Incorporating Dynamic Flight Network in SEIR to Model Mobility between Populations (2010.01408v1)

Abstract: Current efforts of modelling COVID-19 are often based on the standard compartmental models such as SEIR and their variations. As pre-symptomatic and asymptomatic cases can spread the disease between populations through travel, it is important to incorporate mobility between populations into the epidemiological modelling. In this work, we propose to modify the commonly-used SEIR model to account for the dynamic flight network, by estimating the imported cases based on the air traffic volume as well as the test positive rate at the source. This modification, called Flight-SEIR, can potentially enable 1). early detection of outbreaks due to imported pre-symptomatic and asymptomatic cases, 2). more accurate estimation of the reproduction number and 3). evaluation of the impact of travel restrictions and the implications of lifting these measures. The proposed Flight-SEIR is essential in navigating through this pandemic and the next ones, given how interconnected our world has become.

Incorporating Dynamic Flight Network in SEIR to Model Mobility between Populations

The paper "Incorporating Dynamic Flight Network in SEIR to Model Mobility between Populations" presents a novel approach to enhancing traditional epidemiological models by incorporating dynamic networks of flight data, significantly extending the SEIR model's capabilities for modeling highly interconnected populations. The authors propose an adaptation of the classic SEIR model, termed Flight-SEIR, which integrates real-world travel data to better estimate the spread of infectious diseases like COVID-19 between different populations due to air travel.

Key Contributions and Methodologies

1. Integration of Flight Network into SEIR: The core contribution of the paper is the integration of a dynamic flight network into the SEIR model, which traditionally assumes populations as isolated entities. The Flight-SEIR model acknowledges the flow of pre-symptomatic and asymptomatic cases through air travel, thus providing a more realistic framework for understanding pandemic dynamics in a globalized world.
2. Early Detection and Monitoring: The model can predict early outbreaks within a region by identifying likely sources of disease importation based on air traffic data. This feature is particularly relevant for pandemics where containment and mitigation strategies rely on early detection of potential hotspots.
3. Assessment and Implications of Travel Restrictions: By simulating scenarios with varying degrees of travel restrictions, the Flight-SEIR model enables the examination of the effectiveness of such interventions. The model’s results suggest travel-related non-pharmaceutical interventions (NPIs) have a significant impact on disease spread, which could inform policy decisions regarding the implementation or relaxation of travel bans.
4. Accurate Estimation of Reproduction Number (R0): Flight-SEIR provides an improved methodology for estimating the basic reproduction number, R0, by separating external infections from domestic transmission. This leads to a more nuanced understanding of a disease’s spread within populations, reflecting the influence of imported cases on transmission dynamics.
5. Risk Evaluation for Lifting Restrictions: By using projected air traffic data, Flight-SEIR evaluates the risk associated with lifting travel restrictions. The model supports decision-makers in predicting potential spikes in infection rates, thereby assisting in planning and resource allocation.

Results and Analysis

The empirical results presented in the paper underscore the significance of incorporating flight data in epidemiological models. The Flight-SEIR model outperformed traditional SEIR models in several scenarios, providing closer alignment with actual infection data when accounting for the influx of exposed individuals through air travel. Notably, the model demonstrates that even when R0 is less than one, continued exposure from international travel can sustain an outbreak, challenging traditional assumptions of disease extinction under such conditions.

In assessing travel restrictions, the model found substantial differences in infection rates based on the presence or absence of these measures, highlighting the critical role of NPIs in controlling pandemics. The research also revealed significant variations in risk when reopening borders, emphasizing that travel policies should be tailored to specific international connections based on flight volume and prevalence rates in different regions.

Implications for Future Research

The approach outlined in the paper opens several avenues for future research. As more granular and real-time travel data becomes available, it could refine and enhance the predictive accuracy of such models. Moreover, extending the model to incorporate other forms of mobility and integrating with additional compartmental models could provide a more holistic view of pandemic spread. Future investigations might focus on multi-population simulations to evaluate regional interactions more comprehensively.

In conclusion, the incorporation of dynamic flight networks into the SEIR model represents a substantial methodological advancement in epidemiological modeling. It equips researchers and policymakers with a powerful tool for understanding the dynamics of infectious diseases in our interconnected world, ultimately aiding in the formulation of timely and effective public health responses.