Enhancing Spatial Spread Prediction of Infectious Diseases through Integrating Multi-scale Human Mobility Dynamics-Reference-Cited by-同舟云学术

Enhancing Spatial Spread Prediction of Infectious Diseases through Integrating Multi-scale Human Mobility Dynamics

Published:2023-11-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
language:
Short-container-title:

Author:

Tang Yinzhou¹^ORCID,Wang Huandong¹^ORCID,Li Yong¹^ORCID

Affiliation:

1. Tsinghua University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Young Elite Scientists Sponsorship Program by CIC

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589132.3625586

Reference37 articles.

1. Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India

2. Macro-modelling and prediction of epidemic spread at community level

3. Andrew W Bartlow , Carrie Manore , Chonggang Xu , Kimberly A Kaufeld , Sara Del Valle , Amanda Ziemann, Geoffrey Fairchild, and Jeanne M Fair. 2019 . Forecasting zoonotic infectious disease response to climate change: mosquito vectors and a changing environment. Veterinary sciences 6, 2 (2019), 40. Andrew W Bartlow, Carrie Manore, Chonggang Xu, Kimberly A Kaufeld, Sara Del Valle, Amanda Ziemann, Geoffrey Fairchild, and Jeanne M Fair. 2019. Forecasting zoonotic infectious disease response to climate change: mosquito vectors and a changing environment. Veterinary sciences 6, 2 (2019), 40.

4. SIRSi compartmental model for COVID-19 pandemic with immunity loss

5. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation