Covid-19 and Economics Forecasting on Advanced andEmerging Countries

EconoQuantum ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 21-43
Author(s):  
Abraham Ramírez García ◽  
◽  
Ana Lorena Jiménez Preciado ◽  
Keyword(s):  
Machine Learning ◽  
Developing Economies ◽  
Economic Recovery ◽  
Leading Indicators ◽  
Learning Models ◽  
Slow Recovery ◽  
Final Size ◽  
Virus Spreading ◽  
Machine Learning Models

Objective: To estimate the size and the dynamics of the coro-navirus (covid-19) pandemic in Advanced, Emerging, and Developing Economies, and to determine its implications for economic growth.Methodology: A susceptible Infected Recovered (sir) mod-el is implemented, we calculate the size of the pandemic through numerical integration and phase diagrams for covid-19 trajectory; finally, we use ensemble models (ran-dom forest) to forecast economic growth.Results: We confirm that there are differences in pandemic spread and size among countries; likewise, the trajectories show a long-term spiral cycle. Economic recovery is expect-ed to be slow and gradual for most of the economies.Limitations: All countries differ in covid-19 test applica-tions, which could lead to inaccurate total confirmed cases and an imprecise estimate of the pandemic’s spread and size. In addition, there is a lack of leading indicators in some countries, generating a higher mse of some machine learning models. Originality: To implement economic-epidemiological mod-els to analyze the evolution and virus’ spreading through-out time.Conclusions: It is found the pandemic’s final size to be be-tween 74-77%. Likewise, it is demonstrated that covid-19 is endemic, with a constant prevalence of 9 years on av-erage. The spread of the pandemic has caused countries to self-induce in an unprecedented recession with a slow recovery.

scholarly journals "Smart Tanks": An Intelligent High-Throughput Intervention Testing Platform in Daphnia

2021 ◽  
Author(s):  
Yongmin Cho ◽  
Rachael A Jonas-Closs ◽  
Lev Y Yampolsky ◽  
Marc W Kirschner ◽  
Leonid Peshkin
Keyword(s):  
Machine Learning ◽  
Chronological Age ◽  
Complex Behavior ◽  
Learning Models ◽  
Short Term ◽  
Health Span ◽  
Response Profile ◽  
Automated Phenotyping ◽  
Machine Learning Models

We present a novel platform for testing the effect of interventions on life- and health-span of a short-lived semi transparent freshwater organism, sensitive to drugs with complex behavior and physiology - the planktonic crustacean Daphnia magna. Within this platform, dozens of complex behavioural features of both routine motion and response to stimuli are continuously accurately quantified for large homogeneous cohorts via an automated phenotyping pipeline. We build predictive machine learning models calibrated using chronological age and extrapolate onto phenotypic age. We further apply the model to estimate the phenotypic age under pharmacological perturbation. Our platform provides a scalable framework for drug screening and characterization in both life-long and instant assays as illustrated using long term dose response profile of metformin and short term assay of such well-studied substances as caffeine and alcohol.

scholarly journals Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

2020 ◽  
Vol 214 ◽  
pp. 01023
Author(s):  
Linan (Frank) Zhao
Keyword(s):  
Machine Learning ◽  
Age Groups ◽  
Learning Models ◽  
Public Authorities ◽  
Ensemble Machine Learning ◽  
European Public ◽  
Data Driven Approach ◽  
Using Data ◽  
Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

scholarly journals Reducing error propagation for long term energy forecasting using multivariate prediction

10.29007/mbb7 ◽  
2020 ◽  
Author(s):  
Maher Selim ◽  
Ryan Zhou ◽  
Wenying Feng ◽  
Omar Alam
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Short Term ◽  
Energy Forecasting ◽  
Opposite Behavior ◽  
Multivariate Prediction ◽  
Term Energy ◽  
Short And Long Term ◽  
Machine Learning Models

