Mathematical models for infectious diseases surveillance

Abstract

Quantitative methods are unquestionably valuable tools for disease propagation models, and have trackedattention of mathematicians, physicists, and statisticians for decades. Mathematical models for diseasepropagation have become more and more sophisticated, incorporating different sets of heterogeneities inthe hope to be as accurate as possible while still being mathematically and computationally tractable. Sociodemographic characteristics such as age distribution, study and work activities, and local and long-range mobility, have been key to study historical infection data and devise scenario assessment. Recent outbreaks of global concern such as the influenza H1N1pdm09, SARS, MERS-CoV, Ebola, Zika, and thecurrent COVID-19 have tested our ability to produce information that is fundamental for preparedness:reproductive number estimation, importation case probability, short-term predictions of disease evolution,and so on. On the other hand, endemic diseases ask for a complementary approach to disease propagationmodels, which is that of disease surveillance. How do we properly separate baseline activity,characterized by stochastic fluctuations and isolated case bursts, from epidemic activity? In other words,how many weekly cases we need to say that the current season of “disease A” have started? Is that levelthe same everywhere? Does influenza-like illnesses always start in the winter? As for real-timesurveillance, are the available data delivered in a timely manner or is there significant delay between caseoccurrence and official report? In this talk we will go over both approaches highlighting the nuances ofeach, their importance to provide relevant information to public health actions, and, hopefully, spark yourinterest in joining the field.