scholarly journals Early detection of seasonality and second-wave prediction in the COVID-19 pandemic

2020 ◽  
Author(s):  
Márcio Watanabe
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Southern Hemisphere ◽  
Northern Hemisphere ◽  
Time Series Data ◽  
Seasonal Effects ◽  
Series Data ◽  
Seasonal Effect ◽  
Social Distancing ◽  
The Mean

AbstractSeasonality plays an essential role in the dynamics of many infectious diseases. Its confirmation in an emerging infectious disease is usually done using time series data from several years. By using statistical regression methods for time-series data pooled from more than 50 countries from both hemispheres, we show how to determine its presence in a pandemic at the onset of the seasonal period. We measure its expected effect in the mean transmission rate of SARS-coV-2 and predict when further epidemic outbreaks of COVID-19 will occur. The obtained result in the Northern Hemisphere shows that seasonality reduced the mean growth rate in 222.5% in April 2020. A relative reduction greater than 100% should be interpreted as a reduction changing an increasing rate to a decreasing one. In contrast, at the same moment, the seasonal effect in the Southern Hemisphere increased the mean growth rate in 740.3%. Our analysis simultaneously considers other confounding factors to properly separate them from seasonal effects and, in addition, we measure the mean global effect of social-distancing interventions and its relation with income. Future COVID-19 waves are expected to occur in autumn/winter seasons, typically between September and March in the Northern Hemisphere, and between April and September in the Southern Hemisphere. Simulations of a seasonal SEIR model with a social distancing effect are shown to describe the behavior of COVID-19 outbreaks in several countries. These results provide vital information for policy makers to plan their actions against the new coronavirus disease, particularly in the optimization of social-distancing interventions and vaccination schedules. Ultimately, our methods can be used to identify and measure seasonal effects in a future pandemic.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

scholarly journals Observed and simulated time evolution of HCl, ClONO<sub>2</sub>, and HF total column abundances

2011 ◽  
Vol 11 (12) ◽  
pp. 32085-32160 ◽  
Author(s):  
R. Kohlhepp ◽  
R. Ruhnke ◽  
M. P. Chipperfield ◽  
M. De Mazière ◽  
J. Notholt ◽  
...  
Keyword(s):  
Time Series ◽  
Northern Hemisphere ◽  
Time Series Data ◽  
Montreal Protocol ◽  
Irregular Sampling ◽  
Atmospheric Composition ◽  
Series Data ◽  
Anthropogenic Source ◽  
Time Range ◽  
Total Column

Abstract. Time series of total column abundances of hydrogen chloride (HCl), chlorine nitrate (ClONO2), and hydrogen fluoride (HF) were determined from ground-based Fourier transform infrared (FTIR) spectra recorded at 17 sites belonging to the Network for the Detection of Atmospheric Composition Change (NDACC) and located between 80.05° N and 77.82° S. These measurements are compared with calculations from five different models: the two-dimensional Bremen model, the two chemistry-transport models KASIMA and SLIMCAT, and the two chemistry-climate models EMAC and SOCOL. The overall agreement between the measurements and models for the total column abundances and the seasonal cycles is good. Trends of HCl, ClONO2, and HF are calculated from both measurement and model time series data, with a focus on the time range 2000–2009. Their precision is estimated with the bootstrap resampling method. The sensitivity of the trend results with respect to the fitting function, the time of year chosen and time series length is investigated, as well as a bias due to the irregular sampling of the measurements. For the two chlorine species, a decrease is expected during this period because the emission of their prominent anthropogenic source gases (solvents, chlorofluorocarbons (CFCs)) was restricted by the Montreal Protocol 1987 and its amendments and adjustments. As most of the restricted source gases also contain fluorine, the HF total column abundance was also influenced by the above-mentioned regulations in the time period considered. The measurements and model results investigated here agree qualitatively on a decrease of the chlorine species by around −1 % yr−1. The models simulate an increase of HF of around +1 % yr−1. This also agrees well with most of the measurements, but some of the FTIR series in the Northern Hemisphere show a stabilisation or even a decrease in the last few years. In general, for all three gases, the measured trends vary more strongly with latitude and hemisphere than the modelled trends. Relative to the FTIR measurements, the models tend to underestimate the decreasing chlorine trends and to overestimate the fluorine increase in the Northern Hemisphere. At most sites, the models simulate a stronger decrease of ClONO2 than of HCl. In the FTIR measurements, this difference between the trends of HCl and ClONO2 depends strongly on latitude, especially in the Northern Hemisphere.

