scholarly journals COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

2020 ◽  
Author(s):  
Mohamed Aziz Bhouri ◽  
Francisco Sahli Costabal ◽  
Hanwen Wang ◽  
Kevin Linka ◽  
Mathias Peirlinck ◽  
...  
Keyword(s):  
Deep Learning ◽  
Social Behavior ◽  
Reproduction Number ◽  
Time Window ◽  
Human Mobility ◽  
Government Interventions ◽  
Learning Framework ◽  
Mobility Data ◽  
Probabilistic Forecasts ◽  
The Us

This paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Strikingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the basic reproduction number. The model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the proposed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously inform policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.

scholarly journals COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior

2021 ◽  
pp. 113891
Author(s):  
Mohamed Aziz Bhouri ◽  
Francisco Sahli Costabal ◽  
Hanwen Wang ◽  
Kevin Linka ◽  
Mathias Peirlinck ◽  
...  
Keyword(s):  
Deep Learning ◽  
Social Behavior ◽  
Human Mobility ◽  
Learning Study ◽  
The Us

scholarly journals Human Mobility and Epidemic Evolution

2021 ◽  
Author(s):  
Fabio Vanni ◽  
David Lambert ◽  
Luigi Palatella ◽  
Paolo Grigolini
Keyword(s):  
Reproduction Number ◽  
Human Mobility ◽  
Theoretical Framework ◽  
Infection Age ◽  
Mobility Data ◽  
Risk Of Disease ◽  
Age Structured ◽  
Using Data ◽  
Age Structured Model ◽  
The Impact

Abstract The CoViD-19 pandemic ceased to be describable by a susceptible-infected-recovered (SIR) model when lockdowns were enforced. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 (Sudden Acute Respiratory Syndrome Coronavirus 2) in terms of individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.

scholarly journals On the use of aggregated human mobility data to estimate the reproduction number

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fabio Vanni ◽  
David Lambert ◽  
Luigi Palatella ◽  
Paolo Grigolini
Keyword(s):  
Reproduction Number ◽  
Human Mobility ◽  
Theoretical Framework ◽  
Infection Age ◽  
Mobility Data ◽  
Risk Of Disease ◽  
Age Structured ◽  
Using Data ◽  
Age Structured Model ◽  
The Impact

AbstractThe reproduction number of an infectious disease, such as CoViD-19, can be described through a modified version of the susceptible-infected-recovered (SIR) model with time-dependent contact rate, where mobility data are used as proxy of average movement trends and interpersonal distances. We introduce a theoretical framework to explain and predict changes in the reproduction number of SARS-CoV-2 in terms of aggregated individual mobility and interpersonal proximity (alongside other epidemiological and environmental variables) during and after the lockdown period. We use an infection-age structured model described by a renewal equation. The model predicts the evolution of the reproduction number up to a week ahead of well-established estimates used in the literature. We show how lockdown policies, via reduction of proximity and mobility, reduce the impact of CoViD-19 and mitigate the risk of disease resurgence. We validate our theoretical framework using data from Google, Voxel51, Unacast, The CoViD-19 Mobility Data Network, and Analisi Distribuzione Aiuti.

scholarly journals A Survey on Deep Learning for Human Mobility

2023 ◽  
Vol 55 (1) ◽  
pp. 1-44
Author(s):  
Massimiliano Luca ◽  
Gianni Barlacchi ◽  
Bruno Lepri ◽  
Luca Pappalardo
Keyword(s):  
Deep Learning ◽  
Predictive Power ◽  
Human Mobility ◽  
Well Being ◽  
Learning Approaches ◽  
Comprehensive Description ◽  
Mobility Data ◽  
Flow Generation ◽  
Disease Spreading ◽  
Gps Traces

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.

scholarly journals Potential influence of meteorological conditions on early COVID-19 transmission dynamics in 409 cities across 26 countries

2021 ◽  
Author(s):  
Rachel Lowe ◽  
Ben Armstrong ◽  
Sam Abbott ◽  
Sophie Meakin ◽  
Kathleen O'Reilly ◽  
...  
Keyword(s):  
Early Phase ◽  
Reproduction Number ◽  
Time Window ◽  
Meteorological Factors ◽  
Meteorological Conditions ◽  
Transmission Dynamics ◽  
Potential Influence ◽  
Government Interventions ◽  
Government Response ◽  
Pharmaceutical Interventions

