scholarly journals Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Behzad Vahedi ◽  
Morteza Karimzadeh ◽  
Hamidreza Zoraghein
Keyword(s):  
Social Connectedness ◽  
Predictive Power ◽  
Human Mobility ◽  
Human Interaction ◽  
County Level ◽  
Infection Rates ◽  
Baseline Model ◽  
Human Interactions ◽  
Prediction Horizon ◽  
Socioeconomic Composition

AbstractMeasurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19. We evaluate the model on 14 forecast dates between 2020/10/25 and 2021/01/24 over one- to four-week prediction horizons. Comparing our predictions with a Baseline model developed by the COVID-19 Forecast Hub indicates an average 6.46% improvement in prediction Mean Absolute Errors (MAE) over the two-week prediction horizon up to 20.22% improvement in the four-week prediction horizon, pointing to the strong predictive power of our model in the longer prediction horizons.

scholarly journals Predicting County-Level COVID-19 Cases using Spatiotemporal Machine Learning: Modeling Human Interactions using Social Media and Cell-Phone Data

2021 ◽  
Author(s):  
Behzad Vahedi ◽  
Morteza Karimzadeh ◽  
Hamidreza Zoraghein
Keyword(s):  
Predictive Modeling ◽  
Cell Phone ◽  
Social Connectedness ◽  
Predictive Power ◽  
Human Mobility ◽  
Human Interaction ◽  
Gradient Boosting ◽  
County Level ◽  
Human Interactions ◽  
Extreme Gradient Boosting

Abstract Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19. In this study, we first compare the power of Facebook’s social connectedness with cell phone-derived human mobility for predicting county-level new cases of COVID-19. Our experiments show that social connectedness is a better proxy for measuring human interactions leading to new infections. Next, we develop a SpatioTemporal autoregressive eXtreme Gradient Boosting (STXGB) model to predict county-level new cases of COVID-19 in the coterminous US. We evaluate the model on five weekly forecast dates between October 24 and November 28, 2020 over one- to four-week prediction horizons. Comparing our predictions with a baseline Ensemble of 32-models currently used by the CDC indicates an average 58% improvement in prediction RMSEs over two- to four-week prediction horizons, pointing to the strong predictive power of our model.

scholarly journals 694 Insufficient sleep linked with higher COVID-19 infection cases and deaths in the United States

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A271-A271
Author(s):  
Azizi Seixas ◽  
Nicholas Pantaleo ◽  
Samrachana Adhikari ◽  
Michael Grandner ◽  
Giardin Jean-Louis
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
The United States ◽  
Poor Sleep ◽  
County Level ◽  
Infection Rates ◽  
Insufficient Sleep ◽  
Death Rates ◽  
Rural Counties ◽  
The Relationship

Abstract Introduction Causes of COVID-19 burden in urban, suburban, and rural counties are unclear, as early studies provide mixed results implicating high prevalence of pre-existing health risks and chronic diseases. However, poor sleep health that has been linked to infection-based pandemics may provide additional insight for place-based burden. To address this gap, we investigated the relationship between habitual insufficient sleep (sleep <7 hrs./24 hr. period) and COVID-19 cases and deaths across urban, suburban, and rural counties in the US. Methods County-level variables were obtained from the 2014–2018 American community survey five-year estimates and the Center for Disease Control and Prevention. These included percent with insufficient sleep, percent uninsured, percent obese, and social vulnerability index. County level COVID-19 infection and death data through September 12, 2020 were obtained from USA Facts. Cumulative COVID-19 infections and deaths for urban (n=68), suburban (n=740), and rural (n=2331) counties were modeled using separate negative binomial mixed effects regression models with logarithmic link and random state-level intercepts. Zero-inflated models were considered for deaths among suburban and rural counties to account for excess zeros. Results Multivariate regression models indicated positive associations between cumulative COVID-19 infection rates and insufficient sleep in urban, suburban and rural counties. The incidence rate ratio (IRR) for urban counties was 1.03 (95% CI: 1.01 – 1.05), 1.04 (95% CI: 1.02 – 1.05) for suburban, and 1.02 (95% CI: 1.00 – 1.03) rural counties.. Similar positive associations were observed with county-level COVID-19 death rates, IRR = 1.11 (95% CI: 1.07 – 1.16) for urban counties, IRR = 1.04 (95% CI: 1.01 – 1.06) for suburban counties, and IRR = 1.03 (95% CI: 1.01 – 1.05) for rural counties. Level of urbanicity moderated the association between insufficient sleep and COVID deaths, but not for the association between insufficient sleep and COVID infection rates. Conclusion Insufficient sleep was associated with COVID-19 infection cases and mortality rates in urban, suburban and rural counties. Level of urbanicity only moderated the relationship between insufficient sleep and COVID death rates. Future studies should investigate individual-level analysis to understand the role of sleep mitigating COVID-19 infection and death rates. Support (if any) NIH (K07AG052685, R01MD007716, R01HL142066, K01HL135452, R01HL152453

