Was School Closure Effective in Mitigating Coronavirus Disease 2019 (COVID-19)? Time Series Analysis Using Bayesian Inference

Background: Coronavirus disease 2019 (COVID-19) pandemic are causing significant damages to many nations. For mitigating its risk, Japan’s Prime Minister called on all elementary, junior high and high schools nationwide to close beginning March 1, 2020. However, its effectiveness in decreasing disease burden has not been investigated. Methods: We used daily data on the report of COVID-19 and coronavirus infection incidence in Japan until March 31, 2020. Time series analysis were conducted using Bayesian method. Local linear trend models with interventional effect were constructed for number of newly reported cases of COVID-19, including asymptomatic infections. We considered that the effects of intervention start to appear 9 days after the school closure; i.e., on March 9. Results: The intervention of school closure did not appear to decrease the incidence of coronavirus infection. If the effectiveness of school closure began on March 9, mean coefficient α for effectiveness of the measure was calculated to be 0.08 (95% credible interval -0.36 to 0.65), and the actual reported cases were more than predicted, yet with rather wide credible interval. Sensitivity analyses using different dates also showed similar results. Conclusions: School closure carried out in Japan did not show the effectiveness to mitigate the transmission of novel coronavirus infection.

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Forecasting the Primary Demand for a Beer Brand Using Time Series Analysis

Organizacija ◽

10.2478/v10051-008-0013-7 ◽

2008 ◽

Vol 41 (3) ◽

pp. 116-124 ◽

Cited By ~ 6

Author(s):

Danijel Bratina ◽

Armand Faganel

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Discrete Event ◽

Marketing Research ◽

Causality Analysis ◽

Sales Promotions ◽

Current Application ◽

Series Analysis ◽

Daily Data ◽

The Impact

Forecasting the Primary Demand for a Beer Brand Using Time Series AnalysisMarket research often uses data (i.e. marketing mix variables) that is equally spaced over time. Time series theory is perfectly suited to study this phenomena's dependency on time. It is used for forecasting and causality analysis, but their greatest strength is in studying the impact of a discrete event in time, which makes it a powerful tool for marketers. This article introduces the basic concepts behind time series theory and illustrates its current application in marketing research. We use time series analysis to forecast the demand for beer on the Slovenian market using scanner data from two major retail stores. Before our analysis, only broader time spans have been used to perform time series analysis (weekly, monthly, quarterly or yearly data). In our study we analyse daily data, which is supposed to carry a lot of ‘noise’. We show that - even with noise carrying data - a better model can be computed using time series forecasting, explaining much more variance compared to regular regression. Our analysis also confirms the effect of short term sales promotions on beer demand, which is in conformity with other studies in this field.

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TIME SERIES ANALYSIS OF SURFACE DEFORMATION OF BENGALURU CITY USING SENTINEL-1 IMAGES

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-5-473-2018 ◽

2018 ◽

Vol IV-5 ◽

pp. 473-477

Author(s):

N. Ittycheria ◽

D. S. Vaka ◽

Y. S. Rao

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Surface Deformation ◽

Linear Trend ◽

Polarization Data ◽

Persistent Scatterer Interferometry ◽

Advanced Technique ◽

Interferometric Technique ◽

Series Analysis ◽

Persistent Scatterers

Abstract. Persistent Scatterer Interferometry (PSI) is an advanced technique to map ground surface displacements of an area over a period. The technique can measure deformation with a millimeter-level accuracy. It overcomes the limitations of Differential Synthetic Aperture Radar Interferometry (DInSAR) such as geometric, temporal decorrelation and atmospheric variations between master and slave images. In our study, Sentinel-1A Interferometric Wide Swath (IW) mode descending pass images from May 2016 to December 2017 (23 images) are used to identify the stable targets called persistent scatterers (PS) over Bengaluru city. Twenty-two differential interferograms are generated after topographic phase removal using the SRTM 30 m DEM. The main objective of this study is to analyze urban subsidence in Bengaluru city in India using the multi-temporal interferometric technique such as PSI. The pixels with Amplitude Stability Index &geq;0.8 are selected as initial PS candidates (PSC). Later, the PSCs having temporal coherence &gt;0.5 are selected for the time series analysis. The number of PSCs that are identified after final selection are reduced from 59590 to 54474 for VV polarization data and 15611 to 15596 for VH polarization data. It is interesting to note that a very less number of PSC are identified in cross-polarized images (VH). A high number of PSC have identified in co-polarized (VV) images as the vertically oriented urban targets produce a double bounce, which results in a strong return towards the sensor. The velocity maps obtained using VV and VH polarizations show displacement in the range of &plusmn;20mmyear&minus;1. The subsidence and the upliftment observed in the city shows a linear trend with time. It is observed that the eastern part of Bengaluru city shows more subsidence than the western part.

