Information and Communication Technologies
EPIWORK
Developing the Framework for an Epidemic
Forecast Infrastructure
http://www.epiwork.eu
Project no. 231807
D7.7 Yearly activity report with assessment of
lesson learned
Period covered: Month 25th - 36th
Start date of project: February 1st, 2009
Due date of deliverable: Month 36th
Distribution: public
Date of preparation:
Duration:
Actual submission date: March 1st,
2012
Status: Draft
Project Coordinator: Alessandro Vespignani
Project Coordinator Organization Name: ISI Foundation
Lead contractor for this deliverable: ISI Foundation
2
EPIWORK D7.7
Work package participants
The following partners have taken active part in the work leading to the elaboration of this
document, even if they might not have directly contributed writing parts of this document:







ISI
FGC-IGC
TAU
MPG
AIBV
SMI
FFCUL
Change log
Version
1
Date
Amended by
Changes
01/03/12
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 3
Table of contents
1 Project objectives and implementation strategy ................................................ 4
2 Assessment of the third year activities ............................................................... 5
3 Executive summary and highlights of the projects activities ............................ 6
4 Collaboration/integration within the consortium .............................................. 10
5 Exemplary cases of contributions to/from complexity science ...................... 11
6 Exploitation, sustainability and policy relevance. ............................................ 17
7 Problems encountered and corrective actions. ............................................... 20
8 Opportunities and final considerations. ........................................................... 21
Publications ................................................................................................................... 22
International Conferences and seminars ............................................................................ 27
4
EPIWORK D7.7
1 Project objectives and implementation strategy
A fully operational, accurate and reliable epidemic forecast infrastructure nowadays faces
problems related to the lack of appropriate models to understand how an infectious disease
spreads in the real world, lack of extensive and accurate epidemiologically relevant data (from
societal data to epidemic surveillance data), lack of understanding of the interplay among the
various scales of the problem (from the host-pathogen interaction, to human-to-human
transmission, to the interaction with the environment) and, most importantly, lack of
communication among the different areas of research which proceed almost independently,
crucially hampering a significant progress in a highly interdisciplinary field of research. The
present project intends to fill this gap. Through computational thinking, complex systems
concepts and data integration tools relevant for epidemiological understanding at all levels, it
will provide a set of radical, paradigm-changing results enabling a novel approach to the
modelling, forecast and policy making approach to infectious diseases. The projects
overarching goals are:

The identification of general principles and laws that characterize complexity and capture
the essence of complex epidemiological systems.

The development of a collaborative information platform enabling the production of
knowledge, understanding and models from the novel abundance of digital data in
epidemic research.

The development of an open, data driven, computational modelling platform to be used in
epidemic research as well as in policy making for the analysis of global epidemics,
integrating and leveraging on transnational data.

The development, deployment and validation of an Internet-based Monitoring System
(IMS) producing real time data on disease incidence and epidemic spreading.
The project aims at exploring the following work areas as the major research themes directly
matching the objectives of this proposal:

Modelling and theoretical foundations

Data-driven computational platform

ICT monitoring and reporting system.
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 5
The work plan is organized around six distinct scientific work packages (WP1-WP6) whose
parallel scheduling of the work packages is necessary to jump-start the cycle and the Inter-WP
validation. WP1 and WP2 are aimed at exploring theoretical issues in the area of epidemic
modeling in complex, multi-scale systems, structured populations and in the presence of the
dynamical interplay between social and technological factors, seasonality and climate, health
policies implementations, WP3 and WP4 are devoted to the collection and sharing of data on
a computational platform and have a two-way continuous exchange of data and algorithms
with WP1 and WP2. WP5 and WP6 is aimed at the development, set-up and deployment of
innovative web monitoring and data gathering tools that should provide a continuous stream
of data to WP3-WP4 and be informed by constant feedback on the modeling needs in terms of
data gathering by WP1 and WP2. The common research agenda of the consortium teams,
which work in a coordinated way on the various tasks, favors a closer interchange of ideas
and knowledge among the groups and the various components of the project in a truly
interdisciplinary collective effort.