Psychologically Plausible Cognitive Models for Simulating Interactive Human Behaviors

2005 ◽  
Vol 49 (12) ◽  
pp. 1205-1209 ◽  
Author(s):  
Michael L. Bernard ◽  
Patrick Xavier ◽  
Paul Wolfenbarger ◽  
Derek Hart ◽  
Russel Waymire ◽  
...  
Keyword(s):  
Cognitive Modeling ◽  
Human Interaction ◽  
Human Model ◽  
Motor Functioning ◽  
Human Behaviors ◽  
Human Interactions ◽  
Cognitive Framework ◽  
Interaction Modeling ◽  
Cognition Model ◽  
High Level

The intent of Sandia National Laboratories' Human Interactions (HI) project is to demonstrate initial virtual human interaction modeling capability. To accomplish this, we have begun the process of simulating human behavior in a manner that produces life-like characteristics and movement, as well as creating the framework for models that are based on the most current experimental research in cognition, perception, physiology, and cognitive modeling. Currently the simulated human models can sense each other, react to each other, and move about in a simulated 3D environment. A preliminary action generation or motor-level cognition model, which transforms abstract actions generated by high-level cognition to actions that can be carried out by a simulated physical human model, has also been developed. Our work has yielded models of perceptual, spatial, and motor functioning and memory that will be embedded in upgrades to the cognitive framework.

scholarly journals GOSSIP ON WEIGHTED NETWORKS

2012 ◽  
Vol 15 (supp01) ◽  
pp. 1250061 ◽  
Author(s):  
MURSEL TASGIN ◽  
HALUK O. BINGOL
Keyword(s):  
Complex Networks ◽  
Weight Distribution ◽  
Human Interaction ◽  
Interaction Networks ◽  
Weighted Network ◽  
Weighted Networks ◽  
Human Interactions ◽  
Spreading Model ◽  
Good Parameter ◽  
Weighted Complex Networks

In this work, we analyze gossip spreading on weighted networks. We try to define a new metric to classify weighted complex networks using our model. The model proposed here is based on the gossip spreading model introduced by Lind et al. on unweighted networks. The new metric is based on gossip spreading activity in the network, which is correlated with both topology and relative edge weights in the network. The model gives more insight about the weight distribution and correlation of topology with edge weights in a network. It also measures how suitable a weighted network is for gossip spreading. We analyze gossip spreading on real weighted networks of human interactions. Six co-occurrence and seven social pattern networks are investigated. Gossip propagation is found to be a good parameter to distinguish co-occurrence and social pattern networks. As a comparison some miscellaneous networks of comparable sizes and computer generated networks based on ER, BA and WS models are also investigated. They are found to be quite different from the human interaction networks.