Many statistical and machine learning models for prediction make use of historical data as an input and produce single or small numbers of output values. To forecast over many timesteps, it is necessary to run the program recursively. This leads to a compounding of errors, which has adverse effects on accuracy for long forecast periods. In this paper, we show this can be mitigated through the addition of generating features which can have an “anchoring” effect on recurrent forecasts, limiting the amount of compounded error in the long term. This is studied experimentally on a benchmark energy dataset using two machine learning models LSTM and XGBoost. Prediction accuracy over differing forecast lengths is compared using the forecasting MAPE. It is found that for LSTM model the accuracy of short term energy forecasting by using a past energy consumption value as a feature is higher than the accuracy when not using past values as a feature. The opposite behavior takes place for the long term energy forecasting. For the XGBoost model, the accuracy for both short and long term energy forecasting is higher when not using past values as a feature.

scholarly journals Long-Term Impacts of Fair Machine Learning

2019 ◽  
Vol 28 (3) ◽  
pp. 7-11
Author(s):  
Xueru Zhang ◽  
Mohammad Mahdi Khalili ◽  
Mingyan Liu
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Human Beings ◽  
Learning Models ◽  
Real World Data ◽  
World Data ◽  
Fairness Concerns ◽  
Fairness Constraints ◽  
Machine Learning Models

Machine learning models developed from real-world data can inherit potential, preexisting bias in the dataset. When these models are used to inform decisions involving human beings, fairness concerns inevitably arise. Imposing certain fairness constraints in the training of models can be effective only if appropriate criteria are applied. However, a fairness criterion can be defined/assessed only when the interaction between the decisions and the underlying population is well understood. We introduce two feedback models describing how people react when receiving machine-aided decisions and illustrate that some commonly used fairness criteria can end with undesirable consequences while reinforcing discrimination.

Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment

Energy ◽  
2018 ◽  
Vol 158 ◽  
pp. 17-32 ◽  
Author(s):  
Tanveer Ahmad ◽  
Huanxin Chen ◽  
Ronggeng Huang ◽  
Guo Yabin ◽  
Jiangyu Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Energy Prediction ◽  
Term Energy ◽  
Building Environment ◽  
Machine Learning Models

scholarly journals Forecasting the long-term trend of COVID-19 epidemic using a dynamic model

2020 ◽  
Author(s):  
Jichao Sun ◽  
Xi Chen ◽  
Ziheng Zhang ◽  
Shengzhang Lai ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Control Strategies ◽  
Integrated Approach ◽  
Cumulative Number ◽  
Learning Models ◽  
Term Trend ◽  
Intervention Measures ◽  
Long Term Trend ◽  
Machine Learning Models

Abstract Background The current outbreak of coronavirus disease 2019 (COVID-19) has recently been declared as a pandemic and spread over 200 countries and territories. Forecasting the long-term trend of the COVID-19 epidemic can help health authorities determine the transmission characteristics of the virus and take appropriate prevention and control strategies beforehand. Previous studies that applied the traditional epidemic models or machine learning models were subject to underfitting or overfitting problems.Methods We propose a new model named Dynamic-Susceptible-Exposed-Infective-Quarantined (D-SEIQ), by making appropriate modifications of the Susceptible-Exposed-Infective-Recovered (SEIR) model and integrating machine learning based parameter optimization under epidemiological rational constraints. We used the model to predict the long-term reported cumulative numbers of COVID-19 cases in China from 27 January, 2020.Results We evaluated our model on officially reported confirmed cases from three different regions in China, and the results proved the effectiveness of our model in terms of simulating and predicting the trend of COVID-19 outbreak. In China-Excluding-Hubei area within 7 days after the first public report, our model successfully and accurately predicted the 40 days long trend and the exact date of turning point. The predicted cumulative number (12,506) by 10, March 2020 was only 3·8% different with the actual number (13,005). The parameters obtained by our model proved the effectiveness of prevention and intervention strategies on epidemic control in China.Conclusions The integrated approach of epidemic and machine learning models could accurately forecast the long-term trend of COVID-19 outbreak. The learned parameters suggested the effectiveness of intervention measures taken in China.

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