SPLANG-a synthetic poisson-lognormal-based abundance and network generative model for microbial interaction inference algorithms.

Microbes are pervasive and their interaction with each other and the environment can impact fields as diverse as health and agriculture. While network inference and related algorithms that use abundance data from pyrosequencing can infer microbial interaction networks, the ambiguity surrounding the actual underlying networks hampers the validation of these algorithms. This study introduces a generative model to synthesize both the underlying interactive network and observable abundance data, serving as a test bed for the existing and future network inference algorithms. We tested our generative model with four typical network inference algorithms; our results suggest that none of these algorithms demonstrate adequate accuracy for inferring ecologies of non-commensalistic species, either mutualistic or competitive. We further explored the potential for predictability by combining existing algorithms with an oracle algorithm built by fusing the results of several existing algorithms. The oracle algorithm reveals promising improvements in predictability, although it falls short when applied to networks characterized by dense interspecies taxa interactions. Our work underscores the need for the continued development and validation of algorithms to unravel the intricacies of microbial interaction networks.

Changes in physical activity among Canadian adults more than 6 months into the COVID-19 pandemic: a secondary analysis of the INTERACT cohort study.

OBJECTIVE: To estimate the effect of (a) the COVID-19 pandemic and (b) COVID-19 restriction stringency on daily minutes of device-measured moderate-to-vigorous physical activity (MVPA). DESIGN: Physical activity data were collected from the INTerventions, Equity, Research and Action in Cities Team (INTERACT) cohorts in Montreal, Saskatoon and Vancouver before (May 2018 to February 2019, 'phase 1') and during the pandemic (October 2020 to February 2021, 'phase 2'). We estimated the effect of the two exposures by comparing daily MVPA measured (a) before vs during the pandemic (phase 1 vs phase 2) and (b) at different levels of COVID-19 restriction stringency during phase 2. Separate mixed effects negative binomial regression models were used to estimate the association between each exposure and daily MVPA, with and without controlling for confounders. Analyses were conducted on person-days with at least 600 min of wear time. Effect modification by gender, age, income, employment status, education, children in the home and city was assessed via stratification. SETTING: Montreal (Quebec), Saskatoon (Saskatchewan) and Vancouver (British Columbia), Canada. MAIN OUTCOME MEASURE: Daily minutes of MVPA, as measured using SenseDoc, a research-grade accelerometer device. RESULTS: Daily minutes of MVPA were 21% lower in phase 2 (October 2020 to February 2021) compared with phase 1 (May 2018 to February 2019), controlling for gender, age, employment status, household income, education, city, weather and wear time (rate ratio=0.79, 95% CI 0.69, 0.92). This did not appear to be driven by changes in the sample or timing of data collection between phases. The results suggested effect modification by employment, household income and education. Restriction stringency was not associated with daily MVPA between October 2020 and February 2021 (adjusted rate ratio=0.99, 95% CI 0.96, 1.03). CONCLUSIONS: Between October 2020 and February 2021, daily minutes of MVPA were significantly lower than 2 years prior, but were not associated with daily COVID-19 restriction stringency.

Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: Simulation-Driven Approach.

BACKGROUND: Accurate and responsive epidemiological simulations of epidemic outbreaks inform decision-making to mitigate the impact of pandemics. These simulations must be grounded in quantities derived from measurements, among which the parameters associated with contacts between individuals are notoriously difficult to estimate. Digital contact tracing data, such as those provided by Bluetooth beaconing or GPS colocating, can provide more precise measures of contact than traditional methods based on direct observation or self-reporting. Both measurement modalities have shortcomings and are prone to false positives or negatives, as unmeasured environmental influences bias the data. OBJECTIVE: We aim to compare GPS colocated versus Bluetooth beacon-derived proximity contact data for their impacts on transmission models' results under community and types of diseases. METHODS: We examined the contact patterns derived from 3 data sets collected in 2016, with participants comprising students and staff from the University of Saskatchewan in Canada. Each of these 3 data sets used both Bluetooth beaconing and GPS localization on smartphones running the Ethica Data (Avicenna Research) app to collect sensor data about every 5 minutes over a month. We compared the structure of contact networks inferred from proximity contact data collected with the modalities of GPS colocating and Bluetooth beaconing. We assessed the impact of sensing modalities on the simulation results of transmission models informed by proximate contacts derived from sensing data. Specifically, we compared the incidence number, attack rate, and individual infection risks across simulation results of agent-based susceptible-exposed-infectious-removed transmission models of 4 different contagious diseases. We have demonstrated their differences with violin plots, 2-tailed t tests, and Kullback-Leibler divergence. RESULTS: Both network structure analyses show visually salient differences in proximity contact data collected between GPS colocating and Bluetooth beaconing, regardless of the underlying population. Significant differences were found for the estimated attack rate based on distance threshold, measurement modality, and simulated disease. This finding demonstrates that the sensor modality used to trace contact can have a significant impact on the expected propagation of a disease through a population. The violin plots of attack rate and Kullback-Leibler divergence of individual infection risks demonstrated discernible differences for different sensing modalities, regardless of the underlying population and diseases. The results of the t tests on attack rate between different sensing modalities were mostly significant (P<.001). CONCLUSIONS: We show that the contact networks generated from these 2 measurement modalities are different and generate significantly different attack rates across multiple data sets and pathogens. While both modalities offer higher-resolution portraits of contact behavior than is possible with most traditional contact measures, the differential impact of measurement modality on the simulation outcome cannot be ignored and must be addressed in studies only using a single measure of contact in the future.

