Quantifying the Trendiness of Trends.

News media often report that the trend of some public health outcome has changed. These statements are frequently based on longitudinal data, and the change in trend is typically found to have occurred at the most recent data collection time point-if no change had occurred the story is less likely to be reported. Such claims may potentially influence public health decisions on a national level. We propose two measures for quantifying the trendiness of trends. Assuming that reality evolves in continuous time, we define what constitutes a trend and a change in trend, and introduce a probabilistic Trend Direction Index. This index has the interpretation of the probability that a latent characteristic has changed monotonicity at any given time conditional on observed data. We also define an index of Expected Trend Instability quantifying the expected number of changes in trend on an interval. Using a latent Gaussian process model, we show how the Trend Direction Index and the Expected Trend Instability can be estimated in a Bayesian framework, and use the methods to analyse the proportion of smokers in Denmark during the last 20 years and the development of new COVID-19 cases in Italy from 24 February onwards.

Characteristics of Urban Traffic Control Measures in China in the Background of COVID-19

The rapid global spread of the coronavirus disease 2019 (COVID-19) is now a reality. China has taken urban traffic control measures to prevent and control the epidemic, but this has prevented the flow of people between cities. This study investigates the mechanism of the impact of urban traffic control measures on the intercity population flow in China using the one-way causal measurement method. The results show that the impact of urban traffic control measures on the intercity flow of the population changes with time. Based on this, this study makes scientific suggestions for the government on how to reasonably undertake traffic control measures. [ FROM AUTHOR] Copyright of Journal of Advanced Computational Intelligence & Intelligent Informatics is the property of Fuji Technology Press Ltd. 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.)

COVID-19 effects on the Canadian term structure of interest rates

In Canada, COVID-19 pandemic triggered exceptional monetary policy interventions by the central bank, which in March 2020 made multiple unscheduled cuts to its target rate. In this paper we assess the extent to which Bank of Canada interventions affected the determinants of the yield curve. In particular, we apply Functional Principal Component Analysis to the term structure of interest rates. We find that, during the pandemic, the long-run dependence of level and slope components of the yield curve is unchanged with respect to previous months, although the shape of the mean yield curve completely changed after target rate cuts. Bank of Canada was effective in lowering the whole yield curve and correcting the inverted hump of previous months, but it was not able to reduce the exposure to already existing long-run risks.

A Dynamic Interaction Semiparametric Function-on-Scalar Model

Motivated by recent work studying massive functional data, such as the COVID-19 data, we propose a new dynamic interaction semiparametric function-on-scalar (DISeF) model. The proposed model is useful to explore the dynamic interaction among a set of covariates and their effects on the functional response. The proposed model includes many important models investigated recently as special cases. By tensor product B-spline approximating the unknown bivariate coefficient functions, a three-step efficient estimation procedure is developed to iteratively estimate bivariate varying-coefficient functions, the vector of index parameters, and the covariance functions of random effects. We also establish the asymptotic properties of the estimators including the convergence rate and their asymptotic distributions. In addition, we develop a test statistic to check whether the dynamic interaction varies with time/spatial locations, and we prove the asymptotic normality of the test statistic. The finite sample performance of our proposed method and of the test statistic are investigated with several simulation studies. Our proposed DISeF model is also used to analyze the COVID-19 data and the ADNI data. In both applications, hypothesis testing shows that the bivariate varying-coefficient functions significantly vary with the index and the time/spatial locations. For instance, we find that the interaction effect of the population aging and the socio-economic covariates, such as the number of hospital beds, physicians, nurses per 1000 people and GDP per capita, on the COVID-19 mortality rate varies in different periods of the COVID-19 pandemic. The healthcare infrastructure index related to the COVID-19 mortality rate is also obtained for 141 countries estimated based on the proposed DISeF model.

Probabilistic K-means with Local Alignment for Clustering and Motif Discovery in Functional Data

We develop a new method to locally cluster curves and discover functional motifs, that is, typical shapes that may recur several times along and across the curves capturing important local characteristics. In order to identify these shared curve portions, our method leverages ideas from functional data analysis (joint clustering and alignment of curves), bioinformatics (local alignment through the extension of high similarity seeds) and fuzzy clustering (curves belonging to more than one cluster, if they contain more than one typical shape). It can employ various dissimilarity measures and incorporate derivatives in the discovery process, thus exploiting complex facets of shapes. We demonstrate the performance of our method with an extensive simulation study, and show how it generalizes other clustering methods for functional data. Finally, we provide real data applications to Italian Covid-19 death curves and Omics data related to mutagenesis. Supplementary materials for this article are available online. © 2023 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Functional, Multivariate Functional and Spatial PCA: Application to Covid-19 Data in the African Continent

Covid-19 pandemic has negatively impacted many areas, including the economy and health care facilities, and has left more than 5 million deaths worldwide. In this paper, we use functional data analysis methods to describe evolution of the number of cases and the number of deaths of Covid-19 in Africa. We perform functional principal component analysis, Multivariate functional component analysis and spatial component analysis to characterize better the phenomena and spatial data to determine the impact of a region's neighborhood on number of cases. The obtained results allow us to have a better knowledge of the evolution of the pandemic in African continent. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The lock-down effects of COVID-19 on the air pollution indices in Iran and its neighbors.