2 Assessment of the third year activities
In its first year of life the project this report has been a long analysis of the research and
developments produced by the project and to which extent those activities did meet the
schedule proposed in the initial project. As that, the report was an executive summary
reporting or duplicating most of the information already contained in other documents
submitted for the project. The feedback of the reviewers and the Program Manager at the
review meeting made it clear that a more useful assessment report should be structured in a
way that provides a guide through the material submitted for review and a discussion of
specific relevant points in the life of the project and the eventual lessons learned and the
measures eventually implemented to improve the work of the consortium. The second year we
provided a report along the above guidelines that has been appreciated by the reviewers. This
year we provide again an assessment analysis structured in the following sections:
-
Executive summary and highlights of the project activities.
-
Collaboration/integration within the consortium.
-
Exemplary cases of contributions to/from Complexity science.
6
EPIWORK D7.7
-
Exploitation, sustainability and policy relevance.
-
Problems encountered in the second year and corrective actions.
-
Opportunities and
While it is almost impossible not to duplicate information that can be found in deliverables
and other reporting items of the project we intend here to provide an overview of the major
results of the project beyond the rigid structure of deliverables and milestones. We will
therefore try to assess the actual impact of the project achievements and the outlook of the
consortium activities. It is also important to stress that the above structure is meant to address
the recommendations of the past year review meeting. A discussion of each specific
recommendation will be placed in the corresponding section along with the corrective actions
put in place to address the reviewer remarks.
3 Executive summary and highlights of the projects activities
The third year of the project was extremely productive and the consortium has harvested a
large number of scientific results and pushed forward the development of the data and
computational tools. The project has produced an impressive number of scientific
publications (more than 50) and presented at about 100 major international events. In such a
landscape it is not an easy task to single out
the most relevant contributions. For this
reason we have carefully selected those
activities that shows to integrated efforts of
the consortium or explicitly show the
fruitful/promising
dependencies
and
synergies among the various WPs.
Fig.1 A comparison of the per-capita weekly
incidence (black) and the best-fitting model
output (red) from the age structured model
We have also selected some results which
are hardly identifiable with a specific
deliverable/milestone and thus may be
overlooked, despite their relevance, in the conspicuous documentation provided for the
review process.
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 7
The theoretical work of the WP1 and WP2 has
continued the production of an impressive number of
quality scientific publications. The work performed in
this area has touched upon some foundational issues
such as the seasonal and external environmental
drivers and the graph properties of multi-length scale
mobility networks. The LSHTM team has been able
to explain the epidemiological H1N1 pattern in the
UK, resulting in a termination of the summer wave
(as the reproduction number was driven below one),
and the reactivation of the epidemic in September, by
Fig.2 Epidemic curve changes
considerably as the strength of
seasonality d varies although the
final attack rate is found to be
mildly varying.
using an age-structured transmission model giving a
remarkably good fit to the epidemic data (see Fig.1). Noticeably the model was developed by
using data collected with IMS platform about contact patterns, showing the nice linkage
between theoretical development and data gathering.
Extremely relevant is the various streams of work concerning time series analysis and the
role of weather on the relation between influenza and influenza-like illness produced by the
FGC-IGC, TAU and FFCUL Teams (see Fig.2). The results contained in this work could be
extremely relevant in the real time analysis of the IMS platform data offered by
influenzanet.eu. This is a sign that the theoretical work is impacting the more applied work
packages
and
foundational
that
and
the
applied
part of the project are finally
matching together.

The WP2 has produced
a
truly
novel
visualization
technique
the
visualization
algorithm of SPATO
Fig 3: The SPATO algorithm and visualization
approach previously developed within WP2 by the MPG
group has been integrated directly into the GLEaMviz
client. It is now possible to visualize the results of a
simulation by displaying the spreading of a globalscale epidemics upon a shortest-path representation of
the airline transportation network, with different
rescaling options.
