COVID-19 pandemic: Impact of lockdown, contact and non-contact transmissions on infection dynamics (Preprint)

2020 ◽  
Author(s):  
Shovonlal Roy
Keyword(s):  
Time Series Data ◽  
Transmission Rate ◽  
Relative Strength ◽  
Secondary Peak ◽  
Series Data ◽  
Transmission Route ◽  
Transmission Rates ◽  
Relative Contribution ◽  
Route Of Transmission ◽  
Contact Transmission

UNSTRUCTURED COVID-19 coronavirus pandemic has virtually locked down the entire world of human population, and through its rapid and unstoppable spread COVID-19 has essentially compartmentalised the population merely into susceptible, exposed, infected and recovered classes. Adapting the classical epidemic modelling framework, two distinct routes of COVID-19 transmission are incorporated into a model: (a) direct person-to-person contact transmission, and (b) indirect airborne and fomites-driven transmission. The indirect non-contact transmission route needs to explored in models of COVID-19 spread, because evidences show that this route of transmission is entirely viable with hugely uncertain level of relative contribution. This theoretical study based on model simulations demonstrates the following: (1) Not incorporating indirect transmission route in the model leads to underestimation of the basic reproduction number, and hence will impact on the COVID-19 mitigation decisions; (2) Lockdown measures can suppress the primary infection peak, but will lead to a secondary peak whose relative strength and time of occurrence depend on the success and duration of the lockdown measures; (3) To make lockdown effective, a considerable level of reduction in both contact and non-contact transmission rates over a long period is required; (4) To bring down the infection cases below any hypothetical health-care capacity, reduction of non-contact transmission rate is key, and hence active measures should be taken to reduce non-contact transmission (e.g., extensive uses of areal and aerosol disinfectant in public spaces to improve contaminated surfaces and air); (5) Any premature withdrawal of lockdown following the sign of a brief retracement in the infection cases can backfire, and can lead to a quicker, sharper and higher secondary peak, due to reactivation of the two transmission routes. Based on these results, this study recommends that any exit policy from lockdown, should take into account the level of transmission reduction in both routes, the absolute scale of which will vary among countries depending on their health-service capacity, but should be computed using accurate time-series data on infection cases and transmission rates.

scholarly journals COVID-19 pandemic: Impact of lockdown, contact and non-contact transmissions on infection dynamics

2020 ◽  
Author(s):  
Shovonlal Roy
Keyword(s):  
Time Series Data ◽  
Transmission Rate ◽  
Relative Strength ◽  
Secondary Peak ◽  
Series Data ◽  
Transmission Route ◽  
Transmission Rates ◽  
Relative Contribution ◽  
Route Of Transmission ◽  
Contact Transmission

AbstractCOVID-19 coronavirus pandemic has virtually locked down the entire world of human population, and through its rapid and unstoppable spread COVID-19 has essentially compartmentalised the population merely into susceptible, exposed, infected and recovered classes. Adapting the classical epidemic modelling framework, two distinct routes of COVID-19 transmission are incorporated into a model: (a) direct person-to-person contact transmission, and (b) indirect airborne and fomites-driven transmission. The indirect non-contact transmission route needs to explored in models of COVID-19 spread, because evidences show that this route of transmission is entirely viable with hugely uncertain level of relative contribution. This theoretical study based on model simulations demonstrates the following: (1) Not incorporating indirect transmission route in the model leads to underestimation of the basic reproduction number, and hence will impact on the COVID-19 mitigation decisions; (2) Lockdown measures can suppress the primary infection peak, but will lead to a secondary peak whose relative strength and time of occurrence depend on the success and duration of the lockdown measures; (3) To make lockdown effective, a considerable level of reduction in both contact and non-contact transmission rates over a long period is required; (4) To bring down the infection cases below any hypothetical health-care capacity, reduction of non-contact transmission rate is key, and hence active measures should be taken to reduce non-contact transmission (e.g., extensive uses of areal and aerosol disinfectant in public spaces to improve contaminated surfaces and air); (5) Any premature withdrawal of lockdown following the sign of a brief retracement in the infection cases can backfire, and can lead to a quicker, sharper and higher secondary peak, due to reactivation of the two transmission routes. Based on these results, this study recommends that any exit policy from lockdown, should take into account the level of transmission reduction in both routes, the absolute scale of which will vary among countries depending on their health-service capacity, but should be computed using accurate time-series data on infection cases and transmission rates.