scholarly journals Human Interaction with Creators and Fellow Creatures (Study of Communication Relations of XYZ in Alamin Theory)

2019 ◽  
Vol 2 (4) ◽  
pp. 85-98
Author(s):  
Muhammad Aminullah
Keyword(s):  
Qualitative Research ◽  
Content Analysis ◽  
Research Method ◽  
Interaction Process ◽  
Human Interaction ◽  
Analysis Approach ◽  
Human Relations ◽  
Human Interactions ◽  
The Relationship ◽  
The Universe

These studies are important to understand the process of interaction in human relations with the creator and also relationships with fellow creatures. The research method used is a qualitative research based on content analysis approach, with the aim to be able to explore the theory of alamin used in this study. The results found that interaction was formed by the existence of one of the most basic objectives in communication, namely necessity. This concept can be understood that everything needed by humans, then must have a process of relationships in the form of interaction to be able to achieve whatever objects are needed. But the interaction process is different. Therefore human interactions have two goals. First, human interaction with the creator called the relationship X with Y. This interaction is carried out by X to Y in the form of a relationship as creator to X, provider of living facilities to X, trustee Y, and X servant Y. Second is human interaction with the universe this is called the relationship X with Z. This interaction is carried out by X to Z in the form of a Z relationship as a reference for X, a provider of mass space for X, a power provider for X, and a proof of space for the implementation of the X assignment.

scholarly journals Using Data Derived from Cellular Phone Locations to Estimate Visitation to Natural Areas: An Application to Water Recreation in New England, USA.

2019 ◽  
Author(s):  
Nathaniel Merrill ◽  
Sarina F. Atkinson ◽  
Kate K. Mulvaney ◽  
Marisa J. Mazzotta ◽  
Justin Bousquin
Keyword(s):  
New England ◽  
Cell Phone ◽  
Human Mobility ◽  
Cellular Phone ◽  
Natural Areas ◽  
Human Interactions ◽  
Recreation Areas ◽  
Water Recreation ◽  
Using Data ◽  
Consistent Information

We introduce and validate the use of commercially-available datasets of human mobility based on cell phone locational data to estimate visitation to natural areas. By combining this data with on-the-ground observations of visitation to water recreation areas in New England, we fit a model to estimate daily visitation for four months to over 500 sites. The results show the potential for this new big data source of human mobility to overcome limitations in traditional methods of estimating visitation and to provide consistent information at policy-relevant scales. The high-resolution information in both space and time provided by cell phone location derived data creates opportunities for developing next-generation models of human interactions with the natural environment. However, the opaque and rapidly developing methods for processing locational information by the data providers required a calibration and validation against data collected by traditional means to confidently reproduce the desired estimates of visitation.

scholarly journals ACCELERATION TECHNOLOGY ON BOOKS AS CULTURAL MEDIA ON HUMAN COMMUNICATION IN THE ERA OF INTERACTIVE COMMUNICATION

2016 ◽  
pp. 157
Author(s):  
Asthararianty Asthararianty
Keyword(s):  
Interpersonal Communication ◽  
Human Interaction ◽  
Internet Technology ◽  
Human Communication ◽  
Cultural Shift ◽  
Interactive Communication ◽  
Human Interactions ◽  
Reading Culture ◽  
Conventional Manner ◽  
Cultural Media

Dromology is a speed that characterize progress. One of the affected is the culture of reading books. In the past people reading a book in the conventional manner, but in recent years, Internet technology has brought man reading a book in a different way, namely through the e-book. These changes ultimately led to a cultural shift in communication, especially in reading the book. The method used in this research is the study of literature. Results from the study showed that the reading culture (human interactions in a conventional book) has been turned into a reading culture that is synonymous with technology and also acceleration. Characteristics, sensations and experiences have changed. Technology (e-book) has become the new devices in cultured (communication / human interaction). Keywords: book, dromology, interpersonal communication, new culture

scholarly journals Green spaces, especially forest, linked to lower SARS–CoV–2 infection rates: A one–year nationwide study

