Transversal topics

Modelling practices

Modelling practices

Effective modeling practices are essential to ensure that models accurately represent epidemiological systems. Conceptually, this involves selecting appropriate model structures, formulating realistic assumptions, and designing models that capture the essential dynamics of disease transmission and control. Programming aspects encompass the implementation of models using robust and efficient code, leveraging modern software engineering principles such as...

Data

High-quality data is crucial for building and validating epidemiological models, serving as the foundation for accurate and meaningful insights. Ensuring data quality involves addressing aspects such as accuracy, completeness, consistency, and timeliness, which are essential for reliable model outcomes. Adhering to the FAIR principles, making data Findable, Accessible, Interoperable, and Reusable, enhances data management and...

Parameter inference

Parameter inference

Parameter inference involves estimating the values of model parameters that best fit the observed data. Techniques such as Bayesian inference, maximum likelihood estimation, and machine learning are commonly used. Accurate parameter estimation is vital for model credibility and for understanding key factors influencing disease spread. Challenges include dealing with uncertainty, limited data, and computational complexity.

Ensemble modelling

Ensemble modelling

Ensemble modelling combines multiple models to improve predictive performance and account for uncertainty. By integrating diverse modelling approaches, ensemble methods provide more robust forecasts and insights. Intervention ranking leverages ensemble outputs to prioritize control strategies based on effectiveness and feasibility. This approach supports evidence-based decision-making in implementing public health measures and resource allocation.

Forecasting

Forecasting aims to predict the future course of disease outbreaks using epidemiological models. Accurate forecasts are essential for proactive public health responses and for mitigating the impact of epidemics. Key challenges include handling uncertainty, adapting to changing conditions, and integrating real-time data. Advanced forecasting techniques enhance the ability to anticipate trends and inform timely interventions.

Within-host dynamics

Within-host dynamics

Within-host dynamics focus on the interactions between pathogens and the host's biological systems. Modeling these processes helps elucidate mechanisms of infection, immune response, and pathogen evolution. Understanding within-host dynamics is crucial for developing effective treatments, vaccines, and predicting disease outcomes. Challenges include capturing complex biological interactions and integrating multi-scale data.

Phylodynamics

Phylodynamics combines evolutionary biology and epidemiology to study the genetic changes of pathogens over time. By analyzing genetic data, models can trace transmission pathways, estimate mutation rates, and understand the evolutionary pressures shaping pathogens. Phylodynamic approaches provide insights into the spread and adaptation of diseases, informing strategies for surveillance and control.

Inter-species interface

Inter-species interface

The inter-species interface examines the interactions between different species that facilitate disease transmission, such as zoonotic spillover from animals to humans. Modeling these interfaces helps identify critical points of transmission, assess risks, and develop strategies to prevent cross-species outbreaks. Challenges include understanding complex ecological relationships and integrating data across species boundaries.

Multi-scale modelling

Multi-scale modelling

Multi-scale modelling integrates processes occurring at different biological and ecological levels, from molecular interactions to population dynamics. This approach provides a comprehensive understanding of disease systems by linking within-host processes with population-level outcomes. Multi-scale models address the complexity of epidemiological phenomena and enhance the ability to predict and control disease spread across various contexts.

Modelling-policy nexus

Modelling-policy nexus

The modelling-policy nexus explores the relationship between epidemiological models and public health policy-making. Models provide evidence and projections that inform policy decisions, such as implementing control measures or allocating resources. Effective communication and collaboration between modelers and policymakers are essential to ensure that models address relevant questions and that policies are informed by the best...

Interconnecting models

Interconnecting models

Interconnecting epidemiological models with other types of models, such as economic, environmental, or social models, enables a more holistic understanding of disease dynamics and their broader impacts. This integration facilitates the assessment of multifaceted interventions and the exploration of complex interactions between health and other societal factors. Challenges include ensuring compatibility between different modeling frameworks...