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Change of access to emergency care in a repopulated village after the 2011 Fukushima nuclear disaster: a retrospective observational study

BMJ Open ◽

10.1136/bmjopen-2018-023836 ◽

2019 ◽

Vol 9 (2) ◽

pp. e023836 ◽

Cited By ~ 1

Author(s):

Yoshitaka Nishikawa ◽

Masaharu Tsubokura ◽

Yoshimitsu Takahashi ◽

Shuhei Nomura ◽

Akihiko Ozaki ◽

...

Keyword(s):

Time Series ◽

Observational Study ◽

Time Series Analysis ◽

Emergency Care ◽

Credible Interval ◽

Retrospective Observational Study ◽

Series Analysis ◽

Major Disaster ◽

Bayesian Time Series ◽

Fukushima Disaster

ObjectivesSustaining emergency care access is of great concern. The aim of this study is to evaluate access to emergency care in a repopulated village following the 2011 Fukushima disaster.DesignThis research was a retrospective observational study. The primary outcome measure was total emergency medical services (EMS) time. A Bayesian time series analysis was performed to consider local time series trend and seasonality.SettingThe residents in Kawauchi Village, Fukushima, Japan were forced to evacuate after the 2011 Fukushima disaster. As the radiation dose was an acceptable level, the residents began the process of repopulation in April 2012.ParticipantsThis study included patients transported by EMS from January 2009 to October 2015. Patients transported during the evacuation period (from March 2011 to March 2012) were excluded.ResultsA total of 781 patients were transferred by EMS (281 patients before the disaster, 416 after repopulation and 84 during the evacuation period). A Bayesian time series analysis revealed an increase in total EMS time, from the first request call to arrival at a hospital of 21.85 min (95% credible interval 14.2–29.0, Bayesian one-sided tail-area probability p=0.001). After the disaster, 42.3% of patients were transported to a partner hospital.ConclusionsTotal EMS time increased after repopulation of the area affected because of a massive number of hospital closures. Proactive partnerships would be a possible countermeasure in the affected areas after a major disaster.

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Investigation the importance of metabolism on body weight and glucose for 8.5 years using GH-Method: math-physical medicine (No. 300)

Journal of Clinical & Experimental Immunology ◽

10.33140/jcei.05.05.02 ◽

2020 ◽

Vol 5 (5) ◽

Keyword(s):

Time Series ◽

Body Weight ◽

Time Series Analysis ◽

Plasma Glucose ◽

Correlation Coefficients ◽

Annual Data ◽

Physical Medicine ◽

Postprandial Plasma Glucose ◽

Series Analysis ◽

Daily Data

The author uses GH-method: math-physical medicine (MPM) approach to investigate three sets of correlation between: (1) Weight vs. Metabolism Index (2) Glucose vs. Metabolism Index (3) Weight vs. Glucose - Weight is measured in early mornings and Glucose consists of daily average glucose, including both fasting plasma glucose (FPG) and three postprandial plasma glucose (PPG). He utilized time-series analysis on both his “daily data” and his “annual data” for comparison. His selected study period was 8.5 years (3,124 days) from 1/1/2012 through 7/23/2020. The reason for this specific time period was due to his weight (M1) and glucose (M2) data collection starting on 1/1/2012 along with the calculation of his metabolism index (MI) values. It is clear that, through his sophisticated math-physical medicine of metabolism and then statistical method of time-series analysis, all of these three biomarkers, weight, glucose, and metabolism index are proven to be highly correlated to each other. The following order ranking of correlation coefficients remained to be true between daily data and annual data: M1&MI > M2&MI > M1&M2 Daily: 84% > 72% > 61% Annual: 91% > 81% > 67% In other words, if you manage your metabolism (4 medical conditions and 6 lifestyle details) by controlling your disease conditions and monitoring your lifestyle details, your body weight and glucose will reduce accordingly. The author’s analyses is based on his personal biomarkers of two million data within 8.5 years (3,124 days) has further proven a simple and clean conclusion that has already been observed by many clinical physicians and healthcare professionals from their patients.

