scholarly journals Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data

2015 ◽  
Vol 112 (35) ◽  
pp. 11114-11119 ◽  
Author(s):  
Amy Wesolowski ◽  
C. J. E. Metcalf ◽  
Nathan Eagle ◽  
Janeth Kombich ◽  
Bryan T. Grenfell ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Low Income ◽  
Spatial Data ◽  
Disease Transmission ◽  
Travel Behavior ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Cross Sectional ◽  
Unique Source ◽  
Rich Data

Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.

scholarly journals Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks

2016 ◽  
Vol 113 (23) ◽  
pp. 6421-6426 ◽  
Author(s):  
Flavio Finger ◽  
Tina Genolet ◽  
Lorenzo Mari ◽  
Guillaume Constantin de Magny ◽  
Noël Magloire Manga ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Disease Transmission ◽  
Large Scale ◽  
Human Mobility ◽  
Disease Outbreaks ◽  
Mobility Model ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Major Influence ◽  
Mass Gatherings

The spatiotemporal evolution of human mobility and the related fluctuations of population density are known to be key drivers of the dynamics of infectious disease outbreaks. These factors are particularly relevant in the case of mass gatherings, which may act as hotspots of disease transmission and spread. Understanding these dynamics, however, is usually limited by the lack of accurate data, especially in developing countries. Mobile phone call data provide a new, first-order source of information that allows the tracking of the evolution of mobility fluxes with high resolution in space and time. Here, we analyze a dataset of mobile phone records of ∼150,000 users in Senegal to extract human mobility fluxes and directly incorporate them into a spatially explicit, dynamic epidemiological framework. Our model, which also takes into account other drivers of disease transmission such as rainfall, is applied to the 2005 cholera outbreak in Senegal, which totaled more than 30,000 reported cases. Our findings highlight the major influence that a mass gathering, which took place during the initial phase of the outbreak, had on the course of the epidemic. Such an effect could not be explained by classic, static approaches describing human mobility. Model results also show how concentrated efforts toward disease control in a transmission hotspot could have an important effect on the large-scale progression of an outbreak.

scholarly journals Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models

2017 ◽  
Vol 4 (5) ◽  
pp. 160950 ◽  
Author(s):  
Cecilia Panigutti ◽  
Michele Tizzoni ◽  
Paolo Bajardi ◽  
Zbigniew Smoreda ◽  
Vittoria Colizza
Keyword(s):  
Infectious Disease ◽  
Mobile Phone ◽  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Mobile Phone Data ◽  
Disease Models ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Infectious Disease Models

The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.

scholarly journals An Agent-Based Model Simulation of Human Mobility Based on Mobile Phone Data: How Commuting Relates to Congestion

2019 ◽  
Author(s):  
Hao Wu ◽  
Lingbo Liu ◽  
Yang Yu ◽  
Zhenghong Peng ◽  
Hongzan Jiao ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Space ◽  
Road Network ◽  
Model Simulation ◽  
Human Mobility ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Agent Based Model ◽  
Agent Based ◽  
Travel Behaviors

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

scholarly journals Social inequalities in human mobility during the Spanish lockdown and post-lockdown in the Covid-19 pandemic of 2020

2020 ◽  
Author(s):  
Alberto Hernando ◽  
David Mateo ◽  
Ignacio barrios ◽  
Angelo Plastino
Keyword(s):  
Low Income ◽  
Social Inequalities ◽  
Human Mobility ◽  
Rebound Effect ◽  
Relative Difference ◽  
High Income ◽  
Mobile Phone Data ◽  
Radius Of Gyration ◽  
New Normal ◽  
Collateral Effects

AbstractMany countries established strong population lockdowns as a response to the pandemic of COVID-19 in 2020. While these measures proved efficient in stopping the spreading of the virus, they also introduced collateral effects in the economies of these countries. We report in this work that the imprints in mobility of both the lockdown and post-lockdown on the Spanish population are measurable by means of the daily radius of gyration using mobile phone data. We cross mobility with economic data segmented by average salary per person so as to find large inequalities between low- and high-income populations. Indeed, low-income populations typically show a 17% higher radius of gyration than high-income ones during pre-lockdown (8.1 km vs. 6.9 km). However, this relative difference grows to a maximum during lock-down (3.3 km vs. 900 m) since most of the essential workers (carriers, nurses, supermarket cashiers, farmworkers, etc.) belong to the first segment. Post-lockdown shows reversed inequality in the weeks during summer vacations as high-income populations multiplied their pre-lockdown radius by 70% as a rebound effect driven by leisure, while low-income populations recovered their normal pre-lockdown radius. This period is correlated with an extraordinary increase in the number of new Covid cases, which stabilized after the holyday weeks once at the so-called new normal. We find that this new normal emphasizes the pre-lockdown inequalities in mobility between low- and high-income population, increasing the inequality up to a 47%. These results show the relevance of devising measures that could account for potential collateral inequalities.

