Multivariate count time series segmentation with "sums and shares” and Poisson lognormal mixture models: a comparative study using pedestrian flows within a multimodal transport hub

This paper deals with a clustering approach based on mixture models to analyze multidimensional mobility count time-series data within a multimodal transport hub. These time series are very likely to evolve depending on various periods characterized by strikes, maintenance works, or health measures against the Covid19 pandemic. In addition, exogenous one-off factors, such as concerts and transport disruptions, can also impact mobility. Our approach flexibly detects time segments within which the very noisy count data is synthesized into regular spatio-temporal mobility profiles. At the upper level of the modeling, evolving mixing weights are designed to detect segments properly. At the lower level, segment-specific count regression models take into account correlations between series and overdispersion as well as the impact of exogenous factors. For this purpose, we set up and compare two promising strategies that can address this issue, namely the "sums and shares” and "Poisson log-normal” models. The proposed methodologies are applied to actual data collected within a multimodal transport hub in the Paris region. Ticketing logs and pedestrian counts provided by stereo cameras are considered here. Experiments are carried out to show the ability of the statistical models to highlight mobility patterns within the transport hub. One model is chosen based on its ability to detect the most continuous segments possible while fitting the count time series well. An in-depth analysis of the time segmentation, mobility patterns, and impact of exogenous factors obtained with the chosen model is finally performed. © 2023, Springer-Verlag GmbH Germany, part of Springer Nature.

Modeling COVID-19 with big mobility data: Surveillance and reaffirming the people in the data

To better understand the COVID-19 pandemic, public health researchers turned to "big mobility data”—location data collected from mobile devices by companies engaged in surveillance capitalism. Publishing formerly private big mobility datasets, firms trumpeted their efforts to "fight” COVID-19 and researchers highlighted the potential of big mobility data to improve infectious disease models tracking the pandemic. However, these collaborations are defined by asymmetries in information, access, and power. The release of data is characterized by a lack of obligation on the part of the data provider towards public health goals, particularly those committed to a community-based, participatory model. There is a lack of appropriate reciprocities between data company, data subject, researcher, and community. People are de-centered, surveillance is de-linked from action while the agendas of public health and surveillance capitalism grow closer. This article argues that the current use of big mobility data in the COVID-19 pandemic represents a poor approach with respect to community and person-centered frameworks. © The Author(s) 2023.

VISUAL ANALYTICS OF MOBILITY NETWORK CHANGES OBSERVED USING MOBILE PHONE DATA DURING COVID-19 PANDEMIC

The limited exchange between human communities is a key factor in preventing the spread of COVID-19. This paper introduces a digital framework that combines an integration of real mobility data at the country scale with a series of modeling techniques and visual capabilities that highlight mobility patterns before and during the pandemic. The findings not only significantly exhibit mobility trends and different degrees of similarities at regional and local levels but also provide potential insight into the emergence of a pandemic on human behavior patterns and their likely socio-economic impacts. © 2022 IEEE.

Aggregate Learning for Mixed Frequency Data

Large and acute economic shocks such as the 2007-2009 financial crisis and the current COVID-19 infections rapidly change the economic environment. In such a situation, real-time analysis of regional heterogeneity of economic conditions using alternative data is essential. We take advantage of spatio-temporal granularity of alternative data and propose a Mixed-Frequency Aggregate Learning (MF-AGL) model that predicts economic indicators for the smaller areas in real-time. We apply the model for the real-world problem;prediction of the number of job applicants which is closely related to the unemployment rates. We find that the proposed model predicts (i) the regional heterogeneity of the labor market condition and (ii) the rapidly changing economic status. The model can be applied to various tasks, especially economic analysis. © 2022 IEEE.

