Forecasting the Transmission Trends of Respiratory Infectious Diseases
with an Exposure-Risk-Based Model at the Microscopic Level
Abstract
Respiratory infectious diseases (e.g., COVID-19) have brought huge
damages to human society, and the accurate prediction of their
transmission trends is essential for both the health system and
policymakers. Most related studies focus on epidemic trend forecasting
at the macroscopic level, which ignores the microscopic social
interactions among individuals. Meanwhile, current microscopic models
are still not able to sufficiently decipher the individual-based
spreading process and lack valid quantitative tests. To tackle these
problems, we propose an exposure-risk-based model at the microscopic
level, including 4 modules: individual movement, virion-laden droplet
movement, individual exposure risk estimation, and prediction of
transmission trends. Firstly, the front two modules reproduce the
movements of individuals and the droplets of infectors’ expiratory
activities, respectively. Then, the outputs are fed to the third module
to estimate the personal exposure risk. Finally, the number of new cases
is predicted in the final module. By predicting the new COVID-19 cases
in the United States, the performances of our model and 4 other existing
macroscopic or microscopic models are compared. Specifically, the mean
absolute error, root mean square error, and mean absolute percentage
error provided by the proposed model are respectively 2,454.70,
3,170.51, and 3.38% smaller than the minimum results of comparison
models. The quantitative results reveal that our model can accurately
predict the transmission trends from a microscopic perspective, and it
can benefit the further investigation of many microscopic disease
transmission factors (e.g., non-walkable areas and facility layouts).