<p>More than a year since its emergence, there is conflicting evidence on the potential influence of weather conditions on COVID-19 transmission dynamics. Respiratory viral infections often show seasonality, with influenza and other coronaviruses peaking in winter, yet the underlying mechanisms are poorly understood. As SARS-CoV-2 is a new virus to humans, it is difficult to ascertain if seasonal climate variations might have enhanced or reduced transmission in the first pandemic wave given the high proportion of susceptible people and the potential confounding role of different types of non-pharmaceutical interventions adopted at different times after the onset of local outbreaks. We used a two-stage ecological modelling approach to estimate weather-dependent signatures in COVID-19 transmission in the early phase of the pandemic, using a dataset of 3 million COVID-19 cases reported until 31 May 2020, spanning 409 locations in 26 countries. We calculated the effective reproduction number (R<sub>e</sub>) over a city-specific early-phase time-window of 10-20 days, for which local transmission had been established but before non-pharmaceutical interventions had intensified, as measured by the OxCGRT Government Response Index. We calculated mean levels of meteorological factors, including temperature and humidity observed in the same time window used to calculate R<sub>e</sub>. Using a multilevel meta-regression approach, we modelled nonlinear effects of meteorological factors, while accounting for government interventions and socio-demographic factors. A weak non-monotonic association between temperature and R<sub>e</sub> was identified, with a decrease of 0.087 (95% CI: 0.025; 0.148) for an increase in temperature between 10-20°C. Non-pharmaceutical interventions had a greater effect on R<sub>e</sub> with a decrease of 0.285 (95% CI 0.223; 0.347) for a 5th - 95th percentile increase in the government response index. The variation in the effective reproduction number explained by early government interventions was 6 times greater than for mean temperature. We find little evidence of meteorological conditions having influenced the early stages of local epidemics and conclude that population behaviour and governmental intervention are more important drivers of transmission.</p>

scholarly journals Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

2020 ◽  
Author(s):  
Aniruddha Adiga ◽  
Lijing Wang ◽  
Adam Sadilek ◽  
Ashish Tendulkar ◽  
Srinivasan Venkatramanan ◽  
...  
Keyword(s):  
Human Mobility ◽  
The United States ◽  
Global Scale ◽  
International Travel ◽  
Population Mixing ◽  
Government Interventions ◽  
Social Distancing ◽  
Epidemic Dynamics ◽  
The Us ◽  
The Impact

AbstractThis work quantifies the impact of interventions to curtail mobility and social interactions in order to control the COVID-19 pandemic. We analyze the change in world-wide mobility at multiple spatio-temporal resolutions – county, state, country – using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km2. We show that human mobility underwent an abrupt and significant change, partly in response to the interventions, resulting in 87% reduction of international travel and up to 75% reduction of domestic travel. Taking two very different countries sampled from the global spectrum, we observe a maximum reduction of 42% in mobility across different states of the United States (US), and a 68% reduction across the states of India between late March and late April. Since then, there has been an uptick in flows, with the US seeing 53% increase and India up to 38% increase with respect to flows seen during the lockdown. As we overlay this global map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell – often before stay-at-home orders were issued. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility. We find that population mixing has decreased considerably as the pandemic has progressed and are able to measure this effect across the world. Finally, we carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.

scholarly journals Estimating the effect of mobility on SARS-CoV-2 transmission during the first and second wave of the COVID-19 epidemic in Switzerland: a population-based study

2021 ◽  
Author(s):  
Adrian Lison ◽  
Joel Persson ◽  
Nicolas Banholzer ◽  
Stefan Feuerriegel
Keyword(s):  
Reproduction Number ◽  
Human Mobility ◽  
Population Based ◽  
Mobile Phone Data ◽  
Mobility Data ◽  
Daily Travel Distance ◽  
Mode Of Transport ◽  
Second Wave ◽  
Epidemic Wave ◽  
Daily Travel

