scholarly journals Phenomenological and mechanistic models for predicting early transmission data of COVID-19

2021 ◽  
Vol 19 (2) ◽  
pp. 2043-2055
Author(s):  
Takeshi Miyama ◽  
◽  
Sung-mok Jung ◽  
Katsuma Hayashi ◽  
Asami Anzai ◽  
...  
Keyword(s):  
Sir Model ◽  
Exponential Model ◽  
Mechanistic Models ◽  
Mean Square ◽  
Limited Information ◽  
Approximation Model ◽  
Richards Model ◽  
Epidemic Period ◽  
Transmission Data ◽  
Reported Data

<abstract> <p>Forecasting future epidemics helps inform policy decisions regarding interventions. During the early coronavirus disease 2019 epidemic period in January–February 2020, limited information was available, and it was too challenging to build detailed mechanistic models reflecting population behavior. This study compared the performance of phenomenological and mechanistic models for forecasting epidemics. For the former, we employed the Richards model and the approximate solution of the susceptible–infected–recovered (SIR) model. For the latter, we examined the exponential growth (with lockdown) model and SIR model with lockdown. The phenomenological models yielded higher root mean square error (RMSE) values than the mechanistic models. When using the numbers from reported data for February 1 and 5, the Richards model had the highest RMSE, whereas when using the February 9 data, the SIR approximation model was the highest. The exponential model with a lockdown effect had the lowest RMSE, except when using the February 9 data. Once interventions or other factors that influence transmission patterns are identified, they should be additionally taken into account to improve forecasting.</p> </abstract>

scholarly journals A Study on the Generalized Approximation Modeling Method Based on Fitting Sensitivity for Prediction of Engine Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Lin Lin ◽  
Fang Wang ◽  
Shisheng Zhong
Keyword(s):  
Engine Performance ◽  
Grey Model ◽  
Model Parameters ◽  
Bayesian Regularization ◽  
Safety Engineering ◽  
The Novel ◽  
Mean Square ◽  
Approximation Model ◽  
Mean Square Errors ◽  
Fitting Accuracy

Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derived a generalized approximation model that could be used to adjust fitting precision. Approximation precision was combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey model (GM) as an example, we discussed the modeling approach of the novel GM based on fitting sensitivity, analyzed the setting methods and optimization range of model parameters, and solved the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarized the change regularities of the root-mean-square errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

scholarly journals Multitemporal Soil Moisture Retrieval over Bare Agricultural Areas by Means of Alpha Model with Multisensor SAR Data

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Xiang Zhang ◽  
Xinming Tang ◽  
Xiaoming Gao ◽  
Hui Zhao
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Root Mean Square Error ◽  
Field Measurements ◽  
Mean Square ◽  
Approximation Model ◽  
Unknown Parameters ◽  
Sar Data ◽  
Agricultural Areas ◽  
Moisture Estimation

The objective of this research is to optimize the Alpha approximation model for soil moisture retrieval using multitemporal SAR data. The Alpha model requires prior knowledge of soil moisture range to constrain soil moisture estimation. The solution of the Alpha model is an undetermined problem due to the fact that the number of observation equations is less than the number of unknown parameters. This research primarily focused on the optimization of Alpha model by employing multisensor and multitemporal SAR data. The disadvantage of the Alpha model can be eliminated by the combination of multisensor SAR data. The optimized Alpha model was evaluated on the basis of a comprehensive campaign for soil moisture retrieval, which acquired multisensor time series SAR data and coincident field measurements. The agreement between the estimated and measured soil moisture was within a root mean square error of 0.08 cm3/cm3 for both methods. The optimized Alpha model shows an obvious improvement for soil moisture retrieval. The results demonstrated that multisensor and multitemporal SAR data are favorable for time series soil moisture retrieval over bare agricultural areas.

scholarly journals Growth Models for Covid-19 Death Figures of Turkey

2020 ◽  
pp. 1-11
Author(s):  
Muzaffer Balaban
Keyword(s):  
Growth Models ◽  
Exponential Model ◽  
Prediction Errors ◽  
Short Term ◽  
Von Bertalanffy Model ◽  
Peak Point ◽  
Term Forecast ◽  
Reported Data ◽  
Second Wave ◽  
Better Than

This paper presents modeling of the COVID-19 pandemic deaths to understand behavior of it, predict the peak point of the deaths and cases and produces a short-term forecast using the growth models for the reported data of Turkey. The data which is used in this study are gathered of daily announced by Minister of Health. Von Bertalanffy model has outperformed to the other models considered in this study. However, exponential model has predicted the total deaths and total cases better than the others. And, exponential model has given the best prediction errors among them regarding to the death and positive case figures for last months. Observed data have tended to increase since the last days of August. This could mean that the COVİD-19 threat has reached to a critical stage to crack down on prevention of pandemics spread. Or it could sign the beginning of a second wave of epidemics. More studies must be realized to learn more about the pandemic when the new data are available.

