Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. High mobility between areas contribute to the importation of cases, affecting the spread of the disease. While many factors influence local incidence and making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can give rise to autonomous outbreaks that impact separate areas of the contact (social) network. Such mechanism has the potential to boost local incidence and size, making control and tracing measures less effective. In Spain, the high heterogeneity in incidence between similar areas despite the uniform mobility control measures taken suggests that multi-seeding could have played an important role in shaping the spreading of the disease. In this work, we focus on the spreading of SARS-CoV-2 among the $52$ Spanish provinces, showing that local incidence strongly correlates with mobility occurred in the early-stage weeks from and to Madrid, the main mobility hub and where the initial local outbreak unfolded. These results clarify the higher order effects that mobility can have on the evolution of an epidemic and highlight the relevance of its control. ; M.M. is funded by the Conselleria d'Innovaci´o, Recerca i Turisme of the Government of the Balearic Islands and the European Social Fund with grant code FPI/2090/2018. M.M., S.M. and J.J.R. also acknowledge funding from the project Distancia-COVID (CSICCOVID-19) of the CSIC funded by a contribution of AENA, from the Spanish Ministry of Science, Innovation and Universities, the AEI and FEDER (EU) under the grant PACSS (RTI2018-093732-B-C22) and the Maria de Maeztu program for Units of Excellence in R&D (MDM2017-0711). ; No
Monitoring migration flows is crucial to respond to humanitarian crisis and to design efficient policies. This information usually comes from surveys and border controls, but timely accessibility and methodological concerns reduce its usefulness. Here, we propose a method to detect migration flows worldwide using geolocated Twitter data. We focus on the migration crisis in Venezuela and show that the calculated flows are consistent with official statistics at country level. Our method is versatile and far-reaching, as it can be used to study different features of migration as preferred routes, settlement areas, mobility through several countries, spatial integration in cities, etc. It provides finer geographical and temporal resolutions, allowing the exploration of issues not contemplated in official records. It is our hope that these new sources of information can complement official ones, helping authorities and humanitarian organizations to better assess when and where to intervene on the ground. ; MM is funded by the Conselleria d'Innovaci\'o, Recerca i Turisme of the Government of the Balearic Islands and the European Social Fund with grant code FPI/2090/2018. AT acknowledges financial support from the AEI, Spanish National Research Agency, with grant code PTA2017-13872-I and the Government of the Balearic Islands. MM, AT, PC and JJR also acknowledge funding from the Spanish Ministry of Science, Innovation and Universities, the AEI and FEDER (EU) under the grant PACSS (RTI2018-093732-B-C22) and the Maria de Maeztu program for Units of Excellence in R\&D (MDM-2017-0711). We acknowledge support of the publication fee by the CSIC Open Access Publication Support Initiative through its Unit of Information Resources for Research (URICI). ; Peer reviewed
The ongoing SARS-CoV-2 pandemic has been holding the world hostage for several years now. Mobility is key to viral spreading and its restriction is the main non-pharmaceutical interventions to fight the virus expansion. Previous works have shown a connection between the structural organization of cities and the movement patterns of their residents. This puts urban centers in the focus of epidemic surveillance and interventions. Here we show that the organization of urban flows has a tremendous impact on disease spreading and on the amenability of different mitigation strategies. By studying anonymous and aggregated intra-urban flows in a variety of cities in the United States and other countries, and a combination of empirical analysis and analytical methods, we demonstrate that the response of cities to epidemic spreading can be roughly classified in two major types according to the overall organization of those flows. Hierarchical cities, where flows are concentrated primarily between mobility hotspots, are particularly vulnerable to the rapid spread of epidemics. Nevertheless, mobility restrictions in such types of cities are very effective in mitigating the spread of a virus. Conversely, in sprawled cities which present many centers of activity, the spread of an epidemic is much slower, but the response to mobility restrictions is much weaker and less effective. Investing resources on early monitoring and prompt ad-hoc interventions in more vulnerable cities may prove helpful in containing and reducing the impact of future pandemics. ; M.M. is funded by the Conselleria d'Innovació, Recerca i Turisme of the Government of the Balearic Islands and the European Social Fund with grant code FPI/2090/2018. J.A., M.M., S.Meloni and J.J.R. also acknowledge funding from the project Distancia-COVID (CSIC-COVID-19) of the CSIC funded by a contribution of AENA, from the project PACSS RTI2018-093732-B-C22 of the MCIN/AEI/10.13039/501100011033/ and by EU through FEDER funds (A way to make Europe), and also from the Maria ...
Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control. ; M.M.'s salary was funded by the Conselleria d'Innovacio´, Recerca i Turisme of the Government of the Balearic Islands and the European Social Fund with grant code FPI/2090/ 2018. M.M., S.M. and J.J.R. also acknowledge funding from the project Distancia-COVID (CSICCOVID- 19-039) of CSIC integrated in the platform PTI Salud Global and funded by a contribution of AENA, also from the Spanish Ministry of Science, Innovation and Universities, the AEI and FEDER (EU) under the grant PACSS (RTI2018-093732-BC22) and the Maria de Maeztu program for Units of Excellence in R&D (MDM-2017-0711). M.M. acknowledges financial support of the Sorbonne Universite´ Emergence project RISKFLOW. E.P., L. G., C.C. and M.T. gratefully acknowledge the support of the Lagrange Project of the ISI Foundation funded by CRT Foundation. P.B. acknowledges support from Intesa Sanpaolo Innovation Center. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ; Peer reviewed
The gathering and harmonisation of international statistical data in a multidisciplinary environment are key to international comparative analysis and policy work. The availability of timely, accurate statistical information enables policy-makers, practitioners, researchers and other stakeholders to address a wide range of issues in today's rapidly-evolving global economic and social landscape.The use of traditional data such as official administrative statistics however has some shortcomings. Traditional data in general takes long to be published and used because they are subject to a long technical and sometimes political process of harmonization and validation. Also, traditional data does not cover all topics of interest for territorial cohesion.Increasingly, data and information from analysing internet activities or social media can be used for observing territorial development trends. New developments for the availability and use of big data may help to overcome the shortcomings and bring new and interesting opportunities to support policy with up-to-date information relevant for territorial analysis.Currently, the interest from policy makers is growing as the sources for Big Data (Facebook, Google, Twitter, Instagram or blogs for example) contain valuable information, which can normally be hard to gather, and these data can be collected with very short notice. This means that Big Data could provide a more regular, cost-effective and harmonised data collection and provide an opportunity to more easily address new issues of interest.The aim of this ESPON activity is to further develop ways and methodologies for using existing big data sources and platforms to develop and measure indicators for territorial monitoring and analysis. In addition, these methodologies should be applied for indicators measuring the housing dynamics in European cities and the wellbeing of European citizens, in particular related to their housing and living situation. Finally, these methodologies should be made available and applicable to others for measuring these and other aspects in cities.
The gathering and harmonisation of international statistical data in a multidisciplinary environment are key to international comparative analysis and policy work. The availability of timely, accurate statistical information enables policy-makers, practitioners, researchers and other stakeholders to address a wide range of issues in today's rapidly-evolving global economic and social landscape.The use of traditional data such as official administrative statistics however has some shortcomings. Traditional data in general takes long to be published and used because they are subject to a long technical and sometimes political process of harmonization and validation. Also, traditional data does not cover all topics of interest for territorial cohesion.Increasingly, data and information from analysing internet activities or social media can be used for observing territorial development trends. New developments for the availability and use of big data may help to overcome the shortcomings and bring new and interesting opportunities to support policy with up-to-date information relevant for territorial analysis.Currently, the interest from policy makers is growing as the sources for Big Data (Facebook, Google, Twitter, Instagram or blogs for example) contain valuable information, which can normally be hard to gather, and these data can be collected with very short notice. This means that Big Data could provide a more regular, cost-effective and harmonised data collection and provide an opportunity to more easily address new issues of interest.The aim of this ESPON activity is to further develop ways and methodologies for using existing big data sources and platforms to develop and measure indicators for territorial monitoring and analysis. In addition, these methodologies should be applied for indicators measuring the housing dynamics in European cities and the wellbeing of European citizens, in particular related to their housing and living situation. Finally, these methodologies should be made available and applicable to others for measuring these and other aspects in cities.
The gathering and harmonisation of international statistical data in a multidisciplinary environment are key to international comparative analysis and policy work. The availability of timely, accurate statistical information enables policy-makers, practitioners, researchers and other stakeholders to address a wide range of issues in today's rapidly-evolving global economic and social landscape.The use of traditional data such as official administrative statistics however has some shortcomings. Traditional data in general takes long to be published and used because they are subject to a long technical and sometimes political process of harmonization and validation. Also, traditional data does not cover all topics of interest for territorial cohesion.Increasingly, data and information from analysing internet activities or social media can be used for observing territorial development trends. New developments for the availability and use of big data may help to overcome the shortcomings and bring new and interesting opportunities to support policy with up-to-date information relevant for territorial analysis.Currently, the interest from policy makers is growing as the sources for Big Data (Facebook, Google, Twitter, Instagram or blogs for example) contain valuable information, which can normally be hard to gather, and these data can be collected with very short notice. This means that Big Data could provide a more regular, cost-effective and harmonised data collection and provide an opportunity to more easily address new issues of interest.The aim of this ESPON activity is to further develop ways and methodologies for using existing big data sources and platforms to develop and measure indicators for territorial monitoring and analysis. In addition, these methodologies should be applied for indicators measuring the housing dynamics in European cities and the wellbeing of European citizens, in particular related to their housing and living situation. Finally, these methodologies should be made available and ...
