scholarly journals SARS-COV-2 THREE FORCING SEASONALITIES: POLICIES, ENVIRONMENT AND URBAN SPACES

2020 ◽  
Author(s):  
Charles Roberto Telles
Keyword(s):  
Time Series Data ◽  
Data Extraction ◽  
Sir Model ◽  
Urban Spaces ◽  
Series Data ◽  
Predictive Analysis ◽  
The Earth ◽  
Model Equations ◽  
And Control ◽  
North And South

This research investigated if pandemic of SARS-COV-2 follows the Earth seasonality ε comparing countries cumulative daily new infections incidence over Earth periodic time of interest for north and south hemisphere. It was found that no seasonality in this form ε occurs as far as a seasonality forcing behavior ε' assumes most of the influence in SARS-COV-2 spreading patterns. Putting in order ε' of influence, there were identified three main forms of SARS-COV-2 of transmission behavior: during epidemics growth, policies are the main stronger seasonality forcing behavior of the epidemics followed by secondary and weaker environmental and urban spaces driving patterns of transmission. At outbreaks and control phase, environmental and urban spaces are the main seasonality forcing behavior due to policies/ALE limitations to address heterogeneity and confounding scenario of infection. Finally regarding S and R compartments of SIR model equations, control phases are the most reliable phase to predictive analysis. These seasonality forcing behaviors cause environmental driven seasonality researches to face hidden or false observations due to policy/ALE interventions for each country and urban spaces characteristics. And also, it causes policies/ALE limitations to address urban spaces and environmental seasonality instabilities, thus generating posterior waves or uncontrolled patterns of transmission (fluctuations). All this components affect the SARS-COV-2 spreading patterns simultaneously being not possible to observe environmental seasonality not associated intrinsically with policies/ALE and urban spaces, therefore conferring to these three forms of transmission spreading patterns, specific regions of analysis for time series data extraction.

scholarly journals SARS-COV-2: SIR Model Limitations and Predictive Constraints

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 676
Author(s):  
Charles Roberto Telles ◽  
Henrique Lopes ◽  
Diogo Franco
Keyword(s):  
Time Series Data ◽  
Data Extraction ◽  
Sir Model ◽  
Series Data ◽  
Atmospheric Conditions ◽  
Main Research ◽  
Outdoor Spaces ◽  
Model Equations ◽  
Physical Features ◽  
Indoor And Outdoor

Background: The main purpose of this research is to describe the mathematical asymmetric patterns of susceptible, infectious, or recovered (SIR) model equation application in the light of coronavirus disease 2019 (COVID-19) skewness patterns worldwide. Methods: The research modeled severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spreading and dissemination patterns sensitivity by redesigning time series data extraction of daily new cases in terms of deviation consistency concerning variables that sustain COVID-19 transmission. The approach opened a new scenario where seasonality forcing behavior was introduced to understand SARS-COV-2 non-linear dynamics due to heterogeneity and confounding epidemics scenarios. Results: The main research results are the elucidation of three birth- and death-forced seasonality persistence phases that can explain COVID-19 skew patterns worldwide. They are presented in the following order: (1) the environmental variables (Earth seasons and atmospheric conditions); (2) health policies and adult learning education (HPALE) interventions; (3) urban spaces (local indoor and outdoor spaces for transit and social-cultural interactions, public or private, with natural physical features (river, lake, terrain). Conclusions: Three forced seasonality phases (positive to negative skew) phases were pointed out as a theoretical framework to explain uncertainty found in the predictive SIR model equations that might diverge in outcomes expected to express the disease’s behaviour.

Modelling the Nexus Between Foreign Aid and Economic Growth: A Case of Afghanistan and Egypt

2021 ◽  
Vol 39 (2) ◽  
Author(s):  
Muhammed Ashiq Villanthenkodath ◽  
Ubaid Mushtaq
Keyword(s):  
Economic Growth ◽  
Foreign Aid ◽  
Time Series Data ◽  
Trade Openness ◽  
Political Stability ◽  
Series Data ◽  
Long Run ◽  
Short Run ◽  
Annual Time Series ◽  
And Control

This paper tries to explore the existence of a long-run relationship between foreign aid and economic growth by using the data from the two highest foreign aid recipient countries. Using the annual time series data from 1965 to 2017 this study uses several econometric models such as Johansen and Juselius cointegration, Granger causality and vector auto regression to establish the long and short-run relationships among foreign aid inflows and economic growth while also considering financial development and trade openness from both the countries. The empirical results suggest that no long-run relationship exists among foreign aid inflows and economic growth for both the countries. However, unidirectional causality running from foreign aid to economic growth is indicative in both countries. Therefore, the findings in this paper support the adequate need for foreign aid for effective economic growth amid an upright policy environment, related issues of conditionality and political stability. Our results are robust to independent, and control variables and estimation techniques are also on par with robustness.

