2009 H1N1 flu (formerly called “swine flu”) is a new influenza virus that was first detected in the United States in April 2009. Earlier in mid March, the Mexican village of La Gloria had been put under alert as a late outbreak of influenza appeared to be affecting villagers. On April 13th, CDC notified of a case identified as influenza A virus in California that did not match expected subtypes. After determination by CDC that the virus was “swine” influenza A (H1N1), additional US cases were confirmed in California and Texas. On April 24th The WHO announced that the strains of influenza A (H1N1) in the US matched the cases in Mexico. The virus soon started spreading from person-to-person worldwide.. On June 11, 2009, the World Health Organization (WHO) signaled that a pandemic of 2009 H1N1 flu was underway.

The new virus was originally called “swine flu” virus because many of its genes were very similar to influenza viruses that normally occur in pigs (swine) in North America. Nevertheless, this new virus is very different from what normally circulates in North American pigs, since it has two genes from flu viruses that normally circulate in pigs in Europe and Asia, avian genes and human genes.

Advances in modeling and surveillance

In this section we present the contribution that the Epiwork project has given to the study of the 2009 H1N1 pandemic.

Computational modeling platform: GLEaM

The ISI team, involved in developing the Epiwork Computational Modeling Platform is currently using GLEaM to produce projections of the spread of the ongoing H1N1 Flu epidemic.

GLEaM is a discrete stochastic epidemic computational model based on a meta-population approach in which the world is defined in geographical census areas connected in a network of interactions by human travel fluxes corresponding to transportation infrastructures and mobility patterns. The GLEaM 2.0 simulation engine includes a multiscale mobility model integrating different layer of transportation networks ranging from the long range airline connections to the short range daily commuting pattern (read more).

There have been produced several kind of projections collected in the H1N1 flu section:

• winter projections of the spread of the new H1N1 flu in the Northern hemisphere: view winter projections…
• reports on the comparison between the projected spread of the new H1N1 flu and the observed epidemic pattern, for the early phase of the outbreak: view early outbreak comparisons…
• reports on the situation in the continental USA providing results at the state and county level, and at the resolution scale of 1/4°: view early outbreak projections…

Multi-Layer Epidemic Model

The MPG team employed performance computational techniques and multi-layer, large-scale computer simulations to project the time course of the H1N1 flu epidemic in the United States. The simulations yielded projections and risk assessments of the epidemic outbreak in a worst-case scenario, in which no containment measures are taken to mitigate the spread. The approach was based on the current knowledge of the disease parameters and took into account the backbone of spatial spread: a precise estimate of human mobility on spatial scales between a few and a few thousand kilometers. The projections resolved the expected dynamics down to the county scale (3,109 counties in mainland United States). Details of the modeling approach are not yet published but are available online. The key factors in the MPG modeling approach are very accurate human mobility datasets on scales from a few to a few thousand kilometers based on human mobility proxies that included small scale daily commuting traffic, intermediate traffic, and long distance travel by air. The simulations consisted of multiple layers, each layer possessing and increasing degree of accuracy and complexity. The final projections are done with a fully stochastic model that incorporates the inherent randomness in disease dynamics that is particularly important at the onset of an epidemic when the number of infected individuals is small compared to the whole population.

Agent based models.

The FBK team developed a stochastic, spatially structured individual-based model, considering explicit transmission in households, schools and workplaces, to simulate the spatiotemporal spread of an influenza pandemic in Italy and to evaluate the efficacy of interventions based on age-prioritized use of antivirals in terms of cumulative attack rate and excess mortality reduction under different scenarios. Results suggest that governments stockpile of influenza antiviral drugs suffice to treat approximately 25% of their populations. In countries with limited antivirals stockpile, providing prophylaxis to younger individuals is an option that could be taken into account in preparedness plans. In countries where the number of antivirals stockpiled is well below 25% of the population, priority should be decided based on age-specific case fatality rates. However, late detection of cases (administration of antivirals 48 hours after the clinical onset of symptoms) dramatically affects the efficacy of both treatment and prophylaxis (academic paper published on BMC Infectious Diseases). The FBK team has then extended the model to the entire European populations leveraging on the integration of air and railway transportation data. The analysis has shown that the impact of the epidemic in the European countries is highly variable because of marked differences in the socio-demographic structure of the European populations. The cumulative attack rate, R0, and the peak daily attack rate depend heavily on socio-demographic parameters, such as the size of household groups and the fraction of workers and students in the population (academic paper published on Proceedings of the Royal Society B).

ICT monitoring and reporting system: Influenzanet

All Epiwork teams involved in the Epiwork Internet-based Monitoring Systems for Influenza surveillance have carried on an enhanced surveillance during the whole summer. The IMS has been readily implemented in UK in July to cope with the emergency of the rising number of H1N1 cases. The results of the IMS activity in the different countries are collected on the Influenzanet page.

Influenzanet is a system to monitor the activity of influenza-like-illness (ILI) with the aid of volunteers via the internet. It has been operational in The Netherlands and Belgium (since 2003), Portugal (since 2005), Italy (since 2008) and United Kingdom (since 2009), and the current objective is to implement Influenzanet in more European countries. In contrast with the traditional system of sentinel networks of mainly primary care physicians, Influenzanet obtains its data directly from the population. This creates a fast and flexible monitoring system whose uniformity allows for direct comparison of ILI rates between countries.

Any resident of a country where Influenzanet is implemented can participate by completing an online application form, which contains various medical, geographic and behavioural questions. Participants are reminded weekly to report any symptoms they have experienced since their last visit. The incidence of ILI is determined on the basis of a uniform case definition.