(shortest
path
tree
8
EPIWORK D7.7
tomography) extracts mobility networks from their geographical embedding and
visualizes the network based on shortest paths and effective distances. The interactive tool
has been extended and fully integrated in the modeling computational platform of WP4
and allows the visualization of network characteristics, node specific parameters in an
interactive way along with the progression of the epidemic.
The computational platform has been refreshed in the GUI and the main code has been
modified to allow its integration with the SPATO algorithm. In addition to the SPATO
algorithm now the platform integrates several other visualization based on algorithm
developed in WP2 and WP4 (see Fig.3).

The project has delivered publically the epidemic marketplace (EM 2.0) the major
features: new user interface and component integration based on the Drupal ContentManagement System, improved access control management, redesigned web-services.
Another major integration step has been achieved by integrating the EM 2.0 and the
Computational platform GLEaMviz. The users of the two platforms will be authenticated
across a synchronised infrastructure enabling them users to transparently access both
systems with the same credentials. This also enables makes the interaction of the
GLEaMviz client with the Epidemic Marketplace repository very smooth, avoiding
additional user-credential requests upon interaction and therefore allowing seamless single
sign-on access to both platforms. Both platforms will use OpenLDAP software to manage
users
and
Full integration
the computational
and the data
2012 Fig.4:
Copyright
lies withamong
the respective
authors andmodeling
their institutions
repository platforms.
EPIWORK D7.7 9
credentials.

The Internet based monitoring (IMS) system comprises ten platforms in ten different
countries. Influenzanet.org is operational and offers data analysis in real time as collected
from the various IMS platforms.
France has been added as a brand
new participants and Sweden is
fully operational after the test run
of the second year. The total
number of participants is now
above 45,000. The central EpiDB
has been reimplemented as a
collection of agreements/protocols
and ready-made scripts, rather than
as a full server stack based on
REST
technology.
agreements
were
Specifically,
made
on
a
canonical form of the data to be
exported from the various web
platforms (clients). Based on those
agreements each client has its own
script which transforms the data
(which
may,
due
to
Fig.5: Real time analysis of the data collected by
the IMS platforms as offered on influenzanet.eu.
local
differences, be in a slightly different format to the canonical version. We are glad to
announce that CreateNet designed and implemented the EMA platform, a ‘TellMe’
service, which allows to dynamically send ad-hoc questions to mobile users, and a Twitter
client to the various IMS platforms.

This year the project has finalized the organization of the midterm workshop “ Facing the
challenge of infectious diseases” that took place in Courmayeur on 18-20 January 2012.
This workshop consisted in a major event gathering most of the relevant stakeholders
10 EPIWORK D7.7
interested in the Epiwork project. The funding from Epiwork has been complemented
with external sponsorship because of the size reached by the event that has gathered
around 100 researchers, policy makers and institution representatives (including JRC,
WHO, INSERM, ISS, HPA etc.). The workshop detailed presentation is reported in the
specific deliverable D8.3.2.
4 Collaboration/integration within the consortium
One of the major problem in large Integrated project such as Epiwork consists in keeping a
high level of interconnection and collaborations among partners.
The past year one of the specific comments of the review panel was “Integration needs to be
re-enforced. It is critical for next year: Theory, platform, market place and country data”.
This year the project Mantra has been integration-integration-integration. In the previous
higlight we have shown how the various WP are now working exploiting all dependencies.
Let us summarize once more the major integration steps carried out during the third year:
-
WP2 results have been integrated in the WP4 computational platforms.
The
Computational platform integrates also results from WP1 and WP2 concerning contact
patterns, visualization algorithms, age structure.
-
The computational platform WP4 and the Epidemic Marketplace are fully integrated.
This is a major steps that allows to align the development of the two platforms for the
generation and exploitation of data.
-
The IMS platforms are all integrated by the influenzanet.eu database. WP6 is
integrated in WP5 by the start of the IMS Swedish Platform.
-
The WP1 and WP2 are starting to take advantage of the IMS platforms by using data
for the development of noel modeling techniques and validation.
