Publications
Our published Work!
2024
Wu, Yu-Heng; Nordling, Torbjörn E. M.
A structured course of disease dataset with contact tracing information in Taiwan for COVID-19 modelling Journal Article
In: medRxiv, 2024.
Abstract | Links | BibTeX | Tags: COVID-19, Epidemiology, SARS-CoV-2, Structured dataset, Taiwan
@article{Wu2024COVID19dataMedRxiv,
title = {A structured course of disease dataset with contact tracing information in Taiwan for COVID-19 modelling},
author = {Yu-Heng Wu and Torbjörn E. M. Nordling},
url = {https://www.medrxiv.org/content/10.1101/2024.02.28.24303518v1},
doi = {10.1101/2024.02.28.24303518},
year = {2024},
date = {2024-02-01},
journal = {medRxiv},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {The COVID-19 pandemic has flooded open databases with population-level data. However, individual-level structured data, such as the course of disease and contact tracing information, is almost non-existent in open databases. Publish a structured and cleaned COVID-19 dataset with the course of disease and contact tracing information for easy benchmarking of COVID-19 models. We gathered data from Taiwanese open databases and daily news reports. The outcome is a structured quantitative dataset encompassing the course of the disease of Taiwanese individuals, alongside their contact tracing information. Our dataset comprises 579 confirmed cases covering the period from January 21, to November 9, 2020, when the original SARS-CoV-2 virus was most prevalent in Taiwan. The data include features such as travel history, age, gender, symptoms, contact types between cases, date of symptoms onset, confirmed, critically ill, recovered, and dead. We also include the daily summary data at population-level from January 21, 2020, to May 23, 2022. Our data can help enhance epidemiological modelling.},
keywords = {COVID-19, Epidemiology, SARS-CoV-2, Structured dataset, Taiwan},
pubstate = {published},
tppubtype = {article}
}
The COVID-19 pandemic has flooded open databases with population-level data. However, individual-level structured data, such as the course of disease and contact tracing information, is almost non-existent in open databases. Publish a structured and cleaned COVID-19 dataset with the course of disease and contact tracing information for easy benchmarking of COVID-19 models. We gathered data from Taiwanese open databases and daily news reports. The outcome is a structured quantitative dataset encompassing the course of the disease of Taiwanese individuals, alongside their contact tracing information. Our dataset comprises 579 confirmed cases covering the period from January 21, to November 9, 2020, when the original SARS-CoV-2 virus was most prevalent in Taiwan. The data include features such as travel history, age, gender, symptoms, contact types between cases, date of symptoms onset, confirmed, critically ill, recovered, and dead. We also include the daily summary data at population-level from January 21, 2020, to May 23, 2022. Our data can help enhance epidemiological modelling.
2023
Wu, Yu-Heng; Nordling, Torbjörn E. M.
Towards course of disease based epidemiological modelling: motivation and computational optimization Proceedings Article
In: Proceedings 2023 IEEE 47th Annual International Computer Software and Applications Conference (COMPSAC), pp. 213-222, IEEE, Torino, Italy, 2023, ISSN: 07303157.
Abstract | Links | BibTeX | Tags: COVID-19, epidemiological model, firefly optimization, individual data, synthetic dataset
@inproceedings{Wu2023COMPSAC,
title = {Towards course of disease based epidemiological modelling: motivation and computational optimization},
author = {Yu-Heng Wu and Torbjörn E. M. Nordling},
doi = {10.1109/COMPSAC57700.2023.00035},
issn = {07303157},
year = {2023},
date = {2023-06-01},
booktitle = {Proceedings 2023 IEEE 47th Annual International Computer Software and Applications Conference (COMPSAC)},
pages = {213-222},
publisher = {IEEE},
address = {Torino, Italy},
series = {IEEE Annual International Computer Software and Applications Conference (COMPSAC)},
abstract = {The ongoing COVID-19 pandemic has demonstrated the shortcoming of epidemiological modelling for guiding policy decisions.
Due to the lack of public data on infection spread in contact networks and individual courses of disease, current forecasting models rely heavily on unreliable population statistics and ad hoc parameters, resulting in forecasts with high uncertainty. To tackle the problem of insufficient public individual data, we develop an agent-based model to generate a synthetic Taiwanese COVID-19 dataset. We collected COVID-19 data from Taiwanese public databases for the period when the original SARS-CoV-2 virus was most prevalent (Jan.-Oct., 2020) and fit our model to it. We used the Firefly algorithm to optimize the 194 epidemiological parameters and validated the synthetic dataset by comparing it to Taiwanese public data. Here we study the difference between population statistics and individual course of disease data, and computational optimization of our code to reduce run time. The discrepancy between serum prevalence and reported cases, as well as excess deaths and reported deaths, show that population statistics are unreliable. Monte Carlo simulations using our model further exemplify the discrepancy between actual and reported infections. By using Python CProfiler and Snakeviz packages, we iteratively optimize our algorithm and has so far decreased the computation time of the core code from 0.11s to 0.07s. The large computation time implies that we need further optimize the algorithm.},
howpublished = {IEEE 47th Annual International Computer Software and Applications Conference (COMPSAC), Torino, Italy 26-30 Jun. 2023},
keywords = {COVID-19, epidemiological model, firefly optimization, individual data, synthetic dataset},
pubstate = {published},
tppubtype = {inproceedings}
}
The ongoing COVID-19 pandemic has demonstrated the shortcoming of epidemiological modelling for guiding policy decisions.
