12,555 research outputs found
Predicting epidemic evolution on contact networks from partial observations
The massive employment of computational models in network epidemiology calls
for the development of improved inference methods for epidemic forecast. For
simple compartment models, such as the Susceptible-Infected-Recovered model,
Belief Propagation was proved to be a reliable and efficient method to identify
the origin of an observed epidemics. Here we show that the same method can be
applied to predict the future evolution of an epidemic outbreak from partial
observations at the early stage of the dynamics. The results obtained using
Belief Propagation are compared with Monte Carlo direct sampling in the case of
SIR model on random (regular and power-law) graphs for different observation
methods and on an example of real-world contact network. Belief Propagation
gives in general a better prediction that direct sampling, although the quality
of the prediction depends on the quantity under study (e.g. marginals of
individual states, epidemic size, extinction-time distribution) and on the
actual number of observed nodes that are infected before the observation time
Heterogeneous social interactions and the COVID-19 lockdown outcome in a multi-group SEIR model
We study variants of the SEIR model for interpreting some qualitative
features of the statistics of the Covid-19 epidemic in France. Standard SEIR
models distinguish essentially two regimes: either the disease is controlled
and the number of infected people rapidly decreases, or the disease spreads and
contaminates a significant fraction of the population until herd immunity is
achieved. After lockdown, at first sight it seems that social distancing is not
enough to control the outbreak. We discuss here a possible explanation, namely
that the lockdown is creating social heterogeneity: even if a large majority of
the population complies with the lockdown rules, a small fraction of the
population still has to maintain a normal or high level of social interactions,
such as health workers, providers of essential services, etc. This results in
an apparent high level of epidemic propagation as measured through
re-estimations of the basic reproduction ratio. However, these measures are
limited to averages, while variance inside the population plays an essential
role on the peak and the size of the epidemic outbreak and tends to lower these
two indicators. We provide theoretical and numerical results to sustain such a
view
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