scholarly journals Predictive performance of international COVID-19 mortality forecasting models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joseph Friedman ◽  
Patrick Liu ◽  
Christopher E. Troeger ◽  
Austin Carter ◽  
Robert C. Reiner ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Absolute Error ◽  
Decision Makers ◽  
Government Interventions ◽  
World Region ◽  
Mortality Forecasting ◽  
Forecasting Models ◽  
Pandemic Response ◽  
Alternative Scenarios ◽  
Absolute Percent Error

AbstractForecasts and alternative scenarios of COVID-19 mortality have been critical inputs for pandemic response efforts, and decision-makers need information about predictive performance. We screen n = 386 public COVID-19 forecasting models, identifying n = 7 that are global in scope and provide public, date-versioned forecasts. We examine their predictive performance for mortality by weeks of extrapolation, world region, and estimation month. We additionally assess prediction of the timing of peak daily mortality. Globally, models released in October show a median absolute percent error (MAPE) of 7 to 13% at six weeks, reflecting surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. Median absolute error for peak timing increased from 8 days at one week of forecasting to 29 days at eight weeks and is similar for first and subsequent peaks. The framework and public codebase (https://github.com/pyliu47/covidcompare) can be used to compare predictions and evaluate predictive performance going forward.

scholarly journals Predictive performance of international COVID-19 mortality forecasting models

2020 ◽  
Author(s):  
Joseph Friedman ◽  
Patrick Liu ◽  
Christopher E. Troeger ◽  
Austin Carter ◽  
Robert C. Reiner ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Decision Makers ◽  
Sub Saharan Africa ◽  
Mortality Trends ◽  
Government Interventions ◽  
World Region ◽  
Mortality Forecasting ◽  
Forecasting Models ◽  
Sub Saharan ◽  
Alternative Scenarios

AbstractForecasts and alternative scenarios of COVID-19 mortality have been critical inputs into a range of policies and decision-makers need information about predictive performance. We identified n=386 public COVID-19 forecasting models and included n=8 that were global in scope and provided public, date-versioned forecasts. For each, we examined the median absolute percent error (MAPE) compared to subsequently observed mortality trends, stratified by weeks of extrapolation, world region, and month of model estimation. Models were also assessed for ability to predict the timing of peak daily mortality. The MAPE among models released in July rose from 1.8% at one week of extrapolation to 24.6% at twelve weeks. The MAPE at six weeks were the highest in Sub-Saharan Africa (34.8%), and the lowest in high-income countries (6.3%). At the global level, several models had about 10% MAPE at six weeks, showing surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. The framework and publicly available codebase presented here (https://github.com/pyliu47/covidcompare) can be routinely used to compare predictions and evaluate predictive performance in an ongoing fashion.

scholarly journals On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study

2021 ◽  
Vol 13 (24) ◽  
pp. 13599
Author(s):  
Dalton Garcia Borges de Souza ◽  
Erivelton Antonio dos Santos ◽  
Francisco Tarcísio Alves Júnior ◽  
Mariá Cristina Vasconcelos Nascimento
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Absolute Error ◽  
Fuzzy Topsis ◽  
Decision Makers ◽  
Similarity Metric ◽  
Forecasting Models ◽  
Fuzzy Similarity ◽  
Relative Comparisons

Time series cross-validation is a technique to select forecasting models. Despite the sophistication of cross-validation over single test/training splits, traditional and independent metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are commonly used to assess the model’s accuracy. However, what if decision-makers have different models fitting expectations to each moment of a time series? What if the precision of the forecasted values is also important? This is the case of predicting COVID-19 in Amapá, a Brazilian state in the Amazon rainforest. Due to the lack of hospital capacities, a model that promptly and precisely responds to notable ups and downs in the number of cases may be more desired than average models that only have good performances in more frequent and calm circumstances. In line with this, this paper proposes a hybridization of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy sets to create a similarity metric, the closeness coefficient (CC), that enables relative comparisons of forecasting models under heterogeneous fitting expectations and also considers volatility in the predictions. We present a case study using three parametric and three machine learning models commonly used to forecast COVID-19 numbers. The results indicate that the introduced fuzzy similarity metric is a more informative performance assessment metric, especially when using time series cross-validation.