scholarly journals LQG Control of Networked Control Systems with Limited Information

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Qing-Quan Liu ◽  
Fang Jin
Keyword(s):  
Networked Control Systems ◽  
Linear Time ◽  
Sufficient Conditions ◽  
Data Rate ◽  
State Feedback Control ◽  
Mean Square ◽  
Limited Information ◽  
Linear Quadratic ◽  
Linear Time Invariant ◽  
Lqg Control

This paper addresses linear quadratic Gaussian (LQG) control problems for multi-input multioutput (MIMO), linear time-invariant (LTI) systems, where the sensors and controllers are geographically separated and connected via a digital communication channel with limited data rates. An observer-based, quantized state feedback control scheme is employed in order to achieve the minimum data rate for mean square stabilization of the unstable plant. An explicit expression is presented to state the tradeoff between the LQ cost and the data rate. Sufficient conditions on the data rate for mean square stabilization are derived. An illustrative example is given to demonstrate the effectiveness of the proposed scheme.

scholarly journals ORTORECTIFICATION OF SPOT-4 DATA USING RATIONAL POLYNOMIAL COEFFICIENTS

2012 ◽  
Vol 9 (1) ◽  
Author(s):  
Danang Surya Candra
Keyword(s):  
Satellite Orbit ◽  
Parallel Projection ◽  
Mean Square ◽  
Approximation Model ◽  
Positioning Accuracy ◽  
Projection System ◽  
Polynomial Coefficients ◽  
Sensor Model ◽  
Rational Polynomial ◽  
Physical Sensor

Orthorectification  of  satellite  imagery  can  be  done  in  two  ways  i.e.,  rigorous sensor  model  and  the  approximation  model  of  the  satellite’s  orbit.  Dependence  on  physicalparameters,  to  make  rigorous  sensor  model  is  more  complicated  and  difficult  to  apply.  The approximation  model  can be either  Rational Polynomial Coefficients (RPC)  model  or  parallel projection  system.  RPC  is  a  mathematical  model  which  is  not  depends  on  the  sensor.  It  is used to improve the positioning accuracy when the parameter of the physical sensor model is  unknown.  This  study  assessed  orthorectification  of  SPOT-4  using  the  RPC  model  with  7 coefficients. Root Mean Square Error (RMSE) of GCPs obtained from the study  was less than 1  pixel.  RPC  did  not  depend  on  physical  and  satellite  orbit  parameters.  Thus  the  RPC  was simpler and easier to apply.

scholarly journals Calibration and prediction for the inexact SIR model

2022 ◽  
Vol 19 (3) ◽  
pp. 2800-2818
Author(s):  
Yan Wang ◽  
◽  
Guichen Lu ◽  
Jiang Du ◽  
Keyword(s):  
Gaussian Process ◽  
Prediction Accuracy ◽  
Process Model ◽  
Bootstrap Method ◽  
Sir Model ◽  
Prediction Methods ◽  
Wild Bootstrap ◽  
Numerical Studies ◽  
Model Discrepancy ◽  
Reported Data

<abstract><p>A Susceptible Infective Recovered (SIR) model is usually unable to mimic the actual epidemiological system exactly. The reasons for this inaccuracy include observation errors and model discrepancies due to assumptions and simplifications made by the SIR model. Hence, this work proposes calibration and prediction methods for the SIR model with a one-time reported number of infected cases. Given that the observation errors of the reported data are assumed to be heteroscedastic, we propose two predictors to predict the actual epidemiological system by modeling the model discrepancy through a Gaussian Process model. One is the calibrated SIR model, and the other one is the discrepancy-corrected predictor, which integrates the calibrated SIR model with the Gaussian Process predictor to solve the model discrepancy. A wild bootstrap method quantifies the two predictors' uncertainty, while two numerical studies assess the performance of the proposed method. The numerical results show that, the proposed predictors outperform the existing ones and the prediction accuracy of the discrepancy-corrected predictor is improved by at least $ 49.95\% $.</p></abstract>

STABILITY OF STOCHASTIC DELAYED SIR MODEL

2009 ◽  
Vol 09 (02) ◽  
pp. 231-252 ◽  
Author(s):  
GUOTING CHEN ◽  
TIECHENG LI
Keyword(s):  
Steady State ◽  
White Noise ◽  
Sir Model ◽  
Mean Square ◽  
Stochastic Version ◽  
Stability In Probability ◽  
The Stability

A stochastic version of the SIR model is investigated in this paper. The stability in probability of the steady state of the system is proved under suitable conditions on the white noise perturbations. Linearizations of the systems both with and without delay are given and their exponentially mean square stabilities are studied.

