Combinational Recommendation of Vaccinations, Mask-Wearing, and Home-Quarantine to Control Influenza in Megacities: An Agent-Based Modeling Study With Large-Scale Trajectory Data

Abstract

The outbreak of COVID-19 stimulated a new round of discussion on how to deal with respiratory infectious diseases. Influenza viruses have led to several pandemics worldwide. The spatiotemporal characteristics of influenza transmission in modern cities, especially megacities, are not well-known, which increases the difficulty of influenza prevention and control for populous urban areas. For a long time, influenza prevention and control measures have focused on vaccination of the elderly and children, and school closure. Since the outbreak of COVID-19, the public's awareness of measures such as vaccinations, mask-wearing, and home-quarantine has generally increased in some regions of the world. To control the influenza epidemic and reduce the proportion of infected people with high mortality, the combination of these three measures needs quantitative evaluation based on the spatiotemporal transmission characteristics of influenza in megacities. Given that the agent-based model with both demographic attributes and fine-grained mobility is a key planning tool in deploying intervention strategies, this study proposes a spatially explicit agent-based influenza model for assessing and recommending the combinations of influenza control measures. This study considers Shenzhen city, China as the research area. First, a spatially explicit agent-based influenza transmission model was developed by integrating large-scale individual trajectory data and human response behavior. Then, the model was evaluated across multiple intra-urban spatial scales based on confirmed influenza cases. Finally, the model was used to evaluate the combined effects of the three interventions (V: vaccinations, M: mask-wearing, and Q: home-quarantining) under different compliance rates, and their optimal combinations for given control objectives were recommended. This study reveals that adults were a high-risk population with a low reporting rate, and children formed the lowest infected proportion and had the highest reporting rate in Shenzhen. In addition, this study systematically recommended different combinations of vaccinations, mask-wearing, and home-quarantine with different compliance rates for different control objectives to deal with the influenza epidemic. For example, the “V45%-M60%-Q20%” strategy can maintain the infection percentage below 5%, while the “V20%-M60%-Q20%” strategy can maintain the infection percentage below 15%. The model and policy recommendations from this study provide a tool and intervention reference for influenza epidemic management in the post-COVID-19 era.

A pattern shift in SARS-CoV-2 Omicron variant transmission after the city lockdown--observational study based upon daily reported addresses of infected cases

“…To be convinent we used the daily reported address directly to generate one day lag disease transmission graph, this means the infection generation (doubling time) was about 24 hours. This had not been demonstrated but was quite close to the actual data of Omicron variant transmission, 1.28 days in a community in Hong Kong [55]; or the latent period ranged from 33 to75 hours [20]. Aimed to keep pace with the transmission of Omicron variant an ambitious strategy had been proposed and implemented to run the relevent human resources at its full speed.…”

“…The trajectory data of individuals had been used to track potential exposure and the infection 5,9,16,29,48,56 , however, these data were not accessible for the CDC work force and reliable models to identify the persons of potential exposure might be developing. The point-based chronological graph could be viewed as a meso-scale method to visualize and analyze the connectivity (base upon some rules) and interactions between spatial locations.…”

A novel approach to model the role of mobility suppression and vaccinations in containing epidemics in a network of cities

“…The work ( Jewell et al., 2021 ) concludes that mobility is not a reliable indicator of infection rates. In addition, population age distributions and vaccination levels tend to play a crucial role in deciding the best control strategies ( Guo et al., 2022 ; Patel et al., 2021 ; Zhang et al., 2022 ). Vaccination coverage was particularly found impactful in reducing infection risks.…”