Real-time Mobility Research Articles

Adaptive Caching Strategies for IoV Based on LTE Signal Quality Analysis

The Internet of Vehicles (IoV) enhances vehicular communication, promising better connectivity and user experiences. However, maintaining stable data delivery in dynamic vehicular environments is challenging. This paper introduces Mob-Cach, a novel mobility-aware cache management strategy that leverages real-time vehicular mobility information. Our preliminary study on LTE signal quality shows that mobility patterns significantly impact signal degradation. Based on these findings, we developed a dynamic caching framework that adapts to real-time mobility data, optimizing content delivery and network performance. Simulations demonstrate significant improvements in cache hit rates, reduced latency, and overall network efficiency. This research lays a foundation for future advancements in IoV networks, addressing the challenges of mobile and dynamic network topologies.

Altered Heart Rate Variability During Mobile Game Playing and Watching Self-Mobile Gaming in Individuals with Problematic Mobile Game Use: Implications for Cardiac Health.

The surge in mobile gaming, fueled by smartphone and internet accessibility, lacks a comprehensive understanding of physiological changes during gameplay. This study, involving 93 participants (average age 21.75 years), categorized them into Problematic Mobile Gaming (PMG) and non-problematic Mobile Gaming (nPMG) groups based on Problematic Mobile Gaming Questionnaire (PMGQ) scores. The PMGQ is a 12-item scale developed in Taiwan to assess symptoms of problematic mobile gaming. The research delved into heart rate variability (HRV) alterations during real-time mobile gaming and self-gaming video viewing. Results showed that the PMG group significantly presents a lower root mean square of successive differences (RMSSD), and High Frequency (lnHF) than does the nPMG group (F=4.73, p=0.03; F=10.65, p=0.002, respectively) at the baseline. In addition, the PMG group significantly displayed elevated HF and low-frequency to high-frequency (LF/HF) in the mobile-gaming (F=7.59, p=0.007; F=9.31, p=0.003) condition as well as in the watching self-gaming videos (F=9.75, p=0.002; F=9.02, p=0.003) than did the nPMG. The study suggests targeted interventions to mitigate autonomic arousal, offering a potential avenue to address adverse effects associated with problematic mobile gaming behavior. The PMG group displayed increased craving scores after real-time mobile gaming and watching self-gaming video excerpts, unlike the nPMG group. Elevated LF/HF ratios in frequent gaming cases heightened autonomic arousal, presenting challenges in relaxation after mobile gaming. These findings contribute to a nuanced understanding of the complex interplay between mobile gaming activities, physiological responses, and potential intervention strategies.

ZMapp reduces diffusion of Ebola viral particles in fresh human cervicovaginal mucus

ABSTRACT Vaginal transmission from semen of male Ebola virus (EBOV) survivors has been implicated as a potential origin of Ebola virus disease (EVD) outbreaks. While EBOV in semen must traverse cervicovaginal mucus (CVM) to reach target cells, the behaviour of EBOV in CVM is poorly understood. CVM contains substantial quantities of IgG, and arrays of IgG bound to a virion can develop multiple Fc-mucin bonds, immobilizing the IgG/virion complex in mucus. Here, we measured the real-time mobility of fluorescent Ebola virus-like-particles (VLP) in 50 CVM specimens from 17 women, with and without ZMapp, a cocktail of 3 monoclonal IgGs against EBOV. ZMapp-mediated effective trapping of Ebola VLPs in CVM from a subset of women across the menstrual cycle, primarily those with Lactobacillus crispatus dominant microbiota. Our work underscores the influence of the vaginal microbiome on IgG-mucin crosslinking against EBOV and identifies bottlenecks in the sexual transmission of EBOV.

