scholarly journals Complex system properties in the spreading of COVID-19 pandemic

2021 ◽  
Vol 45 (177) ◽  
pp. 1039-1052
Author(s):  
Carlos Quimbay
Keyword(s):  
Complex System ◽  
Time Series ◽  
Structural Properties ◽  
Return Time ◽  
Time Correlation ◽  
Cumulative Number ◽  
Temporal Fluctuation ◽  
The World ◽  
System Properties ◽  
Fluctuation Scaling

The objective of the present study was to show that the spread of the COVID-19 pandemic around the world shows complex system properties such as lognormal laws, temporal fluctuation scaling, and time correlation. First, the daily cumulative number of confirmed cases and deaths is distributed among countries as lognormals such that the time series exhibit a temporal fluctuation scaling. Second, the daily return time series of cases and deaths per day have associated Levy stable distributions and they have time correlation. The idea was to draw attention to the fact that the spread of the COVID-19 pandemic can be seen as a complex system, and, thus, contribute to the identification of the structural properties of this system, which is relevant as it is expected that future stochastic models describing the spread of the COVID-19 pandemic from a microscopic dynamics perspective should be able to explain the emergence of the structural properties identified here.

Modeling COVID-19 Growing Trends to Reveal the Differences in the Effectiveness of Non-Pharmaceutical Interventions among Countries in the World (Preprint)

2020 ◽  
Author(s):  
You Chen ◽  
Yubo Feng ◽  
Chao Yan ◽  
Xinmeng Zhang ◽  
Cheng Gao
Keyword(s):  
Secondary Analysis ◽  
Reproduction Rate ◽  
Cumulative Number ◽  
Linear Regression Models ◽  
Growth Data ◽  
Dynamic Changes ◽  
The World ◽  
Four Levels ◽  
Almost All ◽  
Pharmaceutical Interventions

BACKGROUND Adopting non-pharmaceutical interventions (NPIs) can affect COVID-19 growing trends, decrease the number of infected cases, and thus reduce mortality and healthcare demand. Almost all countries in the world have adopted non-pharmaceutical interventions (NPIs) to control the spread rate of COVID-19; however, it is unclear what are differences in the effectiveness of NPIs among these countries. OBJECTIVE We hypothesize that COVID-19 case growth data reveals the efficacy of NPIs. In this study, we conduct a secondary analysis of COVID-19 case growth data to compare the differences in the effectiveness of NPIs among 16 representative countries in the world. METHODS This study leverages publicly available data to learn patterns of dynamic changes in the reproduction rate for sixteen countries covering Asia, Europe, North America, South America, Australia, and Africa. Furthermore, we model the relationships between the cumulative number of cases and the dynamic reproduction rate to characterize the effectiveness of the NPIs. We learn four levels of NPIs according to their effects in the control of COVID-19 growth and categorize the 16 countries into the corresponding groups. RESULTS The dynamic changes of the reproduction rate are learned via linear regression models for all of the studied countries, with the average adjusted R-squared at 0.96 and the 95% confidence interval as [0.94 0.98]. China, South Korea, Argentina, and Australia are at the first level of NPIs, which are the most effective. Japan and Egypt are at the second level of NPIs, and Italy, Germany, France, Netherlands, and Spain, are at the third level. The US and UK have the most inefficient NPIs, and they are at the fourth level of NPIs. CONCLUSIONS COVID-19 case growth data provides evidence to demonstrate the effectiveness of the NPIs. Understanding the differences in the efficacy of the NPIs among countries in the world can give guidance for emergent public health events. CLINICALTRIAL NA

scholarly journals Modelling spreading of an infection using time series by a novel family of models; fitting the time series of the confirmed cases of COVID-19 in China

2020 ◽  
Vol 9 (s1) ◽  
Author(s):  
Babak Jamshidi ◽  
Shahriar Jamshidi Zargaran ◽  
Mansour Rezaei
Keyword(s):  
Infectious Disease ◽  
Time Series ◽  
Communicable Disease ◽  
Time Series Models ◽  
Cumulative Number ◽  
The Novel ◽  
New Family ◽  
Two Samples ◽  
Almost All ◽  
Number Of Individuals