scholarly journals Social and racial inequalities in COVID-19 risk of hospitalisation and death across Sao Paulo state, Brazil

2020 ◽  
Author(s):  
Sabrina L. Li ◽  
Rafael H. M. Pereira ◽  
Carlos A. Prete ◽  
Alexander E. Zarebski ◽  
Lucas Emanuel ◽  
...  
Keyword(s):  
Low Income ◽  
Respiratory Infections ◽  
Cross Sectional Study ◽  
Sao Paulo ◽  
Mobile Phone Data ◽  
São Paulo ◽  
Cross Sectional ◽  
Paulo State ◽  
Disadvantaged Population ◽  
São Paulo State

Background: Little evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in Sao Paulo state, Brazil and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities. Methods: We conducted a cross-sectional study using hospitalised severe acute respiratory infections (SARI) notified from March to August 2020, in the Sistema de Monitoramento Inteligente de Sao Paulo (SIMI-SP) database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple datasets for individual-level and spatio-temporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour, and comorbidities. Findings: Throughout the study period, patients living in the 40% poorest areas were more likely to die when compared to patients living in the 5% wealthiest areas (OR: 1.60, 95% CI: 1.48 - 1.74) and were more likely to be hospitalised between April and July, 2020 (OR: 1.08, 95% CI: 1.04 - 1.12). Black and Pardo individuals were more likely to be hospitalised when compared to White individuals (OR: 1.37, 95% CI: 1.32 - 1.41; OR: 1.23, 95% CI: 1.21 - 1.25, respectively), and were more likely to die (OR: 1.14, 95% CI: 1.07 - 1.21; 1.09, 95% CI: 1.05 - 1.13, respectively). Interpretation: Low-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to healthcare, adherence to social distancing, and the higher prevalence of comorbidities.

scholarly journals Clustering Weekly Patterns of Human Mobility Through Mobile Phone Data

2018 ◽  
Vol 17 (4) ◽  
pp. 817-830 ◽  
Author(s):  
Etienne Thuillier ◽  
Laurent Moalic ◽  
Sid Lamrous ◽  
Alexandre Caminada
Keyword(s):  
Mobile Phone ◽  
Human Mobility ◽  
Mobile Phone Data

scholarly journals Higher risk of death from COVID-19 in low-income and non-White populations of São Paulo, Brazil

2021 ◽  
Vol 6 (4) ◽  
pp. e004959
Author(s):  
Sabrina L Li ◽  
Rafael H M Pereira ◽  
Carlos A Prete Jr ◽  
Alexander E Zarebski ◽  
Lucas Emanuel ◽  
...  
Keyword(s):  
Low Income ◽  
Respiratory Infections ◽  
Sao Paulo ◽  
Public Hospitals ◽  
Mobile Phone Data ◽  
São Paulo ◽  
Cross Sectional ◽  
Risk Of Death ◽  
Hospitalised Patients ◽  
Multiple Data Sets

IntroductionLittle evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in São Paulo state, Brazil, and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities.MethodsWe conducted a cross-sectional study using hospitalised severe acute respiratory infections notified from March to August 2020 in the Sistema de Monitoramento Inteligente de São Paulo database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple data sets for individual-level and spatiotemporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour and comorbidities.ResultsThroughout the study period, patients living in the 40% poorest areas were more likely to die when compared with patients living in the 5% wealthiest areas (OR: 1.60, 95% CI 1.48 to 1.74) and were more likely to be hospitalised between April and July 2020 (OR: 1.08, 95% CI 1.04 to 1.12). Black and Pardo individuals were more likely to be hospitalised when compared with White individuals (OR: 1.41, 95% CI 1.37 to 1.46; OR: 1.26, 95% CI 1.23 to 1.28, respectively), and were more likely to die (OR: 1.13, 95% CI 1.07 to 1.19; 1.07, 95% CI 1.04 to 1.10, respectively) between April and July 2020. Once hospitalised, patients treated in public hospitals were more likely to die than patients in private hospitals (OR: 1.40%, 95% CI 1.34% to 1.46%). Black individuals and those with low education attainment were more likely to have one or more comorbidities, respectively (OR: 1.29, 95% CI 1.19 to 1.39; 1.36, 95% CI 1.27 to 1.45).ConclusionsLow-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to quality healthcare, ability to self-isolate and the higher prevalence of comorbidities.

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