The updated Italian national plan for preparedness and response to an influenza
pandemic was published in February 2006 , in response to recommendations
and checklists on national influenza pandemic preparedness plans issued
by the World Health Organization .
The Italian plan includes the following preventive measures:
vaccination, prioritising the following
1. personnel of health services and other
2. high risk groups including >=65
years old individuals and all-age individuals with underlying chronic
3. healthy children and adolescents from
2 to 18 years,
4. healthy adults;
||social distancing measures.
To evaluate the impact of these preventive measures on the national population,
a mathematical model was developed by a working group that included researchers
from the Universities of Trento, Pisa and Rome, and the National Institute
of Health (Istituto Superiore di sanità, ISS) The results were published
in an ISS report in December 2006  and are summarised here.
Modelling pandemic influenza transmission and control measures
A SEIR (susceptible; infected but not infectious; infectious; resistent,
that is, immune to re-infection) deterministic model, with a stochastic
simulation component was used. An R0 (the basic reproductive number) of
1.8 was assumed, with a cumulative attack rate (AR) of 35% .
We modelled the impact of vaccination, antiviral prophylaxis and measures
aimed to increase social distancing. For each measure, various scenarios
were considered, assuming different target populations and duration of interventions
As standard parameters, we considered that the target population would
receive the first dose of vaccine 12 weeks after the onset of the index
case in Italy, and the second dose four weeks after that. This two dose
cycle was assumed to be 70% effective, starting 15 days after the administration
of the second dose. Vaccine coverage was fixed at 60%.
Antiviral prophylaxis of uninfected individuals was assumed to reduce susceptibility
to infection by 30% . We supposed that only household contacts of influenza
cases would be treated, limiting the use of antiviral prohlylaxis to a maximum
of 8 weeks after the onset of illness in the index case.
School closure lasting 3 weeks was assumed to start 2, 4, or 8 weeks after
the onset of the index case. As the same time, it was assumed that public
offices not providing essential services would be closed for 4 weeks, and
recreational venues such as theatres and cinemas, for 8 weeks.
In absence of control measures, the epidemic peak would be reached approximately
4 months after the first case onset, with a total of 3 million cases during
the peak week. The epidemic would be over in 7 months, with a cumulative
attack rate of 35% (approximately 20 million cases).
The interventions considered, when implemented singly, would reduce the
cumulative attack rate to, at best, approximately 32% (vaccination only).
Using either prophylaxis with antiviral drugs or social distancing measures
alone would have no effect in reducing the cumulative AR, but would delay
the epidemic peak by approximately one and three weeks, respectively.
Multiple interventions involving vaccination, antiviral prophylaxis and
social distancing measures, would reduce the cumulative attack rate to 20%
at a minimum [range: 20% - 24%], with 8 millions cases avoided.
Modelling results confirm the need to respond to a pandemic with multiple
preventive measures [6-8]. None of the interventions looked at is highly
effective when implemented independently.
These results, which evaluated interventions included in the national prepardness
plan, also show that preparedness is crucial, in order to organise all the
control measures necessary to face an emergency. Timing is also essential,
and measures which at first glance appear to be less important, such as
increasing social distancing, could be extremely useful for delaying the
epidemic peak, allowing time for greater availability of a vaccine, and
thus optimising its impact.
The implementation of multiple measures, including closure of schools and
workplaces requires the involvment of various medical and non-medical structures.
It is therefore essential to properly communicate them the importance of
such actions. Mathematical modelling based on national data are a relevant
tool to assist public health decisors in preparing for responding to a new