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Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis

PLoS ONE ◽

10.1371/journal.pone.0256516 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0256516

Author(s):

Ali Hadianfar ◽

Razieh Yousefi ◽

Milad Delavary ◽

Vahid Fakoor ◽

Mohammad Taghi Shakeri ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Government Policies ◽

World Health ◽

Social Distancing ◽

Social Interventions ◽

Series Analysis ◽

Daily Data ◽

The World ◽

Health Organization

Background Public health policies with varying degrees of restriction have been imposed around the world to prevent the spread of coronavirus disease 2019 (COVID-19). In this study, we aimed to evaluate the effects of the implementation of government policies and the Nowruz holidays on the containment of the COVID-19 pandemic in Iran, using an intervention time series analysis. Methods Daily data on COVID-19 cases registered between February 19 and May 2, 2020 were collected from the World Health Organization (WHO)’s website. Using an intervention time series modeling, the effect of two government policies on the number of confirmed cases were evaluated, namely the closing of schools and universities, and the implementation of social distancing measures. Furthermore, the effect of the Nowruz holidays as a non-intervention factor for the spread of COVID-19 was also analyzed. Results The results showed that, after the implementation of the first intervention, i.e., the closing of universities and schools, no statistically significant change was found in the number of new confirmed cases. The Nowruz holidays was followed by a significant increase in new cases (1,872.20; 95% CI, 1,257.60 to 2,476.79; p<0.001)), while the implementation of social distancing measures was followed by a significant decrease in such cases (2,182.80; 95% CI, 1,556.56 to 2,809.04; p<0.001). Conclusion The Nowruz holidays and the implementation of social distancing measures in Iran were related to a significant increase and decrease in COVID-19 cases, respectively. These results highlight the necessity of measuring the effect of health and social interventions for their future implementations.

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Investigation the importance of metabolism on body weight and glucose for 8.5 years using GH-Method: math-physical medicine (No. 300)

Journal of Clinical & Experimental Immunology ◽

10.33140/jcei.05.05.002 ◽

2020 ◽

Vol 5 (5) ◽

Keyword(s):

Time Series ◽

Body Weight ◽

Time Series Analysis ◽

Plasma Glucose ◽

Correlation Coefficients ◽

Annual Data ◽

Physical Medicine ◽

Postprandial Plasma Glucose ◽

Series Analysis ◽

Daily Data

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Time series analysis in forecasting pririrodno focal infections

Journal of New Medical Technologies eJournal ◽

10.12737/17087 ◽

2015 ◽

Vol 9 (4) ◽

pp. 0-0

Author(s):

Евстегнеева ◽

V. Evstegneeva ◽

Честнова ◽

Tatyana Chestnova ◽

Смольянинова ◽

...

Keyword(s):

Time Series ◽

Regression Analysis ◽

Time Series Analysis ◽

Bank Vole ◽

Linear Trend ◽

Random Variables ◽

Vole Population ◽

Long Term Effects ◽

Series Analysis

Mathematical methods and models used in forecasting problems may relate to a wide variety of topics: from the regression analysis, time series analysis, formulation and evaluation of expert opinions, simulation, systems of simultaneous equations, discriminant analysis, logit and probit models, logical unit decision functions, variance or covariance analysis, rank correlation and contingency tables, etc. In the analysis of the phenomenon over a long timeperiod, for example, the incidence of long-term dynamics with a forecast of further development of the process, you should use the time series, which is influenced by the following factors: • Emerging trends of the series (the trend in cumulative long-term effects of many factors on the dynamics of the phenomenon under study - ascending or descending); • forming a series of cyclical fluctuations related to the seasonality of the disease; • random factors. In our study, we conducted a study to identify cyclical time series of long-term dynamics of morbidity of HFRS and autumn bank vole population. This study was performed using the autocorrelation coefficient. As a result of time-series studies of incidence of HFRS, indicators autumn bank vole population revealed no recurrence, and these figures are random variables, which is confirmed by three tests: nonrepeatability of time series, the assessment increase and decrease time-series analysis of the sum of squares. This shows that a number of indicators of the time series are random variables, contains a strong non-linear trend, to identify which need further analysis, for example by means of regression analysis.

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A Time-Series Analysis of Clostridium difficile and Its Seasonal Association with Influenza

Infection Control and Hospital Epidemiology ◽

10.1086/651095 ◽

2010 ◽

Vol 31 (4) ◽

pp. 382-387 ◽

Cited By ~ 37

Author(s):

Philip M. Polgreen ◽

Ming Yang ◽

Lucas C. Bohnett ◽

Joseph E. Cavanaugh

Keyword(s):

