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

Abstract

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.

Predicting High-Risk Individuals in Disease Outbreaks Using Fuzzy Logic-Based Contact Tracing Model.

“…The social risk as well as the effective reproduction number are high in these regions [21]. The authors in [22] proposed a novel Mob-Cov model. Its main goal is to study the impact of human traveling behavior and individual health conditions on the disease outbreak by combining a stochastic agent-based modeling and hierarchical structures of spatial geographical places.…”

Modeling COVID-19 Transmission in Closed Indoor Settings: An Agent-Based Approach with Comprehensive Sensitivity Analysis

“…COVID-19, caused by SARS-CoV-2, is a highly contagious disease that spreads through various transmission pathways such as physical contact, inhalation of small particles spread by coughing or sneezing, and even indirectly through contaminated surfaces [1,2]. A high probability of infection is present inside closed indoor settings (e.g., schools, factories, or offices) due to a higher likelihood of close contact [3].…”

“…ABMs offer the advantage of incorporating population heterogeneity wherein heterogeneous agents interact with each other and the environment, leading to an emergence of the overall behavior of the system [18]. Similar to compartment and CA models, ABMs have been extensively used to simulate the spread of diseases, including malaria [19], cutaneous leishmaniasis [20], mumps [21], avian influenza [22], AIDS [23], and COVID-19 [1,24]. Besides ABMs, networked metapopulation models can also consider population diversity and spatial aspects [25].…”