Smart Class System Prototyping through Sustainable Infrastructure Based on Mobile Application SPACE (Smart Program - Class Architecture) Integrated Smart Building Management System

Optimizing innovation in the use of health technology in infrastructure will directly impact the conditions of social interaction in the community, including within the scope of the school area. The Smart Class concept collaborates with learning carried out in a hybrid way. However, it is not yet fully optimized by digitizing the existing infrastructure with the concept of managing school facilities and infrastructure. The implementation of the Smart Class uses the intelligent building management system (IBMS), where this system integrates all parts of the building with the Internet of Things (IoT), facilities, and infrastructure and can minimize pandemic transmission in the academic sphere by accessing via mobile app. This research aims to analyze the design of the smart program class architecture integrated monitoring system to implement IoT-integrated health protocols, especially in the middle school scope. This research uses qualitative and quantitative methods with descriptive analysis through the development of user research at the system design stage. The output of this innovation is how schools implement digital technology comprehensively, starting from the application of IoT, sensors, and databases in a real-time mobile app environment. There are three main things: the data input, data processing, and output value. The intervention of school needs can be expanded with the integration of the learning management system in the form of integrated learning management the learning process, which is in line with monitoring the use of infrastructure in schools. Through this Smart Class concept, schools can still operate optimally without any applicable health policies and can provide excellent academic services.
 Keywords: smart class system prototyping, sustainable infrastructure mobile application, SPACE smart building management system

TrapCog: An Anti-Noise, Transferable, and Privacy-Preserving Real-Time Mobile User Authentication System With High Accuracy

The authentication technology of mobile device users has been studied for decades. To balance security, privacy, and usability, motion sensors-based user authentication methods are widely investigated in recent years. However, existing studies meet the problems such as scarcity of training samples, underutilization of data, poor de-noising ability, insufficient transferability, privacy leakage, and low accuracy. To overcome these difficulties, we propose a system, called for Transferable, Real-time, Anti-noise, and Privacy-preserving mobile user COGnition., with the following capabilities: 1) In the phase of data collection, can eliminate man-made noise (mislabeling) through differential training based on down-sampling. 2) In the model training stage, the siamese neural network with Long Short-Term Memory (LSTM) as the sub-network is used to achieve sufficient coverage of sample patterns and the transferability of the model. 3) In the phase of real-world authentication, the privacy of the user is tremendously protected through end-side model deployment and local authentication. Experimental results on a dataset composed of 1,513 users with real-world noise show that TRAP COG has high accuracy and strong transferability, which is much better than state-of-the-art studies.

Exploring the Impact of Charging Behavior on Transportation System in the Era of SAEVs: Balancing Current Request with Charging Station Availability

Shared autonomous electric vehicles (SAEVs) can offer safer, more efficient, and more environmentally friendly real-time mobility services with advanced autonomous driving technologies. In this study, a multi-agent-based simulation model considering SAEVs’ vehicle range and charging behavior is proposed. Based on real-world datasets from the Luohu District in Shenzhen, China, various scenarios with different fleet sizes, charging rates, and vehicle ranges are established to evaluate the impact of these parameters on parking demand, charging demand, vehicle miles traveled (VMT), and response time in the era of SAEVs. The results show there would be much more charging demand than parking demand. Moreover, a larger fleet size and longer vehicle range would lead to more parking demand, more charging demand, and more VMT while increasing the charging rate can dramatically reduce the charging demand and VMT. Average response time can be reduced by increasing the fleet size or the charging rate, and a larger vehicle range leads to longer response time due to the longer time spent recharging. It is worth noting that the VMT generated from relocating from the previous request destination to the origin of the upcoming request accounts for nearly 90% of the total VMT, which should be addressed properly with appropriate scheduling. A charging policy considering current requests and the availability of charging stations was proposed and verified in terms of reducing the response time by 2.5% to 18.9%.

Towards self-organizing railway traffic management: concept and framework

Railway traffic management requires a timely and accurate redefinition of routes and schedules in response to detected perturbations of the original timetable. To date, most of the (automated) solutions to this problem require a central authority to make decisions for all the trains in a given control area. An appealing alternative is to consider trains as intelligent agents able to self-organize and determine the best traffic management strategy. This could lead to more scalable and resilient approaches, that can also take into account the real-time mobility demand. In this paper, we formalize the concept of railway traffic self-organization and we present an original design that enables its real-world deployment. We detail the principles at the basis of the sub-processes brought forth by the trains in a decentralized way, explaining their sequence and interaction. Moreover, we propose a preliminary proof of concept based on a realistic setting representing traffic in a French control area. The results allow conjecturing that self-organizing railway traffic management may be a viable option, and foster further research in this direction.