Comparing Location Data From Smartphone and Dedicated Global Positioning System Devices: Implications for Epidemiologic Research.

In this study, we compared location data from a dedicated Global Positioning System (GPS) device with location data from smartphones. Data from the Interventions, Equity, and Action in Cities Team (INTERACT) Study, a study examining the impact of urban-form changes on health in 4 Canadian cities (Victoria, Vancouver, Saskatoon, and Montreal), were used. A total of 337 participants contributed data collected for about 6 months from the Ethica Data smartphone application (Ethica Data Inc., Toronto, Ontario, Canada) and the SenseDoc dedicated GPS (MobySens Technologies Inc., Montreal, Quebec, Canada) during the period 2017-2019. Participants recorded an average total of 14,781 Ethica locations (standard deviation, 19,353) and 197,167 SenseDoc locations (standard deviation, 111,868). Dynamic time warping and cross-correlation were used to examine the spatial and temporal similarity of GPS points. Four activity-space measures derived from the smartphone app and the dedicated GPS device were compared. Analysis showed that cross-correlations were above 0.8 at the 125-m resolution for the survey and day levels and increased as cell size increased. At the day or survey level, there were only small differences between the activity-space measures. Based on our findings, we recommend dedicated GPS devices for studies where the exposure and the outcome are both measured at high frequency and when the analysis will not be aggregate. When the exposure and outcome are measured or will be aggregated to the day level, the dedicated GPS device and the smartphone app provide similar results.

Synaptotagmin 9 Modulates Spontaneous Neurotransmitter Release in Striatal Neurons by Regulating Substance P Secretion.

Synaptotagmin 9 (SYT9) is a tandem C2 domain Ca2+ sensor for exocytosis in neuroendocrine cells; its function in neurons remains unclear. Here, we show that, in mixed-sex cultures, SYT9 does not trigger rapid synaptic vesicle exocytosis in mouse cortical, hippocampal, or striatal neurons, unless it is massively overexpressed. In striatal neurons, loss of SYT9 reduced the frequency of spontaneous neurotransmitter release events (minis). We delved into the underlying mechanism and discovered that SYT9 was localized to dense-core vesicles that contain substance P (SP). Loss of SYT9 impaired SP release, causing the observed decrease in mini frequency. This model is further supported by loss of function mutants. Namely, Ca2+ binding to the C2A domain of SYT9 triggered membrane fusion in vitro, and mutations that disrupted this activity abolished the ability of SYT9 to regulate both SP release and mini frequency. We conclude that SYT9 indirectly regulates synaptic transmission in striatal neurons by controlling SP release.SIGNIFICANCE STATEMENT Synaptotagmin 9 (SYT9) has been described as a Ca2+ sensor for dense-core vesicle (DCV) exocytosis in neuroendocrine cells, but its role in neurons remains unclear, despite widespread expression in the brain. This article examines the role of SYT9 in synaptic transmission across cultured cortical, hippocampal, and striatal neuronal preparations. We found that SYT9 regulates spontaneous neurotransmitter release in striatal neurons by serving as a Ca2+ sensor for the release of the neuromodulator substance P from DCVs. This demonstrates a novel role for SYT9 in neurons and uncovers a new field of study into neuromodulation by SYT9, a protein that is widely expressed in the brain.

