Infectious Diseases and HIV Conference

Infectious disease modeling has been an important part of epidemiology. It depicts how diseases act and can help researchers and public health officials understand the dynamics of a particular disease, predict possible outbreaks, and make informed decisions.

Thus, in this session, principles of infectious disease modeling and several approaches applied in public health will be discussed. Infectious disease modeling is a simulation of the dynamics and spread of infectious diseases in a population through the use of mathematical and computational methods.

The session will expose the participants to the simple approach of modeling as in basic concepts in R0, dynamics of transmissions and factors affecting the spread of disease. This is done as participants will learn various uses of models for multiple scenarios and different possible outcomes about interventions.

There are several modeling approaches to infectious diseases of different strengths and weaknesses. Critical ones to be covered in this session include compartmental models, such as SIR, SEIR, and agent-based models and network models. Here, the ways through which the models can be built, the types of data that they will require, and the ways through which they may be used to infer understanding about influenza, COVID-19, and malaria will be discussed.

Modeling is crucial in applications relating to public health, whether predicting outbreaks or in trying to evaluate the best possible intervention. The session presents cases describing how models have been used for guidance in campaigns for vaccination, social distancing measures, and allocations of resources during an outbreak.

This will give delegates a feel for how modeling may underpin decision-making, enhance preparedness, and even optimize response efforts during health emergencies. Infectious disease modeling can be truly enlightening but far from easy. Uncertainty as relevant to model construction will include aspects like data quality, issues of parameter estimation, and those assumptions made in the building of a model and sensitivity analysis/model validation for enhancing the reliability of prediction and informing public health strategy.

The Future of Infectious Disease Modeling Future New Technologies Sources of Data Future Possible futures for model development-research applications leveraging real-time data, machine learning, and artificial intelligence to improve model accuracy and responsiveness Value interdisciplinary and how new model approaches also advance public health applications.