Epidemic Spread Modeling for COVID-19 Using Cross-fertilization of Mobility Data

We present an individual-centric model for COVID-19 spread in an urban setting. We first analyze patient and route data of infected patients from January 20, 2020, to May 31, 2020, collected by the Korean Center for Disease Control & Prevention (KCDC) and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of mobility habits and patterns of individuals at the beginning of the pandemic. While the KCDC data offer a wealth of information, they are also by their nature limited. To compensate for their limitations, we use detailed mobility data from Berlin, Germany after observing that mobility of individuals is surprisingly similar in both Berlin and Seoul. Using information from the Berlin mobility data, we cross-fertilize the KCDC Seoul data set and use it to parameterize an agent-based simulation that models the spread of the disease in an urban environment. After validating the simulation predictions with ground truth infection spread in Seoul, we study the importance of each input parameter on the prediction accuracy, compare the performance of our model to state-of-the-art approaches, and show how to use the proposed model to evaluate different what-if counter-measure scenarios. IEEE

Analysis for Data Mobility and Covid-19 Positive Rate with Multilayer Perceptron

COVID-19 has been affecting human mobility to avoid the risk of infection. Movement restriction was one of the government policies to reduce the rate of infection. However, the mobility was still occurred to be recorded during the policy. This action has led to the problem of the number of beds on hospital have to be prepared for the peak of infection. This study developed a model using Multilayer perceptron as a useful theorem in regression analysis to see the fitness approximation over this problem. Five layers neural networks combination have been used to see the performance of the model to reach the best fit of the model. The process of the study includes data acquisition of the influence of community mobility over the positive number of COVID-19, managed hyperparameters, and calculate the results of prediction in the form of the length of time the patient would be infected with COVID-19 from 2020 to 2021. This study found that the infection was happening mostly after 12 days of human mobility activity in public area such as ATM, market, park, and any public area recorded by Google mobility data. It was also showed the number of infections after 12 days in order to prepare the number of beds on hospital. Furthermore, this study found the best model with smallest loss value on 0.01452617616472448 with the gap number of infection from public area as much as 77 persons. © 2022 IEEE.

Leveraging Human Mobility Data for Efficient Parameter Estimation in Epidemic Models of COVID-19

Effectively predicting the evolution of COVID-19 is of great significance to contain the pandemic. Extensive previous studies proposed a great number of SIR variants, which are efficient to capture the transmission characteristics of COVID-19. However, the parameter estimation methods in previous studies are based on data from epidemiological investigations, which inevitably have caused a large delay. The popularity of digital trajectory data world-wide makes it possible to understand epidemic spreading from human mobility perspective. The major advantage of digital trajectory data lies in that the co-location level of a population is reflected at every moment, making it possible to forecast the evolution in advance. We showed that the mobility data contributed by mobile phone users could be exploited to estimate the contact probability between individuals, thus revealing the dynamic transmission of COVID-19. Specifically, we developed an estimation method to obtain human co-location levels and quantified the variations of human mobility during the epidemic. Then, we extended the infection rate with a real-time co-location level to further forecast the transmission of an epidemic, predicting the epidemic size much more accurately than conventional methods. Finally, the proposed method was applied to evaluate the quantitative effect of different non-pharmacological interventions by predicting the epidemic situations with various mobility characteristics. The empirical results and simulations corroborated our theoretical analysis, providing effective guidance to contain the pandemic. IEEE

Spatio-temporal Graph Convolution BiLSTM for COVID-19 Community Mobility

Google COVID-19 community mobility data is important information to reflect the level of social activity and infer economic development. However, the data has complexity and non-linear spatiotemporal characteristics, and it is difficult for traditional prediction algorithms to fit such data with both temporal and spatial characteristics. To address such problems, this paper proposes a novel Spatio-temporal Graph Convolution Bidirectional Long Short Term Memory (STGC-BiLSTM) deep learning model, in which, the spatio-temporal graph convolution module can simultaneously mine the temporal and spatial features, and the prediction module encodes and regresses these features to complete the prediction of Google's mobile indices. The experiments show that the STGC-BiLSTM exhibits superior performance for both single-step and multi-step prediction for the four national datasets. Finally, ablation experiments are used to verify the effects of the spatio-temporal graph convolution module and regularization parameters to further illustrate the effectiveness of the model proposed in this paper. © 2022 IEEE.

