scholarly journals COVID-19 Pandemic Development in Jordan—Short-Term and Long-Term Forecasting

Vaccines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 728
Author(s):  
Tareq Hussein ◽  
Mahmoud H. Hammad ◽  
Pak Lun Fung ◽  
Marwan Al-Kloub ◽  
Issam Odeh ◽  
...  
Keyword(s):  
Early Stage ◽  
Middle Eastern ◽  
Forecast Model ◽  
Third Wave ◽  
Short Term ◽  
Model Based ◽  
Model Merging ◽  
Term Forecast ◽  
Long Term Forecasting

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves’ occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

scholarly journals The approximation of an isolated epidemic process wave using a combination of exponents

2021 ◽  
Vol 57 (4) ◽  
pp. 391-400
Author(s):  
A. N. Avlas ◽  
A. K. Demenchuk ◽  
S. V. Lemeshevskii ◽  
E. K. Makarov
Keyword(s):  
Nonstationary Processes ◽  
Short Term ◽  
Infected People ◽  
Epidemic Process ◽  
Discrete Equations ◽  
Term Forecast ◽  
Long Term Forecasting ◽  
The Republic ◽  
Logistic Functions

The most commonly used methods for the medium- and long-term forecasting of epidemic processes are based on the classical SIR (susceptible – infected – recovered) model and its numerous modifications. In this approach, the dynamics of the epidemic is approximated using the solutions of differential or discrete equations. The forecasting methods based on the approximation of data by functions of a given class are usually focused on obtaining a short-term forecast. They are not used for the long-term forecasts of epidemic processes due to their insufficient efficiency for forecasting nonstationary processes. In this paper, we formulated a hypothesis that the primary waves of the COVID-19 pandemic, which took place in a number of European countries, including the Republic of Belarus, in the spring-summer of 2020 are isolated and therefore can be regarded as processes close to stationary. On the basis of this hypothesis, a method of approximating isolated epidemic process waves by means of generalized logistic functions with an increased number of exponents was proposed. The developed approach was applied to predict the number of infected people in the Republic of Belarus for the period until August 2020 based on data from the beginning of the epidemic until June 12, 2020.

USE OF REGRESSION ANALYSIS IN FORECAST TASKS

2020 ◽  
pp. 22-27
Author(s):  
E.S. Lartseva ◽  
◽  
A.D. Kuznetsova
Keyword(s):  
Regression Analysis ◽  
Animal Population ◽  
Short Term ◽  
Statistical Estimates ◽  
Term Forecast ◽  
Applied Software ◽  
The Family ◽  
The Russian Federation

Based on official statistics on the number, of representatives of the family of non-ruminant cloven-hoofed animals (Artiodactyl) on the territory of the Russian Federation. Using the example of two species: domestic pigs and wild boars, the dynamics of the indicator for the long term is analyzed. Multidirectional trends were revealed for each species. Mathematical models of the dynamics of the livestock were obtained using the methods of regression analysis and applied software. Statistical estimates of the quality of animal population models were obtained. The short-term forecast for 2020 has been fulfilled.

Integration of geological and seismological data in earthquakes occurrence models for Italy: towards a unified model for different forecast perspectives

2019 ◽  
Vol 219 (3) ◽  
pp. 2148-2164
Author(s):  
A M Lombardi
Keyword(s):  
Aftershock Sequence ◽  
Earthquake Forecasting ◽  
Short Term ◽  
Background Rate ◽  
Etas Model ◽  
Term Forecast ◽  
Earthquake Predictability ◽  
Background Seismicity ◽  
Different Types

SUMMARY The operational earthquake forecasting (OEF) is a procedure aimed at informing communities on how seismic hazard changes with time. This can help them live with seismicity and mitigate risk of destructive earthquakes. A successful short-term prediction scheme is not yet produced, but the search for it should not be abandoned. This requires more research on seismogenetic processes and, specifically, inclusion of any information about earthquakes in models, to improve forecast of future events, at any spatio-temporal-magnitude scale. The short- and long-term forecast perspectives of earthquake occurrence followed, up to now, separate paths, involving different data and peculiar models. But actually they are not so different and have common features, being parts of the same physical process. Research on earthquake predictability can help to search for a common path in different forecast perspectives. This study aims to improve the modelling of long-term features of seismicity inside the epidemic type aftershock sequence (ETAS) model, largely used for short-term forecast and OEF procedures. Specifically, a more comprehensive estimation of background seismicity rate inside the ETAS model is attempted, by merging different types of data (seismological instrumental, historical, geological), such that information on faults and on long-term seismicity integrates instrumental data, on which the ETAS models are generally set up. The main finding is that long-term historical seismicity and geological fault data improve the pseudo-prospective forecasts of independent seismicity. The study is divided in three parts. The first consists in models formulation and parameter estimation on recent seismicity of Italy. Specifically, two versions of ETAS model are compared: a ‘standard’, previously published, formulation, only based on instrumental seismicity, and a new version, integrating different types of data for background seismicity estimation. Secondly, a pseudo-prospective test is performed on independent seismicity, both to test the reliability of formulated models and to compare them, in order to identify the best version. Finally, a prospective forecast is made, to point out differences and similarities in predicting future seismicity between two models. This study must be considered in the context of its limitations; anyway, it proves, beyond argument, the usefulness of a more sophisticated estimation of background rate, inside short-term modelling of earthquakes.

An Agenda For Econometric Model Building

1991 ◽  
Vol 3 ◽  
pp. 123-154 ◽  
Author(s):  
Jim Granato
Keyword(s):  
Diagnostic Tests ◽  
Model Building ◽  
Econometric Model ◽  
Short Term ◽  
Correction Model ◽  
Model Based ◽  
Methodological Approaches ◽  
Residual Correction ◽  
Statistical Criteria

This article addresses the lack of cohesion in econometric model building. This incoherence contributes to model building based on statistical criteria—correcting residuals—and not theoretical criteria. The models we build, therefore, are not valid replications of theory. To deal with this problem, an agenda for model building is outlined and discussed. Drawing on the methodological approaches of Hendry, Qin, and Favero (1989), Hendry and Richard (1982, 1983), Sargan (1964), and Spanos (1986), this agenda incorporates a “general to simple” modeling philosophy, a battery of diagnostic tests, reduction theory, and the development of models that include short-term and long-term parameters. A comparison is made between a model based on this agenda and a model based on corrected residuals. The findings show that the agenda-based model outperforms the residual correction model.

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