The COVID-19 pandemic has demonstrated the shortcomings of epidemiological modelling for guiding policy decisions. Moreover, the modelling efforts resulted in many models yielding different predictions, creating a need to compare these predictions to determine which model is most accurate. We introduce a data synthesis algorithm, CovSyn, designed to generate synthetic COVID-19 datasets providing sufficiently detailed information for benchmarking epidemiological models against a known synthetic ground truth. CovSyn utilizes observed infections with contact tracing, testing, course of disease data, and a contact network based on municipality statistics, which categorises connections into household, school, workplace, healthcare, and municipality. The model's initial parameters and boundaries are derived from empirical data, including the first community outbreak of COVID-19 in Taiwan and clinical observations. Comprehensive parameter space exploration for optimal results is done by the Firefly algorithm. We demonstrate it and validate our estimates by comparing state transition times, daily social contacts, and associated secondary attack rates against a structured dataset and clinical observations. Our simulations align with prior research and this dataset. Most state transition times from 10,000 simulations are within uncertainty ranges. Daily contact numbers and their distribution across layers match empirical findings. Our model accurately reproduced the first COVID-19 outbreak in Taiwan, achieving high accuracy with observed cumulative confirmed cases (R^2 = 0.9) across daily, 7-day moving average, and 31-day moving average levels. Each synthetic subject contains demographic data (age, gender, occupation), course of disease (latent/incubation periods, testing, isolation, critical illness, recovery, and death dates), and contact network data including daily interactions with infected and uninfected individuals. Our algorithm offers a valid alternative for developing and benchmarking epidemiological models to advance COVID-19 forecasting research.

The authors have declared no competing interest.

Ministry of Science and Technology in Taiwan (Grants Number MOST 105-2218-E- 006-016-MY2, 105-2911-I-006-518, 107-2634-F-006-009, 110-2222-E-006-010, 789 and National Science and Technology Council 111-2221-E-006-186, 112-2314-B-006- 079, and 113-2314-B-006-069). This research was supported in part by Higher Education Suprout Project, Ministry of Education to Headquarters of University Advancement at National Cheng Kung University (NCKU)

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The CovSyn model, data, and documentation are fully available via GitHub https://github.com/nordlinglab/COVID19-CovSyn