Forecasting the spatial transmission of influenza in the United States

Recurrent outbreaks of seasonal and pandemic influenza create a need for forecasts of the geographic spread of this pathogen. Although it is well established that the spatial progression of infection is largely attributable to human mobility, difficulty obtaining real-time information on human movement has limited its incorporation into existing infectious disease forecasting techniques. In this study, we develop and validate an ensemble forecast system for predicting the spatiotemporal spread of influenza that uses readily accessible human mobility data and a metapopulation model. In retrospective state-level forecasts for 35 US states, the system accurately predicts local influenza outbreak onset,—i.e., spatial spread, defined as the week that local incidence increases above a baseline threshold—up to 6 wk in advance of this event. In addition, the metapopulation prediction system forecasts influenza outbreak onset, peak timing, and peak intensity more accurately than isolated location-specific forecasts. The proposed framework could be applied to emergent respiratory viruses and, with appropriate modifications, other infectious diseases.

Download Full-text

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

Science Advances ◽

10.1126/sciadv.abd6989 ◽

2021 ◽

Vol 7 (10) ◽

pp. eabd6989

Author(s):

Nicole E. Kogan ◽

Leonardo Clemente ◽

Parker Liautaud ◽

Justin Kaashoek ◽

Nicholas B. Link ◽

...

Keyword(s):

Early Warning ◽

Data Streams ◽

Human Mobility ◽

Warning System ◽

State Level ◽

The United States ◽

Digital Data ◽

Mobility Data ◽

Epidemiological Monitoring ◽

Nonpharmaceutical Interventions

Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.

Download Full-text

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

Download Full-text

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 (Preprint)

10.2196/preprints.24432 ◽

2020 ◽

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 DERR1-10.2196/24432

Download Full-text

Potential Urban Rainfall Prediction Measurement System

Water Science & Technology ◽

10.2166/wst.1984.0202 ◽

1984 ◽

Vol 16 (8-9) ◽

pp. 349-362 ◽

Cited By ~ 2

Author(s):

John L Vogel

Keyword(s):

Real Time ◽

The United States ◽

Monitoring Systems ◽

Climatic Region ◽

Convective Storm ◽

Rainfall Prediction ◽

Water Quality Regulations ◽

General Data ◽

Real Time Information ◽

Time Information

Continued growth of urban regions and more stringent water quality regulations have resulted in an increased need for more real-time information about past, present, and future patterns and intensities of precipitation. Detailed, real-time information about precipitation can be obtained using radar and raingages for monitoring and prediction of precipitation amounts. The philosophy and the requirements for the development of real-time radar prediction-monitoring systems are described for climatic region similar to the Midwest of the united States. General data analysis and interpretation techniques associated with rainfall from convective storm systems are presented.

Download Full-text

Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

BMC Public Health ◽

10.1186/s12889-021-10260-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Meng-Chun Chang ◽

Rebecca Kahn ◽

Yu-An Li ◽

Cheng-Sheng Lee ◽

Caroline O. Buckee ◽

...

Keyword(s):

Disease Surveillance ◽

Initial Conditions ◽

Human Mobility ◽

Human Movement ◽

Disease Spread ◽

Spatial Spread ◽

Movement Data ◽

Intercity Travel ◽

Travel Restrictions ◽

The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

Download Full-text

Modelling Representative Population Mobility for COVID-19 Spatial Transmission in South Africa

Frontiers in Big Data ◽

10.3389/fdata.2021.718351 ◽

2021 ◽

Vol 4 ◽

Author(s):

A. Potgieter ◽

I. N. Fabris-Rotelli ◽

Z. Kimmie ◽

N. Dudeni-Tlhone ◽

J. P. Holloway ◽

...

Keyword(s):

Human Mobility ◽

Data Sources ◽

Population Mobility ◽

Mobility Patterns ◽

Mobility Data ◽

The World ◽

Spatial Weight ◽

Data Source ◽

Reduced Mobility ◽

Spatial Transmission

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.

Download Full-text

Differential effects of intervention timing on COVID-19 spread in the United States

Science Advances ◽

10.1126/sciadv.abd6370 ◽

2020 ◽

Vol 6 (49) ◽

pp. eabd6370 ◽

Cited By ~ 4

Author(s):

Sen Pei ◽

Sasikiran Kandula ◽

Jeffrey Shaman

Keyword(s):

United States ◽

Disease Transmission ◽

Human Mobility ◽

The United States ◽

Control Measures ◽

Basic Reproductive Number ◽

Transmission Model ◽

Reproductive Number ◽

Mobility Data ◽

Nonpharmaceutical Interventions

Assessing the effects of early nonpharmaceutical interventions on coronavirus disease 2019 (COVID-19) spread is crucial for understanding and planning future control measures to combat the pandemic. We use observations of reported infections and deaths, human mobility data, and a metapopulation transmission model to quantify changes in disease transmission rates in U.S. counties from 15 March to 3 May 2020. We find that marked, asynchronous reductions of the basic reproductive number occurred throughout the United States in association with social distancing and other control measures. Counterfactual simulations indicate that, had these same measures been implemented 1 to 2 weeks earlier, substantial cases and deaths could have been averted and that delayed responses to future increased incidence will facilitate a stronger rebound of infections and death. Our findings underscore the importance of early intervention and aggressive control in combatting the COVID-19 pandemic.

