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Dataset for: Probabilistic forecasting in infectious disease epidemiology: The thirteenth Armitage lecture
dataset
posted on 2017-07-17, 17:01 authored by Leonhard Held, Sebastian Meyer, Johannes BracherRoutine surveillance of notifiable infectious diseases gives rise to
daily or weekly counts of reported cases stratified by region and age
group. From a public health perspective, forecasts of infectious
disease spread are of central importance. We argue that such forecasts
need to properly incorporate the attached uncertainty, so should be
probabilistic in nature. However, forecasts also need to take into account
temporal dependencies inherent to communicable diseases, spatial
dynamics through human travel, and social contact patterns between age
groups. We describe a multivariate time series model for weekly
surveillance counts on norovirus gastroenteritis from the 12 city
districts of Berlin, in six age groups, from week 2011/27 to week
2015/26. The following year (2015/27 to 2016/26) is used to assess the
quality of the predictions. Probabilistic forecasts of the total
number of cases can be derived through Monte Carlo simulation, but
first and second moments are also available analytically. Final size
forecasts as well as multivariate forecasts of the total number of
cases by age group, by district, and by week are compared across
different models of varying complexity. This leads to a more general
discussion of issues regarding modelling, prediction and evaluation of
public health surveillance data.