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Journal Article Research Support, Non-U.S. Gov't Ppublish 11 citations

PubMed: 35568232 DOI: 10.1016/j.envres.2022.113428

Forecasting the transmission trends of respiratory infectious diseases with an exposure-risk-based model at the microscopic level.

Environ Res 212(Pt C):113428

Authors: Ziwei Cui, Ming Cai, Yao Xiao, Zheng Zhu, Mofeng Yang, Gongbo Chen

Affiliations

  • School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
  • School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
  • School of Intelligent System Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China.
  • College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, Zhejiang, China.
  • Maryland Transportation Institute, Department of Civil and Environmental Engineering, University of Maryland at College Park, Maryland, USA.
  • Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China.

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).

MeSH

Risk, Communicable Diseases, Movement, Covid-19

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