Introduction: The COVID-19 restrictions have a lot of various peripheral negative and positive effects, like economic shocks and decreasing air pollution, respectively. Many studies showed NO2 reduction in most parts of the world. Methods: Iran and its land and maritime neighbors have about 7.4% of the world population and 6.3% and 5.8% of World COVID-19 cases and deaths, respectively. The air pollution indices of them such as CH4 (Methane), CO_1 (CO), H2O (Water), HCHO (Tropospheric Atmospheric Formaldehyde), NO2 (Nitrogen oxides), O3 (ozone), SO2 (Sulfur Dioxide), UVAI_AAI [UV Aerosol Index (UVAI)/Absorbing Aerosol Index (AAI)] are studied from the First quarter of 2019 to the fourth quarter of 2021 with Copernicus Sentinel 5 Precursor (S5P) satellite data set from Google Earth Engine. The outliers are detected based on the depth functions. We use a two-sample t test, Wilcoxon test, and interval-wise testing for functional data to control the familywise error rate. Result: The adjusted p value comparison between Q2 of 2019 and Q2 of 2020 in NO2 for almost all countries is statistically significant except Iraq, UAE, Bahrain, Qatar, and Kuwait. But, the CO and HCHO are not statistically significant in any country. Although CH4, O3, and UVAI_AAI are statistically significant for some countries. In the Q2 comparison for NO2 between 2020 and 2021, only Iran, Armenia, Turkey, UAE, and Saudi Arabia are statistically significant. However, Ch4 is statistically significant for all countries except Azerbaijan. Conclusions: The comparison with and without adjusted p values declares the decreases in some air pollution in these countries. Supplementary Information: The online version contains supplementary material available at 10.1007/s40808-022-01528-x.

Bayesian adaptive selection of basis functions for functional data representation

Considering the context of functional data analysis, we developed and applied a new Bayesian approach via the Gibbs sampler to select basis functions for a finite representation of functional data. The proposed methodology uses Bernoulli latent variables to assign zero to some of the basis function coefficients with a positive probability. This procedure allows for an adaptive basis selection since it can determine the number of bases and which ones should be selected to represent functional data. Moreover, the proposed procedure measures the uncertainty of the selection process and can be applied to multiple curves simultaneously. The methodology developed can deal with observed curves that may differ due to experimental error and random individual differences between subjects, which one can observe in a real dataset application involving daily numbers of COVID-19 cases in Brazil. Simulation studies show the main properties of the proposed method, such as its accuracy in estimating the coefficients and the strength of the procedure to find the true set of basis functions. Despite having been developed in the context of functional data analysis, we also compared the proposed model via simulation with the well-established LASSO and Bayesian LASSO, which are methods developed for non-functional data. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Modeling the evolution of deaths from infectious diseases with functional data models: The case of COVID-19 in Brazil.

In this paper, we apply statistical methods for functional data to explore the heterogeneity in the registered number of deaths of COVID-19, over time. The cumulative daily number of deaths in regions across Brazil is treated as continuous curves (functional data). The first stage of the analysis applies clustering methods for functional data to identify and describe potential heterogeneity in the curves and their functional derivatives. The estimated clusters are labeled with different "levels of alert" to identify cities in a possible critical situation. In the second stage of the analysis, we apply a functional quantile regression model for the death curves to explore the associations with functional rates of vaccination and stringency and also with several scalar geographical, socioeconomic and demographic covariates. The proposed model gave a better curve fit at different levels of the cumulative number of deaths when compared to a functional regression model based on ordinary least squares. Our results add to the understanding of the development of COVID-19 death counts.

Probabilistic K-means with local alignment for clustering and motif discovery in functional data

We develop a new method to locally cluster curves and discover functional motifs, i.e. typical shapes that may recur several times along and across the curves capturing important local characteristics. In order to identify these shared curve portions, our method leverages ideas from functional data analysis (joint clustering and alignment of curves), bioinformatics (local alignment through the extension of high similarity seeds) and fuzzy clustering (curves belonging to more than one cluster, if they contain more than one typical shape). It can employ various dissimilarity measures and incorporate derivatives in the discovery process, thus exploiting complex facets of shapes. We demonstrate the performance of our method with an extensive simulation study, and show how it generalizes other clustering methods for functional data. Finally, we provide real data applications to Italian Covid-19 death curves and Omics data related to mutagenesis. [ FROM AUTHOR]

Exploration of the impact of political ideology disparity on COVID-19 transmission in the United States.

