Conjunction of Factors Triggering Waves of Seasonal Influenza

AbstractUnderstanding the subtle confluence of factors triggering pan-continental, seasonal epidemics of influenza-like illness is an extremely important problem, with the potential to save tens of thousands of lives and billions of dollars every year in the US alone. Beginning with several large, longitudinal datasets on putative factors and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of epidemics. Our analysis included insurance claims for a significant cross-section of the US population in the past decade, human movement patterns inferred from billions of tweets, whole-US weekly weather data covering the same time span as the medical records, data on vaccination coverage over the same period, and sequence variations of key viral proteins. We also explicitly accounted for the spatio-temporal auto-correlations of infectious waves, and a host of socioeconomic and demographic factors. We carried out multiple orthogonal statistical analyses on these diverse, large geo-temporal datasets to bolster and corroborate our findings. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions, the strongest predictor groups are as follows, ranked by importance: (1) the host population’s socio- and ethno-demographic properties; (2) weather variables pertaining to relevant area specific humidity, temperature, and solar radiation; (3) the virus’ antigenic drift over time; (4) the host population’s land-based travel habits, and; (5) the spatio-temporal dynamics’ immediate history, as reflected in the influenza wave autocorrelation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve ≈ 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies.

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Conjunction of factors triggering waves of seasonal influenza

eLife ◽

10.7554/elife.30756 ◽

2018 ◽

Vol 7 ◽

Cited By ~ 20

Author(s):

Ishanu Chattopadhyay ◽

Emre Kiciman ◽

Joshua W Elliott ◽

Jeffrey L Shaman ◽

Andrey Rzhetsky

Keyword(s):

Temporal Dynamics ◽

Human Subjects ◽

Characteristic Curve ◽

Population Level ◽

Host Population ◽

Antigenic Drift ◽

Specific Humidity ◽

Factors Affecting ◽

Out Of Sample ◽

Spatio Temporal

Using several longitudinal datasets describing putative factors affecting influenza incidence and clinical data on the disease and health status of over 150 million human subjects observed over a decade, we investigated the source and the mechanistic triggers of influenza epidemics. We conclude that the initiation of a pan-continental influenza wave emerges from the simultaneous realization of a complex set of conditions. The strongest predictor groups are as follows, ranked by importance: (1) the host population’s socio- and ethno-demographic properties; (2) weather variables pertaining to specific humidity, temperature, and solar radiation; (3) the virus’ antigenic drift over time; (4) the host population’€™s land-based travel habits, and; (5) recent spatio-temporal dynamics, as reflected in the influenza wave auto-correlation. The models we infer are demonstrably predictive (area under the Receiver Operating Characteristic curve 80%) when tested with out-of-sample data, opening the door to the potential formulation of new population-level intervention and mitigation policies.

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Canonical retinotopic shifts under an inverse force field explain predictive remapping

10.1101/2021.09.10.459799 ◽

2021 ◽

Author(s):

Ifedayo-EmmanuEL Adeyefa-Olasupo ◽

Zixuan Xiao ◽

Anirvan S. Nandy

Keyword(s):

Force Field ◽

Temporal Dynamics ◽

Human Subjects ◽

Receptive Fields ◽

Saccadic Eye Movements ◽

Saccade Target ◽

Brain Areas ◽

Predictive Remapping ◽

Spatio Temporal ◽

Inverse Force

ABSTRACTSaccadic eye-movements allow us to bring visual objects of interest to high-acuity central vision. Although saccades cause large displacements of retinal images, our percept of the visual world remains stable. Predictive remapping — the ability of cells in retinotopic brain areas to transiently exhibit spatio-temporal retinotopic shifts beyond the spatial extent of their classical receptive fields — has been proposed as a primary mechanism that mediates this seamless visual percept. Despite the well documented effects of predictive remapping, no study to date has been able to provide a mechanistic account of the neural computations and architecture that actively mediate this ubiquitous phenomenon. Borne out by the spatio-temporal dynamics of peri-saccadic sensitivity to probe stimuli in human subjects, we propose a novel neurobiologically inspired phenomenological model in which the underlying peri-saccadic attentional and oculomotor signals manifest as three temporally overlapping forces that act on retinotopic brain areas. These three forces – a compressive one toward the center of gaze, a convergent one toward the saccade target and a translational one parallel to the saccade trajectory – act in an inverse force field and specify the spatio-temporal window of predictive remapping of population receptive fields.

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Dual-Stream Structured Graph Convolution Network for Skeleton-Based Action Recognition

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3450410 ◽

2021 ◽

Vol 17 (4) ◽

pp. 1-22

Author(s):

Chunyan Xu ◽

Rong Liu ◽

Tong Zhang ◽

Zhen Cui ◽

Jian Yang ◽

...

