scholarly journals Associations Between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

2021 ◽  
Author(s):  
Mostafa Abbas ◽  
Thomas B. Morland ◽  
Eric S. Hall ◽  
Yasser EL-Manzalawy
Keyword(s):  
Functional Data ◽  
Time Series Data ◽  
Principal Component ◽  
State Level ◽  
The United States ◽  
Functional Principal Component Analysis ◽  
Series Data ◽  
Dynamic Correlation ◽  
The Us ◽  
Google Search

ABSTRACTWe utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19 related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

scholarly journals Associations between Google Search Trends for Symptoms and COVID-19 Confirmed and Death Cases in the United States

2021 ◽  
Vol 18 (9) ◽  
pp. 4560
Author(s):  
Mostafa Abbas ◽  
Thomas B. Morland ◽  
Eric S. Hall ◽  
Yasser EL-Manzalawy
Keyword(s):  
Functional Data ◽  
Time Series Data ◽  
Principal Component ◽  
State Level ◽  
The United States ◽  
Functional Principal Component Analysis ◽  
Series Data ◽  
Dynamic Correlation ◽  
The Us ◽  
Google Search

We utilize functional data analysis techniques to investigate patterns of COVID-19 positivity and mortality in the US and their associations with Google search trends for COVID-19-related symptoms. Specifically, we represent state-level time series data for COVID-19 and Google search trends for symptoms as smoothed functional curves. Given these functional data, we explore the modes of variation in the data using functional principal component analysis (FPCA). We also apply functional clustering analysis to identify patterns of COVID-19 confirmed case and death trajectories across the US. Moreover, we quantify the associations between Google COVID-19 search trends for symptoms and COVID-19 confirmed case and death trajectories using dynamic correlation. Finally, we examine the dynamics of correlations for the top nine Google search trends of symptoms commonly associated with COVID-19 confirmed case and death trajectories. Our results reveal and characterize distinct patterns for COVID-19 spread and mortality across the US. The dynamics of these correlations suggest the feasibility of using Google queries to forecast COVID-19 cases and mortality for up to three weeks in advance. Our results and analysis framework set the stage for the development of predictive models for forecasting COVID-19 confirmed cases and deaths using historical data and Google search trends for nine symptoms associated with both outcomes.

scholarly journals TESTING FOR INDONESIAN AND VIET NAM PRICE TRANSMISSION IN THE UNITED STATES TUNA MARKETS

2021 ◽  
Vol 9 (1) ◽  
pp. 61-69
Author(s):  
Puspi Wiranthi
Keyword(s):  
United States ◽  
Time Series Data ◽  
The United States ◽  
Yellowfin Tuna ◽  
Price Transmission ◽  
Series Data ◽  
Viet Nam ◽  
Supporting Evidence ◽  
Asymmetrical Effect ◽  
The Us

This study aims to analyze Indonesia and Viet Nam price transmission as the main exporters of frozen yellowfin tuna to the United States (US) assuming that the market structure is oligopoly. Using monthly time series data of Indonesia, Viet Nam and US frozen yellowfin tuna prices with harmonized system code 03034200 from January 2006 to December 2018 and analyzed through an Asymmetric Error Correction Model (AECM), this study finds that both prices of Indonesia and Viet Nam are integrated to the US prices. Additionally, there are two-way causality relationships between both exporting countries as well as Viet Nam and the US. The short-term price transmission of Viet Nam has an asymmetrical effect on Indonesian prices while on the long-term, the price transmission among three countries occurs symmetrically which indicates that a competitive international market exists. Indonesia’s policy in increasing its market share in the US is not independent, but it is influenced by the price of Viet Nam as its main competitor. The findings of this study are relevant to fill the gap in the literature by providing a supporting evidence regarding price transmission between two main exporters to the US frozen yellowfin tuna market.

scholarly journals Functional Data Analysis of Weather Variables Linked to Fusarium Head Blight Epidemics in the United States

2019 ◽  
Vol 109 (1) ◽  
pp. 96-110 ◽  
Author(s):  
D. A. Shah ◽  
E. D. De Wolf ◽  
P. A. Paul ◽  
L. V. Madden
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Time Series Data ◽  
The United States ◽  
Series Data ◽  
Head Blight ◽  
Weather Variables ◽  
The Mean