Due to the lack of public data on infection spread in contact networks and individual courses of disease, current forecasting models rely heavily on unreliable population statistics and ad hoc parameters, resulting in forecasts with high uncertainty. To tackle the problem of insufficient public individual data, we develop an agent-based model to generate a synthetic Taiwanese COVID-19 dataset. We collected COVID-19 data from Taiwanese public databases for the period when the original SARS-CoV-2 virus was most prevalent (Jan.-Oct., 2020) and fit our model to it. We used the Firefly algorithm to optimize the 194 epidemiological parameters and validated the synthetic dataset by comparing it to Taiwanese public data. Here we study the difference between population statistics and individual course of disease data, and computational optimization of our code to reduce run time. The discrepancy between serum prevalence and reported cases, as well as excess deaths and reported deaths, show that population statistics are unreliable. Monte Carlo simulations using our model further exemplify the discrepancy between actual and reported infections. By using Python CProfiler and Snakeviz packages, we iteratively optimize our algorithm and has so far decreased the computation time of the core code from 0.11s to 0.07s. The large computation time implies that we need further optimize the algorithm.
Due to the lack of public data on infection spread in contact networks and individual courses of disease, current forecasting models rely heavily on unreliable population statistics and ad hoc parameters, resulting in forecasts with high uncertainty. To tackle the problem of insufficient public individual data, we develop an agent-based model to generate a synthetic Taiwanese COVID-19 dataset. We collected COVID-19 data from Taiwanese public databases for the period when the original SARS-CoV-2 virus was most prevalent (Jan.-Oct., 2020) and fit our model to it. We used the Firefly algorithm to optimize the 194 epidemiological parameters and validated the synthetic dataset by comparing it to Taiwanese public data. Here we study the difference between population statistics and individual course of disease data, and computational optimization of our code to reduce run time. The discrepancy between serum prevalence and reported cases, as well as excess deaths and reported deaths, show that population statistics are unreliable. Monte Carlo simulations using our model further exemplify the discrepancy between actual and reported infections. By using Python CProfiler and Snakeviz packages, we iteratively optimize our algorithm and has so far decreased the computation time of the core code from 0.11s to 0.07s. The large computation time implies that we need further optimize the algorithm.
Wu, Yu-Heng; Nordling, Torbjörn E. M.
Towards course of disease based epidemiological modelling: motivation and computational optimization Journal Article
In: medRxiv, 2023.
Abstract | Links | BibTeX | Tags: COVID-19, epidemiological model, firefly optimization, individual data, synthetic dataset
@article{Wu2023COMPSACMedRxiv,
title = {Towards course of disease based epidemiological modelling: motivation and computational optimization},
author = {Yu-Heng Wu and Torbjörn E. M. Nordling},
url = {https://www.medrxiv.org/content/early/2023/05/28/2023.05.24.23290318},
doi = {10.1101/2023.05.24.23290318},
year = {2023},
date = {2023-05-01},
journal = {medRxiv},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {The ongoing COVID-19 pandemic has demonstrated the shortcoming of epidemiological modelling for guiding policy decisions. Due to the lack of public data on infection spread in contact networks and individual courses of disease, current forecasting models rely heavily on unreliable population statistics and ad hoc parameters, resulting in forecasts with high uncertainty. To tackle the problem of insufficient public individual data, we develop an agent-based model to generate a synthetic Taiwanese COVID-19 dataset. We collected COVID-19 data from Taiwanese public databases for the period when the original SARS-CoV-2 virus was most prevalent (Jan.-Oct., 2020) and fit our model to it. We used the Firefly algorithm to optimize the 194 epidemiological parameters and validated the synthetic dataset by comparing it to Taiwanese public data. Here we study the difference between population statistics and individual course of disease data, and computational optimization of our code to reduce run time. The discrepancy between serum prevalence and reported cases, as well as excess deaths and reported deaths, show that population statistics are unreliable. Monte Carlo simulations further exemplify the discrepancy between actual and reported infections. By using Python CProfiler and Snakeviz packages, we iteratively optimize our algorithm and has so far decreased the computation time of the core code from 0.11s to 0.07s. The large computation time implies that we need to further optimize the algorithm.},
keywords = {COVID-19, epidemiological model, firefly optimization, individual data, synthetic dataset},
pubstate = {published},
tppubtype = {article}
}
The ongoing COVID-19 pandemic has demonstrated the shortcoming of epidemiological modelling for guiding policy decisions. Due to the lack of public data on infection spread in contact networks and individual courses of disease, current forecasting models rely heavily on unreliable population statistics and ad hoc parameters, resulting in forecasts with high uncertainty. To tackle the problem of insufficient public individual data, we develop an agent-based model to generate a synthetic Taiwanese COVID-19 dataset. We collected COVID-19 data from Taiwanese public databases for the period when the original SARS-CoV-2 virus was most prevalent (Jan.-Oct., 2020) and fit our model to it. We used the Firefly algorithm to optimize the 194 epidemiological parameters and validated the synthetic dataset by comparing it to Taiwanese public data. Here we study the difference between population statistics and individual course of disease data, and computational optimization of our code to reduce run time. The discrepancy between serum prevalence and reported cases, as well as excess deaths and reported deaths, show that population statistics are unreliable. Monte Carlo simulations further exemplify the discrepancy between actual and reported infections. By using Python CProfiler and Snakeviz packages, we iteratively optimize our algorithm and has so far decreased the computation time of the core code from 0.11s to 0.07s. The large computation time implies that we need to further optimize the algorithm.
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