The spatiotemporal transmission dynamics of COVID-19 among multiple regions: a modeling study in Chinese provinces

Jia, Qiaojuan; Li, Jiali; Lin, Hualiang; Tian, Fei; Zhu, Guanghu

doi:10.1007/s11071-021-07001-1

Cited by 9 publications

(12 citation statements)

References 33 publications

(54 reference statements)

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“…These methods can be categorized into two categories: mechanism-based methods and data-driven methods. Mechanism-based methods simulate disease dynamics by assuming that transmission follows some pre-defined models (e.g., mechanism or compartment models), which are determined using a set of ordinary differential equations (ODEs) [ 9 , 25 – 29 ]. Mechanism-based methods have improved the quantitative characterization of disease transmission; however, ODE-governed assumptions might not hold in reality, thus limiting the practical applicability of such methods.…”

Section: Introductionmentioning

confidence: 99%

“…Mechanism-based methods have improved the quantitative characterization of disease transmission; however, ODE-governed assumptions might not hold in reality, thus limiting the practical applicability of such methods. Although some works [ 27 – 29 ] have incorporated human mobility data into ODEs to yield predictions, the analyses were conducted at the city level and lacked the consideration of fine-scale information. Data-driven methods (e.g., deep learning methods) [ 14 , 30 , 31 ] generally use historical data (e.g., infection numbers) to train models for future predictions.…”

Section: Introductionmentioning

confidence: 99%

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TransCode: Uncovering COVID-19 transmission patterns via deep learning

Ren

Liu

et al. 2023

Infect Dis Poverty

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Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale, especially in densely populated regions. In this study, we aim to discover such fine-scale transmission patterns via deep learning. Methods We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors. First, in Hong Kong, China, we construct the mobility trajectories of confirmed cases using their visiting records. Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution. Integrating the spatial and temporal information, we represent the TransCode via spatiotemporal transmission networks. Further, we propose a deep transfer learning model to adapt the TransCode of Hong Kong, China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises: New York City, San Francisco, Toronto, London, Berlin, and Tokyo, where fine-scale data are limited. All the data used in this study are publicly available. Results The TransCode of Hong Kong, China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns (e.g., the imported and exported transmission intensities) at the district and constituency levels during different COVID-19 outbreaks waves. By adapting the TransCode of Hong Kong, China to other data-limited densely populated metropolises, the proposed method outperforms other representative methods by more than 10% in terms of the prediction accuracy of the disease dynamics (i.e., the trend of case numbers), and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level. Conclusions The fine-scale transmission patterns due to the metapopulation level mobility (e.g., travel across different districts) and contact behaviors (e.g., gathering in social-economic centers) are one of the main contributors to the rapid spread of the virus. Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TransCode: Uncovering COVID-19 transmission patterns via deep learning

Ren

Liu

et al. 2023

Infect Dis Poverty

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“…Progress has been made in various directions, e.g. by taking into account the effects of quarantine [1,2], temporary immunity [3], recurrent outbreaks [4], vaccination [5][6][7][8], different levels of population susceptibility [9], comorbidities [8,10], stratification by age [11][12][13], competitive virus strains of different severity and/or transmissibility [14,15], spatial diffusion [16], human mobility between different regions [17][18][19], availability of testing kits [20], hospital infrastructure [21] and media coverage [22,23] to name a few. In particular, the attempts to combat the disease led to the introduction of social distancing measures on the scales hardly imaginable before [24].…”

Section: Introductionmentioning

confidence: 99%

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

Ilic

Salom

Djordjevic

et al. 2022

Preprint

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While there has been much computational work on the effect of intervention measures, such as vaccination or quarantine, the influence of social distancing on the epidemics' outbursts is not well understood. We present a realistic, analytically solvable, framework for COVID-19 dynamics in the presence of social distancing measures. The model is a generalization of the compartmental SEIR model that accounts for the effects of these measures. We derive a closed-form mathematical expressions for the time dependence of epidemiological observables, in particular, the detected cases and fatalities. These analytical solutions indicate simple quantitative relations between the model variables and epidemiological observables, which give insights into cause-effect connections that underlie the outburst dynamics but are obscured in more standard (numerical) approaches. While the obtained results and conclusions are based on the study of the COVID-19 pandemic, the presented analysis has general applicability to infection outbursts. Our findings are particularly important in the emergence of new pandemics when effective pharmaceutical treatments are unavailable, and one must rely on well-timed and appropriately chosen social mitigation measures.

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“…Models for studying the pandemic spread and the impact of intervention strategies can be broadly divided into two types: partial differential equation (PDE)-based or stochastic differential equation (SDE)-based compartmental models [4][5][6][7][8][9][10][11] and agent-based Monte Carlo models [12][13][14][15][16][17][18][19][20]. The compartmental models are simple and computationally cost-effective, and thus have been deployed to analyze the spread of COVID-19 in various places, including Wuhan [5], Germany [6] and Italy [7].…”

Section: Introductionmentioning

confidence: 99%

“…However, the PDE/SDE-based compartmental models neglect the physical infection process [4][5][6][7][8][9][10][11], which loses accuracy in reflecting real transmission dynamics. Most agent-based models and softwares consider individual contacts using abstract networks and build disease propagation model on these networks.…”

Section: Introductionmentioning

confidence: 99%

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

Chen

Jiang

et al. 2022

Preprint

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The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. To forecast the transmission of COVID-19, a major challenge is the accurate assessment of the multi-scale human mobility and how they impact the infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of places in geography, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behaviour and individual health conditions on the disease outbreak and the probability of zero COVID in the population. Specifically, individuals perform power-law type of local movements within a container and global transport between different-level containers. Frequent long movements inside a small-level container (e.g. a road or a county) and a small population size reduce the local crowdedness of people and the disease infection and transmission. In contrast, travels between large-level containers (e.g. cities and nations) facilitate global disease spread and outbreak. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing total population and local people accumulation as well as restricting global travels help achieve zero-COVID. In summary, the Mob-Cov model considers more realistic human mobility in a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to investigate the pandemic dynamics and plan actions against the disease.

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The spatiotemporal transmission dynamics of COVID-19 among multiple regions: a modeling study in Chinese provinces

Cited by 9 publications

References 33 publications

TransCode: Uncovering COVID-19 transmission patterns via deep learning

TransCode: Uncovering COVID-19 transmission patterns via deep learning

An Analytical Framework for Understanding Infection Progression Under Social Mitigation Measures

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

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