In order to emphasize during the review meeting the integration effort among partners, this
year we have structured the review meetings leaving ample space to demos and presentations
aimed at showing the integration work.
Further integration avenues
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 1
1
Further integration activities are planned for the next year. They have not yet implemented so
that the work pipeline of the various efforts and WPs could match together.
WP1-WP5-WP6): Relying on fitting parameters estimated by incidence data, predictions and
time series analysis it is possible to envision an automated forecast system to become part of
national Influenzanet systems. WP5, through its direct contact with the participants, could
make a feature of this, and make the system more attractive for new countries. The results
obtained this year by the WP1 have great potential and will enrich the data presentation and
analysis offered on influenzanet.eu.
WP3-WP4-WP5 -WP6 : Data gathered by the IMS have to be automatically streamed and
supported in the Epidemic Marketplace. The visualization functionalities of GLEaMviz shall
be used to offer the visual analytics of the IMS data. This can be done by integrating the IMS
platform with the already integrated EM and GLEaMviz platforms. The teams involved in the
system architecture of the three platforms have already started the discussion on the
integration process.
Evidence for the cooperation between partners.
At the level of single partners the sustained level of interaction and collaboration is witnessed
by the number of joint publications and the joint development activities. Theoretical and
computational groups are publishing coauthored papers. FBK and ISI team are collaborating
on the issue of integrating agent based and spatially structured models in the epidemic
platform. Cooperation and collaboration between the teams in WP5 and WP6 has been
continuous. During 2011, the integration of the Epidemic Marketplace and the GLEaMviz
platform has seen the close collaboration of FBK, ISI, FFCUL and MPG. The integration of
the IMS with the EM is bringing all the other teams in the effort.
5 Exemplary cases of contributions to/from complexity science
The second year review panel has explicitly remarked the need to spell out the contribution
to/from complexity science of the project. Here we provide a few exemplary cases of
contributions toward the objective of the call at the origin of the Epiwork funding. All the
following cases reports results obtained because of the exploitation of a systemic approach to
epidemic system, computational thinking the use of techniques and modeling in complexity
science.
12 EPIWORK D7.7
-
Travel restrictions, invasion threshold and critical phase transitions.
The human mobility flows that determine the spreading of infectious diseases and the control
measures based on limiting or constraining human mobility are considered in the contingency
planning of several countries. The target of these control measures is the decrease of travel
to/from the areas affected by the epidemic outbreak and the corresponding decline of infected
individuals reaching countries not yet affected by the epidemic. Most numerical methods and
empirical evidence are pointing out shows even sever regimes of travel reduction would lead
to delays on the order of a few weeks even in the optimistic case of early intervention. It is
unlikely that given the ever-increasing mobility of people travel restrictions could be used
effectively in a future pandemic event.
! "#
$"#
%"#
! "#$
! ($
! $%&$' $
Fig.6:a) At the macroscopic level the system is composed of a heterogeneous
network of subpopulations. The contagion process in one subpopulation (marked
in red) can spread to other subpopulations because of particles diffusing across
subpopulations. b) At the microscopic level, each subpopulation contains a
population of individuals. Within each subpopulation, individuals can mix
homogeneously or according to a subnetwork and can diffuse with rate d from
one subpopulation to another following the edges of the network. c) A critical
value dc of the individuals/particles diffusion identifies a phase transition between
a regime in which the contagion affects a large fraction of the system and one in
which only a small fraction is affected (see the discussion in the text).
