Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of disease import, of changed activity participation rates over time (coming from mobility data), of masks, of indoors vs.\ outdoors leisure activities, and of contact tracing. Results show that the model is able to credibly track the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. The model clearly shows the effects of contact reductions, school closures/vacations, or the effect of moving leisure activities from outdoors to indoors in fall. Sensitivity tests show that all ingredients of the model are necessary to track the current infection dynamics. One interesting result from the mobility data is that behavioral changes of the population mostly happened \textit{before} the government-initiated so-called contact ban came into effect. Similarly, people started drifting back to their normal activity patterns \emph{before} the government officially reduced the contact ban. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, consequences of wearing masks in certain situations, or contact tracing.
Evaluating relative changes leads to additional insights which would remain hidden when only evaluating absolute changes. We analyze a dataset describing mobility of mobile phones in Austria before, during COVID-19 lock-down measures until recent. By applying compositional data analysis we show that formerly hidden information becomes available: we see that the elderly population groups increase relative mobility and that the younger groups especially on weekends also do not decrease their mobility as much as the others.
Measuring traffic performance is critical for public agencies who manage traffic and individuals who plan trips, especially when special events happen. The COVID-19 pandemic has significantly influenced almost every aspect of daily life, including urban traffic patterns. Thus, it is important to measure the impact of COVID-19 on transportation to further guide agencies and residents to properly respond to changes in traffic patterns. However, most existing traffic performance metrics incorporate only a single traffic parameter and measure only the performance of individual corridors. To overco…
Rapid urbanization and climate change trends are intertwined with complex interactions of various social, economic, and political factors. The increased trends of disaster risks have recently caused numerous events, ranging from unprecedented category 5 hurricanes in the Atlantic Ocean to the COVID-19 pandemic. While regions around the world face urgent demands to prepare for, respond to, and to recover from such disasters, large-scale location data collected from mobile phone devices have opened up novel approaches to tackle these challenges. Mobile phone location data have enabled us to obse…
The emergence of SARS-CoV-2 and the coronavirus infectious disease (COVID-19) has become a pandemic. Social (physical) distancing is a key non-pharmacologic control measure to reduce the transmission rate of SARS-COV-2, but high-level adherence is needed. Using daily travel distance and stay-at-home time derived from large-scale anonymous mobile phone location data provided by Descartes Labs and SafeGraph, we quantify the degree to which social distancing mandates have been followed in the U.S. and its effect on growth of COVID-19 cases. The correlation between the COVID-19 growth rate and tra…
Mobile phone data have been widely used to model the spread of COVID-19, however, quantifying and comparing their predictive value across different settings is challenging. Their quality is affected by various factors and their relationship with epidemiological indicators varies over time. Here we adopt a model-free approach based on transfer entropy to quantify the relationship between mobile phone-derived mobility metrics and COVID-19 cases and deaths in more than 200 European subnational regions. We found that past knowledge of mobility does not provide statistically significant information…
COVID-19 is affecting every social sector significantly, including human mobility and subsequently road traffic safety. In this study, we analyze the impact of the pandemic on traffic accidents using two cities, namely Los Angeles and New York City in the U.S., as examples. Specifically, we have analyzed traffic accidents associated with various demographic groups, how traffic accidents are distributed in time and space, and the severity level of traffic accidents that both involve and do not involve other transportation modes (e.g., pedestrians and motorists). We have made the following obser…
Use of mobile phone sensing data to estimate residence and mobility times in urban patches during the COVID-19 epidemic: The case of the 2020 outbreak in Hermosillo, Mexico
The transmission of COVID-19 is dependent on social contacts, the rate of which have varied during the pandemic due to mandated and voluntary social distancing. Changes in transmission dynamics eventually affect hospital admissions and we have used this connection in order to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the infectivity is assumed to depend on mobility data in terms of public transport utilisation and mobile phone usage. The results show that the model can capture the timing of the…
While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resultin…
The COVID-19 pandemic brought unprecedented levels of disruption to the local and regional transportation networks throughout the United States, especially the Motor City: Detroit. That was mainly a result of swift restrictive measures such as statewide quarantine and lock-down orders to confine the spread of the virus. This work is driven by analyzing five types of real-world data sets from Detroit related to traffic volume, daily cases, weather, social distancing index, and crashes from January 2019 to June 2020. The primary goal is figuring out the impacts of COVID-19 on the transportation …
