An integrated deterministic–stochastic approach for forecasting the long-term trajectories of COVID-19

2021 ◽  
pp. 2141001
Author(s):  
Indrajit Ghosh ◽  
Tanujit Chakraborty
Keyword(s):  
Time Series ◽  
Stochastic Approach ◽  
World Population ◽  
Data Sets ◽  
Univariate Time Series ◽  
Reproduction Numbers ◽  
The World ◽  
Long Term Forecasting ◽  
Ar Models

The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic–stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR [Susceptible–Infected–Recovered–Protected–Isolated (SIRCX)] model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model. The estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be [Formula: see text] and [Formula: see text], respectively. ISA model-based results reveal that the number of cases in Italy and Spain between 11 May, 2020–9 June, 2020 will be 10,982 (6383–15,582) and 13,731 (3395–29,013), respectively. Additionally, the expected number of daily cases on 9 July, 2020 for Italy and Spain is estimated to be 30 (0–183) and 92 (0–602), respectively.

scholarly journals An integrated deterministic-stochastic approach for predicting the long-term trajectories of COVID-19

2020 ◽  
Author(s):  
Indrajit Ghosh ◽  
Tanujit Chakraborty
Keyword(s):  
Time Series ◽  
Stochastic Approach ◽  
World Population ◽  
Data Sets ◽  
Major Health ◽  
Univariate Time Series ◽  
The World ◽  
Long Term Forecasting ◽  
Ar Models

The ongoing COVID-19 pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500 thousand reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic-stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time-series is assessed by an extended version of the SIR (SIRCX) model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model.

scholarly journals Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species

2019 ◽  
Author(s):  
David D. Parrish ◽  
Richard G. Derwent ◽  
Simon O'Doherty ◽  
Peter G. Simmonds
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Linear Trend ◽  
Power Series Expansion ◽  
Mathematical Representation ◽  
Seasonal Cycles ◽  
Data Sets ◽  
Significant Information ◽  
Long Term Changes

Abstract. We present an approach to derive a systematic mathematical representation of the statistically significant features of the average long-term changes and seasonal cycle of concentrations of trace tropospheric species. The results for two illustrative data sets (time series of baseline concentrations of ozone and N2O at Mace Head, Ireland) indicate that a limited set of seven or eight parameter values provides this mathematical representation for both example species. This method utilizes a power series expansion to extract more information regarding the long-term changes than can be provided by oft-employed linear trend analyses. In contrast, the quantification of average seasonal cycles utilizes a Fourier series analysis that provides less detailed seasonal cycles than are sometimes represented as twelve monthly means; including that many parameters in the seasonal cycle representation is not usually statistically justified, and thereby adds unnecessary noise to the representation and prevents a clear analysis of the statistical uncertainty of the results. The approach presented here is intended to maximize the statistically significant information extracted from analyses of time series of concentrations of tropospheric species regarding their mean long-term changes and seasonal cycles, including non-linear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

Rapid Population Growth and Long-term Development

2020 ◽  
pp. 164-177
Author(s):  
Giovanni Andrea Cornia
Keyword(s):  
Population Growth ◽  
Resource Availability ◽  
Demographic Transition ◽  
Population Growth Rate ◽  
Physical Capital ◽  
Population Projections ◽  
World Population ◽  
Rapid Population Growth ◽  
The World

This chapter reviews population trends over the last two hundred years and population projections to the end of this century. In 2100 the world population will have stabilized but its geographical distribution will have substantially changed compared to 2015. The chapter then discusses the five stages of the demographic transition, and different neo-Malthusian and non-Malthusian theories of the relation between population growth and economic development. It emphasizes in particular the effects of rapid population growth on land and resource availability, human capital formation, population quality, the accumulation of physical capital, employment, wages, and income inequality. The effects of rapid population growth rate over a given period were found to change in line with the population size and density at the beginning of the period considered.

scholarly journals Comparison of GPS tropospheric delays derived from two consecutive EPN reprocessing campaigns from the point of view of climate monitoring

2016 ◽  
Vol 9 (9) ◽  
pp. 4861-4877 ◽  
Author(s):  
Zofia Baldysz ◽  
Grzegorz Nykiel ◽  
Andrzej Araszkiewicz ◽  
Mariusz Figurski ◽  
Karolina Szafranek
Keyword(s):  
Time Series ◽  
Point Of View ◽  
Trend Test ◽  
Data Sets ◽  
Total Delay ◽  
University Of Technology ◽  
Time Period ◽  
Seasonal Signals ◽  
Linear Trends

Abstract. The main purpose of this research was to acquire information about consistency of ZTD (zenith total delay) linear trends and seasonal components between two consecutive GPS reprocessing campaigns. The analysis concerned two sets of the ZTD time series which were estimated during EUREF (Reference Frame Sub-Commission for Europe) EPN (Permanent Network) reprocessing campaigns according to 2008 and 2015 MUT AC (Military University of Technology Analysis Centre) scenarios. Firstly, Lomb–Scargle periodograms were generated for 57 EPN stations to obtain a characterisation of oscillations occurring in the ZTD time series. Then, the values of seasonal components and linear trends were estimated using the LSE (least squares estimation) approach. The Mann–Kendall trend test was also carried out to verify the presence of linear long-term ZTD changes. Finally, differences in seasonal signals and linear trends between these two data sets were investigated. All these analyses were conducted for the ZTD time series of two lengths: a shortened 16-year series and a full 18-year one. In the case of spectral analysis, amplitudes of the annual and semi-annual periods were almost exactly the same for both reprocessing campaigns. Exceptions were found for only a few stations and they did not exceed 1 mm. The estimated trends were also similar. However, for the reprocessing performed in 2008, the trends values were usually higher. In general, shortening of the analysed time period by 2 years resulted in a decrease of the linear trends values of about 0.07 mm yr−1. This was confirmed by analyses based on two data sets.

