ABSTRACT
The COVID-19 pandemic disrupted typical travel behavior worldwide. In the United States (U.S.), government entities took action to limit its spread through public health messaging to encourage reduced mobility and thus reduce the spread of the virus. Within statewide responses to COVID-19, however, there were different responses locally. Likely some of these variations were a result of individual attitudes toward the government and health messaging, but there is also likely a portion of the effects that were because of the character of the communities. In this research, we summarize county-level characteristics that are known to affect travel behavior for 404 counties in the U.S., and we investigate correlates of mobility between April and September (2020). We do this through application of three metrics that are derived via changepoint analysis—initial post-disruption mobility index, changepoint on restoration of a ‘‘new normal,'' and recovered mobility index. We find that variables for employment sectors are significantly correlated and had large effects on mobility during the pandemic. The state dummy variables are significant, suggesting that counties within the same state behaved more similarly to one another than to counties in different states. Our findings indicate that few travel characteristics that typically correlate with travel behavior are related to pandemic mobility, and that the number of COVID-19 cases may not be correlated with mobility outcomes. © National Academy of Sciences: Transportation Research Board 2021.
ABSTRACT
The focus of this contribution is to show how the course of the pandemic can be retrospectively investigated in terms of change points detection. At this aim, an automatic method based on recursive partitioning is employed, considering the time series of the 14-day notification rate of newly reported COVID-19 cases per 100,000 population collected by the European Centre for Disease Prevention and Control. The application shows that the pandemic, at the individual country level, can be broken into different periods that do not correspond to the common notion of wave as a natural pattern of peaks and valleys implying predictable rises and falls. This retrospective analysis can be useful either to evaluate the implemented measures or to define adequate policies for the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
In the present article, we investigate the impact of the timescale factor on the quality of life index behaviour on specific time intervals characterized by the presence of socio-economic, political, and/or health severe movements such as pandemics and crises. We essentially aim to show that effectively the quality of life evaluation based on a single index as in the existing studies may be described more adequately by a variable index due to the social, political, economic, and also healthy environment. The variability discovered is expressed by the existence and the estimation of a multi-index instead of a single one with relatively too many factors. Our focus is mainly on the effect of the COVID-19 pandemic and crises or crashes on the quality of life. It turns out that the first essays of empirical treatments of such a series bring out a fractal/multifractal aspect. This motivates our main idea reposing on the fractal/multifractal structure of the data to construct a quantitative model based on wavelets combined with change-point analysis. Our model is applied empirically on a sample corresponding to Saudi Arabia as a case of study during the period from January 1990 to December 2021. The end of this period is strongly affected by the COVID-19 pandemic. The sample is based on social media conversations and texts discussing and describing the satisfaction with the quality of life. The study confirms effectively that the role of the timescale factor is more described when considering a multi-index rather than measurement on the whole time interval. Besides, this multi-index is clearly illustrated by means of the multifractal spectra of the data used. © 2023 John Wiley & Sons Ltd.
ABSTRACT
Recent studies have shown that urban ambient noise (UAN) decreased at many sites due to a slowdown in human activities brought by the SARS-CoV-2 (COVID-19) pandemic lockdowns. Such understanding is inferred from the historical record of the noise levels, which may also help us disambiguate noise sources as required for ambient noise tomography, microseismic and other seismic based studies. As UAN is site-specific, and its analysis enables passive situational awareness, therefore, in the present study, we analyzed the temporal variations in UAN before, during and after the social isolation in the metropolitan region of Lima, the capital of Peru, for the very first time. We used continuous waveforms recorded from February 1st to August 31st, 2020, at the Ñaña (NNA) broadband seismic station for the analysis. Results show the temporal changes occur in different frequency ranges;for example, at frequencies >1 Hz, significant changes in the mean daytime amplitudes are observed, which are absent in the lower frequency range (0.1-1, 1-3, 3-5 Hz). A maximum noise reduction of 37% is observed and should be considered for any future application of UAN. The results were verified by comparing with Community Mobility Reports (CMR) provided by Google using statistical change-point analysis. © 2022 Brazilian Geophysical Society.
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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. © 2022 Copyright held by the owner/author(s).
ABSTRACT
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. [ FROM AUTHOR] Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Originating from Wuhan, China, COVID-19 is spreading rapidly throughout the world. The transmission rate is reported to be high for this novel strain of coronavirus, called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), as compared to its predecessors. Major strategies in terms of clinical trials of medicines and vaccines, social distancing, use of personal protective equipment (PPE), and so on are being implemented in order to control the spread. The current study concentrates on lockdown and social distancing policy followed by the Indian Government and evaluates its effectiveness using Bayesian probability model (BPM). The change point analysis (CPA) done through the above approach suggests that the states which implemented the lockdown before the exponential rise of cases are able to control the spread of the disease in a much better and efficient way. The analysis has been done for states of Maharashtra, Gujarat, Madhya Pradesh, Rajasthan, Tamil Nadu, West Bengal, Uttar Pradesh, and Delhi as union territory. The highest value of Δ (delta) is reported for Gujarat and Madhya Pradesh with a value of 9.6 weeks, while the lowest value is 4.7, evidently for Maharashtra which is the worst affected. All of the states indicate a significant correlation (p < 0.05, tstat > tcritical) for Δ, i.e., the difference in the time period of CPA and lockdown with cases per population (CPP) and cases per unit area (CPUA), while weak correlation (p < 0.1 and tstat < tcritical) is exhibited by delta and cases per unit population density (CPD). For both CPP and CPUA, tstat > tcritical indicating a significant correlation, while Pearson's correlation indicates the direction to be negative. Further analysis in terms of identification of high-risk areas has been studied from the Voronoi approach of GIS based on the inputs from BPM. All the states follow the above pattern of high population, high case scenario, and the boundaries of risk zones can be identified by Thiessen polygon (TP) constructed therein. The findings of the study help draw strategic and policy-driven response for India, toward tackling COVID-19 pandemic.
ABSTRACT
The logistics of policy implementation can lead to a delay from when the actual change in behavior occurs, leading to a shift in a time series. Using change point analysis allows for the data to determine where a change in mean, or other parameters, occurred. But when policy is implemented across multiple locations, how can a researcher understand where change points are occurring at across all locations? Can those locations be grouped together based on their change point? We propose a methodology for clustering panels of nonlinear time series and develop diagnostics to assess the clustering. The change point component of the methodology allows for trends and point anomalies to be detected for each time series. This methodology incorporates spatial and demographic information from the locations into the clustering aspect of the methodology. In a practical application of our methodology, we investigate when average counts of emergency department (ED) visits change related to when the Affordable Care Act was enacted, using monthly time series from 88 locations. Using the diagnostic measures developed and innovative data analysis techniques we understand the groupings of these locations and where in time these groups were changing. In another data application we investigated the impact COVID-19 had on crime rates in the city of Chicago. Using our methodology and data visualization tools, we examined if neighborhoods experience a reduction in crime through their change points and how to group these time series together.This paper also explores the use of Gaussian graphical models to understand metabolic networks to assist in the development of new targeted assays. A metabolite can be measured through a well-developed panel, called a targeted assay, or through a mass spectrometer reading. The mass spectrometer measure, an untargeted panel, is poorly measured but can detect all metabolites present in the sample unlike the targeted panel which only measures these few well-studied metabolites. Given the high cost of targeting a metabolite, it is important to investigate the benefits of a possible addition of a metabolite to a targeted panel. We developed a model based on the determination of successful targeting of a metabolite using variables related to the metabolite in the network. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
COVID-19 is a highly infectious disease caused by SARS-CoV-2, first identified in China and spread globally, resulting into pandemic. Transmission of virus takes place either directly through close contact with infected individual (symptomatic/asymptomatic) or indirectly by touching contaminated surfaces. Virus survives on the surfaces from few hours to days. It enters the human body through nose, eyes or mouth. Other sources of contamination are faeces, blood, food, water, semen etc. Parameters such as temperature/relative humidity also play an important role in transmission. As the disease is evolving, so are the number of cases. Proper planning and restriction are helping in influencing the trajectory of the transmission. Various measures are undertaken to prevent infection such as maintaining hygiene, using facemasks, isolation/quarantine, social/physical distancing, in extreme cases lockdown (restricted movement except essential services) in hot spot areas or throughout the country. Countries that introduced various mitigation measures had experienced control in transmission of COVID-19. Python programming is conducted for change point analysis (CPA) using Bayesian probability approach for understanding the impact of restrictions and mitigation methods in terms of either increase or stagnation in number of COVID-19 cases for eight countries. From analysis it is concluded that countries which acted late in bringing in the social distancing measures are suffering in terms of high number of cases with USA, leading among eight countries analysed. The CPA week in comparison with date of lockdown and first reported case strongly correlates (Pearson's r = - 0.86 to - 0.97) to cases, cases per unit area and cases per unit population, indicating earlier the mitigation strategy, lesser the number of cases. The overall paper will help the decision makers in understanding the possible steps for mitigation, more so in developing countries where the fight against COVID-19 seems to have just begun.
ABSTRACT
The year 2020 saw a rapid global spread of the highly contagious novel coronavirus COVID-19. To halt the spread of the disease, decision makers and governments across the world have been forced to limit mobility and human interaction, which led to a complete lockdown and the closure of nonessential businesses and public places in many cities and countries. Although effective in curbing the spread of the disease, such measures have had major social and economic impacts, particularly at locations where a complete lockdown was required. In such unprecedented circumstances, decision makers were faced with the dilemma of deciding on how and when to limit mobility to curb the spread of the disease, while being considerate of the significant economic impacts of enforcing such a lockdown. Limited research in this area meant that decision makers were forced to experiment different courses of action without fully understanding the consequences of those actions. To address this critical gap and to provide decision makers with more insights on how to manage mobility during a global pandemic, this paper conducts statistical change point analysis of mobility data from 10 different countries with the aims of establishing links between mobility trends, COVID-19 infections, and COVID-19 mortality rates across different countries where different policies were adopted. Among other findings, the analysis revealed that slow responders experienced significantly higher mortality rates per 100,000 people and were forced to implement stricter lockdown strategies when compared to early responders. The analysis also shows that operating at 40% level of mobility is achievable if appropriate action is taken early enough. The findings of this study are extremely valuable in helping nations better manage a, highly anticipated, second wave of COVID-19 or any other highly contagious global pandemic.