  We consider two closely related problems: anomaly detection in sensor
networks and testing for infections in human populations. In both problems, we
have $n$ nodes (sensors, humans), and each node exhibits an event of interest
(anomaly, infection) with probability $p$. We want to keep track of the
anomaly/infection status of all nodes at a central location. We develop a
$group$ $updating$ scheme, akin to group testing, which updates a central
location about the status of each member of the population by appropriately
grouping their individual status. Unlike group testing, which uses the expected
number of tests as a metric, in group updating, we use the expected age of
information at the central location as a metric. We determine the optimal group
size to minimize the age of information. We show that, when $p$ is small, the
proposed group updating policy yields smaller age compared to a sequential
updating policy.