Impacts of observation frequency on proximity contact data and modeled transmission dynamics.

Transmission of many communicable diseases depends on proximity contacts among humans. Modeling the dynamics of proximity contacts can help determine whether an outbreak is likely to trigger an epidemic. While the advent of commodity mobile devices has eased the collection of proximity contact data, battery capacity and associated costs impose tradeoffs between the observation frequency and scanning duration used for contact detection. The choice of observation frequency should depend on the characteristics of a particular pathogen and accompanying disease. We downsampled data from five contact network studies, each measuring participant-participant contact every 5 minutes for durations of four or more weeks. These studies included a total of 284 participants and exhibited different community structures. We found that for epidemiological models employing high-resolution proximity data, both the observation method and observation frequency configured to collect proximity data impact the simulation results. This impact is subject to the population's characteristics as well as pathogen infectiousness. By comparing the performance of two observation methods, we found that in most cases, half-hourly Bluetooth discovery for one minute can collect proximity data that allows agent-based transmission models to produce a reasonable estimation of the attack rate, but more frequent Bluetooth discovery is preferred to model individual infection risks or for highly transmissible pathogens. Our findings inform the empirical basis for guidelines to inform data collection that is both efficient and effective.

Wave 1 results of the INTerventions, Research, and Action in Cities Team (INTERACT) cohort study: Examining spatio-temporal measures for urban environments and health.

Built environment interventions have the potential to improve population health and reduce health inequities. The objective of this paper is to present the first wave of the INTErventions, Research, and Action in Cities Team (INTERACT) cohort studies in Victoria, Vancouver, Saskatoon, and Montreal, Canada. We examine how our cohorts compared to Canadian census data and present summary data for our outcomes of interest (physical activity, well-being, and social connectedness). We also compare location data and activity spaces from survey data, research-grade GPS and accelerometer devices, and a smartphone app, and compile measures of proximity to select built environment interventions.

A theory-based model of cumulative activity.

Energy expenditure can be used to examine the health of individuals and the impact of environmental factors on physical activity. One of the more common ways to quantify energy expenditure is to process accelerometer data into some unit of measurement for this expenditure, such as Actigraph activity counts, and bin those measures into physical activity levels. However, accepted thresholds can vary between demographics, and some units of energy measurements do not currently have agreed upon thresholds. We present an approach which computes unique thresholds for each individual, using piecewise exponential functions to model the characteristics of their overall physical activity patterns corresponding to well established sedentary, light, moderate and vigorous activity levels from the literature. Models are fit using existing piecewise fitting techniques and software. Most participants' activity intensity profile is exceptionally well modeled as piecewise exponential decay. Using this model, we find emergent groupings of participant behavior and categorize individuals into non-vigorous, consistent, moderately active, or extremely active activity intensity profiles. In the supplemental materials, we demonstrate that the parameters of the model correlate with demographics of age, household size, and level of education, inform behavior change under COVID lockdown, and are reasonably robust to signal frequency.

Using combined Global Position System and accelerometer data points to examine how built environments and gentrification are associated with physical activity in four Canadian cities.

BACKGROUND: Built and social environments are associated with physical activity. Global Positioning Systems (GPS) and accelerometer data can capture how people move through their environments and provide promising tools to better understand associations between environmental characteristics and physical activity. The purpose of this study is to examine the associations between GPS-derived exposure to built environment and gentrification characteristics and accelerometer-measured physical activity in a sample of adults across four cities. METHODS: We used wave 1 data from the Interventions, Research, and Action in Cities Team, a cohort of adults living in the Canadian cities of Victoria, Vancouver, Saskatoon, and Montreal. A subsample of participants wore a SenseDoc device for 10 days during May 2017-January 2019 to record GPS and accelerometry data. Two physical activity outcomes were derived from SenseDoc data: time spent in light, moderate, and vigorous physical activity; and time spent in moderate or vigorous physical activity. Using corresponding GPS coordinates, we summarized physical activity outcomes by dissemination area-a Canadian census geography that represents areas where 400 to 700 people live- and joined to built (active living space, proximity to amenities, and urban compactness) and gentrification measures. We examined the associations between environmental measures and physical activity outcomes using multi-level negative binomial regression models that were stratified by city and adjusted for covariates (weekday/weekend), home dissemination area, precipitation, temperature) and participant-level characteristics obtained from a survey (age, gender, income, race). RESULTS: We found that adults spent more time being physically active near their homes, and in environments that were more walkable and near parks and less time in urban compact areas, regardless of where participants lived. Our analysis also highlighted how proximity to different amenities was linked to physical activity across different cities. CONCLUSIONS: Our study provides insights into how built environment and gentrification characteristics are associated with the amount of time adults spend being physically active in four Canadian cities. These findings enhance our understanding of the influence that environments have on physical activity over time and space, and can support policies to increase physical activity.

