Periodic Reporting for period 2 - SpatialStructure (How does population structure influence host-pathogen dynamics in mosquito-transmitted diseases?)
Berichtszeitraum: 2023-12-01 bis 2024-11-30
Every year, millions of people are threatened by mosquito-borne diseases such as dengue, chikungunya, Zika, and malaria. Thanks to computational advancements, we can now create agent-based computer models with unprecedented biological realism to study how these diseases spread. While these complex models give us valuable insights, they are often outperformed in disease forecasting by their simpler mathematical counterparts.
One reason for this could be that we still do not fully understand how to accurately model the spatial aspects of disease spread. Currently, in-depth sensitivity analysis for these complex and parameter-rich agent-based models that would help to address this problem are hardly conducted because they tend to be computationally intensive. In this project, we leveraged Gaussian processes (GPs) as a powerful statistical framework to analyze complex agent-based models more efficiently. This in turn allows us to understand which parameters in the agent-based disease transmission models are truly shaping disease dynamics.
We developed an agent-based model to examine disease transmission across different scenarios involving human movement, social structures, and seasonality in infection risk. By training GPs on the simulation outcomes, we were able to build surrogate models that can quickly predict outbreak probability, epidemic severity, and epidemic duration. This approach also enabled comprehensive sensitivity analyses, revealing that human mobility and infection probability after contact are the primary drivers of observing epidemic outbreaks, while seasonality and the timing of disease introduction shape epidemic patterns. When applied to 12 years of empirical dengue incidence data, the GPs identified potential dengue transmission hotspots pointing to areas for further investigation.
In conclusion, this project demonstrates the potential of GPs to efficiently analyze complex epidemiological agent-based models. By using GPs, we gained valuable insights into the key drivers of epidemic dynamics, particularly the interactions between infection probability, human mobility, and seasonality. Importantly, when applied to empirical data, GPs also identified areas that could be important disease transmission hotspots. Our work highlights both the possibilities and challenges of using GPs to explore complex agent-based models, laying the groundwork for more realistic and computationally efficient models in the future.
One reason for this could be that we still do not fully understand how to accurately model the spatial aspects of disease spread. Currently, in-depth sensitivity analysis for these complex and parameter-rich agent-based models that would help to address this problem are hardly conducted because they tend to be computationally intensive. In this project, we leveraged Gaussian processes (GPs) as a powerful statistical framework to analyze complex agent-based models more efficiently. This in turn allows us to understand which parameters in the agent-based disease transmission models are truly shaping disease dynamics.
We developed an agent-based model to examine disease transmission across different scenarios involving human movement, social structures, and seasonality in infection risk. By training GPs on the simulation outcomes, we were able to build surrogate models that can quickly predict outbreak probability, epidemic severity, and epidemic duration. This approach also enabled comprehensive sensitivity analyses, revealing that human mobility and infection probability after contact are the primary drivers of observing epidemic outbreaks, while seasonality and the timing of disease introduction shape epidemic patterns. When applied to 12 years of empirical dengue incidence data, the GPs identified potential dengue transmission hotspots pointing to areas for further investigation.
In conclusion, this project demonstrates the potential of GPs to efficiently analyze complex epidemiological agent-based models. By using GPs, we gained valuable insights into the key drivers of epidemic dynamics, particularly the interactions between infection probability, human mobility, and seasonality. Importantly, when applied to empirical data, GPs also identified areas that could be important disease transmission hotspots. Our work highlights both the possibilities and challenges of using GPs to explore complex agent-based models, laying the groundwork for more realistic and computationally efficient models in the future.
During my fellowship, I engaged in two distinct projects:
1. Understanding Spatial Dynamics in Disease Spread
In this project, I investigated the impact of spatial structure on the accurate prediction of disease spread. I identified a critical dispersal threshold below which disease transmission dynamics exhibit spatial heterogeneity. These findings were validated through agent-based simulations across a continuous two-dimensional landscape. The results of this project are available as a preprint on bioRxiv (https://doi.org/10.1101/2023.06.23.546298).
2. Agent-Based Simulation Framework for Dengue Dynamics
In the second project, I designed and implemented an agent-based disease transmission model. While abstract, the agent-based model is loosely inspired by the transmission dynamics of dengue fever.
The implemented agent-based simulation framework allows seamless transition between simple, panmictic populations and highly structured populations by adjusting a few continuous parameters. To expedite sensitivity analysis, I trained a Gaussian Process (GP) surrogate model on agent-based simulation outcomes to predict disease dynamics. GPs act as an interpolator between sparsely sampled data points and can thereby efficiently approximate computationally intensive agent-based models. This statistical framework allowed me to identify the input parameters and their interactions that have the most influence on the model output.
Variance-based sensitivity revealed that two factors—average human mobility and the infection probability after contact—are the primary drivers of epidemic outbreaks in the agent-based model. Seasonal patterns, along with the timing of the first infectious case, shape how epidemics unfold after they have been established. Although our agent-based model is rather abstract, applying the GPs to real-world data helped identify areas with higher infection probability estimates, which could be interesting for further study. The findings from this project will be shared as a preprint on bioRxiv by December 2024.
1. Understanding Spatial Dynamics in Disease Spread
In this project, I investigated the impact of spatial structure on the accurate prediction of disease spread. I identified a critical dispersal threshold below which disease transmission dynamics exhibit spatial heterogeneity. These findings were validated through agent-based simulations across a continuous two-dimensional landscape. The results of this project are available as a preprint on bioRxiv (https://doi.org/10.1101/2023.06.23.546298).
2. Agent-Based Simulation Framework for Dengue Dynamics
In the second project, I designed and implemented an agent-based disease transmission model. While abstract, the agent-based model is loosely inspired by the transmission dynamics of dengue fever.
The implemented agent-based simulation framework allows seamless transition between simple, panmictic populations and highly structured populations by adjusting a few continuous parameters. To expedite sensitivity analysis, I trained a Gaussian Process (GP) surrogate model on agent-based simulation outcomes to predict disease dynamics. GPs act as an interpolator between sparsely sampled data points and can thereby efficiently approximate computationally intensive agent-based models. This statistical framework allowed me to identify the input parameters and their interactions that have the most influence on the model output.
Variance-based sensitivity revealed that two factors—average human mobility and the infection probability after contact—are the primary drivers of epidemic outbreaks in the agent-based model. Seasonal patterns, along with the timing of the first infectious case, shape how epidemics unfold after they have been established. Although our agent-based model is rather abstract, applying the GPs to real-world data helped identify areas with higher infection probability estimates, which could be interesting for further study. The findings from this project will be shared as a preprint on bioRxiv by December 2024.
The project has made significant strides in advancing the field of epidemiological modeling through the introduction of global sensitivity analysis within the context of agent-based models—an area that has hitherto lacked focused attention on in-depth sensitivity analysis due to computational limitations.
These methodological advancements provide a foundation for more sophisticated sensitivity analyses.
This, in turn, promises a deeper understanding of the underlying dynamics, and holds the potential to inform disease control strategies, thereby contributing to the broader field of public health.
These methodological advancements provide a foundation for more sophisticated sensitivity analyses.
This, in turn, promises a deeper understanding of the underlying dynamics, and holds the potential to inform disease control strategies, thereby contributing to the broader field of public health.