Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model using publicly available datasets (Preprint)

BACKGROUND Coronavirus disease 2019 (COVID-19) pandemic has changed public health policies and personal lifestyles through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment to mitigate disease spread in distressed regions. Current models that predict COVID-19 case counts and spread, such as deep learning, offer limited explainability and generalizability. This creates a gap for highly accurate and robust outbreak prediction models which balance parsimony and fit. OBJECTIVE We seek to leverage various readily accessible datasets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis. METHODS Our methods use the following data inputs: COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model was trained on data between 3/1/20-8/31/20, then tested from 9/1/20 to 10/31/20 against the gold standard to derive confusion matrix statistics. RESULTS The model reported sensitivities of 92%, 90%, and 81% for Indiana, Iowa, and California respectively. The precision in each state was above 85%, and the specificity and accuracy were generally greater than 95%. CONCLUSIONS The parsimonious model provide a generalizable and simple alternative approach to outbreak prediction. Our methodology could be tested on diverse regions to aid government officials and hospitals with resource allocation.

Download Full-text

A comparative study of COVID-19 responses in South Korea and Japan: political nexus triad and policy responses

International Review of Administrative Sciences ◽

10.1177/0020852321997552 ◽

2021 ◽

pp. 002085232199755

Author(s):

M. Jae Moon ◽

Kohei Suzuki ◽

Tae In Park ◽

Kentaro Sakuwa

Keyword(s):

Northeast Asia ◽

Governance Structure ◽

Disease Spread ◽

Policy Outcomes ◽

Governance Structures ◽

Government Officials ◽

Policy Responses ◽

Government Capacity ◽

Institutional Contexts ◽

Policy Problems

Korea and Japan, neighboring democratic countries in Northeast Asia, announced their first COVID-19 cases in January 2020 and witnessed similar patterns of disease spread but adopted different policy approaches to address the pandemic (agile and proactive approach versus cautious and restraint-based approach). Applying the political nexus triad model, this study analyzes and compares institutional contexts and governance structures of Korea and Japan, then examines the differences in policy responses of the two Asian countries. This study first reviews the state of COVID-19 and examines changes in the conventional president-led political nexus triad in Korea and the bureaucracy-led political nexus triad in Japan. Then, this study examines how the differences in institutional contexts and governance structures shaped policy responses and policy outcomes of the two countries in managing the COVID-19 crisis. Points for practitioners • Institutional and governance structure in a society are likely to affect policymaking processes as well as selection of policies among various policy alternatives. • Government officials often need to refer to government capacity as well as citizens’ voluntary participation in resolving wicked policy problems like COVID-19. • Policy decisions made by government officials affect policy outcomes while political environment and political leadership are equally important to policy effectiveness.

Download Full-text

A simple alternative approach to assess the effect of the above-water bow form on the ship added resistance

Ocean Engineering ◽

10.1016/j.oceaneng.2012.09.005 ◽

2013 ◽

Vol 57 ◽

pp. 34-48 ◽

Cited By ~ 6

Author(s):

Ming-Chung Fang ◽

Zi-Yi Lee ◽

Kao-Tuao Huang

Keyword(s):

Added Resistance ◽

Alternative Approach ◽

Simple Alternative

Download Full-text

Evaluation Challenges for Computer-Aided Diagnostic Characterization

Data Mining ◽

10.4018/978-1-4666-2455-9.ch093 ◽

2013 ◽

pp. 1794-1818

Author(s):

William H. Horsthemke ◽

Daniela S. Raicu ◽

Jacob D. Furst ◽

Samuel G. Armato

Keyword(s):

Gold Standard ◽

Prediction Models ◽

Pulmonary Nodules ◽

Ground Truth ◽

Predictive Performance ◽

Reference Standard ◽

Domain Experts ◽

Computer Aided ◽

Visual Characteristics ◽

Aided Diagnosis

Evaluating the success of computer-aided decision support systems depends upon a reliable reference standard, a ground truth. The ideal gold standard is expected to result from the marking, labeling, and rating by domain experts of the image of interest. However experts often disagree, and this lack of agreement challenges the development and evaluation of image-based feature prediction of expert-defined “truth.” The following discussion addresses the success and limitation of developing computer-aided models to characterize suspicious pulmonary nodules based upon ratings provided by multiple expert radiologists. These prediction models attempt to bridge the semantic gap between images and medically-meaningful, descriptive opinions about visual characteristics of nodules. The resultant computer-aided diagnostic characterizations (CADc) are directly usable for indexing and retrieving in content-based medical image retrieval and supporting computer-aided diagnosis. The predictive performance of CADc models are directly related to the extent of agreement between radiologists; the models better predict radiologists’ opinions when radiologists agree more with each other about the characteristics of nodules.