This non-linear and somehow paradoxical feature of global epidemic spreading, on one side
depending on people mobility and on the other side insensitive to its magnitude,
2012  Copyright lies with the respective authors and their institutions
can be
EPIWORK D7.7 1
3
understood mathematically a particle-network framework that extends the heterogeneous
mean-field approach to reaction diffusion systems in networks with arbitrary degree
distribution. Let us consider for instance a simple epidemic process such as the SIR model in
a metapopulation context. In this case each node of the network is a subpopulation or urban
area connected by a transportation system (the edges of the network) that allows individual to
move from one subpopulation to the other (see Fig.6). If we assume that the single population
reproductive number R0>1 of the SIR model and a diffusion rate p on the network for the
individuals we can easily identify two different limits. If p=0 any epidemic occurring in a
given subpopulation will remain confined; no individual could travel to a different
subpopulation and spread the infection across the system. In the limit p very large we have
that individuals are constantly wandering from one subpopulation to the other and the system
is in practice equivalent to a well mixed single population. Since the epidemic has R0>1 it
will spread across the entire system. A transition point between these two regimes is therefore
occurring at a threshold value pc of the diffusion rate. The threshold is defined as the diffusion
rate that allows a sufficient number of infected individuals from each infected subpopulation
to to visit other subpopulations and trigger a new epidemic. In this perspective we can
consider the subpopulation network in a coarse-grained view and provide a characterization of
the invasion dynamics at the level of subpopulations, defining a subpopulation reproductive
number R* that identifies the number of subpopulation epidemics triggered by each infected
subpopulation. As for the basic reproductive number, only if R*> 1 we have that the epidemic
will spread globally. The number R* is depending on the diffusion rate p and therefore define
a global invasion threshold pc for the diffusion rate. Furthermore the global invasion threshold
is affected by the topological fluctuations of the metapopulation network. In particular, the
larger the network heterogeneity, the smaller the value of the diffusion rate above which the
epidemic may globally invade the metapopulation system. The
relevance of this result stems from the fact that the complexity and heterogeneity of the
present time human mobility network is responsible for the fact that travel restrictions,
reducing the rate at which individuals leave an infected region, would not be able to
considerably slow down the global spread unless 90% (or more) effective.
This result has been originally derived in the context of the project and extended to recurrent
mobility patterns by the MPI and ISI. In addition and interestingly this threshold cannot be
uncovered by continuous models as it is related to the stochastic diffusion rate of single
14 EPIWORK D7.7
individuals. It defines a new critical phase transition class that can be found in several multiparticle systems in complex networks. On one side this result is providing answers to a
specific problem in epidemic modeling and scenario analysis by using a complex systems
approach. On the other hand the result can be applied and generalized to other systems and
opens a new window on the effect of stochastic fluctuations on phase transitions and
emerging tipping points. This result indeed defines a general class of models generally
applicable to complex techno-social systems. While the presented result is anchored upon the
example of disease spread, the meta-population approach can be abstracted to the
phenomena of knowledge diffusion, online community formation, information spread, and
technology.
-
Systemic network approach to epidemic diffusion dynamics
In complex networks embedded in the geographical space, diffusion processes often exhibit a
disordered pattern that does not resemble the classic wave-like behavior we are used to
observe in regular systems. In general, the relation of geographic distance and arrival time
correlate well only in the regular lattice while the presence of long range interactions destroys
these correlations (see Fig.7). The algorithm SPATO, developed within the project, reduces
the network to the weighted shortest-path tree of a selected root node, obtaining a local but
simpler view of the network that can be easily visualized. This representation provides
immediately the right relational distance among nodes so that the diffusion dynamics can be
rationalized and visualized according to our physical intuition of a wave like process (see
Fig.7).
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 1
5
Fig.7:radial distance is shortest path distance in a lattice with long range connections.
The SPATO visualization permits the computation of effective speeds, based on effective
distance, which can be used as a predictor.
This results has been integrated in the Computational modeling platform offering a novel
perspective on the large scale spreading of infectious diseases worldwide. However the results
is much more general and applies to all diffusive and contagion processes in complex
networks. Also in this case we have provided the development of methodology and
algorithmic tools to understand the impact of large scale complex features (scale invariance,
extreme heterogeneity, unbounded fluctuations) of interaction, mobility and communication
networks in the behavior of spatially extended models.