BACKGROUND: Based on individual-level studies, previous literature suggested that conservatives and liberals in the United States had different perceptions and behaviors when facing the COVID-19 threat. From a state-level perspective, this study further explored the impact of personal political ideology disparity on COVID-19 transmission before and after the emergence of Omicron. METHODS: A new index was established, which depended on the daily cumulative number of confirmed cases in each state and the corresponding population size. Then, by using the 2020 United States presidential election results, the values of the built index were further divided into two groups concerning the political party affiliation of the winner in each state. In addition, each group was further separated into two parts, corresponding to the time before and after Omicron predominated. Three methods, i.e., functional principal component analysis, functional analysis of variance, and function-on-scalar linear regression, were implemented to statistically analyze and quantify the impact. RESULTS: Findings reveal that the disparity of personal political ideology has caused a significant discrepancy in the COVID-19 crisis in the United States. Specifically, the findings show that at the very early stage before the emergence of Omicron, Democratic-leaning states suffered from a much greater severity of the COVID-19 threat but, after July 2020, the severity of COVID-19 transmission in Republican-leaning states was much higher than that in Democratic-leaning states. Situations were reversed when the Omicron predominated. Most of the time, states with Democrat preferences were more vulnerable to the threat of COVID-19 than those with Republican preferences, even though the differences decreased over time. CONCLUSIONS: The individual-level disparity of political ideology has impacted the nationwide COVID-19 transmission and such findings are meaningful for the government and policymakers when taking action against the COVID-19 crisis in the United States.

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model.

The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.

CellDrift: inferring perturbation responses in temporally sampled single-cell data.

Cells and tissues respond to perturbations in multiple ways that can be sensitively reflected in the alterations of gene expression. Current approaches to finding and quantifying the effects of perturbations on cell-level responses over time disregard the temporal consistency of identifiable gene programs. To leverage the occurrence of these patterns for perturbation analyses, we developed CellDrift (https://github.com/KANG-BIOINFO/CellDrift), a generalized linear model-based functional data analysis method that is capable of identifying covarying temporal patterns of various cell types in response to perturbations. As compared to several other approaches, CellDrift demonstrated superior performance in the identification of temporally varied perturbation patterns and the ability to impute missing time points. We applied CellDrift to multiple longitudinal datasets, including COVID-19 disease progression and gastrointestinal tract development, and demonstrated its ability to identify specific gene programs associated with sequential biological processes, trajectories and outcomes.

Functional Data Analysis for the Detection of Outliers and Study of the Effects of the COVID-19 Pandemic on Air Quality: A Case Study in Gijón, Spain

Air pollution, especially at the ground level, poses a high risk for human health as it can have serious negative effects on the population of certain areas. The high variability of this type of data, which are affected by weather conditions and human activities, makes it difficult for conventional methods to precisely detect anomalous values or outliers. In this paper, classical analysis, statistical process control, and functional data analysis are compared for this purpose. The results obtained motivate the development of a new outlier detector based on the concept of functional directional outlyingness. The validation of this algorithm is perfomed on real air quality data from the city of Gijón, Spain, aiming to detect the proven reduction in NO2 levels during the COVID-19 lockdown in that city. Three more variables (SO2, PM10, and O3) are studied with this technique. The results demonstrate that functional data analysis outperforms the two other methods, and the proposed outlier detector is well suited for the accurate detection of outliers in data with high variability. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

A functional data analysis approach to assess the prognostic value of SARS-CoV-2 infections surrogate data

COVID-19 is characterised by quite diverse prognosis. While the majority of infected individuals present no or very mild symptoms, some individuals develop severe disease requiring intensive care. This work leverages the parameters of a virtual cohort of infected individuals generated by a computational immunology model. In so doing we identify the most relevant immunological parameters for the classification of severe COVID-19 cases. The functional data analysis approach used turns out to be appropriate to analyse the output of the computational model. In this work, we classify the disease prognosis using both statistical models and machine learning algorithms adapted from functional data analysis and we compare their performances. © 2021 IEEE.

Shape-restricted estimation and spatial clustering of COVID-19 infection rate curves.