The effect of mobility and its value for surveillance in different waves of the COVID–19 epidemic is still unclear. In this study, we compared the role of mobility during the first and second epidemic wave in Switzerland by analysing the link between daily travel distances and the effective reproduction number Rt of SARS–CoV–2. Here we used aggregated mobile phone data from a representative panel survey of the Swiss population to measure human mobility. We estimated the effects of reductions in daily travel distance on Rt via a regression model. We compared mobility effects between the first wave (March 2–April 7, 2020) and the second wave (October 1–December 10, 2020) across mode of transport, travel purpose, sociodemographic subgroup and movement radius. We found that human mobility was associated with the effective reproduction number of SARS–CoV–2 during both the first and second epidemic wave in Switzerland. The estimated relative effects of mobility were similar in both waves for all modes of transport, travel purposes, and sociodemographic subgroups but differed by movement radius. Moreover, smaller mobility reductions in the second wave translated into smaller overall reductions of Rt. Mobility data from mobile phones have a continued potential to support real–time surveillance of COVID–19 during epidemic waves.

scholarly journals CITY-SCALE HUMAN MOBILITY PREDICTION MODEL BY INTEGRATING GNSS TRAJECTORIES AND SNS DATA USING LONG SHORT-TERM MEMORY

2020 ◽  
Vol V-4-2020 ◽  
pp. 87-94
Author(s):  
S. Miyazawa ◽  
X. Song ◽  
R. Jiang ◽  
Z. Fan ◽  
R. Shibasaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Short Term Memory ◽  
Human Mobility ◽  
Background Information ◽  
Mobility Prediction ◽  
Short Term ◽  
Term Memory ◽  
Mobility Data ◽  
Long Short Term Memory

Abstract. Human mobility analysis on large-scale mobility data has contributed to multiple applications such as urban and transportation planning, disaster preparation and response, tourism, and public health. However, when some unusual events happen, every individual behaves differently depending on their personal routine and background information. To improve the accuracy of the crowd behavior prediction model, understanding supplemental spatiotemporal topics, such as when, where and what people observe and are interested in, is important. In this research, we develop a model integrating social network service (SNS) data into the human mobility prediction model as background information of the mobility. We employ multi-modal deep learning models using Long short-term memory (LSTM) architecture to incorporate SNS data to a human mobility prediction model based on Global Navigation Satellite System (GNSS) data. We process anonymized interpolated GNSS trajectories from mobile phones into mobility sequence with discretized grid IDs, and apply several topic modeling methods on geo-tagged data to extract spatiotemporal topic features in each spatiotemporal unit similar to the mobility data. Thereafter, we integrate the two datasets in the multi-modal deep learning prediction models to predict city-scale mobility. The experiment proves that the models with SNS topics performed better than baseline models.

Computationally light deep learning framework to recognize cotton leaf diseases

2021 ◽  
pp. 1-16
Author(s):  
Serosh Karim Noon ◽  
Muhammad Amjad ◽  
Muhammad Ali Qureshi ◽  
Abdul Mannan
Keyword(s):  
Deep Learning ◽  
Pathogenic Bacteria ◽  
Computational Cost ◽  
Raw Material ◽  
Cotton Leaf ◽  
Learning Framework ◽  
Accuracy And Precision ◽  
Proposed Model ◽  
The Us ◽  
Almost All

Cotton is an important commodity because of its use in various industries across the globe. It is grown in many countries and is imported/exported as a cash crop due to its large utility. However, cotton yield is adversely affected by the existence of pests, viruses and pathogenic bacteria, etc. For the last one decade or so, several image processing/deep learning-based automatic plant leaf disease recognition methods have been developed but, unfortunately, they rarely address the cotton leaf diseases. The proposed work presents a simple yet efficient deep learning-based framework to recognize cotton leaf diseases. The proposed model is capable of achieving the near ideal accuracy with early convergence to save computational cost of training. Further, due to the unavailability of publicly available datasets for this crop, a dataset is also collected comprising of three diseases namely curl virus, bacterial blight and fusarium wilt in addition to the healthy leaf Images. These images were collected from the Internet and fields of Southern Punjab region in Pakistan where the cotton crop is grown on thousands of acres every year and is exported to the Europe and the US either as a raw material or in the form of knitted industrial/domestic products. Experimental results have shown that almost all variants of our proposed deep learning framework have shown remarkably good recognition accuracy and precision. However, proposed EfficientNet-B0 model achieves 99.95% accuracy in only 152 seconds with best generalization and fast inference.

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