Designing and Prototyping of AI-based Real-time Mobile Detectors for Calisthenic Push-up Exercise

Fitness exercises, including push-ups, are very beneficial to personal health. Many Artificial Intelligence (AI)-based fitness trainers are developed based on human pose estimation models or assisted by Internet of Things (IoT) devices. However, many of them require access to a graphing processing unit (GPU) for model training or IoT sensors to deploy, less accessible for individuals. In our work, we designed and prototyped real-time mobile push-up detectors using three distinctive approaches: (1) Push-up pose classification, (2) Angle-heuristic estimation and (3) Optical flow detection. We trained our deep-learning model with over 2000 images to achieve a high accuracy for real time deployment. Models are tested on our video dataset applied data augmentation techniques to simulate real-world environmental conditions to evaluate model performance based on accuracy metrics (precision, recall, F1 score) and processing frame rate (FPS). From the results, we concluded that the angle-heuristic estimation method has the best overall performance and we analysed the reasons for the relatively poorer performance of the push-up pose classification and optical flow detection methods. All methods developed are capable of working on mobile devices without the need of GPU or IoT sensors.

Alleviating range anxiety with ‘Enroute’: A real-time mobile solution and infrastructure innovation for electric vehicle users

Amidst the growing adoption of electric vehicles (EVs), this study addresses the prominent issue of "range anxiety" encountered by EV users when confronted with critical charging situations during their journeys. The research background emphasises the constraints of the existing charging infrastructure, primarily designed for urban areas, leaving EV drivers susceptible to unforeseen charging emergencies in remote or less-travelled regions. To tackle this challenge, an innovative mobile application that provides a real-time solution by harnessing crowd-sourced data and forging partnerships with charging infrastructure providers was developed. This app assists users in locating available charging stations. It offers the option to book a service that can bring the necessary charging equipment to a specified location (similar to a porter service), ensuring timely assistance. The research methodology involved user surveys and data analysis, specifically emphasising seamless integration with EVs’ onboard systems, making support practical and readily accessible. The results demonstrate the effectiveness of this app in alleviating range anxiety and enhancing EV users’ confidence in long-distance travel. In conclusion, this paper outlines a promising solution to a pressing issue in electric mobility. It underscores the importance of addressing the charging infrastructure gap for the widespread adoption of EVs. The future scope of this research involves further refinement of the app, expanded partnerships with charging networks and Porter service providers, and continued collaboration with EV manufacturers to ensure seamless integration and user satisfaction, ultimately advancing the cause of sustainable transportation.

Unraveling real-time mobile connectivity paradox and emotional ambivalence: a quasi-experimental design from a multi-source perspective

PurposeThis study develops and validates a theoretical model of real-time mobile connectivity, examining how employees' perceptions of their relationship with supervisors influence their emotional experiences. Through quasi-experiments, the authors investigate the behavioral patterns and emotional responses associated with real-time mobile connectivity in organizations, with a focus on messaging apps that indicate message read status. Specifically, they explore how supervisors' attentiveness or inattentiveness in mobile connectivity impacts emotional ambivalence (anxiety and pride) among subordinates. Additionally, they examine the downstream effects of this emotional ambivalence on employees' workplace thriving and job performance across various dimensions.Design/methodology/approachTo address the paradox of real-time mobile connectivity, a quasi-experimental design involving 320 team members from 46 teams was implemented. Multi-level structural equation modeling was employed to analyze within-person variance and evaluate the proposed hypotheses.FindingsThe findings indicate that employees who do not receive timely indications from their supervisors are more likely to experience elevated levels of anxiety, while those who receive prompt indications experience a sense of pride. Moreover, the indirect effects of the real-time mobile connectivity paradox on employee performance, mediated by anxiety (negatively) and pride (positively), are fully explained through workplace thriving.Research limitations/implicationsThis study provides insights into the emotional ambivalence experienced in the workplace due to real-time mobile connectivity, highlighting its implications for organizational competitiveness. Integrating resource conservation theory and cognitive appraisal theory of emotion, the study explores the mediating role of workplace thriving and the impact on employee performance through pride and anxiety. Generalizability requires considering organizational settings and cultural contexts while acknowledging limitations such as a focus on messaging apps and specific samples. Future research should explore these dynamics in diverse contexts and identify additional factors influencing the relationship between real-time mobile connectivity and employee outcomes.Practical implicationsThis study provides valuable insights for managers regarding the significance of message indications, as their attentiveness can elicit emotional reactions from employees that subsequently impact workplace thriving and job performance.Originality/valueThis study pioneers the exploration of the paradox of real-time mobile connectivity in the workplace, uncovering the discrete emotions experienced by employees. Furthermore, it elucidates the subsequent opposing effects on workplace thriving and job performance, contributing to the existing literature and knowledge in this area.