Exploiting mobility data to forecast Covid-19 spread

Infectious diseases are spread through human-human transmissions;thus, the analysis of spatio-temporal mobility data can play a fundamental role to enable epidemic forecasting. This paper presents a data-driven predictive approach that analizes both mobility and infection data to discover spatio-temporal predictive epidemic patterns. Preliminary results, obtained by analyzing data related to mobility and COVID-19 infections in Chicago, show that the approach is promising. © 2022 IEEE.

Spatiotemporal disease case prediction using contrastive predictive coding

Time series prediction models have played a vital role in guiding effective policymaking and response during the COVID-19 pandemic by predicting future cases and deaths at the country, state, and county levels. However, for emerging diseases, there is not sufficient historic data to fit traditional supervised prediction models. In addition, such models do not consider human mobility between regions. To mitigate the need for supervised models and to include human mobility data in the prediction, we propose Spatial Probabilistic Contrastive Predictive Coding (SP-CPC) which leverages Contrastive Predictive Coding (CPC), an unsupervised time-series representation learning approach. We augment CPC to incorporate a covariate mobility matrix into the loss function, representing the relative number of individuals traveling between each county on a given day. The proposal distribution learned by the algorithm is then sampled by the Metropolis-Hastings algorithm to give a final prediction of the number of COVID-19 cases. We find that the model applied to COVID-19 data can make accurate short-term predictions, more accurate than ARIMA and simple time-series extrapolation methods, one day into the future. However, for longer-term prediction windows of seven or more days into the future, we find that our predictions are not as competitive and require future research. © 2022 ACM.

GeoSpread: An Epidemic Spread Modeling Tool for COVID-19 Using Mobility Data

We present an individual-centric agent-based model and a flexible tool, GeoSpread, for studying and predicting the spread of viruses and diseases in urban settings. Using COVID-19 data collected by the Korean Center for Disease Control & Prevention (KCDC), we analyze patient and route data of infected people from January 20, 2020, to May 31, 2020, and discover how infection clusters develop as a function of time. This analysis offers a statistical characterization of population mobility and is used to parameterize GeoSpread to capture the spread of the disease. We validate simulation predictions from GeoSpread with ground truth and we evaluate different what-if counter-measure scenarios to illustrate the usefulness and flexibility of the tool for epidemic modeling. © 2022 Owner/Author.

CovidLens: Visually Understanding the Covid-19 Indicators through the Lens of Mobility Data

Since the onset of the Covid-19 pandemic, an over-whelming amount of related data has been released. In an attempt to gain insights from that data, multiple public data visualization dashboards have been deployed. Differently from such dashboards, which mainly support basic data filtering and visualization of separate datasets, in this work, we propose CovidLens, which: 1) integrates various Covid-19 indicators and is centred around the Google Community Mobility Report dataset, 2) supports similarity search for finding similar and correlated patterns and trends across the integrated datasets, and 3) automatically recommends insightful visualizations that unlocks valuable insights into the pandemic effects. To that end, we will be presenting the employed dataset, together with the design, implementation, and multiple usage scenarios of our proposed CovidLens. © 2022 IEEE.