AbstractIntroductionTime series models are one of the frequently used methods to describe the pattern of spreading an epidemic.MethodsWe presented a new family of time series models able to represent the cumulative number of individuals that contracted an infectious disease from the start to the end of the first wave of spreading. This family is flexible enough to model the propagation of almost all infectious diseases. After a general discussion on competent time series to model the outbreak of a communicable disease, we introduced the new family through one of its examples.ResultsWe estimated the parameters of two samples of the novel family to model the spreading of COVID-19 in China.DiscussionOur model does not work well when the decreasing trend of the rate of growth is absent because it is the main presumption of the model. In addition, since the information on the initial days is of the utmost importance for this model, one of the challenges about this model is modifying it to get qualified to model datasets that lack the information on the first days.

scholarly journals Business Days Time Series Weekly Trend and Seasonality

2021 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Karlis Gutans
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Exchange Rates ◽  
Temperature Data ◽  
Adjustment Method ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Time Series Decomposition ◽  
The World

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

scholarly journals Did the UN convention on the rights of the child reduce child mortality around the world? An interrupted time series analysis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Christopher A. Tait ◽  
Abtin Parnia ◽  
Nishan Zewge-Abubaker ◽  
Wendy H. Wong ◽  
Heather Smith-Cannoy ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Child Mortality ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
Rights Of The Child ◽  
Reduce Child Mortality ◽  
The World

scholarly journals The duration of the active phase on surge-type glaciers: contrasts between Svalbard and other regions

1991 ◽  
Vol 37 (127) ◽  
pp. 388-400 ◽  
Author(s):  
Julian A. Dowdeswell ◽  
Gordon S. Hamilton ◽  
Jon Ove Hagen
Keyword(s):  
Time Series ◽  
North America ◽  
Active Phase ◽  
Aerial Photographs ◽  
Polar Ice ◽  
Phase Duration ◽  
Field Surveys ◽  
The World ◽  
Glacier Surges ◽  
Fast Motion

AbstractMany glaciers in Svalbard and in other glacierized areas of the world are known to surge. However, the time series of observations required to assess the duration of fast motion is very restricted. Data on active-phase duration in Svalbard come from aerial photographs, satellite imagery, field surveys and airborne reconnaissance. Evidence on surge duration is available for eight Svalbard ice masses varying from 3 to 1250 km2. Worldwide, active-phase duration is recorded for less than 50 glaciers. Few observations are available on high polar ice masses. The duration of the active phase is significantly longer for Svalbard glaciers than for surge-type glaciers in other areas from which data are available. In Svalbard, the active phase may last from 3 to 10 years. By contrast, a surge duration of 1–2 years is more typical of ice masses in northwest North America, Iceland and the Pamirs. Ice velocities during the protracted active phase on Svalbard glaciers are considerably lower than those for many surge-type glaciers in these other regions. Mass is transferred down-glacier more slowly but over a considerably longer period. Svalbard surge-type glaciers do not exhibit the very abrupt termination of the active phase, over periods of a few days, observed for several Alaskan glaciers. The duration of the active phase in Svalbard is not dependent on parameters related to glacier size. The quiescent phase is also relatively long (50–500 years) for Svalbard ice masses. Detailed field monitoring of changing basal conditions through the surge cycle is required from surge-type glaciers in Svalbard in order to explain the significantly longer length of the active phase for glaciers in the archipelago, which may also typify other high polar ice masses. The finding that surge behaviour, in the form of active-phase duration, shows systematic differences between different regions and their environments has important implications for understanding the processes responsible for glacier surges.

A comparison of global suicide terrorism by region before and after 9/11 terrorist attacks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmet Guler ◽  
Mustafa Demir
Keyword(s):  
Time Series ◽  
Interrupted Time Series ◽  
Sub Saharan Africa ◽  
Terrorist Attacks ◽  
Suicide Terrorism ◽  
Content Type ◽  
The World ◽  
Before And After ◽  
Sub Saharan ◽  
Global Terrorism Database

