Benchmarking Disease Modeling Techniques on the Philippines’ COVID-19 Dataset
The COVID-19 pandemic has emphasized the importance of timely and accurate prediction of disease outbreaks. Mathematical disease models can help simulate the trajectory of diseases and guide policymakers in identifying priorities and gaps in current policies. This study evaluates the performance, on...
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Archīum Ateneo
2023
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ph-ateneo-arc.discs-faculty-pubs-14182024-06-14T06:35:47Z Benchmarking Disease Modeling Techniques on the Philippines’ COVID-19 Dataset Pulmano, Christian Fernandez, Proceso L, Jr The COVID-19 pandemic has emphasized the importance of timely and accurate prediction of disease outbreaks. Mathematical disease models can help simulate the trajectory of diseases and guide policymakers in identifying priorities and gaps in current policies. This study evaluates the performance, on various metrics, of three different parameter estimation algorithms in compartmental models, i.e., Nelder-Mead, Simulated Annealing, and L-BFGS-B, together with the ARIMA time series modeling, in modeling COVID-19 cases. Using the daily number of confirmed cases of COVID-19 in the Philippines as the dataset, the models were trained on 90 different periods, with each period having 30 days of case data. After training, the models were used to predict the cases up to 30 days later. The Negative Log Likelihood (NLL), time spent, iterations per second, and memory allocation were all measured. The results show that ARIMA performed better in terms of accuracy, time, and space efficiency than each of the other algorithms. This suggests that ARIMA should be preferred for predicting the number of cases. However, policymaking sometimes requires scenario-based modeling, which ARIMA is unable to provide. For such requirements, any of the three compartmental models may be preferred, as each performed generally very well, too. 2023-01-01T08:00:00Z text application/pdf https://archium.ateneo.edu/discs-faculty-pubs/417 https://doi.org/10.5220/0011626400003414 Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo Mathematical Modeling Parameter Estimation ARIMA COVID-19 Artificial Intelligence and Robotics COVID-19 Data Science Other Computer Sciences |
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Mathematical Modeling Parameter Estimation ARIMA COVID-19 Artificial Intelligence and Robotics COVID-19 Data Science Other Computer Sciences |
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Mathematical Modeling Parameter Estimation ARIMA COVID-19 Artificial Intelligence and Robotics COVID-19 Data Science Other Computer Sciences Pulmano, Christian Fernandez, Proceso L, Jr Benchmarking Disease Modeling Techniques on the Philippines’ COVID-19 Dataset |
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The COVID-19 pandemic has emphasized the importance of timely and accurate prediction of disease outbreaks. Mathematical disease models can help simulate the trajectory of diseases and guide policymakers in identifying priorities and gaps in current policies. This study evaluates the performance, on various metrics, of three different parameter estimation algorithms in compartmental models, i.e., Nelder-Mead, Simulated Annealing, and L-BFGS-B, together with the ARIMA time series modeling, in modeling COVID-19 cases. Using the daily number of confirmed cases of COVID-19 in the Philippines as the dataset, the models were trained on 90 different periods, with each period having 30 days of case data. After training, the models were used to predict the cases up to 30 days later. The Negative Log Likelihood (NLL), time spent, iterations per second, and memory allocation were all measured. The results show that ARIMA performed better in terms of accuracy, time, and space efficiency than each of the other algorithms. This suggests that ARIMA should be preferred for predicting the number of cases. However, policymaking sometimes requires scenario-based modeling, which ARIMA is unable to provide. For such requirements, any of the three compartmental models may be preferred, as each performed generally very well, too. |
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text |
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Pulmano, Christian Fernandez, Proceso L, Jr |
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Pulmano, Christian Fernandez, Proceso L, Jr |
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Pulmano, Christian |
title |
Benchmarking Disease Modeling Techniques on the Philippines’ COVID-19 Dataset |
title_short |
Benchmarking Disease Modeling Techniques on the Philippines’ COVID-19 Dataset |
title_full |
Benchmarking Disease Modeling Techniques on the Philippines’ COVID-19 Dataset |
title_fullStr |
Benchmarking Disease Modeling Techniques on the Philippines’ COVID-19 Dataset |
title_full_unstemmed |
Benchmarking Disease Modeling Techniques on the Philippines’ COVID-19 Dataset |
title_sort |
benchmarking disease modeling techniques on the philippines’ covid-19 dataset |
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Archīum Ateneo |
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2023 |
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https://archium.ateneo.edu/discs-faculty-pubs/417 https://doi.org/10.5220/0011626400003414 |
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