In past efforts, input weather variables for Fusarium head blight (FHB) prediction models in the United States were identified after following some version of the window-pane algorithm, which discretizes a continuous weather time series into fixed-length windows before searching for summary variables associated with FHB risk. Functional data analysis, on the other hand, reconstructs the assumed continuous process (represented by a series of recorded weather data) by using smoothing functions, and is an alternative way of working with time series data with respect to FHB risk. Our objective was to functionally model weather-based time series data linked to 865 observations of FHB (covering 16 states and 31 years in total), classified as epidemics (FHB disease index ≥ 10%) and nonepidemics (FHB disease index < 10%). Altogether, 94 different time series variables were modeled by penalized cubic B-splines for the smoothing function, from 120 days pre-anthesis to 20 days post-anthesis. Functional mean curves, standard deviations, and first derivatives were plotted for FHB epidemics relative to nonepidemics. Function-on-scalar regressions assessed the temporal trends of the magnitude and significance of the mean difference between functionally represented weather time series associated with FHB epidemics and nonepidemics. The mean functional weather-variable curve for epidemics started to deviate, in general, from that for nonepidemics as early as 40 days pre-anthesis for several weather variables. The greatest deviations were often near anthesis, the period of maximum susceptibility of wheat to FHB-causing fungi. The most consistent separations between the mean functional curves were seen with the daily averages of moisture-related variables (such as average relative humidity) and with variables summarizing the daily variation in temperature (as opposed to the daily mean). Functional data analysis was useful for extending our knowledge of relationships between weather variables and FHB epidemics.

scholarly journals Mass shootings in Australia and the United States, 1981-2013

2015 ◽  
Vol 1 (3) ◽  
pp. 131-142 ◽  
Author(s):  
Frederic Lemieux ◽  
Samantha Bricknell ◽  
Tim Prenzler
Keyword(s):  
Time Series Data ◽  
Gunshot Wounds ◽  
The United States ◽  
Series Data ◽  
Mass Shootings ◽  
Content Type ◽  
The Us ◽  
The Usa ◽  
Mass Shooting ◽  
Definition Of

Purpose – The purpose of this paper is to compare the incidence and main characteristics of mass shooting events in Australia and the USA in the period 1981-2013. Design/methodology/approach – The study adopted a conservative definition of mass shootings derived from the US Federal Bureau of Investigation, covering four or more fatalities not including the offender. Australian cases were accessed from the Australian Institute of Criminology’s National Homicide Monitoring Programme (NHMP) database and several secondary sources. The US data were collected from the Mother Jones database, a report prepared for Mayors Against Illegal Guns and a New York Police Department report. The time series data were related to changes in firearms regulations in the two jurisdictions. Findings – For Australia, the study identified 13 mass shooting events and 104 fatalities from gunshot wounds. For the USA, there were 73 events and 576 victims. Of note is the fact that all cases in Australia pre-dated the implementation of the restrictive 1996 National Firearms Agreement. In the USA, a small decline was evident during the 1994-2004 Federal Assault Weapon Ban. Incidents and fatalities increased after 2004. Research limitations/implications – Of necessity, the paper adopts a conservative FBI-based definition of mass shootings that limits the number of cases captured. The absence of an official government US database also most likely limits the number of cases identified. Practical implications – The findings lend support to policy considerations regarding regulating access to firearms. Originality/value – The paper is unique in comparing mass shootings in these two jurisdictions over three decades in association with changes in firearms regulation.

scholarly journals Improved Prediction of COVID-19 Transmission and Mortality Using Google Search Trends for Symptoms in the United States

2021 ◽  
Author(s):  
Meshrif Alruily ◽  
Mohamed Ezz ◽  
Ayman Mohamed Mostafa ◽  
Nacim Yanes ◽  
Mostafa Abbas ◽  
...  
Keyword(s):  
Public Health ◽  
Time Series ◽  
Computational Models ◽  
Time Series Data ◽  
Short Term Memory ◽  
Emerging Infectious Diseases ◽  
The United States ◽  
Series Data ◽  
Percentage Error ◽  
Google Search

ABSTRACTAccurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources. Due to the exponential spread of the COVID-19 infection worldwide, several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature. To accelerate scientific and public health insights into the spread and impact of COVID-19, Google released the Google COVID-19 search trends symptoms open-access dataset. Our objective is to develop 7 and 14 -day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms. Specifically, we propose a stacked long short-term memory (SLSTM) architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset. Considering the SLSTM networks trained using historical data only as the base models, our base models for 7 and 14 -day-ahead forecasting of COVID cases had the mean absolute percentage error (MAPE) values of 6.6% and 8.8%, respectively. On the other side, our proposed models had improved MAPE values of 3.2% and 5.6%, respectively. For 7 and 14 -day-ahead forecasting of COVID-19 deaths, the MAPE values of the base models were 4.8% and 11.4%, while the improved MAPE values of our proposed models were 4.7% and 7.8%, respectively. We found that the Google search trends for “pneumonia,” “shortness of breath,” and “fever” are the most informative search trends for predicting COVID-19 transmission. We also found that the search trends for “hypoxia” and “fever” were the most informative trends for forecasting COVID-19 mortality.

scholarly journals Associations between state-level healthcare access and COVID-19 case trajectories in the United States

2020 ◽  
Author(s):  
Shelley H. Liu ◽  
Bian Liu ◽  
Agnes Norbury ◽  
Yan Li
Keyword(s):  
United States ◽  
New York ◽  
Medical Care ◽  
Time Series Data ◽  
Healthcare Access ◽  
Health Spending ◽  
State Level ◽  
The United States ◽  
Series Data ◽  
Medicaid Expansion

Introduction: We conducted an ecological study to determine if state-level healthcare access is associated with trajectories of daily reported COVID-19 cases in the United States. Our focus is on trajectories of daily reported COVID-19 cases, rather than cumulative cases, as trajectories help us identify trends in how the pandemic naturally develops over time, and study the shapes of the curve in different states. Methods: We analyzed data on daily reported confirmed and probable COVID-19 cases from January 21 to June 16, 2020 in 50 states, adjusted for the population size of each state. Cluster analysis for time-series data was used to split the states into clusters that have distinct trajectories of daily cases. Differences in socio-demographic characteristics and healthcare access between clusters were tested. Adjusted models were used to determine if healthcare access is associated with reporting a high trajectory of COVID-19 cases. Results: Two clusters of states were identified. One cluster had a high trajectory of population-adjusted COVID-19 cases, and comprised of 19 states, including New York and New Jersey. The other cluster of states (n=31) had a low trajectory of population-adjusted COVID-19 cases. There were significantly more Black residents (p=0.027) and more nursing facility residents (p=0.001) in states reporting high trajectory of COVID-19 cases. States reporting a high trajectory of COVID-19 cases also had fewer uninsured persons (p=0.005), fewer persons who reported having to forgo medical care due to cost (p=0.016), more registered physicians (p=0.002) and more nurses (p=0.03), higher health spending per capita (p=0.01), fewer residents in Health Professional Shortage Areas per 100,000 population (p=0.027), and higher adoption of Medicaid Expansion (p=0.05). In adjusted models, a higher proportion of uninsured persons (OR: 0.51 [0.25-0.85]; p=0.032), higher proportion of patients who had to forgo medical care due to cost (OR: 0.55 [0.28-0.95]; p=0.048), and no adoption of Medicaid expansion (OR: 0.05 [0-0.59]; p=0.04), were associated with reporting a low trajectory of COVID-19 cases. Conclusion: Our findings from adjusted models suggest that healthcare access can partially explain variations in COVID-19 case trajectories by state.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

scholarly journals Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

2021 ◽  
Author(s):  
Christian Acal ◽  
Ana M. Aguilera ◽  
Annalina Sarra ◽  
Adelia Evangelista ◽  
Tonio Di Battista ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Functional Data ◽  
Central Italy ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Area Of Interest ◽  
Pm 10 ◽  
The Impact ◽  
Monitoring Stations

AbstractFaced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

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