Purpose This study aims to examine the effect of the 9/11 terrorist attacks on suicide terrorism in different regions of the world and changes in the trends in suicide terrorism according to regions before and after 9/11. Design/methodology/approach Using the data obtained from the Global Terrorism Database from 1981 to 2019, the descriptive statistics were computed first and then, independent samples t-tests were run to compare the monthly mean percentage of suicide-terrorism incidents that occurred in each region between the pre-9/11 and the post-9/11 periods. Finally, to statistically assess the effect of the 9/11 attacks and changes in the trends for the dependent variables over time, monthly interrupted time-series analyzes were conducted. Findings The results of monthly interrupted time series analyzes showed that after the 9/11 attacks, the trends for suicide-terrorism rates decreased significantly in three regions including South Asia, the Middle East and North Africa and Europe, while the trend for suicide-terrorism rates increased significantly in Sub-Saharan Africa. However, no statistically significant changes in the trends in suicide-terrorism rates occurred in three regions including North America, East Asia and Central Asia and Southeast Asia before 9/11, during November 2001 or after 9/11. Originality/value This study indicates the critical importance of the 9/11 terrorist attacks in suicide terrorism and its impact on these events in different regions of the world. The research also provides some recommendations concerning the effectiveness of defensive and offensive counterterrorism policies against suicide terrorism.

scholarly journals Changes of China’s agri-food exports to Germany caused by its accession to WTO and the 2008 financial crisis

2015 ◽  
Vol 7 (2) ◽  
pp. 262-279 ◽  
Author(s):  
Zhichao Guo ◽  
Yuanhua Feng ◽  
Thomas Gries
Keyword(s):  
Time Series ◽  
Financial Crisis ◽  
Regression Models ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Middle Part ◽  
Change Points ◽  
Content Type ◽  
The World ◽  
Before And After

Purpose – The purpose of this paper is to investigate changes of China’s agri-food exports to Germany caused by China’s accession to WTO and the global financial crisis in a quantitative way. The paper aims to detect structural breaks and compare differences before and after the change points. Design/methodology/approach – The structural breaks detection procedures in this paper can be applied to find out two different types of change points, i.e. in the middle and at the end of one time series. Then time series and regression models are used to compare differences of trade relationship before and after the detected change points. The methods can be employed in any economic series and work well in practice. Findings – The results indicate that structural breaks in 2002 and 2009 are caused by China’s accession to WTO and the financial crisis. Time series and regression models show that the development of China’s exports to Germany in agri-food products has different features in different sub-periods. Before 1999, there is no significant relationship between China’s exports to Germany and Germany’s imports from the world. Between 2002 and 2008 the former depends on the latter very strongly, and China’s exports to Germany developed quickly and stably. It decreased, however suddenly in 2009, caused by the great reduction of Germany’s imports from the world in that year. But China’s market share in Germany still had a small gain. Analysis of two categories in agri-food trade also leads to similar conclusions. Comparing the two events we see rather different patterns even if they both indicate structural breaks in the development of China’s agri-food exports to Germany. Originality/value – This paper partly originally proposes two statistical algorithms for detecting different kinds of structural breaks in the middle part and at the end of a short-time series, respectively.

scholarly journals FAKTOR – FAKTOR YANG MEMPENGARUHI KONSUMSI GAS DI INDONESIA

2021 ◽  
Vol 3 (2) ◽  
pp. 69
Author(s):  
Rohim Rohim ◽  
Mike Triani
Keyword(s):  
Time Series ◽  
World Bank ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Industrial Growth ◽  
A Value ◽  
The World Bank ◽  
The World ◽  
Gas Consumption

The purpose of this research is to determine (1) the effect of income on gas consumption in Indonesia (2) the effect of population on gas consumption in Indonesia (3) the effect of industrial growth on gas consumption in Indonesia. This type of research is descriptive and associative. The data used in this research is secondary data from Indonesia in the form of time series data from 1970 to 2019 and this data was obtained from official institutions of the World Bank and BP Statistic World. The data were processed using multiple linear regression. The results showed that the income had a negative and significant effect on gas consumption with a probability value of 0.0005 <0.05, the population had a positive and significant effect on gas consumption with a value of prob t-count of 0.0010 <0.05 and industrial growth had a positive and significant effect on gas consumption.  The significant to gas consumption in Indonesia with a value of prob t-count value of 0.5219 <0.05 and suggestions for further researchers to be able to analyze other factors that affecting gas consumption in Indonesia.  Because from the gas sectors, there are still many factors that affected gas consumption until the research results will be better

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