Bayesian Change Point Analysis Research Articles

Trend and Variance Adaptive Bayesian Changepoint Analysis and Local Outlier Scoring

We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesian Changepoints with Outliers (ABCO). We utilize a state-space approach to identify a dynamic signal in the presence of outliers and measurement error with stochastic volatility. We find that global state equation parameters are inadequate for most real applications and we include local parameters to track noise at each time-step. This setup provides a flexible framework to detect unspecified changepoints in complex series, such as those with large interruptions in local trends, with robustness to outliers and heteroskedastic noise. Finally, we compare our algorithm against several alternatives to demonstrate its efficacy in diverse simulation scenarios and two empirical examples on the U.S. economy.

A novel outlier detection method based on Bayesian change point analysis and Hampel identifier for GNSS coordinate time series

The identification and removal of outliers in time series are important problems in numerous fields. In this paper, a novel method (BCP-HI) is proposed to enhance the accuracy of outlier detection in GNSS coordinate time series by combining Bayesian change point (BCP) analysis and the Hampel identifier (HI). By using BCP, change points (cps) in the time series are lidentified, and so the time series is divided into subsegments that have properties of a normal distribution. In each of these separated segments, outliers are detected using HI. Each data element identified as an outlier is corrected by a median filter of window size (w) to obtain the corrected signal. The BCP-HI method was tested on both simulated and real GNSS coordinate time series. Outliers from three different synthetic test datasets with different sampling frequencies and outlier amplitudes were detected with approximately 98% accuracy after processing. After this process, Signal-to-Noise Ratio (SNR) increased from 0.0084 to 10.8714 dB and Root Mean Square (RMS) decreased from 24 to 23 mm. Similarly, for real GNSS data, approximately 98% accuracy was achieved, with an increase in SNR from 0.0003 to 4.4082 dB and a decrease in RMS from 7.6 to 6.6 mm observed. In addition, the output signals after BCP-HI were examined graphically using Lomb–Scargle periodograms and it was observed that clearer power spectrum distributions emerged. When the input and output signals were examined using the Kolmogorov–Smirnov (KS) test, they were found to be statistically similar. These results indicate that the BCP-HI algorithm effectively removes outliers, and enhances processing accuracy and reliability, and improves signal quality.

Impact of pandemic restrictions on travel patterns in urban centres: A case-study of Dublin City, Ireland

The spread of Covid-19 and implementation of various restrictions changed how and why people travel. The present study analyzed three different modes of transportation, to understand the impact of Covid-19 mobility and land-use restrictions on chosen neighbourhoods in Dublin City, Ireland. Classification analysis, Spatial correlation analysis, and Bayesian change point analysis had been conducted using vehicle, cyclist & pedestrian count data from 44 neighbouring or collocated stations to explore the statistical changes in the traffic system characteristics. Apart from reduction in traffic counts, the other impacts were modal shift, rise in cyclist numbers, and similarity between weekday & weekend patterns observed. Analyses could identify that changes in the statistical aspects of traffic system are congruent with the changes in lockdown measures. Overall, this study presented a set of tools to identify the existence and degree of changes in traffic patterns over time due to any mobility or land-use policy changes.

Does Risk Return Trade-off Hold in Base Metal and Bullion Commodity Assets? A Bayesian Change Point Analysis

The risk-return trade-off is fundamental to portfolio investing whose stability is critical for portfolio optimisation. Since the relationship is dynamic, the portfolio manager should know the point of change and, thereafter duration of the changed period with certainty. First, we have done Bayesian change point analysis, and then based on the analysis, the study identifies the regimes having equal statistical variance along with the corresponding average return in two most popular commodities, that is, copper and gold. It is found that risk-return trade-off is not stable. Further, in a higher volatility regime, only gold can be considered as a diversifiable commodity because a positive risk-return trade-off holds. But in a low volatility regime, both commodities lose their diversification properties as the risk-return relation becomes negative.

Efficient Multi-Change Point Analysis to Decode Economic Crisis Information from the S&P500 Mean Market Correlation.