scholarly journals Variability and Sources of Output Growth in Major Oilseeds of Rajasthan

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Kailash Chand Bairwa
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Output Growth ◽  
The State ◽  
Series Data ◽  
Oilseed Crops ◽  
Significant Growth ◽  
Area Effect ◽  
Relative Contribution

Rajasthan state is the second largest oilseeds producer and land coverage in the country. The share of oilseed crops is scheduled the significant growth in area and output in latest 20 years. Nevertheless, compare to wheat and gram, the growth rate of area and production of several oilseeds is less significant and there exist wide instability in their productivity in scattered part of the state. This study investigates to growth, its contributors and variability in area, production and productivity of major oilseed crops. The study period from 1990-91 to 2019-20 was divided into three sub-periods viz., period-I (1990-91 to 2004-05); period-II (2005-06 to 2019-20) and Overall study Period (1990-91 to 2018-19). Time series data were collected from various public E-sources to compute the growth, instability and decomposition in oilseeds production. It was revealed from the analysis that growth of kharif oilseeds was higher than rabi oilseeds. The highest instability (31.78) in production and productivity was reported in period-I for kharif oilseeds. In case of relative contribution, the area effect (416.85) and yield effects (211.10) were more effective in production of taramira and sesame crops, respectively. This analysis suggested that during period –I and II area effect was dominant in changing output of taramira and rapeseed-mustard.

scholarly journals Growth and Decomposition analysis of Rabi Pulse Crops in Rajasthan

2021 ◽  
Vol 66 (3) ◽  
Author(s):  
Harkesh Kumar *, Balai
Keyword(s):  
Growth Rate ◽  
Time Series Data ◽  
Decomposition Analysis ◽  
Output Growth ◽  
Series Data ◽  
Pulse Crops ◽  
Relative Contribution ◽  
Positive Growth ◽  
Decomposition Models ◽  
Mixed Pattern

The present study was conducted to analyze the growth rate and source of output growth in area, production and productivity of rabi pulse crops viz., gram and lentil crops in Rajasthan. The study was solely based on secondary time series data. The study period (1988-89 to 2017-18) has been divided into four periods namely period-I (1988-89 to 1997-98), period-II (1998-99 to 2007-08), period- III (2008-09 to 2017-18) and overall period (1988-89 to 2017-18). Exponential growth and principal decomposition models were used to measure the growth rates and relative contribution of factors in production of gram and lentil crops. The area, production and productivity of gram showed mixed pattern of growth at the rate of -0.46, 1.86 and 2.33 per cent, respectively during overall period. However, the area, production and productivity of lentil were reported positive growth with the magnitude of 11.94, 12.72 and 0.93 per cent, respectively in the state. The study revealed that the mixed growth rate was observed in area, production and productivity of gram while increasing growth was observed in area, production and productivity of lentil. During all the study periods, the expansion in area was effective to increase the production of lentil in Rajasthan. During period-I and II, the production of gram was mainly contributed by expansion in area while in the case of period-III and overall, the interaction effect was more dominant

scholarly journals A Study on Stock Price Prediction using LSTM Model

2021 ◽  
Vol 9 (VI) ◽  
pp. 2618-2622
Author(s):  
Koushal Saini
Keyword(s):  
Stock Market ◽  
Stock Price ◽  
Time Series Data ◽  
Moving Average ◽  
Technical Analysis ◽  
Relative Strength ◽  
Trend Detection ◽  
Series Data ◽  
Future Price ◽  
Stock Price Prediction

Predicting stock price of any stock is a challenging task because the Volatility of stock market the nature of stock price is dynamic, chaotic, noisy and sometimes totally unexpected. The other most difficult task is to analyze and decide financial time series data that improves investment returns and help in minimizing losses. Technical analysis is a method that help in analyzing a stock and predict its future price via evaluating securities. There are already many Indicators and other tools for technical analysis in stock market. Some famous indicators such as SMA (Simple Moving Average), EMA (Exponential Moving Average), WMA (Weight Moving Average), VWMA (Volume Weight Moving Average), DEMA (moving averages), MACD (Moving Average Convergence/Divergence), ADX (Average Di- reactional Movement Index), TDI (Trend Detection Index), Arun, VHF (trend indicators), stochastic, RSI (Relative Strength Index), SMI(Stochastic Momentum Index, volume indicators are also available for technical analysis. Here, we have used the LSTM Model to predict future price of some big companies of stock market in NSE.