scholarly journals Aquaculture Production and Consumption of Fish in India

2021 ◽  
Vol 10 (1) ◽  
pp. 21-26
Author(s):  
Dhanya Sai Das ◽  
R Govindasamy
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Food Security ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Food Protein ◽  
Fish Production ◽  
Total Fish ◽  
The Government

Aquaculture and fisheries emerged as an important source of food, protein, nutrition, livelihood and employment for the majority of the rural population. The fisheries sector has registered a sustainable and astounding growth rate over the last decade. The sector offers an attractive and promising future for employment, livelihood and food security. The study is based on the available secondary data from different aspects of fishery statistics published in Handbook on Fisheries Statistics 2020 by the Government of India and other related articles. Data for the time series analysis was taken from 2001-02 to 2017-18. It is found that the world per capita apparent consumption of fish has been increased by 10.4 kg from the 1960s (i.e., 9.9 kg) to 2016 (i.e., 20.30 kg). By analysing the time-series data, it is evident that the total fish production, including both marines and inland, has shown an astounding growth with a Compound Growth Rate of 4.58. The regression equation was Y = 5.182X – 12267, R2 value was 0.9414 where Y is the total fish production (dependent variable) and X is the total fish seed production (independent variable). There exists a positive relationship between fish seed and fish production in the country. It can be concluded that aquaculture plays a significant role in the country’s GDP rate and food security.

DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik

2020 ◽  
Vol 13 (02) ◽  
pp. 1-8
Author(s):  
Agrienvi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Production Data ◽  
Policy Makers ◽  
Strategic Value ◽  
Modeling And Forecasting ◽  
The Mean ◽  
Arima Modeling

ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced while the data on the production of small chilli carried out the stages of data transformation anddifferencing due to the unstationer of data on variants and the mean. The best ARIMA model that can be applied basedon the smallest AIC and MSE criteria for data on the amount of chili and small chilli production in Central KalimantanProvince is ARIMA (3,1,0).Keywords: modeling of chilli, forecasting of chilli, Autoregresive Integrated Moving Avarage, ARIMA, Box-Jenkins.

scholarly journals Impact of Price and Price Variability on Acreage Allocation in Rice and Wheat Production in Bangladesh

2012 ◽  
Vol 10 (1) ◽  
pp. 23-30
Author(s):  
Basanta Kumar Barmon ◽  
Muntasir Chaudhury
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Wholesale Price ◽  
Series Data ◽  
Wheat Crop ◽  
Price Variability ◽  
Wheat Production ◽  
Long Run ◽  
Short Run

The present study was conducted to estimate the impacts of price and price variability on acreage allocation of rice and wheat production in Bangladesh. Time series data of price and acreage allocation of rice and wheat production during 1983-84 to 2007-08, collected from Bangladesh Bureau of Statistics (BBS) were used in this study. Compound growth rate and Nerlovian models were used. The study indicated that the wholesale price of rice and wheat had significant impact on the allocation of land for rice and wheat production. Significant price variability was found both in case of rice and wheat crop in short-run (SR) and long-run (LR). The values of Nerlovian coefficients of adjustment were found low, which means that although the farmers were adjusting to the changing levels of price, price variability, yield, etc the adjustment was not rapid. Therefore, it may be concluded that the price of rice and wheat should be adjusted rapidly along with allocation of rice and wheat production in Bangladesh.DOI: http://dx.doi.org/10.3329/agric.v10i1.11061The Agriculturists 2012; 10(1): 23-30

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 Functional Data Analysis of Weather Variables Linked to Fusarium Head Blight Epidemics in the United States

