Mapping imported malaria in Bangladesh using parasite genetic and human mobility data

For countries aiming for malaria elimination, travel of infected individuals between endemic areas undermines local interventions. Quantifying parasite importation has therefore become a priority for national control programs. We analyzed epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data to measure the spatial spread of malaria parasites in southeast Bangladesh. We developed a genetic mixing index to estimate the likelihood of samples being local or imported from parasite genetic data and inferred the direction and intensity of parasite flow between locations using an epidemiological model integrating the travel survey and mobile phone calling data. Our approach indicates that, contrary to dogma, frequent mixing occurs in low transmission regions in the southwest, and elimination will require interventions in addition to reducing imported infections from forested regions. Unlike risk maps generated from clinical case counts alone, therefore, our approach distinguishes areas of frequent importation as well as high transmission.

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Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa

eLife ◽

10.7554/elife.43510 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 27

Author(s):

Sofonias Tessema ◽

Amy Wesolowski ◽

Anna Chen ◽

Maxwell Murphy ◽

Jordan Wilheim ◽

...

Keyword(s):

Mobile Phone ◽

Southern Africa ◽

Human Mobility ◽

Genetic Data ◽

Mobile Phone Data ◽

Malaria Surveillance ◽

Travel History ◽

Cross Border ◽

Mobility Data ◽

Regional Coordination

Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.

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The geography of malaria elimination in Bangladesh: combining data layers to estimate the spatial spread of parasites

10.1101/421578 ◽

2018 ◽

Cited By ~ 1

Author(s):

Hsiao-Han Chang ◽

Amy Wesolowski ◽

Ipsita Sinha ◽

Christopher G Jacob ◽

Ayesha Mahmud ◽

...

Keyword(s):

Mobile Phone ◽

Malaria Elimination ◽

Epidemiological Surveillance ◽

Mobile Phone Data ◽

Mobility Patterns ◽

Malaria Parasites ◽

Spatial Spread ◽

Control Programs ◽

Combining Data ◽

Data Layers

AbstractMalaria control programs face difficult resource allocation decisions. Of particular concern for countries aiming for malaria elimination, the regular movement of individuals to and from endemic areas undermines local interventions by reintroducing infections and sustaining local transmission. Quantifying this movement of malaria parasites around a country has become a priority for national control programs, but remains methodologically challenging, particularly in areas with highly mobile populations. Here, we combined multiple data sources to measure the geographical spread of malaria parasites, including epidemiological surveillance data, travel surveys, parasite genetic data, and anonymized mobile phone data. We collected parasite genetic barcodes and travel surveys from 2,090 patients residing in 176 unions in southeast Bangladesh. We developed a genetic mixing index to quantify the likelihood of samples being local or imported. We then inferred the direction and intensity of parasite flow between locations using an epidemiological model, and estimated the proportion of imported cases assuming mobility patterns parameterized using the travel survey and mobile phone calling data. Our results show that each data source provided related but different information about the patterns of geographic spread of parasites. We identify a consistent north/south separation of the Chittagong Hill Tracts region in Bangladesh, and found that in addition to imported infections from forested regions, frequent mixing also occurs in low transmission but highly populated areas in the southwest. Thus, unlike risk maps generated from incidence alone, our maps provide evidence that elimination programs must address ongoing movement of parasites around the lower transmission areas in the southwest.

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

Royal Society Open Science ◽

10.1098/rsos.160950 ◽

2017 ◽

Vol 4 (5) ◽

pp. 160950 ◽

Cited By ~ 19

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.

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Genomics, social media and mobile phone data enable mapping of SARS-CoV-2 lineages to inform health policy in Bangladesh

Nature Microbiology ◽

10.1038/s41564-021-00955-3 ◽

2021 ◽

Author(s):

Lauren A. Cowley ◽

Mokibul Hassan Afrad ◽

Sadia Isfat Ara Rahman ◽

Md Mahfuz Al Mamun ◽

Taylor Chin ◽

...