scholarly journals Internal consistency of the Regional Brewer Calibration Centre for Europe triad during the period 2005–2016

2018 ◽  
Vol 11 (7) ◽  
pp. 4059-4072 ◽  
Author(s):  
Sergio Fabián León-Luis ◽  
Alberto Redondas ◽  
Virgilio Carreño ◽  
Javier López-Solano ◽  
Alberto Berjón ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Relative Standard Deviation ◽  
Data Sets ◽  
Individual Values ◽  
Relative Standard ◽  
The World ◽  
The Mean ◽  
The Individual ◽  
Ozone Column

Abstract. Total ozone column measurements can be made using Brewer spectrophotometers, which are calibrated periodically in intercomparison campaigns with respect to a reference instrument. In 2003, the Regional Brewer Calibration Centre for Europe (RBCC-E) was established at the Izaña Atmospheric Research Center (Canary Islands, Spain), and since 2011 the RBCC-E has transferred its calibration based on the Langley method using travelling standard(s) that are wholly and independently calibrated at Izaña. This work is focused on reporting the consistency of the measurements of the RBCC-E triad (Brewer instruments #157, #183 and #185) made at the Izaña Atmospheric Observatory during the period 2005–2016. In order to study the long-term precision of the RBCC-E triad, it must be taken into account that each Brewer takes a large number of measurements every day and, hence, it becomes necessary to calculate a representative value of all of them. This value was calculated from two different methods previously used to study the long-term behaviour of the world reference triad (Toronto triad) and Arosa triad. Applying their procedures to the data from the RBCC-E triad allows the comparison of the three instruments. In daily averages, applying the procedure used for the world reference triad, the RBCC-E triad presents a relative standard deviation equal to σ = 0.41 %, which is calculated as the mean of the individual values for each Brewer (σ157 = 0.362 %, σ183 = 0.453 % and σ185 = 0.428 %). Alternatively, using the procedure used to analyse the Arosa triad, the RBCC-E presents a relative standard deviation of about σ = 0.5 %. In monthly averages, the method used for the data from the world reference triad gives a relative standard deviation mean equal to σ = 0.3 % (σ157 = 0.33 %, σ183 = 0.34 % and σ185 = 0.23 %). However, the procedure of the Arosa triad gives monthly values of σ = 0.5 %. In this work, two ozone data sets are analysed: the first includes all the ozone measurements available, while the second only includes the simultaneous measurements of all three instruments. Furthermore, this paper also describes the Langley method used to determine the extraterrestrial constant (ETC) for the RBCC-E triad, the necessary first step toward accurate ozone calculation. Finally, the short-term or intraday consistency is also studied to identify the effect of the solar zenith angle on the precision of the RBCC-E triad.

scholarly journals Sustainability in Data and Food

2019 ◽  
Vol 1 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Dean Allemang
Keyword(s):  
Food Production ◽  
Global Scale ◽  
Future Research ◽  
World Population ◽  
Data Sets ◽  
Current State ◽  
The World ◽  
Production And Distribution ◽  
Data Ecosystem ◽  
Global Data

As the world population continues to increase, world food production is not keeping up. This means that to continue to feed the world, we will need to optimize the production and utilization of food around the globe. Optimization of a process on a global scale requires massive data. Agriculture is no exception, but also brings its own unique issues, based on how wide spread agricultural data are, and the wide variety of data that is relevant to optimization of food production and supply. This suggests that we need a global data ecosystem for agriculture and nutrition. Such an ecosystem already exists to some extent, made up of data sets, metadata sets and even search engines that help to locate and utilize data sets. A key concept behind this is sustainability—how do we sustain our data sets, so that we can sustain our production and distribution of food? In order to make this vision a reality, we need to navigate the challenges for sustainable data management on a global scale. Starting from the current state of practice, how do we move forward to a practice in which we make use of global data to have an impact on world hunger? In particular, how do we find, collect and manage the data? How can this be effectively deployed to improve practice in the field? And how can we make sure that these practices are leading to the global goals of improving production, distribution and sustainability of the global food supply? These questions cannot be answered yet, but they are the focus of ongoing and future research to be published in this journal and elsewhere.

Time series analysis of long-term data sets of atmospheric mercury concentrations

2004 ◽  
Vol 380 (3) ◽  
pp. 493-501 ◽  
Author(s):  
Christian Temme ◽  
Ralf Ebinghaus ◽  
J�rgen W. Einax ◽  
Alexandra Steffen ◽  
William H. Schroeder
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Atmospheric Mercury ◽  
Data Sets ◽  
Series Analysis ◽  
Term Data
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