Download Full-text

The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv071 ◽

2015 ◽

Vol 22 (6) ◽

pp. 1120-1125 ◽

Cited By ~ 4

Author(s):

Joy P Ku ◽

Jennifer L Hicks ◽

Trevor Hastie ◽

Jure Leskovec ◽

Christopher Ré ◽

...

Keyword(s):

Data Science ◽

Human Mobility ◽

Wearable Sensors ◽

Human Movement ◽

Science Research ◽

Science Methods ◽

Social Modeling ◽

Mobility Data ◽

Using Data ◽

And Training

Abstract Regular physical activity helps prevent heart disease, stroke, diabetes, and other chronic diseases, yet a broad range of conditions impair mobility at great personal and societal cost. Vast amounts of data characterizing human movement are available from research labs, clinics, and millions of smartphones and wearable sensors, but integration and analysis of this large quantity of mobility data are extremely challenging. The authors have established the Mobilize Center ( http://mobilize.stanford.edu ) to harness these data to improve human mobility and help lay the foundation for using data science methods in biomedicine. The Center is organized around 4 data science research cores: biomechanical modeling, statistical learning, behavioral and social modeling, and integrative modeling. Important biomedical applications, such as osteoarthritis and weight management, will focus the development of new data science methods. By developing these new approaches, sharing data and validated software tools, and training thousands of researchers, the Mobilize Center will transform human movement research.

Download Full-text

Use of open mobile mapping tool to assess human mobility traceability in rural offline populations with contrasting malaria dynamics

PeerJ ◽

10.7717/peerj.6298 ◽

2019 ◽

Vol 7 ◽

pp. e6298 ◽

Cited By ~ 4

Author(s):

Gabriel Carrasco-Escobar ◽

Marcia C. Castro ◽

Jose Luis Barboza ◽

Jorge Ruiz-Cabrejos ◽

Alejandro Llanos-Cuentas ◽

...

Keyword(s):

Malaria Transmission ◽

Human Mobility ◽

Human Movement ◽

Mobile Mapping ◽

Mobility Spectrum ◽

Mobility Data ◽

Mapping Tool ◽

Commercial Logging ◽

Tree Coverage ◽

Transmission Studies

Infectious disease dynamics are affected by human mobility more powerfully than previously thought, and thus reliable traceability data are essential. In rural riverine settings, lack of infrastructure and dense tree coverage deter the implementation of cutting-edge technology to collect human mobility data. To overcome this challenge, this study proposed the use of a novel open mobile mapping tool, GeoODK. This study consists of a purposive sampling of 33 participants in six villages with contrasting patterns of malaria transmission that demonstrates a feasible approach to map human mobility. The self-reported traceability data allowed the construction of the first human mobility framework in rural riverine villages in the Peruvian Amazon. The mobility spectrum in these areas resulted in travel profiles ranging from 2 hours to 19 days; and distances between 10 to 167 km. Most Importantly, occupational-related mobility profiles with the highest displacements (in terms of time and distance) were observed in commercial, logging, and hunting activities. These data are consistent with malaria transmission studies in the area that show villages in watersheds with higher human movement are concurrently those with greater malaria risk. The approach we describe represents a potential tool to gather critical information that can facilitate malaria control activities.

Download Full-text

The Twitter Social Mobility Index: Measuring Social Distancing Practices With Geolocated Tweets (Preprint)

10.2196/preprints.21499 ◽

2020 ◽

Author(s):

Paiheng Xu ◽

Mark Dredze ◽

David A Broniatowski

Keyword(s):

United States ◽

South Carolina ◽

Social Mobility ◽

State Level ◽

The United States ◽

Mobility Index ◽

Social Distancing ◽

Mobility Data ◽

Response Planning ◽

Twitter Data

BACKGROUND Social distancing is an important component of the response to the COVID-19 pandemic. Minimizing social interactions and travel reduces the rate at which the infection spreads and “flattens the curve” so that the medical system is better equipped to treat infected individuals. However, it remains unclear how the public will respond to these policies as the pandemic continues. OBJECTIVE The aim of this study is to present the Twitter Social Mobility Index, a measure of social distancing and travel derived from Twitter data. We used public geolocated Twitter data to measure how much users travel in a given week. METHODS We collected 469,669,925 tweets geotagged in the United States from January 1, 2019, to April 27, 2020. We analyzed the aggregated mobility variance of a total of 3,768,959 Twitter users at the city and state level from the start of the COVID-19 pandemic. RESULTS We found a large reduction (61.83%) in travel in the United States after the implementation of social distancing policies. However, the variance by state was high, ranging from 38.54% to 76.80%. The eight states that had not issued statewide social distancing orders as of the start of April ranked poorly in terms of travel reduction: Arkansas (45), Iowa (37), Nebraska (35), North Dakota (22), South Carolina (38), South Dakota (46), Oklahoma (50), Utah (14), and Wyoming (53). We are presenting our findings on the internet and will continue to update our analysis during the pandemic. CONCLUSIONS We observed larger travel reductions in states that were early adopters of social distancing policies and smaller changes in states without such policies. The results were also consistent with those based on other mobility data to a certain extent. Therefore, geolocated tweets are an effective way to track social distancing practices using a public resource, and this tracking may be useful as part of ongoing pandemic response planning.

Download Full-text