scholarly journals Convergent cross sorting for estimating dynamic coupling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Leo Breston ◽  
Eric J. Leonardis ◽  
Laleh K. Quinn ◽  
Michael Tolston ◽  
Janet Wiles ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Simulated Data ◽  
Brain Regions ◽  
Relative Strength ◽  
Series Data ◽  
Dynamic Coupling ◽  
Natural Systems ◽  
Noise Characteristics ◽  
Wide Range

AbstractNatural systems exhibit diverse behavior generated by complex interactions between their constituent parts. To characterize these interactions, we introduce Convergent Cross Sorting (CCS), a novel algorithm based on convergent cross mapping (CCM) for estimating dynamic coupling from time series data. CCS extends CCM by using the relative ranking of distances within state-space reconstructions to improve the prior methods’ performance at identifying the existence, relative strength, and directionality of coupling across a wide range of signal and noise characteristics. In particular, relative to CCM, CCS has a large performance advantage when analyzing very short time series data and data from continuous dynamical systems with synchronous behavior. This advantage allows CCS to better uncover the temporal and directional relationships within systems that undergo frequent and short-lived switches in dynamics, such as neural systems. In this paper, we validate CCS on simulated data and demonstrate its applicability to electrophysiological recordings from interacting brain regions.

Relative Strenght Index, Moving Average Convergence-Divergence on Stock Performance and Fundamental Analysis as Moderating

2021 ◽  
Vol 6 (2) ◽  
pp. 27-30
Author(s):  
Ronni Andri Wijaya ◽  
Yamasitha Yamasitha ◽  
Elfiswandi Elfiswandi ◽  
Lusiana Lusiana
Keyword(s):  
Sampling Method ◽  
Time Series Data ◽  
Stock Exchange ◽  
Moving Average ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Relative Strength ◽  
Series Data ◽  
Stock Performance ◽  
Equity Ratio

This study aims to determine the effect of Relative Strength Index (RSI) and Moving Average Convergence-divergence (MACD) on stock performers with Debt to Equity Ratio (DER) as a Moderation variable in Financing companies listed on the Indonesian Stock Exchange (IDX). Sampling in the study using purpose sampling method obtained 14 companies with time series data. The analysis method used in this study is multiple linear regression analysis using eview. The results show that Relative Strenth Index (RSI) partially has a positive and significant effect on stock performance, Moving Average Convergence-divergence (MACD) partially has a positive and significant effect on stock performance, Relative Strenth Index (RSI) has a positive and significant effect on stock performance. which is moderated by Debt to Equity Ratio (DER), Moving Average Convergence-divergence (MACD) has a positive and significant effect on the Performance of Shares moderated by Debt to Equity Ratio (DER).

scholarly journals International Real Estate Review

2019 ◽  
Vol 22 (2) ◽  
pp. 231-274
Author(s):  
Mohsen Bahmani-Oskooee ◽  
◽  
Seyed Ghodsi ◽  
Keyword(s):  
Time Series Data ◽  
Relative Strength ◽  
Series Data ◽  
Asymmetric Effects ◽  
Currency Depreciation ◽  
Long Run ◽  
Short Run ◽  
Dollar Depreciation ◽  
The Cost ◽  
The U.S

Currency depreciation is said to affect domestic output in either direction, depending on the relative strength of its impact on next exports and the cost of imported inputs. Since increased net exports and eventual economic growth affect the demand for housing and increased cost of imported materials that are used in housing construction affects the supply of housing, we assume that currency depreciation could have an impact on housing output. We test our assumption by using time-series data from each of the states in the U.S. and show that when a linear model is estimated, dollar depreciation has short-run effects in 41 states and long-run effects in only three states. However, when dollar depreciation is separated from appreciation and a nonlinear model is estimated, we find short-run asymmetric effects in all of the states and long-run asymmetric effects in 32 states. Additional analysis reveals that while dollar depreciation increases housing output in 10 states, dollar appreciation hurts the output in 11 states, thus supporting the expansionary depreciation of the dollar in the U.S. housing market.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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