Identifying macroeconomic events that are responsible for dramatic changes of economy is of particular relevance to understanding the overall economic dynamics. We introduce an open-source available efficient Python implementation of a Bayesian multi-trend change point analysis, which solves significant memory and computing time limitations to extract crisis information from a correlation metric. Therefore, we focus on the recently investigated S&P500 mean market correlation in a period of roughly 20 years that includes the dot-com bubble, the global financial crisis, and the Euro crisis. The analysis is performed two-fold: first, in retrospect on the whole dataset and second, in an online adaptive manner in pre-crisis segments. The online sensitivity horizon is roughly determined to be 80 up to 100 trading days after a crisis onset. A detailed comparison to global economic events supports the interpretation of the mean market correlation as an informative macroeconomic measure by a rather good agreement of change point distributions and major crisis events. Furthermore, the results hint at the importance of the U.S. housing bubble as a trigger of the global financial crisis, provide new evidence for the general reasoning of locally (meta)stable economic states, and could work as a comparative impact rating of specific economic events.

Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times

Change-point analysis (CPA) is a method for detecting abrupt changes in parameter(s) underlying a sequence of random variables. It has been applied to detect examinees’ aberrant test-taking behavior by identifying abrupt test performance change. Previous studies utilized maximum likelihood estimations of ability parameters, focusing on detecting one change point for each examinee. This article proposes a Bayesian CPA procedure using response times (RTs) to detect abrupt changes in examinee speed, which may be related to aberrant responding behaviors. The lognormal RT model is used to derive a procedure for detecting aberrant RT patterns. The method takes the numbers and locations of the change points as parameters in the model to detect multiple change points or multiple aberrant behaviors. Given the change points, the corresponding speed of each segment in the test can be estimated, which enables more accurate inferences about aberrant behaviors. Simulation study results indicate that the proposed procedure can effectively detect simulated aberrant behaviors and estimate change points accurately. The method is applied to data from a high-stakes computerized adaptive test, where its applicability is demonstrated.

A Bayesian change point analysis re-examines the 4.2 ka BP event in southeast Europe and southwest Asia

The 4.2 ka BP event is a hypothesized global drought and cooling event and is claimed to be supported by proxy records from a wide range of geographical locations. However, recent studies have presented contradictory data that call into question the validity of this hypothetical event in certain regions, leading to renewed interest in defining its true geographical and temporal extent. This study applies a Bayesian model designed to test the existence of abrupt change points to paleoclimate time series from southeast Europe and southwest Asia, which are claimed to support this hypothesis. The results are discussed in relation to previous studies on the geographical extent of the 4.2 ka BP event. The results suggest that the evidence for the 4.2 ka BP event may not be as strong as previously thought. While this analysis supports the event that occurred in the central Mediterranean and Greece, it does not appear to have had an impact on proxy data from Anatolia and the Levant. Further proxy data are needed to confirm the existence of this potential climate event and better constrain its geographical extent.

Barley systemic bioelectrical changes detect pathogenic infection days before the first disease symptoms

Plant electrome corresponds to the emergent set of plant bioelectrical activity. The infection of plant tissues by different fungi alters this bioelectrical activity and can originate different electrical signatures, which can be used to identify the potential pathogenic agent. In this work, we evaluated the systemic variations of the electrome of barley triggered by the inoculation of Blumeria graminis and Bipolaris sorokiniana on the leaves. Therefore, approximately 50 days-old barley plants were placed within a Faraday cage under controlled conditions. A pair of needle electrodes was inserted in the culm for the recording of bioelectrical data. The plants were then inoculated with the pathogen in the last completely expanded leaf. Each pathogen was inoculated in different plants. Plants exposed to the same procedures as the inoculated ones, but without the fungi, served as controls. The electromes were recorded through 48 h, 24 h of which before the fungi inoculation or the control treatment. The bioelectrical data collected were analysed as time series through the following techniques: descriptive statistics, Bayesian change point (BCP) analysis, approximate entropy (ApEn), and autocorrelation. The results of these analyses were used as features, hence creating the dataset used in the machine learning (cluster analysis). The results indicate that the inoculation of pathogenic fungi culminated in a greater electrome variation (EV) when compared with the control (simulated inoculation). The pathogen inoculation generated peculiarities in the EV distribution for each pathogen. Therefore, the inoculation of B. graminis or B. sorokiniana created an electrical signature specific for each pathogen. This signature allows identifying the presence of pathogenic infection in the first minutes after contact with the host surface.