The study of regional COVID-19 daily reported cases is used to understand pattern of spread and disease progression over time. These data are challenging to model due to noise that is present, which arises from failures in reporting, false positive tests, etc., and the spatial dependence between regions. In this work, we extend a recently developed Bayesian modeling framework for inference of functional data to jointly estimate and cluster daily reported cases data from US states, while accounting for spatial dependence between US states. Shape-restriction allows us to directly infer the number of extrema of a smooth infection rate curve that underlies noisy data. Other parameters in the model account for the relative timing of extrema, and the magnitude and severity of infection rates. We incorporate mobility behavior of each US state's population into an informative prior model to account for the spatial dependence between US states. Our model corroborates past work that shows that different US states have indeed experienced COVID-19 differently, but that there are regional patterns within the US. The modeling results can be used to assess severity of infection in individual US states and trends of neighboring US states to aid pandemic planning. Retrospectively, this model can be used to see which factors (governmental, behavioral, etc.) are associated with the varying shapes of infection rate curves, which is left as future work.

Functional data analysis: Application to daily observation of COVID-19 prevalence in France

In this paper we use the technique of functional data analysis to model daily hospitalized, deceased, Intensive Care Unit (ICU) cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments in France while our response variables are numbers of vaccinations, deaths, infected, recovered and tests in France. These sets of data were considered before and after vaccination started in France. After smoothing our data set, analysis based on functional principal components method was performed. Then, a clustering using k-means techniques was done to understand the dynamics of the pandemic in different French departments according to their geographical location on France map. We also performed canonical correlations analysis between variables. Finally, we made some predictions to assess the accuracy of the method using functional linear regression models. © 2022 the Author(s), licensee AIMS Press.

An Exploratory Analysis of COVID-19 in Latin America Using Functional Principal Component Analysis

Even though there already exists a wide variety of epidemiological models, it’s worthwhile to apply Functional Data Analysis (FDA) techniques to study the shapes of the COVID-19 pandemic in Latin America. In the present work we use Functional Principal Component Analysis (FPCA) to make an exploratory study on a dataset formed by the total cases per million, new cases, new tests, and stringency index of 6 Latin American countries, namely: Mexico, Ecuador, Chile, Peru, Cuba, and Colombia;obtained from the first confirmed case reported to January 2021, measured daily. We identify an increasing pattern in all of the variables and the interesting case of Cuba concerning the management of the pandemic, as well as the influence of stringency index over the growth curve of positive cases, and the mean perturbations with functional principal components (FPC) of the variables. Finally, we suggest more FDA techniques to carry out further studies to get a broad perspective of COVID-19 in Latin America. © 2021, Springer Nature Switzerland AG.

A look at the spatio-temporal mortality patterns in Italy during the COVID-19 pandemic through the lens of mortality densities.

With the tools and perspective of Object Oriented Spatial Statistics, we analyze official daily data on mortality from all causes in the provinces and municipalities of Italy for the year 2020, the first of the COVID-19 pandemic. By comparison with mortality data from 2011 to 2019, we assess the local impact of the pandemic as perturbation factor of the natural spatio-temporal death process. For each Italian province and year, mortality data are represented by the densities of time of death during the calendar year. Densities are regarded as functional data belonging to the Bayes space B2. In this space, we use functional-on-functional linear models to predict the expected mortality in 2020, based on mortality in previous years, and we compare predictions with actual observations, to assess the impact of the pandemic. Through spatial downscaling of the provincial data down to the municipality level, we identify spatial clusters characterized by mortality densities anomalous with respect to the surroundings. The proposed analysis pipeline could be extended to indexes different from death counts, measured at a granular spatio-temporal scale, and used as proxies for quantifying the local disruption generated by the pandemic.

Learning delay dynamics for multivariate stochastic processes, with application to the prediction of the growth rate of COVID-19 cases in the United States.

Delay differential equations form the underpinning of many complex dynamical systems. The forward problem of solving random differential equations with delay has received increasing attention in recent years. Motivated by the challenge to predict the COVID-19 caseload trajectories for individual states in the U.S., we target here the inverse problem. Given a sample of observed random trajectories obeying an unknown random differential equation model with delay, we use a functional data analysis framework to learn the model parameters that govern the underlying dynamics from the data. We show the existence and uniqueness of the analytical solutions of the population delay random differential equation model when one has discrete time delays in the functional concurrent regression model and also for a second scenario where one has a delay continuum or distributed delay. The latter involves a functional linear regression model with history index. The derivative of the process of interest is modeled using the process itself as predictor and also other functional predictors with predictor-specific delayed impacts. This dynamics learning approach is shown to be well suited to model the growth rate of COVID-19 for the states that are part of the U.S., by pooling information from the individual states, using the case process and concurrently observed economic and mobility data as predictors.