Download Full-text

Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities

Journal of Risk and Financial Management ◽

10.3390/jrfm13050092 ◽

2020 ◽

Vol 13 (5) ◽

pp. 92

Author(s):

Katarina Valaskova ◽

Pavol Durana ◽

Peter Adamko ◽

Jaroslav Jaros

Keyword(s):

Prediction Models ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Confusion Matrix ◽

Predictive Ability ◽

Early Warning Systems ◽

Emerging Countries ◽

Bankruptcy Prediction ◽

Financial Health ◽

Prediction Ability

The risk of corporate financial distress negatively affects the operation of the enterprise itself and can change the financial performance of all other partners that come into close or wider contact. To identify these risks, business entities use early warning systems, prediction models, which help identify the level of corporate financial health. Despite the fact that the relevant financial analyses and financial health predictions are crucial to mitigate or eliminate the potential risks of bankruptcy, the modeling of financial health in emerging countries is mostly based on models which were developed in different economic sectors and countries. However, several prediction models have been introduced in emerging countries (also in Slovakia) in the last few years. Thus, the main purpose of the paper is to verify the predictive ability of the bankruptcy models formed in conditions of the Slovak economy in the sector of agriculture. To compare their predictive accuracy the confusion matrix (cross tables) and the receiver operating characteristic curve are used, which allow more detailed analysis than the mere proportion of correct classifications (predictive accuracy). The results indicate that the models developed in the specific economic sector highly outperform the prediction ability of other models either developed in the same country or abroad, usage of which is then questionable considering the issue of prediction accuracy. The research findings confirm that the highest predictive ability of the bankruptcy prediction models is achieved provided that they are used in the same economic conditions and industrial sector in which they were primarily developed.

Download Full-text

Local relative motion in general relativity

Canadian Journal of Physics ◽

10.1139/p79-280 ◽

1979 ◽

Vol 57 (12) ◽

pp. 2066-2071

Author(s):

F. I. Cooperstock ◽

D. W. Hobill

Keyword(s):

Gravitational Wave ◽

Relative Motion ◽

Reference Frames ◽

Physical Significance ◽

Cryogenic Cooling ◽

Cautionary Note ◽

Geodesic Deviation ◽

Wave Detector ◽

Alternative Approach ◽

Simple Alternative

The distinction is drawn between problems in which single particle motion has physical significance and those in which relative motion between pairs of particles must be considered. Local relative motion is considered from the standpoint of the equation of geodesic deviation, expressed in arbitrary coordinates and in geodesic Fermi coordinates. A simple alternative approach to geodesic deviation using synchronous reference frames is described. Examples of relative motion in the Schwarzschild field and in a gravitational wave are discussed. Criticism of the efficacy of cryogenic cooling to enhance gravitational wave detector sensitivity is shown to be invalid. However, a cautionary note is expressed with regard to the necessity of a local observer to detect deviations from local planeness.

Download Full-text

Performance of three molecular tests for SARS-CoV-2 on a university campus estimated jointly with Bayesian latent class modeling

10.1101/2021.07.31.21261425 ◽

2021 ◽

Author(s):

Alex Perkins ◽

Melissa Stephens ◽

Wendy Alvarez Barrios ◽

Sean M. Cavany ◽

Liz Rulli ◽

...

Keyword(s):

Gold Standard ◽

Latent Class ◽

Credible Interval ◽

University Campus ◽

Rt Pcr ◽

Molecular Tests ◽

Latent Class Modeling ◽

Major Disadvantage ◽

Alternative Approach ◽

University Setting

Accurate tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been critical in efforts to control its spread. The accuracy of molecular tests for SARS-CoV-2 has been assessed numerous times, usually in reference to a gold standard diagnosis. One major disadvantage of that approach is the possibility of error due to inaccuracy of the gold standard, which is especially problematic for evaluating testing in a real-world surveillance context. We used an alternative approach known as Bayesian latent class modeling (BLCM), which circumvents the need to designate a gold standard by simultaneously estimating the accuracy of multiple tests. We applied this technique to a collection of 1,716 tests of three types applied to 853 individuals on a university campus during a one-week period in October 2020. We found that reverse transcriptase polymerase chain reaction (RT-PCR) testing of saliva samples performed at a campus facility had higher sensitivity (median: 0.923; 95% credible interval: 0.732-0.996) than RT-PCR testing of nasal samples performed at a commercial facility (median: 0.859; 95% CrI: 0.547-0.994). The reverse was true for specificity, although the specificity of saliva testing was still very high (median: 0.993; 95% CrI: 0.983-0.999). An antigen test was less sensitive and specific than both of the RT-PCR tests. These results suggest that RT-PCR testing of saliva samples at a campus facility can be an effective basis for surveillance screening to prevent SARS-CoV-2 transmission in a university setting.