Uncovering COVID-19 infection determinants in Portugal: towards an evidence-based spatial susceptibility index to support epidemiological containment policies

BackgroundCOVID-19 caused the largest pandemic of the twenty-first century forcing the adoption of containment policies all over the world. Many studies on COVID-19 health determinants have been conducted, mainly using multivariate methods and geographic information systems (GIS), but few attempted to demonstrate how knowing social, economic, mobility, behavioural, and other spatial determinants and their effects can help to contain the disease. For example, in mainland Portugal, non-pharmacological interventions (NPI) were primarily dependent on epidemiological indicators and ignored the spatial variation of susceptibility to infection.MethodsWe present a data-driven GIS-multicriteria analysis to derive a spatial-based susceptibility index to COVID-19 infection in Portugal. The cumulative incidence over 14 days was used in a stepwise multiple linear regression as the target variable along potential determinants at the municipal scale. To infer the existence of thresholds in the relationships between determinants and incidence the most relevant factors were examined using a bivariate Bayesian change point analysis. The susceptibility index was mapped based on these thresholds using a weighted linear combination.ResultsRegression results support that COVID-19 spread in mainland Portugal had strong associations with factors related to socio-territorial specificities, namely sociodemographic, economic and mobility. Change point analysis revealed evidence of nonlinearity, and the susceptibility classes reflect spatial dependency. The spatial index of susceptibility to infection explains with accuracy previous and posterior infections. Assessing the NPI levels in relation to the susceptibility map points towards a disagreement between the severity of restrictions and the actual propensity for transmission, highlighting the need for more tailored interventions.ConclusionsThis article argues that NPI to contain COVID-19 spread should consider the spatial variation of the susceptibility to infection. The findings highlight the importance of customising interventions to specific geographical contexts due to the uneven distribution of COVID-19 infection determinants. The methodology has the potential for replication at other geographical scales and regions to better understand the role of health determinants in explaining spatiotemporal patterns of diseases and promoting evidence-based public health policies.

Identifying dominant industrial sectors in market states of the S&P 500 financial data

Understanding and forecasting changing market conditions in complex economic systems like the financial market is of great importance to various stakeholders such as financial institutions and regulatory agencies. Based on the finding that the dynamics of sector correlation matrices of the S&P 500 stock market can be described by a sequence of distinct states via a clustering algorithm, we try to identify the industrial sectors dominating the correlation structure of each state. For this purpose, we use a method from explainable artificial intelligence (XAI) on daily S&P 500 stock market data from 1992 to 2012 to assign relevance scores to every feature of each data point. To compare the significance of the features for the entire data set we develop an aggregation procedure and apply a Bayesian change point analysis to identify the most significant sector correlations. We show that the correlation matrix of each state is dominated only by a few sector correlations. Especially the energy and IT sector are identified as key factors in determining the state of the economy. Additionally we show that a reduced surrogate model, using only the eight sector correlations with the highest XAI-relevance, can replicate of the cluster assignments. In general our findings imply an additional dimension reduction of the dynamics of the financial market.

A Case Series Exploration of Multi-Regional Expression Heterogeneity in Triple-Negative Breast Cancer Patients.

Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor. We obtained tissue specimens from 10 TNBC patients and conducted RNA sequencing analysis of 2-4 regions per tumor. We developed a novel analysis framework to dissect and characterize different types of variability: between-patients (inter-tumoral heterogeneity), between-patients across regions (inter-tumoral and region heterogeneity), and within-patient, between-regions (regional intratumoral heterogeneity). We performed a Bayesian changepoint analysis to assess and classify regional variability as low (convergent) versus high (divergent) within each patient feature (TNBC and PAM50 subtypes, immune, stroma, tumor counts and tumor infiltrating lymphocytes). Gene expression signatures were categorized into three types of variability: between-patients (108 genes), between-patients across regions (183 genes), and within-patients, between-regions (778 genes). Based on the between-patient gene signature, we identified two distinct patient clusters that differed in menopausal status. Significant intratumoral divergence was observed for PAM50 classification, tumor cell counts, and tumor-infiltrating T cell abundance. Other features examined showed a representation of both divergent and convergent results. Lymph node stage was significantly associated with divergent tumors. Our results show extensive intertumoral heterogeneity and regional ITH in gene expression and image-derived features in TNBC. Our findings also raise concerns regarding gene expression based TNBC subtyping. Future studies are warranted to elucidate the role of regional heterogeneity in TNBC as a driver of treatment resistance.

Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis.

Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks by using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. The aim of this study was to assess the variation in the timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability by using the example of seasonal influenza epidemic in 24 countries. We obtained influenza-related reports between January 2013 and December 2019 from 2 EBS systems, that is, HealthMap and the World Health Organization Epidemic Intelligence from Open Sources (EIOS), and weekly virological influenza counts for the same period from FluNet as the gold standard. Influenza epidemic periods were detected based on report frequency by using Bayesian change point analysis. Timely sensitivity, that is, outbreak detection within the first 2 weeks before or after an outbreak onset was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. Overall, while monitoring the frequency of EBS reports over 7 years in 24 countries, we detected 175 out of 238 outbreaks (73.5%) but only 22 out of 238 (9.2%) within 2 weeks before or after an outbreak onset; in the best case, while monitoring the frequency of health-related reports, we identified 2 out of 6 outbreaks (33%) within 2 weeks of onset. The positive predictive value varied between 9% and 100% for HealthMap and from 0 to 100% for EIOS, and timeliness of detection ranged from 13% to 94% for HealthMap and from 0% to 92% for EIOS, whereas system specificity was generally high (59%-100%). The number of EBS reports available within a country, the human development index, and the country's geographical location partially explained the high variability in system performance across countries. We documented the global variation of EBS performance and demonstrated that monitoring the report frequency alone in EBS may be insufficient for the timely detection of outbreaks. In particular, in low- and middle-income countries, low data quality and report frequency impair the sensitivity and timeliness of disease surveillance through EBS. Therefore, advances in the development and evaluation and EBS are needed, particularly in low-resource settings.

Leveraging Mobile Sensing and Bayesian Change Point Analysis to Monitor Community-scale Behavioral Interventions: A Case Study on COVID-19

During pandemics, effective interventions require monitoring the problem at different scales and understanding the various tradeoffs between efficacy, privacy, and economic burden. To address these challenges, we propose a framework where we perform Bayesian change-point analysis on aggregate behavior markers extracted from mobile sensing data collected during the COVID-19 pandemic. Results generated by 598 participants for up to four months reveal rich insights: We observe an increase in smartphone usage around February 10th, followed by an increase in email usage around February 27th and, finally, a large reduction in participant’s mobility around March 13th. These behavior changes overlapped with important news events and government directives such as the naming of COVID-19, a spike in the number of reported cases in Europe, and the declaration of national emergency by President Trump. We also show that our detected change points align with changes in large scale external sources, including number of COVID-19 tweets, COVID-19 search traffic, and a large-scale foot traffic data collected by SafeGraph, providing further validation of our method. Our results show promise towards the feasibility of using mobile sensing to understand communities’ responses to public health interventions.

Response of active catchment water storage capacity to a prolonged meteorological drought and asymptotic climate variation

Abstract. Studies on the hydrological response to continuous extreme and asymptotic climate change can improve our ability to cope with intensified water-related problems. Most of the literature focused on the runoff response to climate change, while neglecting the impacts of the potential variation in the active catchment water storage capacity (ACWSC) that plays an essential role in the transfer of climate inputs to the catchment runoff. This study aims to systematically identify the response of the ACWSC to a long-term meteorological drought and asymptotic climate change. First, the time-varying parameter is derived to reflect the ACWSC periodic and abrupt variations in both drought and non-drought periods. Second, the change points and varying patterns of the ACWSC are analyzed based on the Bayesian change point analysis with multiple evaluation criteria. Finally, various catchment properties and climate characteristics are used to explore the possible relationship between these variables and the temporal variation characteristics of the ACWSC. The catchments that suffered from a prolonged meteorological drought in southeast Australia were selected as the case study. Results indicate that: (1) the increase in amplitude change in the ACWSC is observed in 83/92 catchments during the prolonged drought period, and significant shifts in the mean value of the ACWSC are detected in 77/92 catchments; (2) the average response time of the ACWSC for all 92 catchments with significant changes is 641.3 d; (3) the values of the ACWSC changed significantly in the catchments with small areas, low elevations, small slope ranges, large forest coverage, and high soil water-holding capacities. This study could enhance our understanding of the variations in catchment property under climate change.