Novel Methods of Identifying Individual and Neighborhood Risk Factors for Loss to Follow-Up After Ophthalmic Screening.

Residence in a middle-class neighborhood correlated with lower follow-up compared with residence in more affluent neighborhoods. The most common explanations for not following up were the process of making an appointment and lack of symptoms. To explore which individual-level and neighborhood-level factors influence follow-up as recommended after positive ophthalmic and primary care screening in a vulnerable population using novel methodologies. From 2017 to 2018, 957 participants were screened for ophthalmic disease and cardiovascular risk factors as part of the Real-Time Mobile Teleophthalmology study. Individuals who screened positive for either ophthalmic or cardiovascular risk factors were contacted to determine whether or not they followed up with a health care provider. Data from the Social Vulnerability Index, a novel virtual auditing system, and personal demographics were collected for each participant. A multivariate logistic regression was performed to determine which factors significantly differed between participants who followed up and those who did not. As a whole, the study population was more socioeconomically vulnerable than the national average (mean summary Social Vulnerability Index score=0.81). Participants whose neighborhoods fell in the middle of the national per capita income distribution had a lower likelihood of follow-up compared with those who resided in the most affluent neighborhoods (relative risk ratio=0.21, P -value<0.01). Participants cited the complicated process of making an eye care appointment and lack of symptoms as the most common reasons for not following up as instructed within 4 months. Residence in a middle-class neighborhood, difficulty accessing eye care appointments, and low health literacy may influence follow-up among vulnerable populations.

Combining biosignals with RFID to develop a multimodal-shared control interface

The paper presents an approach to develop assistive devices by combining multimodal biosignals and radio frequency identification (RFID). The brain and eye signals have been used as multimodal biosignals to control the movement of a robot in four directions and help reach near the object following a predefined path. RFID shared control over object identification, and the gripper arm connected at the end effector of the robot performs pick and place operations. Horizontal electrooculography (EOG) has been used for x-directional movement control and electroencephalography (EEG) signal obtained by visual stimulus, called steady-state visual-evoked potential (SSVEP) has been used for y-directional movement control of a robot. The SSVEP signal has also been used to ring an alarm in case of an emergency call by the user. Two parameters classification accuracy (CA) and information transfer rate (ITR) have been calculated for the performance evaluation of the proposed multimodal-shared control model and have shown improved results as compared to previous literature. The results also proved that the proposed model can be used for real-time mobility assistive applications.

Development of Adaptive Realtime Mobile Based Traffic Management System for Congested Urban Cities

