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 2454.70, 3170.51, 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).

Keywords: COVID-19; Environmental epidemiology; Exposure risk; Microscopic model; Public health; Respiratory infectious diseases.

Fig. 1

The flowchart of our model.

Fig. 2

The sketch map of the individuals moving in the simulation space.

Fig. 3

Schematic diagram of (a) the computational domain with source and susceptible manikins, and (b) the numerical simulation computational domain.

Fig. 4

15 representative x-z planes in the three-dimensional schematic diagram of the computational domain.

Fig. 5

$M_s\left(t^{\text{cg}}\right)$ versus $t^{\text{cg}}$ at different times: (a) $T^{\text{step}}=0.02\text{s}$; (b) $T^{\text{step}}=0.03\text{s}$; (c) $T^{\text{step}}=0.04\text{s}$; (d) $T^{\text{step}}=0.05\text{s}$.

Fig. 6

Schematic diagram of data relations used in this case.

Fig. 7

The sketch map of the simulation space in this case.

Fig. 8

The number of individuals varies in different exposure risk segments.

Fig. 9

The average total-effect of parameters $\alpha$ and $\beta$ in each training day.

Fig. 10

MAE varies with α and β. (a) The side view of $\alpha -\beta$ when $\alpha \in [0.50,100.00]$ μg and $\beta \in [1.00\times {10}^{-5},2.00\times {10}^{-3}]$. (b–d) The front view and 2 side views when MAE are less than 6500.

Fig. 11

(a) MAE, RMSE, and (b) MAPE change with the number of days randomly selected as the training set.

Fig. 12

The actual values and different model results of $C^{\text{new}}$ change with the date.