An Extended SIR Model to Explore the Impact of Syndromic Data Sources on Social Distancing Policy

摘要

Epidemics such as seasonal influenza are a major worldwide public health concern, and therefore early outbreak detection and outbreak management are prioritized goals of public health professionals. Syndromic surveillance focuses on discovering the earliest possible indicators of a health problem, and therefore much of the focus in on pre-diagnostic data. Information technology has created new opportunities for syndromic surveillance, for example, geographical internet search data can now estimate the probability that a random physician visit was related to an influenza outbreak. However, there are also important challenges in adopting this use of new technology, and the potential harmful side-effects (in terms of public confidence) if the real-time data models are not sufficiently robust. This paper presents an exploratory model that captures the dynamics of information quality, and the potential effect of syndromic information quality on social distancing measures.