Keyword(s):

Mobile Phone ◽

Rural Areas ◽

National Level ◽

Control Measures ◽

Mobile Phone Data ◽

Population Mobility ◽

Spatial Spread ◽

Mobility Data ◽

Mass Migration ◽

First Case

AbstractGenomics, combined with population mobility data, used to map importation and spatial spread of SARS-CoV-2 in high-income countries has enabled the implementation of local control measures. Here, to track the spread of SARS-CoV-2 lineages in Bangladesh at the national level, we analysed outbreak trajectory and variant emergence using genomics, Facebook ‘Data for Good’ and data from three mobile phone operators. We sequenced the complete genomes of 67 SARS-CoV-2 samples (collected by the IEDCR in Bangladesh between March and July 2020) and combined these data with 324 publicly available Global Initiative on Sharing All Influenza Data (GISAID) SARS-CoV-2 genomes from Bangladesh at that time. We found that most (85%) of the sequenced isolates were Pango lineage B.1.1.25 (58%), B.1.1 (19%) or B.1.36 (8%) in early-mid 2020. Bayesian time-scaled phylogenetic analysis predicted that SARS-CoV-2 first emerged during mid-February in Bangladesh, from abroad, with the first case of coronavirus disease 2019 (COVID-19) reported on 8 March 2020. At the end of March 2020, three discrete lineages expanded and spread clonally across Bangladesh. The shifting pattern of viral diversity in Bangladesh, combined with the mobility data, revealed that the mass migration of people from cities to rural areas at the end of March, followed by frequent travel between Dhaka (the capital of Bangladesh) and the rest of the country, disseminated three dominant viral lineages. Further analysis of an additional 85 genomes (November 2020 to April 2021) found that importation of variant of concern Beta (B.1.351) had occurred and that Beta had become dominant in Dhaka. Our interpretation that population mobility out of Dhaka, and travel from urban hotspots to rural areas, disseminated lineages in Bangladesh in the first wave continues to inform government policies to control national case numbers by limiting within-country travel.

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Mobile phone data reveals the importance of pre-disaster inter-city social ties for recovery after Hurricane Maria

Applied Network Science ◽

10.1007/s41109-019-0221-5 ◽

2019 ◽

Vol 4 (1) ◽

Cited By ~ 1

Author(s):

Takahiro Yabe ◽

Satish V. Ukkusuri ◽

P. Suresh C. Rao

Keyword(s):

Mobile Phone ◽

Large Scale ◽

Human Mobility ◽

Recovery Process ◽

Mobile Phone Data ◽

Mobility Patterns ◽

Social Forms ◽

Recovery Processes ◽

Mobility Data ◽

New Perspective

Abstract Recent disasters have shown the existence of large variance in recovery trajectories across cities that have experienced similar damage levels. Case studies of such events reveal the high complexity of the recovery process of cities, where inter-city dependencies and intra-city coupling of social and physical systems may affect the outcomes in unforeseen ways. Despite the large implications of understanding the recovery processes of cities after disasters for many domains including critical services, disaster management, and public health, little work have been performed to unravel this complexity. Rather, works are limited to analyzing and modeling cities as independent entities, and have largely neglected the effect that inter-city connectivity may have on the recovery of each city. Large scale mobility data (e.g. mobile phone data, social media data) have enabled us to observe human mobility patterns within and across cities with high spatial and temporal granularity. In this paper, we investigate how inter-city dependencies in both physical as well as social forms contribute to the recovery performances of cities after disasters, through a case study of the population recovery patterns of 78 Puerto Rican counties after Hurricane Maria using mobile phone location data. Various network metrics are used to quantify the types of inter-city dependencies that play an important role for effective post-disaster recovery. We find that inter-city social connectivity, which is measured by pre-disaster mobility patterns, is crucial for quicker recovery after Hurricane Maria. More specifically, counties that had more influx and outflux of people prior to the hurricane, were able to recover faster. Our findings highlight the importance of fostering the social connectivity between cities to prepare effectively for future disasters. This paper introduces a new perspective in the community resilience literature, where we take into account the inter-city dependencies in the recovery process rather than analyzing each community as independent entities.

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Complete trajectory reconstruction from sparse mobile phone data

EPJ Data Science ◽

10.1140/epjds/s13688-019-0206-8 ◽

2019 ◽

Vol 8 (1) ◽

Cited By ~ 1

Author(s):

Guangshuo Chen ◽

Aline Carneiro Viana ◽

Marco Fiore ◽

Carlos Sarraute

Keyword(s):

Mobile Phone ◽

Mobile Communications ◽

Large Scale ◽

Human Mobility ◽

Mobile Phone Data ◽

Tensor Factorization ◽

Trajectory Reconstruction ◽

Mobility Data ◽

Communication Events ◽

A New Technique

Abstract Mobile phone data are a popular source of positioning information in many recent studies that have largely improved our understanding of human mobility. These data consist of time-stamped and geo-referenced communication events recorded by network operators, on a per-subscriber basis. They allow for unprecedented tracking of populations of millions of individuals over long periods that span months. Nevertheless, due to the uneven processes that govern mobile communications, the sampling of user locations provided by mobile phone data tends to be sparse and irregular in time, leading to substantial gaps in the resulting trajectory information. In this paper, we illustrate the severity of the problem through an empirical study of a large-scale Call Detail Records (CDR) dataset. We then propose Context-enhanced Trajectory Reconstruction, a new technique that hinges on tensor factorization as a core method to complete individual CDR-based trajectories. The proposed solution infers missing locations with a median displacement within two network cells from the actual position of the user, on an hourly basis and even when as little as 1% of her original mobility is known. Our approach lets us revisit seminal works in the light of complete mobility data, unveiling potential biases that incomplete trajectories obtained from legacy CDR induce on key results about human mobility laws, trajectory uniqueness, and movement predictability.