A Bayesian Change Point Analysis of the USD/CLP Series in Chile from 2018 to 2020: Understanding the Impact of Social Protests and the COVID-19 Pandemic

Exchange rates are determined by factors such as interest rates, political stability, confidence, the current account on balance of payments, government intervention, economic growth and relative inflation rates, among other variables. In October 2019, an increased climate of citizen discontent with current social policies resulted in a series of massive protests that ignited important political changes in Chile. This event along with the global COVID-19 pandemic were two major factors that affected the value of the US dollar and produced sudden changes in the typically stable USD/CLP (Chilean Peso) exchange rate. In this paper, we use a Bayesian approach to detect and locate change points in the currency exchange rate process in order to identify and relate these points with the important dates related to the events described above. The implemented method can successfully detect the onset of the social protests, the beginning of the COVID-19 pandemic in Chile and the economic reactivation in the US and Europe. In addition, we evaluate the performance of the proposed MCMC algorithms using a simulation study implemented in Python and R.

Assessing the effect of non-pharmaceutical interventions on COVID-19 transmission in Spain, 30 August 2020 to 31 January 2021.

BackgroundAfter a national lockdown during the first wave of the COVID-19 pandemic in Spain, regional governments implemented different non-pharmaceutical interventions (NPIs) during the second wave.AimTo analyse which implemented NPIs significantly impacted effective reproduction number (Rt) in seven Spanish provinces during 30 August 2020–31 January 2021.MethodsWe coded each NPI and levels of stringency with a ‘severity index’ (SI) and computed a global SI (mean of SIs per six included interventions). We performed a Bayesian change point analysis on the Rt curve of each province to identify possible associations with global SI variations. We fitted and compared several generalised additive models using multimodel inference, to quantify the statistical effect on Rt of the global SI (stringency) and the individual SIs (separate effect of NPIs).ResultsThe global SI had a significant lowering effect on the Rt (mean: 0.16 ± 0.05 units for full stringency). Mandatory closing times for non-essential businesses, limited gatherings, and restricted outdoors seating capacities (negative) as well as curfews (positive) were the only NPIs with a significant effect. Regional mobility restrictions and limited indoors seating capacity showed no effect. Our results were consistent with a 1- to 3-week-delayed Rt as a response variable.ConclusionWhile response measures implemented during the second COVID-19 wave contributed substantially to a decreased reproduction number, the effectiveness of measures varied considerably. Our findings should be considered for future interventions, as social and economic consequences could be minimised by considering only measures proven effective.

Decadal Change of Meiyu Onset over Yangtze River and Its Causes

Meiyu onset marks the beginning of the rainfall season in the densely populated Yangtze River Basin, whether the Meiyu initiates early or late in June, and thus has a profound effect on the several hundred million people living there. Applying a Bayesian change-point analysis to data from 1960–2014, we objectively detected an abrupt change of Meiyu onset around 2002. The Meiyu onset date averaged over 2002–2014 was 19 June, delayed by about two weeks compared to that of 1989–2001 (6 June). This decadal change is attributable to the distinct amplitude of moisture transport toward the Yangtze River Basin induced by the changes in climatological intraseasonal oscillation (CISO). The CISO emerges as the annual cycle interacts with the transient intraseasonal perturbations. The wet/dry phases of the CISO are consistent with the climatological active/break stages of the East Asian summer monsoon. In early June, the northwestward-propagating CISO convective/cyclonic anomalies over the western North Pacific (WNP) show weaker amplitude during the earlier-onset epoch compared to the delayed-onset epoch. Thus, relative to the delayed onset epoch, a quasi-barotropic anticyclonic CISO anomaly appears over the WNP in early June during the earlier-onset years. This anticyclonic anomaly was conducive to the westward extension of the WNP subtropical high, conveying warm, moist air from the tropics toward the Yangtze River Basin for the rainy season onset. Model experiments suggest that the decadal changes in WNP CISO intensity were associated with the epochal changes in large-scale background circulation and sea surface temperature over the WNP.