Keyword(s):

Action Recognition ◽

Temporal Dynamics ◽

Recognition Task ◽

Human Movement ◽

Body Part ◽

Human Action ◽

Human Motion ◽

Body Parts ◽

Dynamic Interactions ◽

Spatio Temporal

In this work, we propose a dual-stream structured graph convolution network ( DS-SGCN ) to solve the skeleton-based action recognition problem. The spatio-temporal coordinates and appearance contexts of the skeletal joints are jointly integrated into the graph convolution learning process on both the video and skeleton modalities. To effectively represent the skeletal graph of discrete joints, we create a structured graph convolution module specifically designed to encode partitioned body parts along with their dynamic interactions in the spatio-temporal sequence. In more detail, we build a set of structured intra-part graphs, each of which can be adopted to represent a distinctive body part (e.g., left arm, right leg, head). The inter-part graph is then constructed to model the dynamic interactions across different body parts; here each node corresponds to an intra-part graph built above, while an edge between two nodes is used to express these internal relationships of human movement. We implement the graph convolution learning on both intra- and inter-part graphs in order to obtain the inherent characteristics and dynamic interactions, respectively, of human action. After integrating the intra- and inter-levels of spatial context/coordinate cues, a convolution filtering process is conducted on time slices to capture these temporal dynamics of human motion. Finally, we fuse two streams of graph convolution responses in order to predict the category information of human action in an end-to-end fashion. Comprehensive experiments on five single/multi-modal benchmark datasets (including NTU RGB+D 60, NTU RGB+D 120, MSR-Daily 3D, N-UCLA, and HDM05) demonstrate that the proposed DS-SGCN framework achieves encouraging performance on the skeleton-based action recognition task.

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Nonlinear Spatio-Temporal Dynamics and Chaos in Semiconductors

10.1017/cbo9780511524615 ◽

2001 ◽

Cited By ~ 136

Author(s):

Eckehard Schöll

Keyword(s):

Temporal Dynamics ◽

Spatio Temporal

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Spatio-temporal dynamics of ascidian larval recruitment and colony abundance in a non-indigenous Newfoundland population

Marine Ecology Progress Series ◽

10.3354/meps12437 ◽

2017 ◽

Vol 585 ◽

pp. 99-112 ◽

Cited By ~ 2

Author(s):

KCK Ma ◽

D Deibel ◽

JB Lowen ◽

CH McKenzie

Keyword(s):

Temporal Dynamics ◽

Larval Recruitment ◽

Spatio Temporal

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Spatial and temporal dynamics of Pacific capelin Mallotus catervarius in the Gulf of Alaska: implications for ecosystem-based fisheries management

Marine Ecology Progress Series ◽

10.3354/meps13211 ◽

2020 ◽

Vol 637 ◽

pp. 117-140 ◽

Cited By ~ 1

Author(s):

DW McGowan ◽

ED Goldstein ◽

ML Arimitsu ◽

AL Deary ◽

O Ormseth ◽

...

Keyword(s):

Temporal Dynamics ◽

Marine Ecosystem ◽

Gulf Of Alaska ◽

Data Series ◽

Limited Information ◽

Important Species ◽

Multiple Data ◽

Spatial And Temporal Dynamics ◽

Spawning Areas ◽

Spatio Temporal

Pacific capelin Mallotus catervarius are planktivorous small pelagic fish that serve an intermediate trophic role in marine food webs. Due to the lack of a directed fishery or monitoring of capelin in the Northeast Pacific, limited information is available on their distribution and abundance, and how spatio-temporal fluctuations in capelin density affect their availability as prey. To provide information on life history, spatial patterns, and population dynamics of capelin in the Gulf of Alaska (GOA), we modeled distributions of spawning habitat and larval dispersal, and synthesized spatially indexed data from multiple independent sources from 1996 to 2016. Potential capelin spawning areas were broadly distributed across the GOA. Models of larval drift show the GOA’s advective circulation patterns disperse capelin larvae over the continental shelf and upper slope, indicating potential connections between spawning areas and observed offshore distributions that are influenced by the location and timing of spawning. Spatial overlap in composite distributions of larval and age-1+ fish was used to identify core areas where capelin consistently occur and concentrate. Capelin primarily occupy shelf waters near the Kodiak Archipelago, and are patchily distributed across the GOA shelf and inshore waters. Interannual variations in abundance along with spatio-temporal differences in density indicate that the availability of capelin to predators and monitoring surveys is highly variable in the GOA. We demonstrate that the limitations of individual data series can be compensated for by integrating multiple data sources to monitor fluctuations in distributions and abundance trends of an ecologically important species across a large marine ecosystem.

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