Temperature and mortality on the roof of the world: A time-series analysis in three Tibetan counties, China

AbstractIntroductionThe world is not on track to achieve the goals for immunization coverage and equity described by the World Health Organization’s Global Vaccine Action Plan. In India, only 62% of children had received a full course of basic vaccines in 2016. We evaluated the Intensified Mission Indradhanush (IMI), a campaign-style intervention to increase routine immunization coverage and equity in India, implemented in 2017-2018.MethodsWe conducted a comparative interrupted time-series analysis using monthly district-level data on vaccine doses delivered, comparing districts participating and not participating in IMI. We estimated the impact of IMI on coverage and under-coverage (defined as the proportion of children who were unvaccinated) during the four-month implementation period and in subsequent months.FindingsDuring implementation, IMI increased delivery of thirteen infant vaccines by between 1.6% (95% CI: −6.4, 10.2%) and 13.8% (3.0%, 25.7%). We did not find evidence of a sustained effect during the 8 months after implementation ended. Over the 12 months from the beginning of implementation, IMI reduced under-coverage of childhood vaccination by between 3.9% (−6.9%, 13.7%) and 35.7% (−7.5%, 77.4%). The largest estimated effects were for the first doses of vaccines against diptheria-tetanus-pertussis and polio.InterpretationIMI had a substantial impact on infant immunization delivery during implementation, but this effect waned after implementation ended. Our findings suggest that campaign-style interventions can increase routine infant immunization coverage and reach formerly unreached children in the shorter term, but other approaches may be needed for sustained coverage improvements.FundingBill & Melinda Gates Foundation.

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Clustering the countries for quantifying the status of Covid-19 through time series analysis

Information Discovery and Delivery ◽

10.1108/idd-03-2021-0034 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Madurapperumage Erandathi ◽

William Yu Chung Wang ◽

Chih-Chia Hsieh

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Financial Stability ◽

Health Facilities ◽

Safety Measures ◽

Content Type ◽

Series Analysis ◽

The World ◽

The Status ◽

The Government

Purpose This study aims to use financial stability and health facilities of countries, to cluster them for making a more consensus environment for manifesting the status of Covid-19 in a justifiable manner. The scarcity of the categorisation of the countries of the world in a common platform, and the requirement of manifesting the pandemic status such as Covid-19 in a justifiable manner create the demanding requirement. This study mainly focusses on assisting to generate a liable manifesto to criticise the span of viral infection of the severe acute respiratory syndrome coronavirus-2 over the globe. Design/methodology/approach Data for this study has been gathered from official websites of the World Bank, and the world in data. The Louvain clustering method has been used to cluster the countries based on their financial strength and health facilities. The resulted clusters are visualised using Silhouette plots. The anomalies of the clusters had been used to quantify the pandemic situation. The status of Covid-19 has been manifested with the time series analysis through python programming. Findings The countries of the world have been clustered into seven, where developed countries divided into three clusters and the countries with transition economies and developing clustered together into four clusters. The time series analysis of recognised anomalies of the clusters assist to monitor the government responses and analyse the efficiency of used safety measures against the pandemic. Originality/value This study’s resulted clusters are highly valuable as a division of countries of the whole world for evaluating the health systems and for the regional levels. Further, the results of time series analysis are beneficial in monitoring the government responses and analysing the efficiency of used safety measures against the pandemic.

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Non-Linear Properties of the VIX Index

International Journal of Productivity Management and Assessment Technologies ◽

10.4018/ijpmat.2017070102 ◽

2017 ◽

Vol 5 (2) ◽

pp. 16-24

Author(s):

L. Magafas ◽

M. Hanias ◽

A. Tavlatou ◽

P. Kostantaki

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Strange Attractor ◽

Series Analysis ◽

The World ◽

Non Linear ◽

The Future ◽

Stock Indexes

This paper applies non-linear methods to analyze and predict the daily VIX index which is one of the most important stock indexes in the world. The aim of the analysis is to quantitatively show if the corresponding time series is a deterministic chaotic one and if one or more days ahead prediction can be achieved. The research employs Grassberger and Procaccia's methodology in the time series analysis in order to estimate the correlation and minimum embedding dimensions of the corresponding strange attractor. To achieve from the sample a multistep ahead prediction, the article gives the average for overall neighbours' projections of k-steps into the future. These results make the present work a valuable tool for traders, investors, and funds.

