Predicting COVID-19 Spread from Large-Scale Mobility Data

Schwabe, Amray; Persson, Joel; Feuerriegel, Stefan

doi:10.1145/3447548.3467157

Cited by 21 publications

(19 citation statements)

References 35 publications

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“…While the smart devices are GPS-tracked, the locations of the transaction data can be found at retail outlets, leisure facilities and other public amenities. Among the public datasets currently available, the Google COVID-19 Community Mobility Reports have been widely used for forecasting cases of infection and providing insights on how to use mobility characteristics efficiently (Wang and Yamamoto 2020;Bryant and Elofsson 2020;Achterberg et al 2020;Schwabe et al 2021;Garcı ´a-Cremades et al 2021). Sufficient mobility records in both spatial and temporal dimensions enable the training of machine learning models that require large amount of data.…”

Section: The Google Datamentioning

confidence: 99%

“…Including a time-varying or static prior close to the true contact network may improve the performance of a NIPA across logistic function, sigmoid curves and LSTM. A mobility-marked Hawkes model was proposed for case prediction in the early stages of the pandemic (Schwabe et al 2021). The study retooled to adopt a Hawkes process to capture the transmission dynamics.…”

Section: The Google Datamentioning

confidence: 99%

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Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

Zhao¹,

Han

Wang

et al. 2022

Stoch Environ Res Risk Assess

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At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease’s spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.

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Section: The Google Datamentioning

confidence: 99%

Section: The Google Datamentioning

confidence: 99%

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

Zhao¹,

Han

Wang

et al. 2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

show abstract

“…While the smart devices are GPS-tracked, the locations of the transaction data can be found at retail outlets, leisure facilities and other public amenities. Among the public datasets currently available, the Google COVID-19 Community Mobility Reports have been widely used for forecasting cases of infection and providing insights on how to use mobility characteristics efficiently [30][31][32][33][34]. Sufficient mobility records in both spatial and temporal dimensions enable the training of machine learning models that require large amount of data.…”

Section: The Google Datamentioning

confidence: 99%

“…Including a time-varying or static prior close to the true contact network may improve the performance of a NIPA across logistic function, sigmoid curves and LSTM. A mobility-marked Hawkes model was proposed for case prediction in the early stages of the pandemic [33]. The study retooled to adopt a Hawkes process to capture the transmission dynamics.…”

Section: The Google Datamentioning

confidence: 99%

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

Zhao

Han

Wang

et al. 2022

Preprint

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At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease’s spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, mobility data with a lag of 8 days is most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.

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“…Many prediction models for the first outbreak wave have been proposed to anticipate the infection and death cases [9,28,40,41]. One critical input for these models is the mobility data [6,19,22], which describes population movements and is positively related to the disease infections [49].…”

Section: Introductionmentioning

confidence: 99%

Multiwave COVID-19 Prediction via Social Awareness-Based Graph Neural Networks using Mobility and Web Search Data

Xue,

Yabe,

Tsubouchi

et al. 2021

Preprint

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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. SAB-GNN 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 _ANONYMOUS_COMPANY_ under strict privacy protection rules. Results show our model outperforms other baselines including ST-GNN and MPNN+LSTM. 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.

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Predicting COVID-19 Spread from Large-Scale Mobility Data

Cited by 21 publications

References 35 publications

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection

Multiwave COVID-19 Prediction via Social Awareness-Based Graph Neural Networks using Mobility and Web Search Data

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