Previously we were assimilating only the positive records. That is, if node 342 enters hospital at some time window t we would assimilate the risk <H_{342}(t)> = 1. Now we also assimilate the risk of <H_i(t)>=0 for all i !=342. Note this is a more expensive task, as it always requires the assimilation of the state H (and for death records D) for all nodes.
We demonstrate the use case in the simple_epidemic_with_da_health_and_death_records.py. We create observations negative_health_record and negative_death_record, by setting a bool_type=False.
We set the risk of - for example H - for of every node with statuses !='H' to a small value near zero. In practice this must be much smaller than we have set previously, as it is applied to many more nodes in the network (and thus the implication on the whole population is more significant).
Previously we were assimilating only the positive records. That is, if node
342
enters hospital at some time windowt
we would assimilate the risk<H_{342}(t)> = 1
. Now we also assimilate the risk of<H_i(t)>=0
for alli !=342
. Note this is a more expensive task, as it always requires the assimilation of the stateH
(and for death recordsD
) for all nodes.We demonstrate the use case in the
simple_epidemic_with_da_health_and_death_records.py
. We create observationsnegative_health_record
andnegative_death_record
, by setting abool_type=False
.We set the risk of - for example
H
- for of every node with statuses!='H'
to a small value near zero. In practice this must be much smaller than we have set previously, as it is applied to many more nodes in the network (and thus the implication on the whole population is more significant).Co-authored (in spirit) with @agarbuno