congestion in Nigeria, focusing on Port Harcourt. The study analyzed four main routes: Location, Rumuokoro, Rumuokwuta, and GRA Junction. Field surveys collected traffic volume and video recordings of cars traveling the indicated routes between 7 am and 7 pm. The Rivers State Command of the Federal Road Safety Corps provided further data. A mobile-based Traffic Management System was designed and simulated using Proteus. The technology transmits real-time traffic data from congested areas to a local server. This data is easily accessible on mobile devices. Python software was used to classify traffic congestion as heavy (0-14km/hr), medium (15-28km/hr), and free (29km/hr and above). Background subtraction was used for vehicle image analysis. The system can detect road traffic, correctly determine car speeds, count the number of vehicles, and send this traffic information to consumers via mobile devices via online communications. After simulating 1200 traffic data instances, 36 had mistaken, while 1164 were classified. This represents good accuracy, with 97% accuracy and 3% inaccuracy. The traffic data will be maintained in a database to help researchers identify places with recurring traffic congestion. These findings suggest that authorities should build alternative routes. It can also help build traffic-congestion-reducing policy review, organization, and enforcement initiatives. Keywords: Background Subtraction Technique, Mobile devices, Proteus, Traffic Congestion, Python

Securing Group Patient Communication in 6G-Aided Dynamic Ubiquitous Healthcare with Real-Time Mobile DNA Sequencing.

(1) Background: With an advanced technique, third-generation sequencing (TGS) provides services with long deoxyribonucleic acid (DNA) reads and super short sequencing time. It enables onsite mobile DNA sequencing solutions for enabling ubiquitous healthcare (U-healthcare) services with modern mobile technology and smart entities in the internet of living things (IoLT). Due to some strict requirements, 6G technology can efficiently facilitate communications in a truly intelligent U-healthcare IoLT system. (2) Research problems: conventional single user-server architecture is not able to enable group conversations where "multiple patients-server" communication or "patient-patient" communication in the group is required. The communications are carried out via the open Internet, which is not a trusted channel. Since heath data and medical information are very sensitive, security and privacy concerns in the communication systems have become extremely important. (3) Purpose: the author aims to propose a dynamic group-based patient-authenticated key distribution protocol for 6G-aided U-healthcare services enabled by mobile DNA sequencing. In the protocol, an authenticated common session key is distributed by the server to the patients. Using the key, patients in a healthcare group are allowed to securely connect with the service provider or with each other for specific purposes of communication. (4) Results: the group key distribution process is protected by a secure three-factor authentication mechanism along with an efficient sequencing-device-based single sign-on (SD-SSO) solution. Based on traceable information stored in the server database, the proposed approach can provide patient-centered services which are available on multiple mobile devices. Security robustness of the proposed protocol is proven by well-known verification tools and a detailed semantic discussion. Performance evaluation shows that the protocol provides more functionality and incurs a reasonable overhead in comparison with the existing works.

Fairness-Based 3-D Multi-UAV Trajectory Optimization in Multi-UAV-Assisted MEC System

Unmanned aerial vehicles (UAVs) -assisted mobile edge computing (MEC) communication system has recently gained increasing attention. In this paper, we investigate a 3D multi-UAV trajectory optimization based on ground devices (GDs) selecting the target UAV for task computing. Specifically, we first design a 3D dynamic multi-UAV-assisted MEC system in which GDs have real-time mobility and task update. Next, we formulate the system communication, computation, and flight energy consumption as objective functions based on fairness among UAVs. Then, to pursue fairness among UAVs, we theoretically deduce and mathematically prove the optimal GDs’ selectivity and offloading strategy, that is, how GDs select the optimal UAV for task offloading and how much to offload. While ensuring the optimal offloading strategy and GDs’ selectivity between UAVs and GDs at each step, we model UAV trajectories as a sequence of location updates of all UAVs and apply a multi-agent deep deterministic policy gradient (MADDPG) algorithm to find the optimal solution. Simulation results demonstrate that we achieve the minimum energy consumption under the premise of fairness and the efficiency of model processing tasks.