scholarly journals Modeling the COVID-19 spread, a case study of Egypt

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Assem S. Deif ◽  
Sahar A. El-Naggar
Keyword(s):  
Sir Model ◽  
Logistic Growth ◽  
Approximate Estimate ◽  
Epidemic Size ◽  
Final Epidemic Size ◽  
Logistic Growth Model ◽  
Reported Data ◽  
High Degree ◽  
Second Wave

AbstractIn this article, the authors applied a logistic growth model explaining the dynamics of the spread of COVID-19 in Egypt. The model which is simple follows well-known premises in population dynamics. Our aim is to calculate an approximate estimate of the total number of infected persons during the course of the disease. The model predicted—to a high degree of correctness—the timing of the pandemic peak$$t_{{\text{m}}}$$ t m and the final epidemic size$$P$$ P ; the latter was foreseen by the model long before it was announced by the Egyptian authorities. The estimated values from the model were also found to match significantly with the nation reported data during the course of the disease. The period in which we applied the model was from the first of April 2020 until the beginning of October of the same year. By the time the manuscript was returned for revision, the second wave swept through Egypt and the authors felt obliged to renew their study. Finally, a comparison is made with the SIR model showing that ours is much simpler; yet leading to the same results.

scholarly journals Spilocaea oleagina in Olive Groves of Southern Spain: Survival, Inoculum Production, and Dispersal

Plant Disease ◽  
2013 ◽  
Vol 97 (12) ◽  
pp. 1549-1556 ◽  
Author(s):  
J. R. Viruega ◽  
J. Moral ◽  
L. F. Roca ◽  
N. Navarro ◽  
A. Trapero
Keyword(s):  
Control Strategies ◽  
Exponential Model ◽  
Tree Canopy ◽  
Southern Spain ◽  
Limited Information ◽  
Spore Trap ◽  
Disease Epidemiology ◽  
Inoculum Production ◽  
Pathogen Survival ◽  
Fallen Leaves

Olive scab caused by the mitosporic fungus Spilocaea oleagina is the most important foliar disease of olive. Limited information is available on pathogen survival and disease epidemiology; however, this information is essential for development of new control strategies. Pathogen survival and inoculum production on infected olive leaves and conidial dispersal were evaluated during 4 years in an olive orchard of the susceptible ‘Picual’ in southern Spain. Infected leaves in the tree canopy were important for pathogen survival and conidia production. The number of conidia per square centimeter of scab lesion and their viability varied greatly throughout the seasons and between years; conidial density in lesions was highest (about 1 to 5 × 105 conidia cm–2) from November to February in favorable years. Conidial density declined sharply in other periods of the year (becoming zero in summer) or in less favorable years. The pathogen did not form new conidia in scab lesions, although some pseudothecia-like structures and chlamydospores were detected on fallen leaves. Under humid conditions, the pathogen could not be detected on fallen leaves after 3 months because the leaves were colonized by saprophytic fungi. The dispersal of conidia as a function of distance from infected leaves in the tree canopy was well described by an exponential model which, together with the lack of conidia in a Burkard spore trap, showed that conidia were mainly rain-splash dispersed. Some trapped conidia were attached to olive leaf trichomes, suggesting that detached trichomes might enhance wind dispersal of conidia.

scholarly journals Admin vs. questionnaire data: Can we replace ‘highest qualification’ questions with admin data?

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Stephan Tietz ◽  
Nicola Haines ◽  
Brogan Taylor
Keyword(s):  
Policy Development ◽  
Labour Force ◽  
National Level ◽  
Further Education ◽  
Accurate Information ◽  
Quality Information ◽  
Limited Information ◽  
High Quality ◽  
High Quality Information ◽  
Reported Data

Information on qualifications is used widely across central and local government to inform service delivery and policy development; main user requirements are for highest level of qualifications and, no qualifications. We explored the feasibility of using administrative data to derive high quality information on educational qualifications held. For this feasibility research, we used data supplied by the Department for Education. This covered 14-25y/o from the three funding streams in England: primary and secondary education, further education and higher education. We compared our results at national level with the 2011 Census and Labour Force Survey/Annual Population Surveys (LFS/APS). We also undertook linkage to the Census to compare our results at case-level. We were able to derive a highest level of qualification for more than 96% of individuals in the data. There is a high level of agreement at national level when compared to the Census and LFS/APS. Differences are likely due to mode of data collection and the accuracy of differentiating between full and partial attainment as limited information was available in the feasibility dataset. Moreover, we successfully linked 84% of 14-25y/o on the English Census. We found that highest qualification level as derived from admin data agreed with 57% of Census records and either agreed or was within one level for 84% of records. Disagreement patterns were similar to the ones observed by the Census Quality Survey, which suggest that they are driven by mode effects. We demonstrated that we can produce high quality information on highest level of qualification for a large proportion of first-time entrants to the labour market. We also opened the door to providing more accurate information on highest level of qualification achieved by individuals than self-reported data since it does not rely on respondents recall ability or proxy responses.

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