scholarly journals Sonification of Astronomical Data

2011 ◽  
Vol 7 (S285) ◽  
pp. 133-136 ◽  
Author(s):  
Wanda L. Diaz-Merced ◽  
Robert M. Candey ◽  
Nancy Brickhouse ◽  
Matthew Schneps ◽  
John C. Mannone ◽  
...  
Keyword(s):  
Solar Wind ◽  
Time Series Data ◽  
Solar Wind Plasma ◽  
Series Data ◽  
Astronomical Data ◽  
X Ray ◽  
The Earth ◽  
Polar Type ◽  
Mass Ejection ◽  
The Impact

AbstractThis document presents Java-based software called xSonify that uses a sonification technique (the adaptation of sound to convey information) to promote discovery in astronomical data. The prototype is designed to analyze two-dimensional data, such as time-series data. We demonstrate the utility of the sonification technique with examples applied to X-ray astronomy and solar data. We have identified frequencies in the Chandra X-Ray observations of EX Hya, a cataclysmic variable of the intermediate polar type. In another example we study the impact of a major solar flare, with its associated coronal mass ejection (CME), on the solar wind plasma (in particular the solar wind between the Sun and the Earth), and the Earth's magnetosphere.

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

scholarly journals Potensi dan Tantangan Pengembangan Sektor Unggulan di Kalimantan Barat

2016 ◽  
Vol 5 (2) ◽  
pp. 94
Author(s):  
Dinarjad Achmad
Keyword(s):  
Time Series ◽  
Water Supply ◽  
Time Series Data ◽  
Water Area ◽  
Primary Objective ◽  
Series Data ◽  
West Kalimantan ◽  
The Earth ◽  
Descriptive Method ◽  
Land Water

The primary objective of this study was to analyze the potential and challenges of superior sectordevelopment in West Kalimantan. Superior sectors here interpreted as a sector that producesgoods that can be exported. Descriptive method and time series data for 7 years (2007- 2013) wasused as the tools and materials to perform the analysis.The results showed that the based on ofnatural resources (land, water area and the river, fill the earth) and geography, West Kalimantanhave a greater potential for superior sector development, but there are several challenges to thedevelopment potential of the superior sector, including: (1) resource human (HR) is still weak.(2) Infrastructure (electricity, gas and water supply, road and port export) are limited. (3)Marketing and networking is still weak 

scholarly journals Adults newly infected with hiv in burundi: a box-jenkins arima approach

2020 ◽  
Vol 4 ◽  
pp. 48-56
Author(s):  
Smartson P. NYONI ◽  
Thabani NYONI
Keyword(s):  
Time Series Data ◽  
Hiv Infections ◽  
Series Data ◽  
Annual Number ◽  
Optimal Model ◽  
Applied Model ◽  
Annual Time Series ◽  
And Control ◽  
Blue Print ◽  
Annual Time

Using annual time series data on the number of adults (ages 15 and above) newly infected with HIV in Burundi from 1990 – 2018, the study predicts the annual number of adults who will be newly infected with HIV over the period 2019 – 2025. The study applied the Box-Jenkins ARIMA methodology. The diagnostic ADF tests as well as correlogram analysis show that the G series under consideration is an I (2) variable. Based on the AIC, the study presents the ARIMA (0, 2, 1) model as the optimal model. The residual correlogram and the inverse roots of the applied model further reveal that the presented model is stable and suitable for forecasting new HIV infections in adults in Burundi. The results of the study indicate that the number of new HIV infections in adults in Burundi will most likely decline, over the period 2019 – 2023, from approximately 698 to almost 90 new HIV infections. By 2025, Burundi could experience her first zero new HIV infections in adults! This implies that, despite the fact that Vision Burundi 2025 is a highly ambitious blue-print; Vision Burundi 2025 will largely be achieved as far as HIV/AIDS prevention and control is concerned.

scholarly journals Inference for Ecological Dynamical Systems: A Case Study of Two Endemic Diseases

2012 ◽  
Vol 2012 ◽  
pp. 1-13
Author(s):  
Daniel A. Vasco
Keyword(s):  
Time Series ◽  
Statistical Theory ◽  
Time Series Data ◽  
Sample Path ◽  
Sir Model ◽  
Series Data ◽  
Parameter Inference ◽  
Transmission Process ◽  
Endemic Diseases

A Bayesian Markov chain Monte Carlo method is used to infer parameters for an open stochastic epidemiological model: the Markovian susceptible-infected-recovered (SIR) model, which is suitable for modeling and simulating recurrent epidemics. This allows exploring two major problems of inference appearing in many mechanistic population models. First, trajectories of these processes are often only partly observed. For example, during an epidemic the transmission process is only partly observable: one cannot record infection times. Therefore, one only records cases (infections) as the observations. As a result some means of imputing or reconstructing individuals in the susceptible cases class must be accomplished. Second, the official reporting of observations (cases in epidemiology) is typically done not as they are actually recorded but at some temporal interval over which they have been aggregated. To address these issues, this paper investigates the following problems. Parameter inference for a perfectly sampled open Markovian SIR is first considered. Next inference for an imperfectly observed sample path of the system is studied. Although this second problem has been solved for the case of closed epidemics, it has proven quite difficult for the case of open recurrent epidemics. Lastly, application of the statistical theory is made to measles and pertussis epidemic time series data from 60 UK cities.

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