- Computational thinking and synthetic contact matrices
The accurate characterization of the structure of social contacts in mathematical and
computational models of infectious disease transmission is a key element in the assessment of
the impact of epidemic outbreaks, and the accuracy in the evaluation of the effective control
measures. For instance, the transmissibility potential of a disease and the final epidemic size
strongly depend on the mixing patterns between individuals of the population, which in turn
16 EPIWORK D7.7
depend on socio-demographic parameters (e.g. household size, fraction of workers and
students in the population).}. For this reason, several efforts have been recently carried out in
order to obtain contact data with
the aim of quantifying ``who
meets whom (where, when, how
long and how often)'. Empirical
data collection on a large-scale is
however extremely difficult and
although several models tackling
both new emerging epidemics and
endemic diseases have introduced
a
significant
amount
of
information on contact patterns it
is clear that the increasing use of
data-driven models in the support
Fig.8: Linear regression model with zero intercept
for POLYMOD matrices Pn against those from the
synthetic model, Mn.
of public health decisions is
calling for novel approaches to the
estimation of mixing patterns in
human populations.
In a collaboration between the ISI and the FBK the project has developed a general
computational approach to derive mixing patterns from routinely collected socio-demographic
data. In particular the project has generated the contact matrices by age of 26 European
countries for which we are in the position to construct a synthetic society in the computer by
integrating available social and census data. The approach is based on the simulation of a
virtual society of agents that allows the estimate of contact matrices by age in different social
contexts: household, school, workplace and general community. Those matrices are
appropriately combined in order to obtain the overall ``adequate'' total contacts matrix for
influenza like illness. The method has been validated by comparing the obtained contact
matrices by age with the results of the Polymod study the first large-scale survey on social
mixing patterns relevant to infectious disease transmission (see Fig.8). The proposed method
is extremely general and can be readily exported to other countries in the world for which the
necessary social and demographic data can be gathered. We consider this approach an
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 1
7
important step in order to overcome the current difficulties in real data gathering. Furthermore
the computational path to the estimate of contact matrices represents a convenient scheme for
the introduction of detailed individual based information in a wide range of modeling
approaches working at the population level. In this case we have used a computational
thinking approach to develop a novel technique for the generation of hybrid models
integrating synthetic and real world data.
6 Exploitation, sustainability and policy relevance.
The review panel on the second year has noted that “Exploitation should be taken seriously,
also linking to the question of sustainability of the tools and the project itself. “
The consortium has always considered the exploitation of results and the sustainability of the
project beyond the Epiwork funding a priority. Along the years and with solid results to show
the consortium has been able to progressively engage many several National and International
agencies triggering interactions and collaborations.
All teams of the consortium have been actively involved in the data gathering, computational
analysis and monitoring in close contact with national and International agencies. The project
has developed collaborations and interactions with the ECDC, The JRC and the WHO and
other international agencies concerning the modeling and computational activities. We are
also partnering with the Joint Research Center (JRC) of the European Commission at Ispra,
Italy. The Joint Research Centre is a research based policy support organization and an
integral part of the European Commission. The mission of the JRC is to provide customerdriven scientific and technical support for the conception, development, implementation and
monitoring of EU policies. As a service of the European Commission, the JRC functions as a
reference centre of science and technology for the Union. The objective of the collaboration is
to share tools, including the GLEaM platform to improve models for crisis management and
decision making of emerging public health threats. We are proud to announce that the first
version of the GLEaMviz Simulator has been released to JRC where usability tests have been
started. The project is part of the ECDC discussion group on the formation of a modeling
coordinated group and Epiwork has been among the presented project at ECDC Infectious
Disease Modeling Meeting, ECDC, Stockholm, Sweden, November 14-15, 2011. During the
next WP5 meeting that will be held in Stockholm in May 2012, the WP5 Consortium plans to
18 EPIWORK D7.7
start a further collaboration with the ECDC to design the future life of the Influenzanet
platform and the relationship of the WP5 Consortium with the ECDC. We are in contact with
representative from the WHO for the development of a collaboration within their activities
concerning pandemic and highly pathogenic diseases. Epiwork in Europe has also direct
contacts through teams leaders with several National Health Institutes:
o
Belgian Pandemic crisis team, Belgium
o
Health Protection Agency, UK (The Flusurvey data are available to the Health
Protection Agency, the national body responsible for infectious disease surveillance, and they
produce a weekly summary of the Flusurvey findings for their weekly influenza report)
o
National Institute of health, Italy
o
INSERM, France, now a subcontractor of WP5 (The Grippenet.fr data are available to
the Reseau Sentinelles, the national body responsible for infectious disease surveillance, and
they produce a weekly summary of the Grippenet.fr findings for their weekly influenza
report)
o
RIVM, the Netherlands
It is worth remarking that HPA and INSERM are directly involved in the dissemination of
data from the respective national IMS platform and Italy is working on a similar plan for the
next year.
