Infectious diseases are a leading cause of morbidity and mortality worldwide, particularly in low- and middle-income countries according to the World Health Organization. Consequently, they cause significant economic and social burden in the form of high healthcare costs and productivity disruptions.

Public health officials turn to evidence as the basis for their health policies to control the spread of infections. However, many nuanced factors can influence variations in the Effective Reproductive Number (the number of infections caused by a single infected individual) of communicable diseases, making it difficult to answer such basic questions as:

• Can we reduce cases enough to eliminate infection?
• What is the best timing for interventions?
• Can we permanently prevent infections?
• How cost effective is a given intervention?

Fortunately, dynamic mathematical models can be used to help answer these questions and to select the most effective and cost-efficient mitigation strategies. In this whitepaper, we explain the basic measures used to quantify infectious disease transmission and introduce the principles of dynamic modelling. A case study drawn from actual practice demonstrates how dynamic transmission modelling can aid in infectious disease policy making.