The gathering and harmonisation of international statistical data in a multidisciplinary environment are key to international comparative analysis and policy work. The availability of timely, accurate statistical information enables policy-makers, practitioners, researchers and other stakeholders to address a wide range of issues in today's rapidly-evolving global economic and social landscape.The use of traditional data such as official administrative statistics however has some shortcomings. Traditional data in general takes long to be published and used because they are subject to a long technical and sometimes political process of harmonization and validation. Also, traditional data does not cover all topics of interest for territorial cohesion.Increasingly, data and information from analysing internet activities or social media can be used for observing territorial development trends. New developments for the availability and use of big data may help to overcome the shortcomings and bring new and interesting opportunities to support policy with up-to-date information relevant for territorial analysis.Currently, the interest from policy makers is growing as the sources for Big Data (Facebook, Google, Twitter, Instagram or blogs for example) contain valuable information, which can normally be hard to gather, and these data can be collected with very short notice. This means that Big Data could provide a more regular, cost-effective and harmonised data collection and provide an opportunity to more easily address new issues of interest.The aim of this ESPON activity is to further develop ways and methodologies for using existing big data sources and platforms to develop and measure indicators for territorial monitoring and analysis. In addition, these methodologies should be applied for indicators measuring the housing dynamics in European cities and the wellbeing of European citizens, in particular related to their housing and living situation. Finally, these methodologies should be made available and applicable to others for measuring these and other aspects in cities.
The gathering and harmonisation of international statistical data in a multidisciplinary environment are key to international comparative analysis and policy work. The availability of timely, accurate statistical information enables policy-makers, practitioners, researchers and other stakeholders to address a wide range of issues in today's rapidly-evolving global economic and social landscape.The use of traditional data such as official administrative statistics however has some shortcomings. Traditional data in general takes long to be published and used because they are subject to a long technical and sometimes political process of harmonization and validation. Also, traditional data does not cover all topics of interest for territorial cohesion.Increasingly, data and information from analysing internet activities or social media can be used for observing territorial development trends. New developments for the availability and use of big data may help to overcome the shortcomings and bring new and interesting opportunities to support policy with up-to-date information relevant for territorial analysis.Currently, the interest from policy makers is growing as the sources for Big Data (Facebook, Google, Twitter, Instagram or blogs for example) contain valuable information, which can normally be hard to gather, and these data can be collected with very short notice. This means that Big Data could provide a more regular, cost-effective and harmonised data collection and provide an opportunity to more easily address new issues of interest.The aim of this ESPON activity is to further develop ways and methodologies for using existing big data sources and platforms to develop and measure indicators for territorial monitoring and analysis. In addition, these methodologies should be applied for indicators measuring the housing dynamics in European cities and the wellbeing of European citizens, in particular related to their housing and living situation. Finally, these methodologies should be made available and applicable to others for measuring these and other aspects in cities.
The gathering and harmonisation of international statistical data in a multidisciplinary environment are key to international comparative analysis and policy work. The availability of timely, accurate statistical information enables policy-makers, practitioners, researchers and other stakeholders to address a wide range of issues in today's rapidly-evolving global economic and social landscape.The use of traditional data such as official administrative statistics however has some shortcomings. Traditional data in general takes long to be published and used because they are subject to a long technical and sometimes political process of harmonization and validation. Also, traditional data does not cover all topics of interest for territorial cohesion.Increasingly, data and information from analysing internet activities or social media can be used for observing territorial development trends. New developments for the availability and use of big data may help to overcome the shortcomings and bring new and interesting opportunities to support policy with up-to-date information relevant for territorial analysis.Currently, the interest from policy makers is growing as the sources for Big Data (Facebook, Google, Twitter, Instagram or blogs for example) contain valuable information, which can normally be hard to gather, and these data can be collected with very short notice. This means that Big Data could provide a more regular, cost-effective and harmonised data collection and provide an opportunity to more easily address new issues of interest.The aim of this ESPON activity is to further develop ways and methodologies for using existing big data sources and platforms to develop and measure indicators for territorial monitoring and analysis. In addition, these methodologies should be applied for indicators measuring the housing dynamics in European cities and the wellbeing of European citizens, in particular related to their housing and living situation. Finally, these methodologies should be made available and applicable to others for measuring these and other aspects in cities.
