside of Hubei Province between 20 January and 19 February 2020, we estimate the distribution of secondary infection sizes, frequency of super spreading events, serial intervals and age-stratified hazard of infection. Young and older people have higher risks of being infected with households while males 65+ of age are responsible for a disproportionate number of household infections. Meaning: This report is the first large-scale analysis of the household and social transmission events in the COVID-19 pandemic.

Higher immune response induced by vaccination in Houhai acupoint relates to the lymphatic drainage of the injection site.

Houhai acupoint (HA) is a site for acupuncture stimulation, located in the fossa between the anus and tail base in animals. To evaluate HA as a potential immunization site, the immune responses were compared when HA and the conventional site nape were vaccinated in rats. The results showed that injection of a porcine epidemic diarrhea virus (PEDV) vaccine in HA induced significantly higher IgG, IgG1, IgG2, splenocyte proliferation and mRNA expression of IL-2, IL-4 and IFN-γ than in the nape. To search for the underlying mechanisms, the draining lymph nodes for HA and the nape were investigated. When rats were injected in HA with Indian ink, 11 lymph nodes including caudal mesenteric lymph node and bilateral gluteal lymph nodes, posterior inguinal lymph nodes, lumbar lymph nodes, internal iliac lymph nodes and popliteal lymph nodes were visibly stained with the ink and injection of a model antigen ovalbumin (OVA) in HA resulted in detection of OVA by western blotting while in the same lymph nodes only a pair of lymph nodes (central brachial lymph nodes) were observed when Indian ink or OVA was injected in the nape. IL-2 mRNA expression was detected in all the lymph nodes when PEDV vaccine was injected. Therefore, the enhanced immune response elicited by vaccination in HA may be attributed to more lymphocytes activated.

Four-Month Changes in Air Quality during and after the COVID-19 Lockdown in Six Megacities in China

The pandemic of coronavirus disease 2019 (COVID-19) resulted in a stringent lockdown in China to reduce the infection rate. We adopted a machine learning technique to analyze the air quality impacts of the COVID-19 lockdown from January to April 2020 for six megacities with different lockdown durations. Compared with the scenario without lockdowns, we estimated that the lockdown reduced ambient NO2 concentrations by 36–53% during the most restrictive periods, which involved Level-1 public health emergency response control actions. Several cities lifted the Level-1 control actions during February and March, and the avoided NO2 concentrations subsequently dropped below 10% in late April. Traffic analysis during the same periods in Beijing and Chengdu confirmed that traffic emission changes were a major factor in the substantial NO2 reduction, but they were also associated with increased O3 concentrations. The lockdown also reduced PM2.5 concentrations, although heavy pollution episodes occurred on certain days due to the enhanced formation of secondary aerosols in association with the increased atmospheric oxidizing capacity. We also observed that the changes in air pollution levels decreased as the lockdown was gradually eased in various cities.