Towards course of disease based epidemiological modelling: motivation and computational optimization

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.