Estimating the SARS-CoV-2 infected population fraction and the infection-to-fatality ratio: a data-driven case study based on Swedish time series data

AbstractWe demonstrate that finite impulse response (FIR) models can be applied to analyze the time evolution of an epidemic with its impact on deaths and healthcare strain. Using time series data for COVID-19-related cases, ICU admissions and deaths from Sweden, the FIR model gives a consistent epidemiological trajectory for a simple delta filter function. This results in a consistent scaling between the time series if appropriate time delays are applied and allows the reconstruction of cases for times before July 2020, when RT-PCR testing was not widely available. Combined with randomized RT-PCR study results, we utilize this approach to estimate the total number of infections in Sweden, and the corresponding infection-to-fatality ratio (IFR), infection-to-case ratio (ICR), and infection-to-ICU admission ratio (IIAR). Our values for IFR, ICR and IIAR are essentially constant over large parts of 2020 in contrast with claims of healthcare adaptation or mutated virus variants importantly affecting these ratios. We observe a diminished IFR in late summer 2020 as well as a strong decline during 2021, following the launch of a nation-wide vaccination program. The total number of infections during 2020 is estimated to 1.3 million, indicating that Sweden was far from herd immunity.

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Estimating the SARS-CoV-2 infected population fraction and the infection to-fatality ratio: A data-driven case study based on Swedish time series data

10.1101/2021.05.27.21257900 ◽

2021 ◽

Author(s):

Andreas Wacker ◽

Anna Jöud ◽

Bo Bernhardsson ◽

Philip Gerlee ◽

Fredrik Gustafsson ◽

...

Keyword(s):

Time Series ◽

Time Series Data ◽

Least Squares Method ◽

Finite Impulse Response ◽

Herd Immunity ◽

Late Summer ◽

Epidemiological Data ◽

Series Data ◽

Filter Function ◽

Study Results

Aim: To estimate the COVID-19 infection-to-fatality ratio (IFR), infection-to-case ratio (ICR), and infection-to-ICU admission ratio (IIAR) in Sweden; to suggest methods for time series reconstruction and prediction. Methods: We optimize a set of simple finite impulse response (FIR) models comprising of a scaling factor and time-delay between officially reported cases, ICU admissions and deaths time series using the least squares method. Combined with randomized PCR study results, we utilize this simple model to estimate the total number of infections in Sweden, and the corresponding IFR. Results: The model class provides a good fit between ICU admissions and deaths throughout 2020. Cases fit consistently from July 2020, by when PCR tests had become broadly available. We observe a diminished IFR in late summer as well as a strong decline during 2021, following the launch of a nation-wide vaccination program. The total number of infections during 2020 is estimated to $1.3$ million. Conclusions: A FIR model with a delta filter function describes the evolution of epidemiological data in Sweden well. The fact that we found IFR, ICR and IIAR constant over large parts of 2020 is in contrast with claims of healthcare adaptation or mutated virus variants importantly affecting these ratios. The model allows us to retrospectively estimate the COVID-19 epidemiological trajectory, and conclude that Sweden was far from herd immunity by the end of 2020.

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Assessment of Online Deliberative Quality: New Indicators Using Network Analysis and Time-Series Analysis

Sustainability ◽

10.3390/su13031187 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1187

Author(s):

Bokyong Shin ◽

Mikko Rask

Keyword(s):

Time Series ◽

Network Analysis ◽

Time Series Analysis ◽

Time Series Data ◽

Collective Intelligence ◽

Series Data ◽

Online Data ◽

Series Analysis ◽

Study Results ◽

Online Deliberation

Online deliberation research has recently developed automated indicators to assess the deliberative quality of much user-generated online data. While most previous studies have developed indicators based on content analysis and network analysis, time-series data and associated methods have been studied less thoroughly. This article contributes to the literature by proposing indicators based on a combination of network analysis and time-series analysis, arguing that it will help monitor how online deliberation evolves. Based on Habermasian deliberative criteria, we develop six throughput indicators and demonstrate their applications in the OmaStadi participatory budgeting project in Helsinki, Finland. The study results show that these indicators consist of intuitive figures and visualizations that will facilitate collective intelligence on ongoing processes and ways to solve problems promptly.

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Time Series Analysis of Individual Music Therapy in Dementia

GeroPsych ◽

10.1024/1662-9647/a000123 ◽

2015 ◽

Vol 28 (3) ◽

pp. 113-122 ◽

Cited By ~ 12

Author(s):

Arthur Schall ◽

Julia Haberstroh ◽

Johannes Pantel

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Music Therapy ◽

Time Series Data ◽

Well Being ◽

Research Area ◽

Series Data ◽

Communication Behavior ◽

Series Analysis ◽

Study Results

Abstract. This pilot study evaluated the effects of individual music therapy on communication behavior and emotional well-being in persons with advanced dementia. Contrary to more common qualitative designs in dementia-related music therapy research, we used time series analysis of videographed music therapy sessions. This quantitative statistical method for modeling and explaining time-dependent processual data has not been employed in this research area before. Based on aggregated time series data, the results showed positive and statistically significant intervention effects on participants’ communication behavior, situational well-being, and their expression of positive emotions. The study results indicate that, by choosing appropriate outcomes, video-based time series analysis is sensitive and well suited to displaying the effects of music therapy in dementia care.

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Investigating PM2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

Scientific Reports ◽

10.1038/s41598-020-72722-z ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Haiyue Fu ◽

Yiting Zhang ◽

Chuan Liao ◽

Liang Mao ◽

Zhaoya Wang ◽

...