Positron-emitting radiotracers spatially resolve unexpected biogeochemical relationships linked with methane oxidation in Arctic soils.

Arctic soils are marked by cryoturbic features, which impact soil-atmosphere methane (CH4 ) dynamics vital to global climate regulation. Cryoturbic diapirism alters C/N chemistry within frost boils by introducing soluble organic carbon and nutrients, potentially influencing microbial CH4 oxidation. CH4 oxidation in soils, however, requires a spatio-temporal convergence of ecological factors to occur. Spatial delineation of microbial activity with respect to these key microbial and biogeochemical factors at relevant scales is experimentally challenging in inherently complex and heterogeneous natural soil matrices. This work aims to overcome this barrier by spatially linking microbial CH4 oxidation with C/N chemistry and metagenomic characteristics. This is achieved by using positron-emitting radiotracers to visualize millimeter-scale active CH4 uptake areas in Arctic soils with and without diapirism. X-ray absorption spectroscopic speciation of active and inactive areas shows CH4 uptake spatially associates with greater proportions of inorganic N in diapiric frost boils. Metagenomic analyses reveal Ralstonia pickettii associates with CH4 uptake across soils along with pertinent CH4 and inorganic N metabolism associated genes. This study highlights the critical relationship between CH4 and N cycles in Arctic soils, with potential implications for better understanding future climate. Furthermore, our experimental framework presents a novel, widely applicable strategy for unraveling ecological relationships underlying greenhouse gas dynamics under global change.

Why machine learning (ML) has failed physical activity research and how we can improve.

Measuring physical activity is a critical issue for our understanding of the health benefits of human movement. Machine learning (ML), using accelerometer data, has become a common way to measure physical activity. ML has failed physical activity measurement research in four important ways. First, as a field, physical activity researchers have not adopted and used principles from computer science. Benchmark datasets are common in computer science and allow the direct comparison of different ML approaches. Access to and development of benchmark datasets are critical components in advancing ML for physical activity. Second, the priority of methods development focused on ML has created blind spots in physical activity measurement. Methods, other than cut-point approaches, may be sufficient or superior to ML but these are not prioritised in our research. Third, while ML methods are common in published papers, their integration with software is rare. Physical activity researchers must continue developing and integrating ML methods into software to be fully adopted by applied researchers in the discipline. Finally, training continues to limit the uptake of ML in applied physical activity research. We must improve the development, integration and use of software that allows for ML methods' broad training and application in the field.

The Effect of Season and Neighbourhood-Built Environment on Home Area Sedentary Behaviour in 9-14 Year Old Children.

There is little understanding of how the built environment shapes activity behaviours in children over different seasons. This study sought to establish how seasonal weather patterns, in a given year in a mid-western Canadian city, affect sedentary time (SED) in youth and how the relationship between season and SED are moderated by the built environment in their home neighbourhood. Families with children aged 9-14 years were recruited from the prairie city of Saskatoon, Canada. Location-specific, device-based SED was captured in children during three timeframes over a one-year period using GPS-paired accelerometers. Multilevel models are presented. Children accumulated significantly greater levels of SED in spring but significantly less SED in the fall months in comparison to the winter months. Children living in neighbourhoods with the highest density of destinations accumulated significantly less SED while in their home area in comparison to their counterparts, and this effect was more pronounced in the spring and summer months. On weekends, the rise in sedentariness within the home area was completely diminished in children living in neighbourhoods with the greatest number of destinations and highest activity friendliness. These results suggested that increasing neighbourhood amenities can lead to a reduced sedentariness of youth, though more so in the warmers months of the year.

The future of activity space and health research.

We discuss the future of activity space and health research in the context of a recently published systematic review. Our discussion outlines a number of elements for reflection among the research community. We need to think beyond activity space and reconceptualize exposure in era of high volume, high precision location data. We need to develop standardized methods for understanding global positioning system data. We must adopt replicable scientific computing processes and machine learning models. Finally, we must embrace modern notions of causality in order to contend with the conceptual challenges faced by our research field.