Google and Apple mobility data as predictors for European tourism during the COVID-19 pandemic: A neural network approach

Research background: The COVID-19 pandemic has caused unprecedented disruptions to the global tourism industry, resulting in significant impacts on both human and economic activities. Travel restrictions, border closures, and quarantine measures have led to a sharp decline in tourism demand, causing businesses to shut down, jobs to be lost, and economies to suffer. Purpose of the article: This study aims to examine the correlation and causal relationship between real-time mobility data and statistical data on tourism, specifically tourism overnights, across eleven European countries during the first 14 months of the pandemic. We analyzed the short longitudinal connections between two dimensions of tourism and related activities. Methods: Our method is to use Google and Apple's observational data to link with tourism statistical data, enabling the development of early predictive models and econometric models for tourism overnights (or other tourism indices). This approach leverages the more timely and more reliable mobility data from Google and Apple, which is published with less delay than tourism statistical data. Findings &amp; value added: Our findings indicate statistically significant correlations between specific mobility dimensions, such as recreation and retail, parks, and tourism statistical data, but poor or insignificant relations with workplace and transit dimensions. We have identified that leisure and recreation have a much stronger influence on tourism than the domestic and routine-named dimensions. Additionally, our neural network analysis revealed that Google Mobility Parks and Google Mobility Retail &amp; Recreation are the best predictors for tourism, while Apple Driving and Apple Walking also show significant correlations with tourism data. The main added value of our research is that it combines observational data with statistical data, demonstrates that Google and Apple location data can be used to model tourism phenomena, and identifies specific methods to determine the extent, direction, and intensity of the relationship between mobility and tourism flows.

BELIEVE: Privacy-Aware Secure Multi-Party Computation for Real-Time Connected and Autonomous Vehicles and Micro-Mobility Data Validation Using Blockchain—A Study on New York City Data

Connected and autonomous vehicles, as well as micro-mobility devices (e-scooters, e-bikes, and smartphones), constantly generate tremendous amounts of mobility data, comprising spatio-temporal trajectories and other sensory data, the sharing of which can assist in different safety and intelligent transportation system applications. However, constant sharing of such data for real-time mobility decisions could violate the privacy of respective users. At the same time, malicious users could generate and share false data, which would be potentially harmful for stakeholders relying on this data for their safety and mobility decisions. In this paper, we propose a new framework: Blockchain-Enabled Location Identification and Efficient Validation with Encryption” (BELIEVE)—a blockchain-based platform with multi-party computation (MPC)—to securely validate data from a mobility source without violating the user’s privacy. The smart contractin BELIEVE first enables a source to initiate the mobility data validation process, then enables mobile peers in the vicinity to reach a consensus on the data validation using MPC without requiring the source to share raw data, thereby preserving the user’s privacy. The spatio-temporal validation is then updated on a distributed ledger (Inter Planetary File System) as an immutable transaction. Real-time data validation is challenging, considering constant mobility alongside the reduction of resource consumption at the devices and, therefore, to address this, we employ an adaptive approach to the problem where the validation frequency as well as storage on the blockchain are dynamically decided by the nodes, based on the network conditions. We evaluate BELIEVE using a simulation model developed for a portion of New York City, U.S., transportation network and it achieves lower delays and overhead.

Forecasting hospital-level COVID-19 admissions using real-time mobility data

BackgroundFor each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts.MethodsLarge-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume.ResultsIncorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic.ConclusionsThe high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users’ contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.

A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting.

The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.

Estimating the transmission dynamics of SARS-CoV-2 Omicron BF.7 in Beijing after adjustment of the zero-COVID policy in November-December 2022.

We tracked the effective reproduction number (Rt) of the predominant severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron BF.7 in Beijing in November-December 2022 by fitting a transmission dynamic model parameterized with real-time mobility data to (i) the daily number of new symptomatic cases on 1-11 November (when China's zero-COVID interventions were still strictly enforced) and (ii) the proportion of individuals who participated in online polls on 10-22 December and self-reported to have been test-positive since 1 November. After China's announcement of 20 measures to transition from zero-COVID, we estimated that Rt increased to 3.44 (95% credible interval (CrI): 2.82-4.14) on 18 November and the infection incidence peaked on 11 December. We estimated that the cumulative infection attack rate (IAR; that is, proportion of the population infected since 1 November) in Beijing was 75.7% (95% CrI: 60.7-84.4) on 22 December 2022 and 92.3% (95% CrI: 91.4-93.1) on 31 January 2023. Surveillance programs should be rapidly set up to monitor the evolving epidemiology and evolution of SARS-CoV-2 across China.