We all hold models of reality in our heads, into which we feed our observations. This is how we make sense of the world. Mathematical models follow the same principle: They reduce complexity. The spread of infectious diseases is a highly complex process that we can never follow in every detail. The great advantage of models is that they enable us to map the process systematically by incorporating evidence and breaking it down into essential mechanisms. They are like a movie projector that shows us how a disease will play out in the future.
I calculate how infectious diseases spread and identify which interventions and vaccination strategies are most effective. During the Covid-19 pandemic, we saw that there are an incredible number of potential interventions. The clinical approach is to try to prevent serious illness in people who develop symptoms. This is essential, but rarely sufficient in the context of a pandemic. If the virus spreads unchecked, hospitals could become overwhelmed within weeks of admitting the first seriously ill patient.
This is where models can support systematic decision-making. Differential equations can be used to describe the spread of disease. We start with situational awareness – trying to understand how many people are infected. From this we can work out how bad it can get, how the disease spreads – and how best to contain it. This area of research is now being strengthened in Germany: A modeling network is being set up, and I am the first vaccine modeler to be appointed to the Standing Committee on Vaccination.
To build reliable models, we need expertise in medicine, especially immunologists and epidemiologists. We base our models on surveillance data – the routine collection of data about cases of infectious diseases, which represent only the tip of the iceberg. To describe the dynamics of infectious diseases, we also rely on carriage studies, which record who is infected and who can spread the disease. In addition, we use serological data that map the immune response and provide information on who became infected and who did not, allowing us to draw conclusions about the spread of the disease. We take all this evidence into account when weighing up the options: What is the benefit of one intervention versus another? What burden of disease can be avoided? And what are the costs? In Germany, for example, we are currently considering what preventive measures are appropriate for respiratory syncytial virus.
I work here in Berlin at the Charité Center for Global Health, so I factor in the global perspective as well. Our models help make vaccination programs more efficient, minimizing costs for poorer countries and maintaining, or even increasing, global vaccination rates. For example, they show how herd immunity can be used: Vaccinating children against pneumococcal disease not only helps them, but also benefits adults even though they have not been protected directly themselves, because vaccinating children greatly reduces the spread of the disease.
My models have been used to inform health policy strategies. In the early days of Ebola, we worked with Médecins Sans Frontières in Sierra Leone to predict the spread of the disease. This led to a fundamental rethink of the scale of the problem on the ground. At the World Health Organization, my models for dengue fever and pneumococcal disease have informed global vaccination recommendations.
Each infectious disease has its own characteristics. My goal is to model their complexity and subtleties to promote structured thinking that will improve decision-making. The key optimization variable for me is saving lives.