2021 ◽  
Author(s):  
Bin Jiang ◽  
Yuwen Yang ◽  
Long Chen ◽  
Xueming Liu ◽  
Xueying Wu ◽  
...  
Keyword(s):  
Disease Incidence ◽  
Negative Association ◽  
Green Spaces ◽  
Sensitivity Analyses ◽  
Walking Distance ◽  
County Level ◽  
Risk Of Infection ◽  
Infection Rates ◽  
Weighted Analysis ◽  
One Year

This study examined the associations between green spaces and one–years' worth of SARS–CoV–2 infection rates across all 3,108 counties in the contiguous United States after controlling for multiple categories of confounding factors. We found green spaces at the county level have a significant negative association with infection rates. Among all types of green spaces, forest yields the most consistent and strongest negative association. Sensitivity analyses confirmed the negative association of forest across five urbanicity levels, and the strength of the association increases as disease incidence increases across five time periods. Although forest located in moderately urbanized counties yields the strongest association, the negative pattern of significant associations holds across all five urbanicity levels. A population–weighted analysis revealed that proximity to forest within a moderate walking distance (≤ 1.0–1.4 km) may provide the greatest protection against the risk of infection.

scholarly journals Critical Mobility, a practical criterion and early indicator for regional COVID-19 resurgence

2020 ◽  
Author(s):  
Marcus O Freitag ◽  
Johanes Schmude ◽  
Carlo Siebenschuh ◽  
Gustavo Stolovitzky ◽  
Hendrik Hamann ◽  
...  
Keyword(s):  
Predictive Power ◽  
Human Mobility ◽  
Face Mask ◽  
Data Driven ◽  
Southern Us ◽  
Data Driven Approach ◽  
The Usa ◽  
Temporal Model ◽  
Opening Up ◽  
Actual Mobility

The sharp reduction of human mobility in March 2020, as observed by anonymized cellphone data, has played an important role in thwarting a runaway COVID-19 pandemic. As the world is reopening, the risks of new flare-ups are rising. We report a data-driven approach, grounded in strong correlation between mobility and growth in COVID-19 cases two weeks later, to establish a spatial-temporal model of "critical mobility" maps that separate relatively safe mobility levels from dangerous ones. The normalized difference between the current and critical mobility has predictive power for case trajectories during the "opening-up" phases. For instance, actual mobility has risen above critical mobility in many southern US counties by the end of May, foreshadowing the latest virus resurgence. Encouragingly, critical mobility has been rising throughout the USA, likely due to face mask-wearing and social distancing measures. However, critical mobility is still well below pre-COVID mobility levels in most of the country suggesting continued mobility-reduction is still necessary.

scholarly journals Using network science to propose strategies for effectively dealing with pandemics: The COVID-19 example

2020 ◽  
Author(s):  
Helena A. Herrmann ◽  
Jean-Marc Schwartz
Keyword(s):  
Predictive Power ◽  
Network Science ◽  
Health Care Systems ◽  
Source Code ◽  
Interaction Network ◽  
Human Interaction ◽  
Disease Modelling ◽  
Epidemiological Models ◽  
Global Spread ◽  
Care Systems

SummaryThe global spread of Coronavirus Disease 2019 (COVID-19) is overwhelming many health-care systems. As a result, epidemiological models are being used to inform policy on how to effectively deal with this pandemic. We note that the majority of existing models do not take into account differences in the amount of interactions between individuals (i.e. the underlying human interaction network). Using network science we demonstrate how this network of interactions can be used to predict the spread of the virus and to inform policy on the most successful mitigation and suppression strategies. Although applicable to disease modelling in general, our results emphasize how network science can improve the predictive power of current COVID-19 epidemiological models. We provide commented source code for all our analyses so that they can easily be integrated into current and future epidemiological models.

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