Spatiotemporal Transmission Model to Simulate an Interregional Epidemic Spreading

Abstract

Infectious disease spread is a spatiotemporal process with significant regional differences that can be affected by multiple factors, such as human mobility and manner of contact. From a geographical perspective, the simulation and analysis of an epidemic can promote an understanding of the contagion mechanism and lead to an accurate prediction of its future trends. The existing methods fail to consider the mutual feedback mechanism of heterogeneities between the interregional population interaction and the regional transmission conditions (e.g., contact probability and the effective reproduction number). This disadvantage oversimplifies the transmission process and reduces the accuracy of the simulation results. To fill this gap, a general model considering the spatiotemporal characteristics is proposed, which includes compartment modeling of population categories, flow interaction modeling of population movements, and spatial spread modeling of an infectious disease. Furthermore, the correctness of a theoretical hypothesis for modeling and prediction accuracy of this model was tested with a synthetic data set and a real-world COVID-19 data set in China, respectively. The theoretical contribution of this article was to verify that the interplay of multiple types of geospatial heterogeneities dramatically influences the spatial spread of infectious disease. This model provides an effective method for solving infectious disease simulation problems involving dynamic, complex spatiotemporal processes of geographical elements, such as optimization of lockdown strategies, analyses of the medical resource carrying capacity, and risk assessment of herd immunity from the perspective of geography. Key Words: geospatial heterogeneities, health geography, interregional population interaction, spatiotemporal analysis, transmission modeling.

传染病传播是一个具有显著区域差异的时空过程, 受到多种因素的影响, 例如人类的流动性和接触方式。从地理学角度来看, 对流行病的模拟和分析, 可以促进对传染机理的理解, 从而准确预测其未来趋势。现有方法没有考虑区域间人口互动与区域传染条件之间(例如, 接触概率和有效繁殖数量)异质性的反馈机理, 过于简化了传播过程, 降低了模拟的准确性。为了填补这一空白, 本文提出一个考虑时空特征的通用模型, 包括人口类型的分区模型、人口流动的流动交互模型、传染病的空间传播模型。通过合成数据和中国新冠肺炎真实数据, 本文验证了模拟和预测精度的理论假设的正确性。本文的理论贡献是: 证实了多种空间异质性的相互作用显著影响了传染病的空间传播。为了模拟具有动态复杂时空过程的传染病(例如, 从地理角度优化封锁策略、分析医疗资源承载力、群体免疫风险评估), 本文提供了一种有效的方法。

La propagación de las enfermedades infecciosas es un proceso espaciotemporal de significativas diferencias regionales que puede ser afectado por numerosos factores, tales como la movilidad humana y el modo del contacto. Desde una perspectiva geográfica, la simulación y el análisis de una epidemia puede promover el entendimiento del mecanismo de contagio y llevar a una predicción precisa de sus tendencias futuras. Los métodos disponibles fallan por no considerar el mecanismo del feedback mutuo de las heterogeneidades entre la interacción interregional de la población y las condiciones regionales de la transmisión (e.g., la probabilidad de contacto, el número efectivo de reproducción). Esta desventaja simplifica en demasía el proceso de la transmisión y disminuye la exactitud de los resultados de la simulación. Para salvar esta laguna, se propone un modelo general que toma en cuenta las características espaciotemporales, entre las cuales se incluyen la modelización por compartimentos de las categorías de la población, la modelización de la interacción de flujos de los movimientos de la población y la modelización de la propagación de una enfermedad infecciosa en el espacio. Aún más, lo correcto de una hipótesis teórica para la modelización y predicción de la exactitud de este modelo se puso a prueba con un conjunto de datos sintéticos y un conjunto de datos sobre el COVID-19 del mundo real en China, respectivamente. La contribución teórica de este artículo consistió en verificar que la interacción de múltiples tipos de heterogeneidades geoespaciales influye dramáticamente en la propagación espacial de la enfermedad infecciosa. Este modelo provee un método efectivo para la solución de problemas de la simulación de enfermedades infecciosas que involucran procesos espaciotemporales dinámicos y complejos de elementos geográficos, tales como la optimización de las estrategias de la cuarentena, los análisis de la capacidad de carga de los recursos médicos y la evaluación de los riesgos de la inmunidad de rebaño, desde la perspectiva de la geografía.

关键词:

• 地理空间异质性
• 健康地理
• 区域间人口互动
• 时空分析
• 传播模拟。

Palabras clave:

• análisis espaciotemporal
• geografía médica
• heterogeneidades geoespaciales
• modelización del contagio

Acknowledgments

We are grateful to Ling Bian and three anonymous reviewers for their insightful comments. Haiping Zhang served as the corresponding author for this article.

Supplemental Material

Supplemental data for this article can be accessed on the publisher’s site at https://doi.org/10.1080/24694452.2023.2216296. Experimental details and code in this study can be accessed at STTM-supporting_material-final.pdf.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by National Natural Science Foundation of China (No. 42201455 and 41930102).

Notes on contributors

Zitong Li

ZITONG LI is an Assistant Researcher in the Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China. E-mail: [email protected]. His research interests include GIScience, geosocial process simulation, and cultural geography.

Haiping Zhang

HAIPING ZHANG is a Postdoctoral Fellow in the Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China. E-mail: [email protected]. His research interests include GIScience, geographic big data and spatial intelligence, and human geography.

Ding Chen

DING CHEN is an Assistant Researcher in the Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China. E-mail: [email protected]. His research interests include geomatics.

Canyu Chen

CANYU CHEN is an Assistant Researcher in the Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China. E-mail: [email protected]. His research interests include geomatics.

Renyu Chen

RENYU CHEN is an Assistant Researcher in the Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China. E-mail: [email protected]. His research interests include geographical modeling and analysis.

Nuozhou Shen

NUOZHOU SHEN is an Assistant Researcher in the Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China. E-mail: [email protected]. His research interests include social system simulation and urban computing.

Yi Huang

YI HUANG is a Lecturer in the School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China. E-mail: [email protected]. His research interests include geoinformatics and geographic big data and spatial intelligence.

Liyang Xiong

LIYANG XIONG is Associate Professor in the Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China. E-mail: [email protected]. His research interests include spatial analysis and geomorphic evolution simulation.

Guoan Tang

GUOAN TANG is Professor in the Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing, 210023, China. E-mail: [email protected]. His research interests include GIScience and geomorphology.