scholarly journals Quantitative Longitudinal Predictions of Alzheimer’s Disease by Multi-Modal Predictive Learning

2021 ◽  
Vol 79 (4) ◽  
pp. 1533-1546
Author(s):  
Mithilesh Prakash ◽  
Mahmoud Abdelaziz ◽  
Linda Zhang ◽  
Bryan A. Strange ◽  
Jussi Tohka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Models ◽  
Predictive Performance ◽  
Absolute Error ◽  
Disease Stage ◽  
Disease Assessment ◽  
Disease Category ◽  
Modal Data ◽  
Predictive Learning

Background: Quantitatively predicting the progression of Alzheimer’s disease (AD) in an individual on a continuous scale, such as the Alzheimer’s Disease Assessment Scale-cognitive (ADAS-cog) scores, is informative for a personalized approach as opposed to qualitatively classifying the individual into a broad disease category. Objective: To evaluate the hypothesis that the multi-modal data and predictive learning models can be employed for future predicting ADAS-cog scores. Methods: Unimodal and multi-modal regression models were trained on baseline data comprised of demographics, neuroimaging, and cerebrospinal fluid based markers, and genetic factors to predict future ADAS-cog scores for 12, 24, and 36 months. We subjected the prediction models to repeated cross-validation and assessed the resulting mean absolute error (MAE) and cross-validated correlation (ρ) of the model. Results: Prediction models trained on multi-modal data outperformed the models trained on single modal data in predicting future ADAS-cog scores (MAE12, 24 & 36 months= 4.1, 4.5, and 5.0, ρ12, 24 & 36 months= 0.88, 0.82, and 0.75). Including baseline ADAS-cog scores to prediction models improved predictive performance (MAE12, 24 & 36 months= 3.5, 3.7, and 4.6, ρ12, 24 & 36 months= 0.89, 0.87, and 0.80). Conclusion: Future ADAS-cog scores were predicted which could aid clinicians in identifying those at greater risk of decline and apply interventions at an earlier disease stage and inform likely future disease progression in individuals enrolled in AD clinical trials.

Forecasting Model of Short-Term Traffic Flow Based on Cloud Model

2011 ◽  
Vol 94-96 ◽  
pp. 38-42
Author(s):  
Qin Liu ◽  
Jian Min Xu
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Cloud Model ◽  
Prediction Method ◽  
Absolute Error ◽  
Short Term ◽  
Guangzhou City ◽  
Time Prediction ◽  
Real Time Traffic ◽  
Absolute Percent Error

In order to improve the prediction precision of the short-term traffic flow, a prediction method of short-term traffic flow based on cloud model was proposed. The traffic flow was fit by cloud model. The history cloud and the present cloud were built by historical traffic flow and present traffic flow. The forecast cloud is produced by both clouds. Then, combining with the volume of the short-term traffic flow of an intersection in Guangzhou City, the model was calculated and simulated through programming. Max Absolute Error (MAE) and Mean Absolute percent Error (MAPE) were used to estimate the effect of prediction. The simulation results indicate that this prediction method is effective and advanced. The change of the historical and real time traffic flow is taken into account in this method. Because the short-term traffic flow is dealt with as a whole, the error of prediction is avoided. The prediction precision and real-time prediction are satisfied.

Forecasting and Technical Comparison of Inflation in Turkey With Box-Jenkins (ARIMA) Models and the Artificial Neural Network

2022 ◽  
pp. 1194-1216
Author(s):  
Erkan Işığıçok ◽  
Ramazan Öz ◽  
Savaş Tarkun
Keyword(s):  
Consumer Price Index ◽  
Predictive Performance ◽  
Central Banks ◽  
Decision Makers ◽  
Inflation Expectations ◽  
Arima Models ◽  
Goods And Services ◽  
The Future ◽  
Alternative Techniques ◽  
Artificial Neural

Inflation refers to an ongoing and overall comprehensive increase in the overall level of goods and services price in the economy. Today, inflation, which is attempted to be kept under control by central banks or, in the same way, whose price stability is attempted, consists of continuous price changes that occur in all the goods and services used by the consumers. Undoubtedly, in terms of economy, in addition to the realized inflation, inflation expectations are also gaining importance. This situation requires forecasting the future rates of inflation. Therefore, reliable forecasting of the future rates of inflation in a country will determine the policies to be applied by the decision-makers in the economy. The aim of this study is to predict inflation in the next period based on the consumer price index (CPI) data with two alternative techniques and to examine the predictive performance of these two techniques comparatively. Thus, the first of the two main objectives of the study are to forecast the future rates of inflation with two alternative techniques, while the second is to compare the two techniques with respect to statistical and econometric criteria and determine which technique performs better in comparison. In this context, the 9-month inflation in April-December 2019 was forecast by Box-Jenkins (ARIMA) models and Artificial Neural Networks (ANN), using the CPI data which consist of 207 data from January 2002 to March 2019 and the predictive performance of both techniques was examined comparatively. It was observed that the results obtained from both techniques were close to each other.

scholarly journals P112 Prediction of free Ceftriaxone concentration in children: disease and maturation do matter

2019 ◽  
Vol 104 (6) ◽  
pp. e64.2-e64
Author(s):  
H-Y Shi ◽  
X Huang ◽  
Q Li ◽  
Wu Y-E ◽  
MW Khan ◽  
...  
Keyword(s):  
Linear Regression ◽  
Mean Absolute Error ◽  
Predictive Ability ◽  
Predictive Performance ◽  
Absolute Error ◽  
Regression Equations ◽  
Free Concentration ◽  
In Vivo Binding ◽  
Mean Prediction Error

BackgroundTo evaluate the predictive ability of the existing formula to measure free ceftriaxone levels in children, and optimize the formula by adding disease and maturation factors.MethodsFifty children receiving ceftriaxone were evaluated, and the predictive performance of the different equations were assessed by mean absolute error (MAE), mean prediction error (MPE) and linear regression of predicted vs. actual free levels.ResultsThe average free ceftriaxone concentration was 2.11 ± 9.51µg/ml. The predicted free concentration was 1.15 ± 4.39µg/ml with the in vivo binding equation, which increased to 1.58 ± 7.73µg/ml and 2.01 ± 9.53µg/ml when adjusted for age (disease adapted equation), and age and albumin (disease-maturation equation) respectively. The average MAE values were 0.48 (in vivo banding equation), 0.34 (disease adapted equation) and 0.41 (disease maturation equation). The average MPE values were -0.41 (in vivo binding equation), 0.14 (disease adapted equation) and 0.09 (disease maturation equation). The respective linear regression equations and coefficients were y=1.8647x+1.0731(R2=0.7398), y=1.1455x+0.8414(R2=0.8674), and y=0.9664x(R2=0.8641) for the in vivo binding, disease adapted and disease maturation equations respectively.ConclusionCompared to the in vivo binding equation, the disease adapted and disease maturation equations showed lower MAE and MPE values, and the latter showed the lowest MPE value. In addition, the slope of the disease maturation equation was closer to 1 compared to the other two. Therefore, the optimized disease maturation equation should be used to measure free ceftriaxone levels in children.Disclosure(s)Nothing to disclose.

scholarly journals Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic

2020 ◽  
Vol 17 (15) ◽  
pp. 5574 ◽  
Author(s):  
Mohammed Al Zobbi ◽  
Belal Alsinglawi ◽  
Omar Mubin ◽  
Fady Alnajjar
Keyword(s):  
Machine Learning ◽  
Infection Rate ◽  
Global Economy ◽  
Data Analytics ◽  
Measurement Method ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Decision Makers ◽  
Machine Learning Classification ◽  
Data Analyses