Download Full-text

Pre-season screening currently has no value for injury prediction: The development and internal validation of a multivariable prognostic model to predict indirect muscle injury risk in elite football (soccer) players

10.1101/19012054 ◽

2019 ◽

Author(s):

Tom Hughes ◽

Richard D. Riley ◽

Michael J. Callaghan ◽

Jamie C. Sergeant

Keyword(s):

Prognostic Factors ◽

Muscle Injury ◽

Injury Risk ◽

Prognostic Model ◽

Missing Values ◽

Prediction Models ◽

Health Examination ◽

List Type ◽

Muscle Injuries ◽

Parsimonious Model

ABSTRACTBackgroundIn elite football (soccer), periodic health examination (PHE) could provide prognostic factors to predict injury risk.ObjectiveTo develop and internally validate a prognostic model to predict individual indirect (non-contact) muscle injury (IMI) risk during a season in elite footballers, only using PHE-derived candidate prognostic factors.MethodsRoutinely collected preseason PHE and injury data were used from 119 players over 5 seasons (1st July 2013 to 19th May 2018). Ten candidate prognostic factors (12 parameters) were included in model development. Multiple imputation was used to handle missing values. The outcome was any time-loss, index indirect muscle injury (I-IMI) affecting the lower extremity. A full logistic regression model was fitted, and a parsimonious model developed using backward-selection to remove non-significant factors. Predictive performance was assessed through calibration, discrimination and decision-curve analysis, averaged across all imputed datasets. The model was internally validated using bootstrapping and adjusted for overfitting.ResultsDuring 317 participant-seasons, 138 I-IMIs were recorded. The parsimonious model included only age and frequency of previous IMIs; apparent calibration was perfect but discrimination was modest (C-index = 0.641, 95% confidence interval (CI): 0.580 to 0.703), with clinical utility evident between risk thresholds of 37-71%. After validation and overfitting adjustment, performance deteriorated (C-index = 0.580; calibration-in-the-large =-0.031, calibration slope =0.663).ConclusionThe selected PHE data were insufficient prognostic factors from which to develop a useful model for predicting IMI risk in elite footballers. Further research should prioritise identifying novel prognostic factors to improve future risk prediction models in this field.Trial registration numberNCT03782389KEY POINTSFactors measured through preseason screening generally have weak prognostic strength for future indirect muscle injuries and further research is needed to identify novel, robust prognostic factors.Because of sample size restrictions, and until the evidence base improves, it is likely that any further attempts at creating a prognostic model at individual club level would also suffer from poor performance.The value of using preseason screening data to make injury predictions or to select bespoke injury prevention strategies remains to be demonstrated, so screening should only be considered as useful for detection of salient pathology or for rehabilitation/ performance monitoring purposes at this time.

Download Full-text

Prediksi Customer Churn dengan Algoritma Decision Tree C4.5 Berdasarkan Segmentasi Pelanggan untuk Mempertahankan Pelanggan pada Perusahaan Retail

Jurnal RESISTOR (Rekayasa Sistem Komputer) ◽

10.31598/jurnalresistor.v1i1.219 ◽

2018 ◽

Vol 1 (1) ◽

pp. 16-24

Author(s):

Ni Wayan Wardani ◽

Gede Rasben Dantes ◽

Gede Indrawan

Keyword(s):

Decision Tree ◽

Prediction Models ◽

Confusion Matrix ◽

Customer Segmentation ◽

Customer Churn ◽

Rfm Model ◽

Performance Results ◽

Retail Company ◽

Number Of Customers ◽

A Company

Customer is a very important asset for retail companies. This is the reason why retail companies should plan and use a fairly clear strategy in treating customers. With the large number of customers, the problem that must be faced is how to identify the characteristics of all customers and able to retain existing customers in order not to stop buying and moving to a competitor retail company. By applying the concept of CRM, a company can identify customers by segmenting customers while also being able to implement customer retention programs by predicting potential churn on each customer class. The data used comes from UD.Mawar Sari. Customer segmentation process uses RFM model to get customer class. UD. Mawar Sari customer class is dormant, everyday, golden and superstar. The construction of prediction models using the Decision Tree C4.5. The application of the prediction model obtains performance results, that is: Dormant: Recall 97.51%, Precision 75.18%, Accuracy 76.18%. Everyday: Recall 100%, Precision 99.04%, Accuracy 99.04%. Golden: Recall 100%, Precision 98.84%, Accuracy 98.84%. Superstar: Recall 96.15%, Precision 99.43%, Accuracy 95.63%. Results of the evaluation with confusion matrix it can be concluded that the dormant customer class is a potentially churn customer class.

Download Full-text

Optinalysis: A New Approach of Symmetry Detection and Similarity Measurement through a Looking-Glass

10.20944/preprints201905.0295.v2 ◽

2019 ◽

Author(s):

Kabir Bindawa Abdullahi

Keyword(s):

Sequence Comparison ◽

Genomic Sequences ◽

Similarity Measurement ◽

Symmetry Detection ◽

New Approach ◽

Alternative Approach ◽

Simple Alternative ◽

Looking Glass

Optinalysis, as a method of symmetry detection, is a new algorithm that intrametrically (within elements or variables) or intermetrically (between elements or variables) computes and compares two or more univariate or multi-clustered or multivariate sequences as a mirror-like reflection of each other (optics-like manner), hence the name is driven. Optinalysis is based by the principles of reflection and moment about a symmetrical line which detects symmetry that reflects a similarity measurement. This proposed methodology was validated in comparison with Pearson method of skewness detection, and also with some algorithms for pairewise alignment and comparison of genomic sequences (Needle, Stretcher, Water, Matcher) on EMBL-EBI website. A results comparison shows that optinalysis is more advance, more sensitive, more inferential and simple alternative approach of skewness detection and pairewise sequence comparison.

Download Full-text