In response to the COVID-19 pandemic, governments have implemented policies to curb the spread of the novel virus. Little is known about how these policies impact various groups in society. This paper explores the relationship between social distancing policies, traffic volumes and air quality and how they impact various socioeconomic groups. This study aims to understand how disparate communities respond to Stay-at-Home Orders and other social distancing policies to understand how human behavior in response to policy may play a part in the prevalence of COVID-19 positive cases. We collected d…
The COVID-19 outbreak greatly limited human activities and reduced primary emissions particularly from urban on-road vehicles, but coincided with Beijing experiencing pandemic haze, raising the public concerns of the validity and effectiveness of the imposed traffic policies to improve the air pollution. Here, we explored the relationship between local vehicle emissions and the winter haze in Beijing before and during the COVID-19 lockdown period based on an integrated analysis framework, which combines a real-time on-road emission inventory, in-situ air quality observations and a localized ch…
This work considers the sensitivity of commute travel times in US metro areas due to potential changes in commute patterns, for example caused by events such as pandemics. Permanent shifts away from transit and carpooling can add vehicles to congested road networks, increasing travel times. Growth in the number of workers who avoid commuting and work from home instead can offset travel time increases. To estimate these potential impacts, 6-9 years of American Community Survey commute data for 118 metropolitan statistical areas are investigated. For 74 of the metro areas, the average commute tr…
Countries and cities around the world have resorted to unprecedented mobility restrictions to combat Covid-19 transmission. Here we exploit a natural experiment whereby Colombian cities implemented varied lockdown policies based on ID number and gender to analyse the impact of these policies on urban mobility. Using mobile phone data, we find that the restrictiveness of cities' mobility quotas (the share of residents allowed out daily according to policy advice) does not correlate with mobility reduction Instead, we find that larger, wealthier cities with more formalized and complex industrial…
Metapopulation models have been a popular tool for the study of epidemic spread over a network of highly populated nodes (cities, provinces, countries) and have been extensively used in the context of the ongoing COVID-19 pandemic. In the present work, we revisit such a model, bearing a particular case example in mind, namely that of the region of Andalusia in Spain during the period of the summer-fall of 2020 (i.e., between the first and second pandemic waves). Our aim is to consider the possibility of incorporation of mobility across the province nodes focusing on mobile-phone time dependent…
This chapter is written for the welfare of the society, questioning and enlightening the effects of the increment or decrement in the percentage of quality of air causing pollution due to the rise in the traffic post lockdown due to COVID 19 in metro cities, specifically in Delhi. In this chapter, we address the question about people's preference in moving in the shared taxis to their workplaces or their reluctance and denial of the idea of moving in the shared vehicle because of the fear of getting infected. The sensitivity of the situation will compel the people to move in a single occupied …
The COVID-19 pandemic has caused, in general, a sharp reduction in traffic and industrial activities. This in turn leaded to a reduction in air pollution around the world. It is important to quantity the amount of that reduction in order to estimate the influence weight of traffic and industrial activities over the total variation of air quality. The aim of this paper is to evaluate the impact of the COVID-19 outbreak on air pollution in Israel, which is considered one of the countries with a higher air pollution than other Western countries. The results reveal two main findings: 1. During the…
Utilization of multiple trajectories of a dynamical system model provides us with several benefits in approximation of time series. For short term predictions a high accuracy can be achieved via switches to new trajectory at any time. Different long term trends (tendency to different stationary points) of the phase portrait characterize various scenarios of the process realization influenced by externalities. The dynamical system's phase portrait analysis helps to see if the equations properly describe the reality. We also extend the dynamical systems approach (discussed in \cite{R5}) to the d…
Activity-based models appeared as an answer to the limitations of the traditional trip-based and tour-based four-stage models. The fundamental assumption of activity-based models is that travel demand is originated from people performing their daily activities. This is why they include a consistent representation of time, of the persons and households, time-dependent routing, and microsimulation of travel demand and traffic. In spite of their potential to simulate traffic demand management policies, their practical application is still limited. One of the main reasons is that these models requ…
The newly released Orange D4D mobile phone data base provides new insights into the use of mobile technology in a developing country. Here we perform a series of spatial data analyses that reveal important geographic aspects of mobile phone use in Cote d'Ivoire. We first map the locations of base stations with respect to the population distribution and the number and duration of calls at each base station. On this basis, we estimate the energy consumed by the mobile phone network. Finally, we perform an analysis of inter-city mobility, and identify high-traffic roads in the country.