Automated Machine Learning for COVID-19 Forecasting

When responding to a pandemic situation, policy makers rely on forecasts of the spread. In the context of the current COVID-19 pandemic, various sophisticated epidemic and machine learning models have been used for forecasting. These models, however, rely on carefully selected architectures and detailed data that is often only available for specific regions. Automated machine learning (AutoML) addresses these challenges by allowing to automatically create forecasting pipelines in a data-driven manner, resulting in high-quality predictions. In this paper we study the role of open data along with AutoML systems in acquiring high-performance forecasting models for COVID-19. Here, we adapted the AutoML framework auto-sklearn to the time series forecasting task and introduced two variants for multi-step ahead COVID-19 forecasting which we refer to as (a) multi-output and (b) repeated single output forecasting. We studied the usefulness of anonymized open mobility data sets (place visits, and the use of different transportation modes) in addition to open mortality data. We evaluated three drift adaptation strategies to deal with concept drifts in data by (i) refitting our models on part of the data, (ii) the full data, or (iii) retraining the models completely. We compared the performance of our AutoML methods in terms of RMSE with five baselines on two testing periods (over 2020 and 2021). Our results show that combining mobility features and mortality data improves forecasting accuracy. Furthermore, we show that when faced with concept drifts, our method refitted on recent data using place visits mobility features outperforms all other approaches for 22 of the 26 countries considered in our study. Author

Multiwave COVID-19 Prediction from Social Awareness Using Web Search and Mobility Data

Recurring outbreaks of COVID-19 have posed enduring effects on global society, which calls for a predictor of pandemic waves using various data with early availability. Existing prediction models that forecast the first outbreak wave using mobility data may not be applicable to the multiwave prediction, because the evidence in the USA and Japan has shown that mobility patterns across different waves exhibit varying relationships with fluctuations in infection cases. Therefore, to predict the multiwave pandemic, we propose a Social Awareness-Based Graph Neural Network (SAB-GNN) that considers the decay of symptom-related web search frequency to capture the changes in public awareness across multiple waves. Our model combines GNN and LSTM to model the complex relationships among urban districts, inter-district mobility patterns, web search history, and future COVID-19 infections. We train our model to predict future pandemic outbreaks in the Tokyo area using its mobility and web search data from April 2020 to May 2021 across four pandemic waves collected by Yahoo Japan Corporation under strict privacy protection rules. Results demonstrate our model outperforms state-of-the-art baselines such as ST-GNN, MPNN, and GraphLSTM. Though our model is not computationally expensive (only 3 layers and 10 hidden neurons), the proposed model enables public agencies to anticipate and prepare for future pandemic outbreaks. © 2022 Owner/Author.

Interactive dashboards to study relations between early COVID-19 outbreaks and human mobility trends

Outbreaks of the COVID-19 pandemic, caused by the SARS-CoV-2 virus, have led to the creation of social distancing and lockdown policies to reduce the spread of the virus. Consequently, public/private transportation services, schools, workplaces, and retail stores' operations were disrupted. We gather user mobility reports worldwide to learn impacts of early COVID-19 outbreaks on human mobility patterns and trends. Building time series of six types of activities tracked in the Google Community Mobility Reports (CMR), we develop visualization tools and interactive dashboards for linking mobility and COVID-19 infection data at different levels (from county- and state-level in the US, to country level for the rest of the world). We show that the relationship between mobility and COVID-19 infection changes over time, and therefore the stage of the pandemic is essentially important for understanding how containment policies can affect infections and deaths caused by the COVID-19 pandemic. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

A novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization for short-term load forecasting during the COVID-19 pandemic

The coronavirus disease 2019 pandemic has significantly impacted the world. The sudden decline in electricity load demand caused by strict social distancing restrictions has made it difficult for traditional models to forecast the load demand during the pandemic. Therefore, in this study, a novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization is proposed for short-term load forecasting during the pandemic. First, a knowledge base containing mobility data is constructed, which can reflect the changes in visitor volume in different regions and buildings based on mobile services. Therefore, the sudden decline in load can be analyzed according to the socioeconomic behavior changes during the pandemic. Furthermore, a new transfer deep learning model is proposed to address the problem of limited mobility data associated with the pandemic. Moreover, reinforcement learning is employed to optimize the hyperparameters of the proposed model automatically, which avoids the manual adjustment of the hyperparameters, thereby maximizing the forecasting accuracy. To enhance the hyperparameter optimization efficiency of the reinforcement-learning agents, a new advance forecasting method is proposed to forecast the state-action values of the state space that have not been traversed. The experimental results on 12 real-world datasets covering different countries and cities demonstrate that the proposed model achieves high forecasting accuracy during the coronavirus disease 2019 pandemic. © 2022 - IOS Press. All rights reserved.

