부산대학교 도서관

The outbreak of the severe acute respiratory syndrome coronavirus 2 started in Wuhan, China, towards the end of 2019 and spread worldwide. The rapid spread of the disease can be attributed to many factors including its high infectiousness and the high rate of human mobility around the world. Although travel/movement restrictions and other non-pharmaceutical interventions aimed at controlling the disease spread were put in place during the early stages of the pandemic, these interventions did not stop COVID-19 spread. To better understand the impact of human mobility on the spread of COVID-19 between regions, we propose a hybrid gravity-metapopulation model of COVID-19. Our modeling framework has the flexibility of determining mobility between regions based on the distances between the regions or using data from mobile devices. In addition, our model explicitly incorporates time-dependent human mobility into the disease transmission rate, and has the potential to incorporate other factors that affect disease transmission such as facemasks, physical distancing, contact rates, etc. An important feature of this modeling framework is its ability to independently assess the contribution of each factor to disease transmission. Using a Bayesian hierarchical modeling framework, we calibrate our model to the weekly reported cases of COVID-19 in thirteen local health areas in Metro Vancouver, British Columbia (BC), Canada, from July 2020 to January 2021. We consider two main scenarios in our model calibration: using a fixed distance matrix and time-dependent weekly mobility matrices. We found that the distance matrix provides a better fit to the data, whilst the mobility matrices have the ability to explain the variance in transmission between regions. This result shows that the mobility data provides more information in terms of disease transmission than the distances between the regions. Author summary: Mathematical modelling was at the forefront of the fight against COVID-19. Many models were used to study the disease dynamics and the effectiveness of intervention strategies to control the disease spread. Due to the high infectiousness of COVID-19 and the high rate of human mobility, understanding the disease spread between regions became important. Many of the previously developed models for studying disease dynamics between regions described human mobility based on either the distances between the regions or using other forms of mobility data. In addition, some of these models can only be used to study the spread of COVID-19 from an epicentre to neighbouring regions/cities. These models are suitable for only the early stages of the disease outbreak. We developed a hybrid gravity-metapopulation modelling framework for studying the spread of diseases between regions. Our model provides the flexibility of using the distances between the regions and mobile device data as a proxy for human mobility between regions. Furthermore, our framework is suitable for studying disease dynamics at any epidemic stage. It accounts for disease spread from each region to the remaining regions, irrespective of the number of reported cases in each region and their population sizes. [ABSTRACT FROM AUTHOR]