scholarly journals Telecoupled Food Trade Affects Pericoupled Trade and Intracoupled Production

2019 ◽  
Vol 11 (10) ◽  
pp. 2908 ◽  
Author(s):  
Anna Herzberger ◽  
Min Gon Chung ◽  
Kelly Kapsar ◽  
Kenneth A. Frank ◽  
Jianguo Liu
Keyword(s):  
Temporal Dynamics ◽  
Moving Average ◽  
Trade Networks ◽  
Autoregressive Integrated Moving Average ◽  
Food Trade ◽  
Political Landscape ◽  
Moving Average Models ◽  
Trade Relationships ◽  
Spatio Temporal ◽  
The Impact

Technology, transportation and global appetites have transformed trade relationships between near and distant countries. The impact of distant food demand on local agricultural production and trade has attracted considerable scientific scrutiny, yet little is known about how distant trade affects trade relationships and production between adjacent countries. In this paper, we explore this important issue by examining international food trade and agriculture production, which represent how distant places are connected through trade networks. By analyzing patterns of soybean, corn and wheat trading between 1991–2016 under the framework of metacoupling (human-nature interactions within, as well as between adjacent and distant systems), this study provides new insights into the spatio-temporal dynamics of trade flows. Results reveal that telecoupled (between distant countries) trade interacts with the geo-political landscape to enhance or offset intracoupled (within country) production and pericoupled (between neighboring countries) trade. Evidence from the literature and the results of autoregressive integrated moving average models indicate that when restrictions are placed on distant export routes, pericoupled trade increased. The extent to which the telecoupled food trade affected the pericoupled trade and intracoupled processes holds implications for the true extent of production driven by distant demands.

Intervention Modeling

2017 ◽  
Author(s):  
Richard McCleary ◽  
David McDowall ◽  
Bradley J. Bartos
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Intervention Model ◽  
Noise Component ◽  
Autoregressive Integrated Moving Average ◽  
Noise Function ◽  
Modeling Strategy ◽  
The Impact ◽  
True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

scholarly journals Spatio-temporal dynamics of schistosomiasis in Rwanda between 2001 and 2012: impact of the national Neglected Tropical Disease control programme

2017 ◽  
Vol 12 (1) ◽  
Author(s):  
Elias Nyandwi ◽  
Tom Veldkamp ◽  
Frank Badu Osei ◽  
Sherif Amer
Keyword(s):  
Temporal Dynamics ◽  
Public Health Problem ◽  
Incidence Rates ◽  
Control Programme ◽  
Scan Statistics ◽  
Major Public Health Problem ◽  
Implementation Phase ◽  
Incidence Data ◽  
Spatio Temporal ◽  
The Impact

Schistosomiasis is recognised as a major public health problem in Rwanda. We aimed to identify the spatio-temporal dynamics of its distribution at a fine-scale spatial resolution and to explore the impact of control programme interventions. Incidence data of Schistosoma mansoni infection at 367 health facilities were obtained for the period 2001-2012. Disease cluster analyses were conducted using spatial scan statistics and geographic information systems. The impact of control interventions was assessed for three distinct sub-periods. Findings demonstrated persisting, emerging and re-emerging clusters of schistosomiasis infection across space and time. The control programme initially caused an abrupt increase in incidence rates during its implementation phase. However, this was followed by declining and disappearing clusters when the programme was fully in place. The findings presented should contribute to a better understanding of the dynamics of schistosomiasis distribution to be used when implementing future control activities, including prevention and elimination efforts.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals Effectiveness and Forecasting of Interest Rate Reversal BI 7-Day Repo Rate in Indonesia: Lower Bound on Monetary Policy?