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Radius of Gyration as predictor of COVID-19 deaths trend with three-weeks offset

10.1101/2021.01.30.21250708 ◽

2021 ◽

Author(s):

Alberto Hernando ◽

David Mateo ◽

Jordi Bayer ◽

Ignacio Barrios

Keyword(s):

Mobile Phone ◽

Human Mobility ◽

Critical Value ◽

Mobile Phone Data ◽

Critical Threshold ◽

Radius Of Gyration ◽

Mobility Data ◽

Collateral Effects ◽

Candidate Predictor ◽

Nonpharmaceutical Interventions

AbstractTotal and perimetral lockdowns were the strongest nonpharmaceutical interventions to fight against Covid-19, as well as the with the strongest socioeconomic collateral effects. Lacking a metric to predict the effect of lockdowns in the spreading of COVID-19, authorities and decision-makers opted for preventive measures that showed either too strong or not strong enough after a period of two to three weeks, once data about hospitalizations and deaths was available. We present here the radius of gyration as a candidate predictor of the trend in deaths by COVID-19 with an offset of three weeks. Indeed, the radius of gyration aggregates the most relevant microscopic aspects of human mobility into a macroscopic value, very sensitive to temporary trends and local effects, such as lockdowns and mobility restrictions. We use mobile phone data of more than 13 million users in Spain during a period of one year (from January 6th 2020 to January 10th 2021) to compute the users’ daily radius of gyration and compare the median value of the population with the evolution of COVID-19 deaths: we find that for all weeks where the radius of gyration is above a critical value (70% of its pre-pandemic score) the number of weekly deaths increases three weeks after. The reverse also stands: for all weeks where the radius of gyration is below the critical value, the number of weekly deaths decreased after three weeks. This observation leads to two conclusions: i) the radius of gyration can be used as a predictor of COVID-19-related deaths; and ii) partial mobility restrictions are as effective as a total lockdown as far the radius of gyration is below this critical value.BackgroundAuthorities around the World have used lockdowns and partial mobility restrictions as major nonpharmaceutical interventions to control the expansion of COVID-19. While effective, the efficiency of these measures on the number of COVID-19 cases and deaths is difficult to quantify, severely limiting the feedback that can be used to tune the intensity of these measures. In addition, collateral socioeconomic effects challenge the overall effectiveness of lockdowns in the long term, and the degree by which they are followed can be difficult to estimate. It is desirable to find both a metric to accurately monitor the mobility restrictions and a predictor of their effectiveness.MethodsWe correlate the median of the daily radius of gyration of more than 13M users in Spain during all of 2020 with the evolution of COVID-19 deaths for the same period. Mobility data is obtained from mobile phone metadata from one of the major operators in the country.ResultsThe radius of gyration is a predictor of the trend in the number of COVID-19 deaths with 3 weeks offset. When the radius is above/below a critical threshold (70% of the pre-pandemic score), the number of deaths increases/decreases three weeks later.ConclusionsThe radius of gyration can be used to monitor in real time the effectiveness of the mobility restrictions. The existence of a critical threshold suggest that partial lockdowns can be as efficient as total lockdowns, while reducing their socioeconomic impact. The mechanism behind the critical value is still unknow, and more research is needed.

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Existing human mobility data sources poorly predicted the spatial spread of SARS-CoV-2 in Madagascar

10.1101/2021.07.30.21261392 ◽

2021 ◽

Author(s):

Tanjona Ramiadantsoa ◽

C. Jessica E. Metcalf ◽

Antso Hasina Raherinandrasana ◽

Santatra Randrianarisoa ◽

Benjamin L. Rice ◽

...