Countries highly exposed to incoming traffic from China were expected to be at the highest risk of COVID-19 spread. However, COVID-19 case numbers (infection levels) are negatively correlated with incoming traffic-level. Moreover, infection levels are positively correlated with population-size, while the latter should only affect infection-level once herd immunity is reached. These could be explained if a low-virulence strain (LVS) began spreading a few months earlier from China, providing immunity from the later emerging known SARS-CoV-2 high-virulence strain (HVS). We find that the dynamics …
A country-wide `lock-down' imposed on the 23red March 2020 in the UK had a significant impact on the UK's anthropogenic movements. The closure of work-places and restrictions imposed on visiting friends and family has radically reduced the amount of traffic on the roads. In this short communication, we use data from UK air-quality sensors to quantify air pollution trends pre- and post-lock-down. While we detect large falls in nitrogen dioxide at levels not seen over the last decade, trends in other pollutants were mixed especially when compared to historical data. It suggests that the implicat…
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior ex…
The main focus of this study is to collect and prepare data on air passengers traffic worldwide with the scope of analyze the impact of travel ban on the aviation sector. Based on historical data from January 2010 till October 2019, a forecasting model is implemented in order to set a reference baseline. Making use of airplane movements extracted from online flight tracking platforms and on-line booking systems, this study presents also a first assessment of recent changes in flight activity around the world as a result of the COVID-19 pandemic. To study the effects of air travel ban on aviati…
A mathematical model for the COVID-19 pandemic spread, which integrates age-structured Susceptible-Exposed-Infected-Recovered-Deceased dynamics with real mobile phone data accounting for the population mobility, is presented. The dynamical model adjustment is performed via Approximate Bayesian Computation. Optimal lockdown and exit strategies are determined based on nonlinear model predictive control, constrained to public-health and socio-economic factors. Through an extensive computational validation of the methodology, it is shown that it is possible to compute robust exit strategies with r…
The worldwide outbreak of COVID-19 has posed a dire threat to the public. Human mobility has changed in various ways over the course of the pandemic. Despite current studies on common mobility metrics, research specifically on state-to-state mobility is very limited. By leveraging the mobile phone location data from over 100 million anonymous devices, we estimate the population flow between all states in the United States. We first analyze the temporal pattern and spatial differences of between-state flow from January 1, 2020 to May 15, 2020. Then, with repeated measures ANOVA and post-hoc ana…
Unlike the lockdown measures taken in some countries or cities, the Japanese government declared a "State of Emergency" (SOE) under which people were only requested to reduce their contact with other people by at least 70 %, while some local governments also implemented their own mobility-reduction measures that had no legal basis. The effects of these measures are still unclear. Thus, in this study, we investigate changes in travel patterns in response to the COVID-19 outbreak and related policy measures in Japan using longitudinal aggregated mobile phone data. Specifically, we consider daily…
The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility is at the epicenter of that change, as the greatest facilitator for the spread of the virus. To study the change in mobility, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to possible future crisis, we need to properly understand all mobility data sources at our disposal. Our work is dedicated to the study of private mobility sources, gathered and released by large technological companies. This data is of special interest because, unlike most pu…
In the wake of the COVID-19 pandemic many countries implemented containment measures to reduce disease transmission. Studies using digital data sources show that the mobility of individuals was effectively reduced in multiple countries. However, it remains unclear whether these reductions caused deeper structural changes in mobility networks, and how such changes may affect dynamic processes on the network. Here we use movement data of mobile phone users to show that mobility in Germany has not only been reduced considerably: Lockdown measures caused substantial and long-lasting structural cha…
In the fight against the racketeering and terrorism, knowledge about the structure and the organization of criminal networks is of fundamental importance for both the investigations and the development of efficient strategies to prevent and restrain crimes. Intelligence agencies exploit information obtained from the analysis of large amounts of heterogeneous data deriving from various informative sources including the records of phone traffic, the social networks, surveillance data, interview data, experiential police data, and police intelligence files, to acquire knowledge about criminal net…
In this paper we deal with the study of travel flows and patterns of people in large populated areas. Information about the movements of people is extracted from coarse-grained aggregated cellular network data without tracking mobile devices individually. Mobile phone data are provided by the Italian telecommunication company TIM and consist of density profiles (i.e. the spatial distribution) of people in a given area at various instants of time. By computing a suitable approximation of the Wasserstein distance between two consecutive density profiles, we are able to extract the main direction…
Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analyzing millions of anonymous mobile phone users' visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are compu…
Countries around the world implement nonpharmaceutical interventions (NPIs) to mitigate the spread of COVID-19. Design of efficient NPIs requires identification of the structure of the disease transmission network. We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with an agent-based epidemiological model. We assume a Watts-Strogatz small-world network which allows to distinguish contacts within clustered cl…
The D4D-Senegal challenge is an open innovation data challenge on anonymous call patterns of Orange's mobile phone users in Senegal. The goal of the challenge is to help address society development questions in novel ways by contributing to the socio-economic development and well-being of the Senegalese population. Participants to the challenge are given access to three mobile phone datasets. This paper describes the three datasets. The datasets are based on Call Detail Records (CDR) of phone calls and text exchanges between more than 9 million of Orange's customers in Senegal between January …
A computational methodology is introduced to minimize infection opportunities for people suffering some degree of lockdown in response to a pandemic, as is the 2020 COVID-19 pandemic. Persons use their mobile phone or computational device to request trips to places of their need or interest indicating a rough time of day: `morning', `afternoon', `night' or `any time' when they would like to undertake these outings as well as the desired place to visit. An artificial intelligence methodology which is a variant of Genetic Programming studies all requests and responds with specific time allocatio…
The Orange "Data for Development" (D4D) challenge is an open data challenge on anonymous call patterns of Orange's mobile phone users in Ivory Coast. The goal of the challenge is to help address society development questions in novel ways by contributing to the socio-economic development and well-being of the Ivory Coast population. Participants to the challenge are given access to four mobile phone datasets and the purpose of this paper is to describe the four datasets. The website http://www.d4d.orange.com contains more information about the participation rules. The datasets are based on ano…
The capability of traffic-information systems to sense the movement of millions of users and offer trip plans through mobile phones has enabled a new way of optimizing city traffic dynamics, turning transportation big data into insights and actions in a closed-loop and evaluating this approach in the real world. Existing research has applied dynamic Bayesian networks and deep neural networks to make traffic predictions from floating car data, utilized dynamic programming and simulation approaches to identify how people normally travel with dynamic traffic assignment for policy research, and in…
In the context of preventing and fighting crime, the analysis of mobile phone traffic, among actors of a criminal network, is helpful in order to reconstruct illegal activities on the base of the relationships connecting those specific individuals. Thus, forensic analysts and investigators require new advanced tools and techniques which allow them to manage these data in a meaningful and efficient way. In this paper we present LogAnalysis, a tool we developed to provide visual data representation and filtering, statistical analysis features and the possibility of a temporal analysis of mobile …
Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon again…
In response to the coronavirus disease 2019 (COVID-19) pandemic, governments have encouraged and ordered citizens to practice social distancing, particularly by working and studying at home. Intuitively, only a subset of people have the ability to practice remote work. However, there has been little research on the disparity of mobility adaptation across different income groups in US cities during the pandemic. The authors worked to fill this gap by quantifying the impacts of the pandemic on human mobility by income in Greater Houston, Texas. In this paper, we determined human mobility using p…
COVID-19 has spread from China across Europe and the United States and has become a global pandemic. In countries of the Global South, due to often weaker socioeconomic options and health care systems, effective local countermeasures remain debated. We combine large-scale socioeconomic and traffic survey data with detailed agent-based simulations of local transportation to analyze COVID-19 spreading in a regional model for the Nelson Mandela Bay Municipality in South Africa under a range of countermeasure scenarios. The simulations indicate that any realistic containment strategy, including th…
In 2020, countries affected by the COVID-19 pandemic implemented various non-pharmaceutical interventions to contrast the spread of the virus and its impact on their healthcare systems and economies. Using Italian data at different geographic scales, we investigate the relationship between human mobility, which subsumes many facets of the population's response to the changing situation, and the spread of COVID-19. Leveraging mobile phone data from February through September 2020, we find a striking relationship between the decrease in mobility flows and the net reproduction number. We find tha…
