Background

Surveillance systems in public health are traditionally syndromic, only documenting the number of cases in a population. Recent technological advances have produced more accurate and sophisticated data at an even more affordable cost. Multiplex serology allows to semi-automatically process blood samples for several dozens of antigens at once, which informs the immunological status of a population and thus the potential risk of an outbreak before it occurs.

The development of digital social networks (Twitter, Facebook, etc.) and the popularity of personal mobile technologies such as smartphones or wearable tracking devices have generated massive real-time data that can be used to characterise the interactions between individuals in a population. From these data, various types of epidemiological parameters can be estimated. Unfortunately, the task remains cumbersome due to the lack of easy-to-use analytical tools.

Aims

This study aims to develop an R package that will offer easy-to-use and efficient tools to infer infectious disease parameters based on serological and social contact data. The first version will be based on the book “Modeling Infectious Disease Parameters Based on Serological and Social Contact Data – A Modern Statistical Perspective” (Hens et al. 2012), which compiles a wide spectrum of methods developed since the eighties. Subsequent development versions will include more recent methods developed over the past decade, including those currently in development by Facebook’s Data for Good engineers.