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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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Regression and Time Series Analysis of the World Oil Peak of Production: Another Look

Mathematical Geosciences ◽

10.1007/s11004-008-9159-3 ◽

2008 ◽

Vol 40 (6) ◽

pp. 653-670 ◽

Cited By ~ 13

Author(s):

Peter Caithamer

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Series Analysis ◽

The World ◽

Oil Peak

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Analysis and Forecast of Global Civil Aviation Accidents for the Period 1942-2016

Mathematical Problems in Engineering ◽

10.1155/2019/5710984 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Yafei Li

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Change Point ◽

Research Result ◽

Sudden Change ◽

Civil Aviation ◽

Aviation Accidents ◽

Series Analysis ◽

The World

With the increase of global civil aviation transportation, more and more researchers pay attention to the analysis of civil aviation accidents. Time series analysis can obtain the variation law in a large amount of data, and there is no research result of aviation accident time series yet. Based on the Mann-Kendall trend analysis and mutation analysis methods, this paper studied the change trend of accidents and casualties in different flight stages of civil aviation and built ARIMA (Autoregressive Integrated Moving Average model) time series analysis model to predict the number of civil aviation accidents and casualties by the long-term data in the world. (1) The number of civil aviation accidents fluctuates generally in the world; from 1942 to 2016, there were two fluctuation periods of civil aviation accidents. (2) The number of global civil aviation casualties from 1942 to 2016 showed a parabola trend of increasing first and then decreasing. The highest number of casualties appeared in 1972, which was 2373; on the different flight stages, the number of accidents was different. In the air route and approach phase, the number of accidents was the most, and the number of casualties was more than other flight phases, accounting for about 50% of the whole flight phase. (3) In addition to the land phase, the number of accidents showed a significant decrease in other flight phases; while the air route and total number of casualties decreased significantly, the number of casualties at other flight phases did not decrease significantly. There were no sudden changes in the number of global civil aviation accidents and approach casualties. (4) The sudden change point of the global civil aviation casualties was 2013, the sudden change point of the air route stage accidents was 1980, the sudden change point of approach stage accidents was 2012, and the sudden change point of air route stage casualties was 2006. According to the ARIMA (1,0,1) model, the numbers of global civil aviation accidents and casualties were predicted to 2025. Through time series research, we have explored the variation law in the historical data of long-term aviation accidents and predicted the possible changes of future aviation accidents, providing data reference for aviation safety research.

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Cesarean section rates before and after the Implementation of Health Sector Evolution Plan in Iran: an interrupted time series analysis

Current Women s Health Reviews ◽

10.2174/1573404817666210604105751 ◽

2021 ◽

Vol 17 ◽

Author(s):

Abbas Alipour ◽

Leyla Khazaei ◽

Soheila Khodakarim

Keyword(s):

Time Series ◽

Retrospective Study ◽

Time Series Analysis ◽

Health Sector ◽

Interrupted Time Series ◽

Interrupted Time Series Analysis ◽

Series Analysis ◽

The World ◽

Before And After ◽

Iranian Health System

Background: Today, the issue challenging cesarean delivery is its overuse as the conventional method of birth in the world, especially in developing countries. The Iranian health system implemented the Health Sector Evolution Plan (HSEP) in 2014 to reduce CS rates. Objective: To evaluate the effect of the implementation of the HSEP on the rate of CS in an Iranian subpopulation (Qom province) in 13 years. Methods: In this longitudinal retrospective study, the trend of CS rate over 13 years (2005-2017) in Qom province has been studied by using an interrupted time series analysis. Results: In the period after implementing the HSEP, the rate of CS decreases by 0.1% monthly slope compared to the previous period, which was not statistically significant. This trend is more definite among governmental hospitals. In contrast, the pattern observed in non-governmental hospitals has a monthly increased trend. Conclusion : After more than four years of implementation of the HSEP, it has not been able to significantly reduce the overall CSs during this period in Qom province and has failed to reach the plan's goals.

