Mitigation Measures for Pandemic Influenza in Italy: An Individual Based Model Considering Different Scenarios

Abstract

BackgroundIndividual-based models can provide the most reliable estimates of the spread of infectious diseases. In the present study, we evaluated the diffusion of pandemic influenza in Italy and the impact of various control measures, coupling a global SEIR model for importation of cases with an individual based model (IBM) describing the Italian epidemic.Methodology/Principal FindingsWe co-located the Italian population (57 million inhabitants) to households, schools and workplaces and we assigned travel destinations to match the 2001 census data. We considered different R0 values (1.4; 1.7; 2), evaluating the impact of control measures (vaccination, antiviral prophylaxis -AVP-, international air travel restrictions and increased social distancing). The administration of two vaccine doses was considered, assuming that first dose would be administered 1-6 months after the first world case, and different values for vaccine effectiveness (VE). With no interventions, importation would occur 37–77 days after the first world case. Air travel restrictions would delay the importation of the pandemic by 7–37 days. With an R0 of 1.4 or 1.7, the use of combined measures would reduce clinical attack rates (AR) from 21–31% to 0.3–4%. Assuming an R0 of 2, the AR would decrease from 38% to 8%, yet only if vaccination were started within 2 months of the first world case, in combination with a 90% reduction in international air traffic, closure of schools/workplaces for 4 weeks and AVP of household and school/work close contacts of clinical cases. Varying VE would not substantially affect the results.ConclusionsThis IBM, which is based on country-specific demographic data, could be suitable for the real-time evaluation of measures to be undertaken in the event of the emergence of a new pandemic influenza virus. All preventive measures considered should be implemented to mitigate the pandemic.

Choosing pandemic parameters for pandemic preparedness planning: A comparison of pandemic scenarios prior to and following the influenza A(H1N1) 2009 pandemic

“…Eventually 18 articles were included for the first search, as displayed in Fig. 1 [7,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

“…The infectious period ranges from 1.5 to 4.1 days in eight modelling studies describing pre-2009 parameters, when not including the study by Carrat et al who state it to be less than ten days but not what the mean period length is [18]. One study states it to be 1.5 days, based on the literature [21], and another states it to be 1.6 days, due to calibration to equal a certain generation time [25]. The other articles give a period of three or more days.…”

“…For example, many of the pre-2009 influenza models use a contact or travel matrix [11][12][13][14][15][16][17][18][20][21][22][23][24][25][26]. An analysis by Flasche et al uncovered a possible role for the absolute humidity and the timing of importations (extinction probability), mixing patterns and the absolute humidity as underlying factors [68].…”

The role of population heterogeneity and human mobility in the spread of pandemic influenza

“…Because of their complexity, these models have been developed only at the country level, also including some European countries (Ferguson et al 2006;Ciofi degli Atti et al 2008).…”

An individual-based model of hepatitis A transmission

“…Recently, highly detailed individual-based models have been developed for evaluating the effectiveness of control measures for diseases such as pandemic influenza (Germann et al, 2006;Ferguson et al, 2005Ferguson et al, , 2006Longini et al, 2005Longini et al, , 2004Ciofi degli Atti et al, 2008) or fighting back a bioterroristic attack (e.g., by employing smallpox virus) (Eubank et al, 2004;Longini et al, 2006;Riley and Ferguson, 2006;Halloran et al, 2002). As highlighted by Riley (Riley, 2007), the need arises "to develop a simple model of household demographics, so that these largescale models can be extended to the investigation of long-time scale human pathogens".…”