Stepwise evolution of the Asian summer monsoon during the Holocene revealed by a stalagmite record from Heifeng Cave, Southwest China

The abrupt changes of the Asian summer monsoon (ASM) during the Holocene have long been of interest to environmental scientists. Here, RAMPFIT and Bayesian Change Point (BCP) analyses are applied to analyze the stalagmite δ18O record from Heifeng Cave in southern China, and the results show that it is characterized by a distinct stepwise pattern of variation which can be divided into six stages (Stages, S) and five transitional phases (Transitions, T). In the early Holocene, when Northern Hemisphere summer insolation (NHSI) was at its maximum, the ASM underwent step-like increases in strength, comprising two stages. With the decrease in NHSI during the middle to late-Holocene, the weakening of ASM intensity was characterized by further step-like changes comprising three stages. The transitional phases of the monsoon are broadly correlative with millennial-scale monsoon weakening events associated with Bond events in the North Atlantic region, suggesting that the stepwise evolution of the ASM during the Holocene was caused by the combined effects of changes in NHSI and Bond events. We propose that Bond events may have acted as triggers that caused the ASM to cross a critical threshold, prompting a shift from one regime to another.

Reducing the refuge effect: using private‐land hunting to mitigate issues with hunter access

AbstractHunting pressure alters the spatial ecology of many ungulate species. During hunting periods, game species often select areas that have reduced hunter accessibility by increasing use of forest cover, remaining farther from roads, or moving to private land as a means of refuge. Our objectives were to determine if elk (Cervus canadensis) in central Utah, USA, shifted distribution towards areas of refuge (private land) in response to hunting and whether allocating hunting permits to refuge areas could reduce selection of refugia by elk. We captured 445 elk between January 2015 and March 2017 on the Wasatch Front in central Utah and fitted each individual with a global positioning system collar. We used logistic regression models and Bayesian change point analyses to determine differences in daily use of public land in response to the addition of private‐land hunting. We detected shifts in elk distribution at the beginning of the archery hunting season and during the rifle season, suggesting elk selected for private land as refuge from hunters. At the conclusion of the hunting season, we detected a shift back to public land. The addition of private‐land hunting increased use of public land by elk during the rifle season (from 29% in 2015 to 41% in 2016). Our results show that the distribution of elk across a matrix of public and private land can be influenced by manipulating hunting pressure. Hunter access, the ability of hunters to access target game species, can be a concern in areas with private‐land refuges. This can be particularly problematic when management strategies rely on hunter harvest to maintain population objectives; however, this problem can be mitigated by the introduction of hunting on private lands, if landowners provide hunter access.

Changes in Device-Measured Physical Activity Patterns in U.K. Adults Related to the First COVID-19 Lockdown

The response to COVID-19 resulted in behavioral restrictions to tackle the spread of infection. Initial data indicates that step counts were impacted by lockdown restrictions; however, there is little evidence regarding changes of light and moderate to vigorous physical activity (MVPA) behavioral intensities. In this study, participants were asked to provide longitudinal wearable data from Fitbit devices over a period of 30 weeks, from December 2019 to June 2020. Self-assessed key worker status was captured, along with wearable estimates of steps, light activity, and MVPA. Bayesian change point analyses of data from 97 individuals found that there was a sharp decrease of 1,473 steps (95% credible interval [CI] [−2,218, −709]) and light activity minutes (41.9; 95% CI [−54.3, −29.3]), but an increase in MVPA minutes (11.7; 95% CI [2.9, 19.4]) in the mean weekly totals for nonkey workers. For the key workers, the total number of steps (207; 95% CI [−788, 1,456]) and MVPA minutes increased (20.5; 95% CI [12.6, 28.3]) but light activity decreased by an average of 46.9 min (95% CI [−61.2, −31.8]). Interestingly, the change in steps was commensurate with that observed during Christmas (1,458; 95% CI [−2,286, −554]) for nonkey workers and behavioral changes occurred at different time points and rates depending on key worker status. Results indicate that there were clear behavioral modifications before and during the initial COVID-19 lockdown period, and future research should assess whether any behavioral modifications were sustained over time.