The future sustainability of the project is obviously related to the above contacts and their
unfolding in the next year. The direct involvement of National Institutes in the IMS platforms,
as in the French partnership, is extremely welcome and guarantees the sustainability of the
IMS platforms. We are confident that other national Institutes will take over in Italy and in the
UK. The Netherlands and Belgium platforms are run on a different SME model that appear to
find sustainability through different mechanisms. It is possible to imagine that the two models
live together and provide a core group of partner to influenzanet.eu well beyond the horizon
of the Epiwork project.
The sustainability of the EM and the computational modelling platforms is linked to two
major resource generating channels. The first one is the adoption of the platforms from major
national and international agencies. The second issue is the gathering of resources through
new contracts and grants aimed at the future R&D of the platforms. The project is well
positioned in terms of interest for the adoption of the current platform and it is securing the
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 1
9
resources for the future R&D development with the start of new grants aimed at specific
research directions such as single country implementation, new diseases modelling, economic
impact analysis etc. Funding agencies interested in taking over the project are both in Europe
and the USA. Finally, the Epiwork project is playing an important role within one of the
finalist flagship initiatives as a demonstrator and innovator accelerator within the FuturICT
initiative.
Although a final assessment on the sustainability of the project will be possible only in 2013,
the consortium is confident that the present tools and infrastructure will be supported and
continued to develop after the end of the project.
Let us finally comment on the policy making relevance of the project that is a key factor in
advocating for the project sustainability and exploitation. The relevance to policy making of
EPIWORK can be made explicit in several areas of monitoring, planning and running health
systems, contingency plans etc..
-The first one is the introduction of a novel approach to real time monitoring of disease
incidence. Traditional health monitoring infrastructures have a determinant role in protecting
populations, but are generally hierarchically organized, and information often travels slowly.
The participatory platform that EPIWORK is developing allows the real time gathering of
data, improving coverage and accessibility, thus allowing a prompt analysis of impact and the
evaluation of the effectiveness of mitigation and containment measures devised by policy
makers. Our monitoring platform represents a proposal for an innovative health monitoring
systems as well as a new way to engage the public as participants in the public health process.
- The development of data-driven computational platforms for the simulation of realistic
disease spreading scenario is enabling agencies and policy makers with tools that can be
defined "decision science applications". The tools allows to explore scenarios and develop
contingency plans while analyzing solutions with local, national and global impact.
- We have been already working and discussing with the JRC, the ECDC and other national
agencies in analyzing the H1N1 "situation". The work has been performed in real time and
continues with the analysis of data accrued after the epidemic (serological studies etc.).
20 EPIWORK D7.7
- More in general, in the context of future development of the project, let me report an excerpt
of the abstract of a session I have participated at the Global Health Meeting and sponsored by
the WHO: “One can enhance health systems performance through better use of knowledge
and evidence, which are generated through research and practice and packaged as programs
and interventions. Scaling up
interventions, towards universal coverage and diffusing
innovations more widely, requires modeling the dynamics of system change in real world
conditions, which is currently not widely employed. Complex systems analysis can be used to
combat this deficit. It promises better models of how change happens, and constraints and
enablers that we can influence to achieve better performance”.