Keyword(s):

Time Series ◽

Air Pollutants ◽

Time Series Data ◽

Meteorological Factors ◽

Ensemble Empirical Mode Decomposition ◽

Series Data ◽

Daily Maximum ◽

Temporal Scales ◽

Correlation Pattern ◽

Study Results

Abstract It remains unclear on how PM2.5 interacts with other air pollutants and meteorological factors at different temporal scales, while such knowledge is crucial to address the air pollution issue more effectively. In this study, we explored such interaction at various temporal scales, taking the city of Nanjing, China as a case study. The ensemble empirical mode decomposition (EEMD) method was applied to decompose time series data of PM2.5, five other air pollutants, and six meteorological factors, as well as their correlations were examined at the daily and monthly scales. The study results show that the original PM2.5 concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM2.5 concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM10, SO2 and NO2 is synchronous with that of PM2.5. At both daily and monthly scales, PM2.5 was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM2.5 was positively correlated with O3, daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.

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Analysis and Application of Grey-Markov Chain Model in Tax Forecasting

Journal of Mathematics ◽

10.1155/2021/9918411 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Huidi Zhang ◽

Yimao Chen

Keyword(s):

Time Series ◽

Markov Chain ◽

Time Series Data ◽

Markov Chain Model ◽

Series Data ◽

Forecasting Model ◽

Chain Model ◽

Grey Forecasting ◽

Forecasting Methods ◽

Study Results

Tax data is a typical time series data, which is subject to the interaction and influence of economic and political factors and has dynamic and highly nonlinear characteristics. The key to correct tax forecasting is the choice of forecasting algorithm. Traditional tax forecasting methods, such as factor scoring method, factor regression method, and system adjustment method, have a certain guiding role in actual work, but there are still many shortcomings, such as the limitation from the distribution and size of sample data and difficulty of grasping the nonlinear phenomena in economic system. Grey-Markov chain model formed by the combination of grey forecasting and Markov chain forecasting can not only reveal the general developmental trend of time series data, but also predict their state change patterns. Based on the summary and analysis of previous research works, this paper expounds the current research status and significance of tax forecasting, elaborates the development background, current status, and future challenges of the Grey-Markov chain model, introduces the basic principles of grey forecasting model and Markov chain model, constructs the Grey-Markov chain model, analyzes the model’s residual error and posteriori error tests, conducts the analysis of Grey-Markov chain model, performs grey forecasting model construction and its state division, implements the calculation of transition probability matrix and the determination of tax forecasting value, discusses the application of the Grey-Markov chain model in tax forecasting, and finally carries out a simulation experiment and its result analysis. The study results show that, compared with separate grey forecasting, Markov chain forecasting, and other commonly used time series forecasting methods, the Grey-Markov chain model increases the accuracy of tax forecasts by an average of 2.3–13.1%. This indicates that the combinative forecasting of Grey-Markov chain model can make full use of the information provided by time series data for tax analysis and forecasting. It can not only avoid the influence of economic, political, and human subjective factors, but also have simple calculations, higher accuracy, and stronger practicality. The study results of this paper provide a reference for further researches on the analysis and application of Grey-Markov chain model in tax forecasting.

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Sustainable Determinants That Affect Tourist Arrival Forecasting

Sustainability ◽

10.3390/su13179659 ◽

2021 ◽

Vol 13 (17) ◽

pp. 9659

Author(s):

Sergej Gricar ◽

Tea Baldigara ◽

Violeta Šugar

Keyword(s):

Time Series ◽

Sensitivity Analysis ◽

Co2 Emissions ◽

Quantitative Methods ◽

Time Series Data ◽

Weather Conditions ◽

Series Data ◽

Secondary Sources ◽

Study Results ◽

Negative Effect

This study considers diversification effects and significant influences on tourist arrivals as a vital export direction. Different quantitative methods, namely a cointegrated-autoregressive model, panels, sentiment and sensitivity analysis, were used in this study. The time-series data for Croatia and Slovenia were isolated from several secondary sources. The variables examined in this approach are tourist arrivals, precipitations, sunny days, earthquakes, microbes and CO2 emissions. The study results showed that there is a severe negative effect on tourist arrivals defined by viruses. Moreover, there is a significant decisive effect of weather conditions on tourist arrivals. Nevertheless, it is necessary to move past Covid-19 pandemic discussions to yield more accurate tourism supply forecasts, while demand is already somehow low since the beginning of 2020. The primary significance is to develop a broader thinking about the impacts of CO2 emissions on the tourism escorted to official tourist websites.

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Electricity Consumption and Economic Growth: A Time-Series Study on Pakistan

Global Economics Review ◽

10.31703/ger.2020(v-i).03 ◽

2020 ◽

Vol V (I) ◽

pp. 24-37

Author(s):

Raza Muhammad khan ◽

Sohail Farooq ◽

Muhammad Akram Gilal

Keyword(s):

Economic Growth ◽

Time Series ◽

Economic Development ◽

Time Series Data ◽

Electricity Consumption ◽

Series Data ◽

Study Results ◽

Level Of Economic Development ◽

Sustainable Economic Growth

In Socio-Economic development, the role of energy is very crucial, and often its consumption indicates the level of economic development and is considered as a vital ingredient of sustainable economic growth. Electricity as a key input variable plays a supreme role in the process of industrialization (IND), Economic growth (GDP) as well as technological progress (TEC) of a country. By utilizing time-series data from 1975 up to 2017, the study attempted to examine the nexus between EC and GDP of Pakistan in the Cobb Douglas production function framework. The study results reveal that EC, labor force, capital stocks, and technology, have positively and significantly affect Pakistans GDP growth. The study concluded and recommended that economic performance cannot be improved in the absence of continuous usage of electricity. Therefore, to achieve the desired economic growth path, uninterrupted electricity consumption is pivotal.

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

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.

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