2018 ◽  
Vol 9 (1) ◽  
pp. 171-180
Author(s):  
I Gede Sanica ◽  
I Ketut Nurcita ◽  
I Made Mastra ◽  
Desak Made Sukarnasih
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Time Series Data ◽  
Moving Average ◽  
Series Data ◽  
Autoregressive Integrated Moving Average ◽  
Implementation Of Monetary Policy ◽  
Repo Rate ◽  
The Right ◽  
The Impact

AbstractThis study aims to analyze effectivity and forecast of interest rate BI 7-Day Repo Rate as policy reference in the implementation of monetary policy. The method was used in this study contains Vector Autoregression (VAR) to estimate effectivity of BI 7-Day Repo Rate and Autoregressive Integrated Moving Average (ARIMA) to forecast of BI 7-Day Repo Rate. Period of observation in this study used time series data during 2016.4 until 2017.6. The result of this research shows that the transformation of the BI Rate to BI 7-Day Repo Rate is the right step in the monetary policy operation in the effort to reach deepening of the financial market and strengthen the interbank money market structure so that it will decrease loan interest rate and encourage credit growth. The effectiveness of the use of BI 7 Day-Repo Rate on price stability is indicated by the positive relationship between the benchmark interest rate and inflation compared to the BI Rate. The impact of BI 7-Day Repo Rate on economic growth that tends to be positive. Forecasting the use of BI 7-Day Repo Rate shows good results with declining value levels, so this will encourage deepening the financial markets.

scholarly journals Intervention analysis with autoregressive integrated moving average models for time series of urban air pollutants

2021 ◽  
Vol 2139 (1) ◽  
pp. 012002
Author(s):  
L A Manco-Perdomo ◽  
L A Pérez-Padilla ◽  
C A Zafra-Mejía
Keyword(s):  
Air Pollutants ◽  
Moving Average ◽  
Air Pollutant ◽  
Urban Air ◽  
Intervention Analysis ◽  
Autoregressive Integrated Moving Average ◽  
Pollutant Concentrations ◽  
Moving Average Models ◽  
Regulatory Interventions ◽  
Air Pollutant Concentrations

Abstract The objective of this paper is to show an intervention analysis with autoregressive integrated moving average models for time series of air pollutants in a Latin American megacity. The interventions considered in this study correspond to public regulations for the control of urban air quality. The study period comprised 10 years. Information from 10 monitoring stations distributed throughout the megacity was used. Modelling showed that setting maximum emission limits for different pollution sources and improving fuel were the most appropriate regulatory interventions to reduce air pollutant concentrations. Modelling results also suggested that these interventions began to be effective between the first 4 days-15 days after their publication. The models developed on a monthly timescale had a short autoregressive memory. The air pollutant concentrations at a given time were influenced by the concentrations of up to three months immediately preceding. Moving average term of the models showed fluctuations in time of the air pollutant concentrations (3 months - 14 months). Within the framework of the applications of physics for the air pollution control, this study is relevant for the following findings: the usefulness of autoregressive integrated moving average models to temporal simulate air pollutants, and for its suitable performance to detect and quantify regulatory interventions.

Estimating the Impact of Non-pharmaceutical Interventions Against COVID-19 on Mumps Incidence in Sichuan, China

2021 ◽  
Author(s):  
Wenqiang Zhang ◽  
Rongsheng Luan
Keyword(s):  
Public Health ◽  
Respiratory Virus ◽  
Moving Average ◽  
Sarima Model ◽  
Potential Reduction ◽  
Autoregressive Integrated Moving Average ◽  
Health Measures ◽  
Virus Incidence ◽  
The Impact ◽  
Pharmaceutical Interventions

Abstract Background: A series of social and public health measures have been implemented to contain coronavirus disease 2019 (COVID-19) in China. We examined the impact of non-pharmaceutical interventions against COVID-19 on mumps incidence as an agent to determine the potential reduction in other respiratory virus incidence.Methods: We modelled mumps incidence per month in Sichuan using a seasonal autoregressive integrated moving average (SARIMA) model, based on the reported number of mumps cases per month from 2017-2020. Results: The epidemic peak of mumps in 2020 is lower than in the preceding years. Whenever compared with the projected cases or the average from corresponding periods in the preceding years (2017-2019), the reported cases in 2020 markedly declined (P<0.001). From January to December, the number of mumps cases was estimated to decrease by 36.3% (33.9% - 38.8%), 34.3% (31.1% - 37.8%), 68.9% (66.1% - 71.6%), 76.0% (73.9% - 77.9%), 67.0% (65.0% - 69.0%), 59.6% (57.6% - 61.6%), 61.1% (58.8% - 63.3%), 49.2% (46.4% - 52.1%), 24.4% (22.1% - 26.8%), 30.0% (27.5% - 32.6%), 42.1% (39.6% - 44.7%), 63.5% (61.2% - 65.8%), respectively. The total number of mumps cases in 2020 was estimated to decrease by 53.6% (52.9% - 54.3%).Conclusion: Our study shows that non-pharmaceutical interventions against COVID-19 have had an effective impact on mumps incidence in Sichuan, China.

Predicting change in biogeochemical potential of subsurface systems with changing hydrogeological conditions

2021 ◽  
Author(s):  
Swamini Khurana ◽  
Falk Heße ◽  
Martin Thullner
Keyword(s):  
Water Quality ◽  
Preferential Flow ◽  
Nutrient Loading ◽  
Temporal Dynamics ◽  
Reaction Network ◽  
Climate Scenario ◽  
Ecosystem Functions ◽  
External Forcing ◽  
Spatio Temporal ◽  
The Impact

&lt;p&gt;In a changing climate scenario, we expect weather event patterns to change, both in frequency and in intensity. The subsequent impacts of these changing patterns on ecosystem functions are of great interest. Water quality particularly is critical due to public health concerns. Already, seasonal variation of water quality has been attributed to varying microbial community assemblages and nutrient loading in the corresponding water body but the contribution of the variations in the quantity of groundwater recharge is a missing link. It is thus beneficial to establish links between external forcing such as changing infiltration rate or recharge on nutrient cycling in the subsurface. We undertake this study to investigate the impact of temporal variation in external forcing on the biogeochemical potential of spatially heterogeneous subsurface systems using a numerical modeling approach. We used geostatistical tools to generate spatial random fields by considering difference combinations of the variance in the log conductivity field and the anisotropy of the domain. Tuning these two parameters assists in effective representation of a wide variety of geologic materials with varying intensity of preferential flow paths in the heterogeneous domain. We ran simulations using OGS#BRNS that enables us to combine a flexibly defined microbial mediated reaction network with the mentioned spatially heterogeneous domains in transient conditions. We propose that a combination of estimated field indicators of Damk&amp;#246;hler number, Peclet number (transformed Damk&amp;#246;hler number: Da&lt;sub&gt;t&lt;/sub&gt;), and projected temporal dynamics in surface conditions can assist us in predicting the change in biogeochemical potential of the subsurface system. Preliminary results indicate that we miss potentially critical variations in reactive species concentration if we neglect spatio-temporal heterogeneities for regimes where 1&lt;Da&lt;sub&gt;t&lt;/sub&gt;&lt;40. For regimes characterized by values outside this range, we propose that spatio-temporal heterogeneities due to subsurface structure and changing hydrological forcing may not be relevant.&lt;/p&gt;

scholarly journals Transfer Function Analysis: Modelling Residential Building Costs in New Zealand by Including the Influences of House Price and Work Volume

Buildings ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 152
Author(s):  
Linlin Zhao ◽  
Jasper Mbachu ◽  
Zhansheng Liu ◽  
Huirong Zhang
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Transfer Function ◽  
Moving Average ◽  
Cost Data ◽  
Autoregressive Integrated Moving Average ◽  
Work Volume ◽  
Moving Average Models ◽  
Function Models ◽  
Transfer Function Models

An accurate cost estimate not only plays a key role in project feasibility studies but also in achieving a final successful outcome. Conventionally, estimating cost typically relies on the experience of professionals and cost data from previous projects. However, this process is complex and time-consuming, and it is challenging to ensure the accuracy of the estimates. In this study, the bivariate and multivariate transfer function models were adopted to estimate and forecast the building costs of two types of residential buildings in New Zealand: Low-rise buildings and high-rise buildings. The transfer function method takes advantage of the merits of univariate time series analysis and the power of explanatory variables. In the dynamic project conduction environment, simply including building cost data in the cost forecasting models is not valid for making predictions, because the change in demand must be considered. Thus, the time series of house prices and work volume were used to explain exogenous effects in the transfer function model. To demonstrate the effectiveness of transfer function models, this study compared the results generated by the transfer function models with autoregressive integrated moving average models. According to the forecasting performance of the models, the proposed approach achieved better results than autoregressive integrated moving average models. The proposed method can provide accurate cost estimates that can help stakeholders in project budget planning and management strategy making at the early stage of a project.

Sign in / Sign up

Export Citation Format

Share Document