Keyword(s):

Public Health ◽

Mobile Phone ◽

Gravity Model ◽

Human Mobility ◽

Health Planning ◽

Data Availability ◽

Disease Spread ◽

Mobile Phone Data ◽

Migration Flow ◽

Mobility Data

For emerging epidemics such as the COVID-19 pandemic, quantifying travel is a key component of developing accurate predictive models of disease spread to inform public health planning. However, in many LMICs, traditional data sets on travel such as commuting surveys as well as non-traditional sources such as mobile phone data are lacking, or, where available, have only rarely been leveraged by the public health community. Evaluating the accuracy of available data to measure transmission-relevant travel may be further hampered by limited reporting of suspected and laboratory confirmed infections. Here, we leverage case data collected as part of a COVID-19 dashboard collated via daily reports from the Malagasy authorities on reported cases of SARS-CoV-2 across the 22 regions of Madagascar. We compare the order of the timing of when cases were reported with predictions from a SARS-CoV-2 metapopulation model of Madagascar informed using various measures of connectivity including a gravity model based on different measures of distance, Internal Migration Flow data, and mobile phone data. Overall, the models based on mobile phone connectivity and the gravity-based on Euclidean distance best predicted the observed spread. The ranks of the regions most remote from the capital were more difficult to predict but interestingly, regions where the mobile phone connectivity model was more accurate differed from those where the gravity model was most accurate. This suggests that there may be additional features of mobility or connectivity that were consistently underestimated using all approaches, but are epidemiologically relevant. This work highlights the importance of data availability and strengthening collaboration among different institutions with access to critical data - models are only as good as the data that they use, so building towards effective data-sharing pipelines is essential.

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Incorporating human mobility data improves forecasts of Dengue fever in Thailand

Scientific Reports ◽

10.1038/s41598-020-79438-0 ◽

2021 ◽

Vol 11 (1) ◽

Cited By ~ 1

Author(s):

Mathew V. Kiang ◽

Mauricio Santillana ◽

Jarvis T. Chen ◽

Jukka-Pekka Onnela ◽

Nancy Krieger ◽

...

Keyword(s):

Time Series ◽

Dengue Fever ◽

Human Mobility ◽

Epidemiological Data ◽

Early Warning Systems ◽

Environmental Data ◽

Mobile Phone Data ◽

Effective Vaccine ◽

Spatial Spread ◽

Mobility Data

AbstractOver 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.

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Monitoring the Spatial Spread of COVID-19 and Effectiveness of Control Measures Through Human Movement Data: Proposal for a Predictive Model Using Big Data Analytics

JMIR Research Protocols ◽

10.2196/24432 ◽

2020 ◽

Vol 9 (12) ◽

pp. e24432

Author(s):

Zhenlong Li ◽

Xiaoming Li ◽

Dwayne Porter ◽

Jiajia Zhang ◽

Yuqin Jiang ◽

...

Keyword(s):

Public Health ◽

Big Data ◽

Infectious Diseases ◽

Spatial Scales ◽

Human Mobility ◽

Human Movement ◽

Data Sources ◽

Mobile Phone Data ◽

Spatial Spread ◽

Mobility Data

Background Human movement is one of the forces that drive the spatial spread of infectious diseases. To date, reducing and tracking human movement during the COVID-19 pandemic has proven effective in limiting the spread of the virus. Existing methods for monitoring and modeling the spatial spread of infectious diseases rely on various data sources as proxies of human movement, such as airline travel data, mobile phone data, and banknote tracking. However, intrinsic limitations of these data sources prevent us from systematic monitoring and analyses of human movement on different spatial scales (from local to global). Objective Big data from social media such as geotagged tweets have been widely used in human mobility studies, yet more research is needed to validate the capabilities and limitations of using such data for studying human movement at different geographic scales (eg, from local to global) in the context of global infectious disease transmission. This study aims to develop a novel data-driven public health approach using big data from Twitter coupled with other human mobility data sources and artificial intelligence to monitor and analyze human movement at different spatial scales (from global to regional to local). Methods We will first develop a database with optimized spatiotemporal indexing to store and manage the multisource data sets collected in this project. This database will be connected to our in-house Hadoop computing cluster for efficient big data computing and analytics. We will then develop innovative data models, predictive models, and computing algorithms to effectively extract and analyze human movement patterns using geotagged big data from Twitter and other human mobility data sources, with the goal of enhancing situational awareness and risk prediction in public health emergency response and disease surveillance systems. Results This project was funded as of May 2020. We have started the data collection, processing, and analysis for the project. Conclusions Research findings can help government officials, public health managers, emergency responders, and researchers answer critical questions during the pandemic regarding the current and future infectious risk of a state, county, or community and the effectiveness of social/physical distancing practices in curtailing the spread of the virus. International Registered Report Identifier (IRRID) DERR1-10.2196/24432

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