Intrinsic dimensionality of human behavioral activity data.

Patterns of spatial behavior dictate how we use our infrastructure, encounter other people, or are exposed to services and opportunities. Understanding these patterns through the analysis of data commonly available through commodity smartphones has become an important arena for innovation in both academia and industry. The resulting datasets can quickly become massive, indicating the need for concise understanding of the scope of the data collected. Some data is obviously correlated (for example GPS location and which WiFi routers are seen). Codifying the extent of these correlations could identify potential new models, provide guidance on the amount of data to collect, and even provide actionable features. However, identifying correlations, or even the extent of correlation, is difficult because the form of the correlation must be specified. Fractal-based intrinsic dimensionality directly calculates the minimum number of dimensions required to represent a dataset. We provide an intrinsic dimensionality analysis of four smartphone datasets over seven input dimensions, and empirically demonstrate an intrinsic dimension of approximately two.

Structural equation modeling of a winnowed soil microbiome identifies how invasive plants re-structure microbial networks.

The development of microbial networks is central to ecosystem functioning and is the hallmark of complex natural systems. Characterizing network development over time and across environmental gradients is hindered by the millions of potential interactions among community members, limiting interpretations of network evolution. We developed a feature selection approach using data winnowing that identifies the most ecologically influential microorganisms within a network undergoing change. Using a combination of graph theory, leave-one-out analysis, and statistical inference, complex microbial communities are winnowed to identify the core organisms responding to external gradients or functionality, and then network development is evaluated against these externalities. In a plant invasion case study, the winnowed microbial network became more influential as the plant invasion progressed as a result of direct plant-microbe links rather than the expected indirect plant-soil-microbe links. This represents the first use of structural equation modeling to predict microbial network evolution, which requires identification of keystone taxa and quantification of the ecological processes underpinning community structure and function patterns.

INTERACT: A comprehensive approach to assess urban form interventions through natural experiments.

BACKGROUND: Urban form interventions can result in positive and negative impacts on physical activity, social participation, and well-being, and inequities in these outcomes. Natural experiment studies can advance our understanding of causal effects and processes related to urban form interventions. The INTErventions, Research, and Action in Cities Team (INTERACT) is a pan-Canadian collaboration of interdisciplinary scientists, urban planners, and public health decision makers advancing research on the design of healthy and sustainable cities for all. Our objectives are to use natural experiment studies to deliver timely evidence about how urban form interventions influence health, and to develop methods and tools to facilitate such studies going forward. METHODS: INTERACT will evaluate natural experiments in four Canadian cities: the Arbutus Greenway in Vancouver, British Columbia; the All Ages and Abilities Cycling Network in Victoria, BC; a new Bus Rapid Transit system in Saskatoon, Saskatchewan; and components of the Sustainable Development Plan 2016-2020 in Montreal, Quebec, a plan that includes urban form changes initiated by the city and approximately 230 partnering organizations. We will recruit a cohort of between 300 and 3000 adult participants, age 18 or older, in each city and collect data at three time points. Participants will complete health and activity space surveys and provide sensor-based location and physical activity data. We will conduct qualitative interviews with a subsample of participants in each city. Our analysis methods will combine machine learning methods for detecting transportation mode use and physical activity, use temporal Geographic Information Systems to quantify changes to urban intervention exposure, and apply analytic methods for natural experiment studies including interrupted time series analysis. DISCUSSION: INTERACT aims to advance the evidence base on population health intervention research and address challenges related to big data, knowledge mobilization and engagement, ethics, and causality. We will collect ~ 100 TB of sensor data from participants over 5 years. We will address these challenges using interdisciplinary partnerships, training of highly qualified personnel, and modern methodologies for using sensor-based data.

Multiscale entropy rate analysis of complex mobile agents.

Accurate prediction of the motion of objects is a central scientific goal. For deterministic or stochastic processes, models exist which characterize motion with a high degree of reliability. For complex systems, or those where objects have a degree of agency, characterizing motion is far more challenging. The information entropy rate of motion through a discrete space can place a limit on the predictability of even the most complex or history-dependent actor, but the variability in measured encountered locations is inexorably tied to the spatial and temporal resolutions of those measurements. This relation depends on the path of the actor in ways that can be used to derive a general law in closed form relating the mobility entropy rate to different spatial and temporal resolutions, and the path properties within each cell along the path. Correcting for spatial and temporal effects through regression yields the path properties and a measure of mobility entropy rate robust to changes in dimension, allowing comparison of mobility entropy rates between datasets. Employing this measure on empirical datasets yields novel findings, from the similarity of taxicabs to drifters, to the predictable motions of undergraduates, to the browsing habits of Canadian moose.