Time Series ◽

Clostridium Difficile ◽

Time Series Analysis ◽

Correlation Functions ◽

Cross Correlation ◽

Linear Trend ◽

International Classification Of Diseases ◽

Series Data ◽

Series Analysis ◽

Classification Of Diseases

Objective.To characterize the temporal progression of the monthly incidence of Clostridium difficile infections (CDIs) and to determine whether the incidence of CDI is related to the incidence of seasonal influenza.Design.A retrospective study of patients in the Nationwide Inpatient Sample during the period from 1998 through 2005.Methods.We identified all hospitalizations with a primary or secondary diagnosis of CDI with use of International Classification of Diseases, 9th Revision, Clinical Modification codes, and we did the same for influenza. The incidence of CDI was modeled as an autoregression about a linear trend. To investigate the association of CDI with influenza, we compared national and regional CDI and influenza series data and calculated cross-correlation functions with data that had been prewhitened (filtered to remove temporal patterns common to both series). To estimate the burden of seasonal CDI, we developed a proportional measure of seasonal CDI.Results.Time-series analysis of the monthly number of CDI cases reveals a distinct positive linear trend and a clear pattern of seasonal variation (R2 = 0.98). The cross-correlation functions indicate that influenza activity precedes CDI activity on both a national and regional basis. The average burden of seasonal (ie, winter) CDI is 23%.Conclusions.The epidemiologic characteristics of CDI follow a pattern that is seasonal and associated with influenza, which is likely due to antimicrobial use during influenza seasons. Approximately 23% of average monthly CDI during the peak 3 winter months could be eliminated if CDI remained at summer levels.

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Comparison of daily flows simulated for the year 2060 on the Kaczawa River for various scenarios of climate change by simple time series analysis

E3S Web of Conferences ◽

10.1051/e3sconf/201910000041 ◽

2019 ◽

Vol 100 ◽

pp. 00041

Author(s):

Leszek Kuchar ◽

Ewa Broszkiewicz-Suwaj ◽

Slawomir Iwanski ◽

Leszek Jelonek

Keyword(s):

Climate Change ◽

Time Series ◽

Time Series Analysis ◽

Hydrological Model ◽

Meteorological Data ◽

Change Scenario ◽

Series Analysis ◽

Daily Data ◽

Data Generator ◽

Odra River

In this paper a time series analysis for daily flow simulations according three climate change scenario for Kaczawa River a left side tributary of the Odra River in south-west Poland is presented. The flow sequences were simulated using the hydrological model MIKE SHE and the spatial SWGEN meteorological data generator. Meteorological data for the hydrological model were generated based on data from 24 meteorological stations and 35-year daily data from the Institute of Meteorology and Water Management of the National Research Institute (IMGW). Data were generated for future climate condition for 2060 according GISS Model E, HadCM3, and GFDL R15 scenarios as well for the present conditions. The year 2000 was used as a reference year. The results obtained on the basis of a simple time series analysis point to small changes in flows for current and simulated conditions for 2060 for the Kaczawa River.

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Investigation the importance of metabolism on body weight and glucose for 8.5 years using GH-Method: math-physical medicine (No. 300)

Journal of Bioscience & Biomedical Engineering ◽

10.47485/2693-2504.1017 ◽

2020 ◽

Keyword(s):

Time Series ◽

Body Weight ◽

Time Series Analysis ◽

Plasma Glucose ◽

Correlation Coefficients ◽

Annual Data ◽

Physical Medicine ◽

Postprandial Plasma Glucose ◽

Series Analysis ◽

Daily Data

The author uses GH-method: math-physical medicine (MPM) approach to investigate three sets of correlation between: Weight vs. Metabolism Index Glucose vs. Metabolism Index Weight vs. Glucose – Weight is measured in early mornings and Glucose consists of daily average glucose, including both fasting plasma glucose (FPG) and three postprandial plasma glucose (PPG). He utilized time-series analysis on both his “daily data” and his “annual data” for comparison. His selected study period was 8.5 years (3,124 days) from 1/1/2012 through 7/23/2020. The reason for this specific time period was due to his weight (M1) and glucose (M2) data collection starting on 1/1/2012 along with the calculation of his metabolism index (MI) values. It is clear that, through his sophisticated math-physical medicine of metabolism and then statistical method of time-series analysis, all of these three biomarkers, weight, glucose, and metabolism index are proven to be highly correlated to each other. The following order ranking of correlation coefficients remained to be true between daily data and annual data: M1&MI > M2&MI > M1&M2 Daily : 84% > 72% > 61% Annual : 91% > 81% > 67% In other words, if you manage your metabolism (4 medical conditions and 6 lifestyle details) by controlling your disease conditions and monitoring your lifestyle details, your body weight and glucose will reduce accordingly. The author’s analyses is based on his personal biomarkers of two million data within 8.5 years (3,124 days) has further proven a simple and clean conclusion that has already been observed by many clinical physicians and healthcare professionals from their patients.

Download Full-text