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Time Series Analysis of Price of Coffee in Case of Mettu Town, Ilu Ababor Zone, Oromia Regional State, Ethiopia

Asian Journal of Dairy and Food Research ◽

10.18805/ajdfr.dr-204 ◽

2021 ◽

Author(s):

Dereje Gebeyehu Ababu ◽

Azmeraw Misganaw Getahun

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Price Change ◽

Secondary Data ◽

Original Data ◽

Cash Crop ◽

Series Analysis ◽

Cash Crops ◽

Diagnostic Checking ◽

The World

Background: Coffee is one of the most important cash crops across the world and major source of export earnings. Coffee has been and remains the leading cash crop and export commodity of Ethiopia. The aim of this study was to estimate and predict the price change of coffee in Mettu town. Methods: In this study both descriptive and inferential statistics were used to analyze secondary data that were collected from Mettu town of Ethiopian Commodity Exchange office sector. A total of 120 months of price of coffee was included in this study. Time series analysis was used to estimate the parameter and for forecast the future values of price change of coffee. Result: The original data was not stationary and become stationary after second order differencing. The results showed that the price change of coffee was increasing from time to time. After that the data tested the order MA and AR are identified by using the ACF and PACF. Then the model was selected by using AIC. Since, ARMA (1, 2, 1) for price change of coffee was lower values of AIC found to be the most appropriate model to fit the data of the price change of coffee. After the model was fitted, the diagnostic checking has been applied by using the ACF residual and normality checking. So that the model fitted is appropriate for the price change of coffee. All the forecast values are found between the lower and the upper interval then we can say that forecasted value is accurate.

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Interrupted time series analysis of the implementation of social distancing policy, its lifting and the mandate of wearing face masks in Iran to mitigate against COVID-19

10.21203/rs.3.rs-83125/v1 ◽

2020 ◽

Author(s):

Mandana Saki ◽

Masoud Behzadifar ◽

Meysam Behzadifar ◽

Mahboubeh Khaton Ghanbari ◽

Ahad Bakhtiari ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Interrupted Time Series ◽

Interrupted Time Series Analysis ◽

Economic Activities ◽

Policy Makers ◽

Social Distancing ◽

Series Analysis ◽

The World ◽

The Impact

Abstract BackgroundCOVID-19 was first reported in Wuhan, China, and has spread rapidly around the world. The purpose of this study was to investigate the effects of implementing social distancing policy, and the impact of its lifting, with the resumption of social contacts and activities, as well as the effects of mandating face masks on the temporal trend of new COVID-19 cases in Iran. Methods We employed the interrupted time series analysis (ITSA), which is a very valuable method that can be used to evaluate the impact of the implementation of various policies in the health sector to help health policy-makers make effective decisions. Daily data were collected from the Ministry of Health and Medical Education and the World Health Organization from 954 public hospitals and health center settings. Data were extracted 14 days before and after the implementation of each policy. Results were computed with their 95% confidence interval (CI) and p-values equal to or less than 0.05 were considered as statistically significant. All data were analyzed using the open-source software R Version 3.6.1 using the “nlme” and “car” packages.ResultsThe slope of changes in new confirmed cases following the implementation of the social distancing policy decreased by 118.79 (P <0.001). With the resumption of social and economic activities in all provinces except for Tehran, initially the number of new daily confirmed cases was 3300, which was statistically significant (P <0.001). The slope of changes due to the implementation of this policy was 47.89 (P <0.001). A similar trend was detected with the resumption of social and economic activities in Tehran. With the implementation of the policy of mandatory use of masks, the slope of changes showed a decrease of 25.84 (P <0.001). Conclusion Given the absence of effective drugs and vaccines against COVID-19, policy-makers have implemented non-pharmacological interventions to reduce the transmission of the disease and prevent more deaths. Social distancing may be unsustainable in the long-term, while wearing masks is both a cost-effective and efficacious measure to curb disease transmission.

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