External Ion Access in the Na/K Pump: Kinetics of Na+, K+, and Quaternary Amine Interaction.

Na/K pumps build essential ion gradients across the plasmalemma of animal cells by coupling the extrusion of three Na+, with the import of two K+ and the hydrolysis of one ATP molecule. The mechanisms of selectivity and competition between Na+, K+, and inhibitory amines remain unclear. We measured the effects of external tetrapropylammonium (TPA+) and ethylenediamine (EDA2+) on three different Na/K pump transport modes in voltage-clamped Xenopus oocytes: 1) outward pump current (IP), 2) passive inward H+ current at negative voltages without Na+ or K+ (IH), and 3) transient charge movement reporting the voltage-dependent extracellular binding/release of Na+ (QNa). Both amines competed with K+ to inhibit IP. TPA+ inhibited IH without competing with H+, whereas EDA2+ did not alter IH at pH 7.6. TPA+ competed with Na+ in QNa measurements, reducing Na+-apparent affinity, evidenced by a ∼-75 mV shift in the charge-voltage curve (at 20 mM TPA+) without reduction of the total charge moved (Qtot). In contrast, EDA2+ and K+ did not compete with Na+ to inhibit QNa; both reduced Qtot without decreasing Na+-apparent affinity. EDA2+ (15 mM) right-shifted the charge-voltage curve by ∼+50 mV. Simultaneous occlusion of EDA2+ and Na+ by an E2P conformation unable to reach E1P was demonstrated by voltage-clamp fluorometry. Trypsinolysis experiments showed that EDA2+-bound pumps are much more proteolysis-resistant than Na+-, K+-, or TPA+-bound pumps, therefore uncovering unique EDA2+-bound conformations. K+ effects on QNa and IH were also evaluated in pumps inhibited with beryllium fluoride, a phosphate mimic. K+ reduced Qtot without shifting the charge-voltage curve, indicating noncompetitive effects, and partially inhibited IH to the same extent as TPA+ in non-beryllium-fluorinated pumps. These results demonstrate that K+ interacts with beryllium-fluorinated pumps inducing conformational changes that alter QNa and IH, suggesting that there are two external access pathways for proton transport by IH.

How many days are enough?: capturing routine human mobility.

Wedding mobile phone sensor technology and human spatial behaviour has great potential. The ubiquity of Global Positioning Systems (GPS) technology has made gathering data about human mobility simpler, more precise, and with higher fidelity, providing minute-by-minute records of the locations of cohorts from dozens of participants. While this data provides a strong basis for Geographic Information Science research, it also constitutes an invasion of the participants' privacy and can provide more information than researchers require to answer their questions. As an ethical and practical consideration, researchers should gather only as much data as they need. In this paper, we take three weeks of GPS traces from over a hundred student participants in mobile phone-based tracking studies and show that fewer than 14 days of data is necessary to establish complete activity spaces. We define 'complete' as the point at which marginal information gains become negligible according to a pairwise temporal analysis of the Kullback-Leibler (KL) divergence of the spatial (bivariate) histogram through time. For the fixed level of information difference, observable in the data, impacts due to individual variability, population composition, and spatial resolution are evident. However, all populations at each level of resolution examined in the paper demonstrated convergence to low divergence levels occurred within a matter of days, and to negligible information gain in less than two weeks. The methods described in the paper represent a novel metric useful to understand the interaction between measurements and information in human mobility.

A glossary for big data in population and public health: discussion and commentary on terminology and research methods.

The volume and velocity of data are growing rapidly and big data analytics are being applied to these data in many fields. Population and public health researchers may be unfamiliar with the terminology and statistical methods used in big data. This creates a barrier to the application of big data analytics. The purpose of this glossary is to define terms used in big data and big data analytics and to contextualise these terms. We define the five Vs of big data and provide definitions and distinctions for data mining, machine learning and deep learning, among other terms. We provide key distinctions between big data and statistical analysis methods applied to big data. We contextualise the glossary by providing examples where big data analysis methods have been applied to population and public health research problems and provide brief guidance on how to learn big data analysis methods.