Coronavirus Disease 2019 (COVID-19) has affected day to day life and slowed down the global economy. Most countries are enforcing strict quarantine to control the havoc of this highly contagious disease. Since the outbreak of COVID-19, many data analyses have been done to provide close support to decision-makers. We propose a method comprising data analytics and machine learning classification for evaluating the effectiveness of lockdown regulations. Lockdown regulations should be reviewed on a regular basis by governments, to enable reasonable control over the outbreak. The model aims to measure the efficiency of lockdown procedures for various countries. The model shows a direct correlation between lockdown procedures and the infection rate. Lockdown efficiency is measured by finding a correlation coefficient between lockdown attributes and the infection rate. The lockdown attributes include retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, residential, and schools. Our results show that combining all the independent attributes in our study resulted in a higher correlation (0.68) to the dependent value Interquartile 3 (Q3). Mean Absolute Error (MAE) was found to be the least value when combining all attributes.

scholarly journals ENVIRONMENTAL SCENARIOS FOR MANDATORY BIO-FUEL BLENDING TARGETS: AN APPLICATION OF INTUITIVE LOGICS DOI:10.7444/fsrj.v2i1.23

2010 ◽  
Vol 2 (1) ◽  
pp. 99-136
Author(s):  
Marco Antonio Conejero ◽  
Marcos Fava Neves ◽  
Mairun Junqueira Alves Pinto
Keyword(s):  
Environmental Analysis ◽  
Correlation Matrix ◽  
Energy Production ◽  
Driving Forces ◽  
Production Systems ◽  
Decision Makers ◽  
Alternative Scenarios ◽  
Fuel Blending ◽  
Focal Issue ◽  
Planning Production

Scenarios depicting targets concerning mandatory bio-fuel blending are critical to the strategic planning of food and bio-energy production chains and their design is the purpose of this paper. Each scenario tells a story about how various elements might interact under given conditions. The method herein utilized is primarily based on Schoemaker´s (1995) and Schwartz´s (1991) earlier proposals. A six step framework is followed: i) identify the focal issue; ii) summarize current mandatory blending targets; iii) identify the driving forces as of a macro-environmental analysis; iv) validate driving forces with specialists; v) rank such key forces by importance before uncertainties, building a correlation matrix; vi) design the scenarios. Finally, three alternative scenarios, relative to the adoption on behalf of countries, by the year 2020, of mandatory bio-fuel blending targets, are proposed which might guide these countries’ decision makers when planning production systems.

scholarly journals Exploring the use of Water Cycle Optimization Algorithm for Foreign Exchange Prediction

2019 ◽  
Vol 8 (10) ◽  
pp. 680-684
Keyword(s):  
Exchange Rates ◽  
Foreign Exchange ◽  
Performance Metrics ◽  
Water Cycle ◽  
Mean Absolute Error ◽  
Predictive Performance ◽  
Absolute Error ◽  
Mean Square ◽  
United States Dollar ◽  
Artificial Neural Network Ann

The aim of this paper is to model a network and predict the exchange price of United States Dollar to Indian Rupees using daily exchange rates from Dec 18, 1991-Jul 19, 2007. In this paper, Water Cycle Optimization (WCA) technique has been used to optimize the Artificial Neural Network (ANN) for Foreign Exchange prediction on the basis of their predictive performance. The performance metrics considered for the evaluation of the models are root mean square error (RMSE) and mean absolute error (MAE). The tabulated outcome shows the efficiency of the model over other popular models

scholarly journals Modelling the first wave of the COVID-19 epidemic in the Czech Republic and the role of government interventions

2020 ◽  
Author(s):  
Ondrej Majek ◽  
Ondrej Ngo ◽  
Jiri Jarkovsky ◽  
Martin Komenda ◽  
Jarmila Razova ◽  
...  
Keyword(s):  
Czech Republic ◽  
Reproduction Number ◽  
Epidemiological Model ◽  
Decision Makers ◽  
Short Term ◽  
Role Of Government ◽  
The Czech Republic ◽  
Government Interventions

In the Czech Republic, the first COVID-19 cases were confirmed on 1 March 2020; early population interventions were adopted in the following weeks. A simple epidemiological model was developed to help decision-makers understand the course of the epidemic and perform short-term predictions. In this paper, we present the use of the model and estimated changes in the reproduction number (decrease from > 2.00 to < 1.00 over March and April) following adopted interventions.

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