2019 ◽  
Vol 109 (1) ◽  
pp. 96-110 ◽  
Author(s):  
D. A. Shah ◽  
E. D. De Wolf ◽  
P. A. Paul ◽  
L. V. Madden
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Time Series Data ◽  
The United States ◽  
Series Data ◽  
Head Blight ◽  
Weather Variables ◽  
The Mean

In past efforts, input weather variables for Fusarium head blight (FHB) prediction models in the United States were identified after following some version of the window-pane algorithm, which discretizes a continuous weather time series into fixed-length windows before searching for summary variables associated with FHB risk. Functional data analysis, on the other hand, reconstructs the assumed continuous process (represented by a series of recorded weather data) by using smoothing functions, and is an alternative way of working with time series data with respect to FHB risk. Our objective was to functionally model weather-based time series data linked to 865 observations of FHB (covering 16 states and 31 years in total), classified as epidemics (FHB disease index ≥ 10%) and nonepidemics (FHB disease index < 10%). Altogether, 94 different time series variables were modeled by penalized cubic B-splines for the smoothing function, from 120 days pre-anthesis to 20 days post-anthesis. Functional mean curves, standard deviations, and first derivatives were plotted for FHB epidemics relative to nonepidemics. Function-on-scalar regressions assessed the temporal trends of the magnitude and significance of the mean difference between functionally represented weather time series associated with FHB epidemics and nonepidemics. The mean functional weather-variable curve for epidemics started to deviate, in general, from that for nonepidemics as early as 40 days pre-anthesis for several weather variables. The greatest deviations were often near anthesis, the period of maximum susceptibility of wheat to FHB-causing fungi. The most consistent separations between the mean functional curves were seen with the daily averages of moisture-related variables (such as average relative humidity) and with variables summarizing the daily variation in temperature (as opposed to the daily mean). Functional data analysis was useful for extending our knowledge of relationships between weather variables and FHB epidemics.

scholarly journals Growth Factor: an Important Factor in Determining the Fate of Outbreaks

2021 ◽  
Author(s):  
Yousef Alimohamadi ◽  
Mojtaba Sepandi ◽  
Taher Teymouri ◽  
Hadiseh Hosamirudsari
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Growth Factor ◽  
Exponential Growth ◽  
Inflection Point ◽  
Time Series Data ◽  
Healthy People ◽  
Series Data ◽  
The Social ◽  
Effective Role

Introduction: Epidemic curves are a type of time series data consisting of the number of events that occur over a period of time. The time unit in this data can be a day, a week, or a month, etc. Methods: In the current letter, the authors tried to explain the growth factor and its effect on epidemic curves by using some literature. Results: In the outbreaks setting, the number of cases can increase with different patterns. When the number of cases is increasing exponentially, it means that the number of cases is increasing at a certain speed, which is determined by a factor called an exponential growth factor. When this factor is greater than one, it means that the cases are increasing exponentially, and when this coefficient is equal to 1, it means that we have reached an inflection point that we will face a change in the growth rate of the cases. Conclusion: Some factors such as reducing the contact between infected and healthy people, run the social distancing program, and so on can have an effective role in decreasing epidemic growth factor and controlling the epidemic.

Growth Rate of Pulses in Eastern Uttar Pradesh a Zone-wise Analysis

2021 ◽  
Vol 66 (3) ◽  
Author(s):  
Ekta Pandey
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Time Series Data ◽  
Uttar Pradesh ◽  
Series Data ◽  
The North ◽  
North Eastern ◽  
Eastern Uttar Pradesh ◽  
Negative Growth ◽  
Made In

Attempts are made in this paper to investigate the trend of pulses in Eastern Uttar Pradesh, as well as their instability and non-linear model. This time series data on pulses pertains to the period 1980-1981 to 2014-15 and includes information on the area, production, and productivity of pulses. Pulses have had negative growth in terms of area, production, and productivity in all three zones of Eastern Uttar Pradesh, namely, the North Eastern plain zone, the Eastern plain zone, and the Vindhyan zone. Since 1980-81, there has been a rise in the area and output of pulses in the Vindhyan zone, as seen by the percentage change. The Eastern plain zone has the most stable pulse crop in terms of instability

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