Human Mobility based Pandemic Prediction Model

In December 2019, the coronavirus disease 2019 (COVID-19) outbreak was first reported in Wuhan, China, and later has expanded all over India and across the world. The dynamics of pandemic spatial spread have gotten a lot of interest among researchers. Human mobility on a huge scale has increased the transmission of the epidemic. This paper offers a prediction model for coronavirus cases based on epidemic data and population mobility data. The study integrates the human mobility data with the historical cases to predict future COVID-19 cases. The findings indicate that population mobility may adequately explain the spread of coronavirus. The effectiveness of our proposed model is demonstrated by the coefficient of determination-R2 with the prediction value of 0.962. The proposed model can be used as a resource for epidemic prevention and control decision support. © 2021 IEEE.

Mobility-as-a-Service Challenges and Opportunities in the Post-Pandemic

In December 2019, the world experienced a pandemic that called into question what we always took for granted, such as our freedom of movement. Tough restrictions imposed across the world were necessary to stem the transmission of the COVID-19 virus and have largely affected the mobility and transport sector. In a first phase, due to the mandatory confinement that forced people not to leave their houses;in a second phase, when the measures eased and people started to have the need to move again, it was necessary to look for alternative means of transport that avoided the gathering of people. In view of the advances that were being made in recent years towards a Mobility-as-a-Service paradigm that advocates multimodal and shared transport, the pandemic has raised many challenges. In this paper, a statistical analysis of the mobility data made available by Apple from January 2020 to March 2021 is presented, where the reduction in the use of public transport becomes evident, leading us to question what the future of Mobility-as-a-Service will be as its foundation advocates, among other aspects, the use of a shared transport model. Despite the challenges that the pandemic has brought to Mobility-as-a-Service, a set of opportunities are presented that can be used in the short and medium term to strengthen the paradigm and enhance its massive adoption. © 2021 IEEE.

Impact of Community Mobility and Weather Variability on COVID-19 Case in the Provinces of Java Island

COVID-19 is easy to transmit from one infected person to a susceptible person through droplets. Human mobility and weather variable become the factors affecting COVID-19. However, the most influence variable needs to be investigated to effectively control COVID-19 spread. This paper studied the correlation between COVID-19, community mobility and weather variability in Java Island. We used the confirmed cases of COVID-19, community mobility data and weather data from the beginning of March 2020 until the end of February 2021 in each province of Java Island. Two decision tree-based models (Random Forest and XGBoost) in four experimental setups were implemented in this paper. We found that there is similarity trend between Random Forest and XGBoost method in prediction results. The performance of both has also no significant difference. The Capital City of Jakarta, Banten and the Special Region of Yogyakarta shows the best prediction result in the third experiment which used the community mobility variable as features. While, West Java shows the best result with a combination of all weather variables and mobility, Central Java and East Java with the combination of temperature and mobility. This shows that the community mobility gives an impact on COVID-19 cases in all provinces. The correlation analysis found that the community mobility percentage change in transit stations has a significant role in predicting COVID-19 cases. Based on the model performance, the prediction of COVID-19 cases in the Capital City of Jakarta has the best result. While the Special Region of Yogyakarta has the highest error. © 2021 ACM.

Unsupervised data mining on spatial-temporal passenger mobility and survey data during Covid-19

Traditionally, survey data and travel data are considered and analyzed independently. By being able to combine survey data with the respective trip data, this paper analyzes patterns between quantitative mobility data and qualitative survey responses. Firstly, we apply spatial-temporal clustering on the mobility data to understand travel patterns. Secondly, we utilize association rule mining to understand the differences between the clusters. Lastly, we apply association rule mining on the combined mobility and survey data set to understand the perception of Covid-19 related measurements in public transportation. With the created association rules, public transportation authorities can comprehend how different measurements affect the awareness of their services. © 2021 IEEE.