7 Problems encountered and corrective actions.
Although the project has kept sustained progress and has provided al the deliverables as
scheduled, we have encountered problems this year specifically related to the subcontracting
partners.
a) Despite numerous efforts and contacts, including a trip to Bilbao to a meeting of 15
Spanish epidemiologists and modellers from the Gripometro group and an introduction
from EU advisor Prof. Carlos Bousoño from Madrid, the WP5 Consortium did not receive
an adequate reaction to introduce the IMS in Spain. Dr. José Ramasco from IFISC at
Palma de Mallorca was very helpful in this process. In the end, he even considered his
institute to be a partner. However, his management was reluctant to develop a Spanish
IMS, due to all the investments needed and despite our offer to invest part of our Epiwork
sub contract budget. The WP5 Consortium will continue to persuade contacts in Spain to
be partner in the months to come.
b) The major problem has however occurred in Germany, Switzerland and Austria where the
IMS has had a slow start. At this moment, the German sub-contractor ExploSYS GmbH
faces very disappointing results in recruiting volunteers in the three countries. The
subcontractor, a small company in Germany, seems to be unable to realise the required PR
results despite its many efforts. In order to realise a minimum of 1.000 to 2.000 volunteers
in Germany and a 500-800 in Austria and 500-800 in Switzerland, the WP5 consortium
has started to take the following measures:
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 2
1
-
direct involvement of AI BV, which exploits successfully the GGM in Belgium and
the Netherlands;
-
recruitment of a coordinator in Switzerland, next month, and in Austria, later this year;
-
contacts with public health institutions in order to expand cooperation.
Unfortunately as of today (march 10, 2012) ExploSYS appears to be unable to deliver
according to its contractual obligations. At the end of the current epidemic season (concurrent
with the review meeting) the consortium will consider the German situation. Unless in
presence a drastic change in the performance of ExploSYS, the consortium management will
take all the appropriate measure to ensure the quality standard and reputation of the project.
This will include the cessation of the collaboration with ExploSYS. The costs incurred by the
subcontracting will be evaluated and considered not eligible unless the subcontractor has
delivered according to the contractual obligations. All decision regarding this matter will be
taken according to the management procedures established in the ANNEX I of the Epiwork
contract.
8 Opportunities and final considerations.
The project has performed well in the third year, coordination issues has been very limited
and the only major problem is occurred with subcontracting. In addition some important
opportunity windows have opened for the project:
1. the integration of the platforms has been progressing smoothly and it is possible to
envision the creation of specific API for the computational modelling part that would
open the project to external open source contributions.
2. New technologies not existing at the time of the proposal (Android and IOS devices)
are obviously natural extension to our mobile component of the IMS platforms.
3. The theoretical results concerning age structured transmission and modelling call for
extension to the research program in order to fully exploit the results of the project.
4. The consortium has contacts in Hungary, Bulgaria and Estonia. Meanwhile, more and
more organisations in other countries are becoming interested to start an initiative as
the IMS. The European database is an excellent basis for a rapid expansion of the
22 EPIWORK D7.7
IMS/Influenzanet throughout Europe and other parts of the world. We are optimistic
that in 2013, in more than ten countries an IMS has been launched.
At the same time WP2 activities, although very successful, have been carried out with a
considerable under-spending for one of the teams involved in the project (MPI). The project
board has therefore discussed and agreed on the following lines of action:
a) a small redistributions of funds in order to introduce two/three new deliverables in the
last year of the project.
b) the consortium will seek a no-cost extension of six months to the project. This will
allow to complete the 2012-2013 influenza season monitoring campaign under the
activity of the project and to provide a full assessment of the IMS activities for the full
influenza season.
The above lines of actions will be discussed during the review meeting and will eventually
lead (if accepted) to a final amendment to the Annex I.
Publications
1. Coelho FC, Codeço CT, Gomes MGM (2011) A Bayesian framework for parameter
estimation in dynamical models. PLoS ONE 6(5):e19616.
2. van Noort SP, Aguas R, Ballesteros S, Gomes MGM (2012) The role of weather on the
relation between influenza and influenza-like illness. J Theor Biol 298C:131-137.
3. Aguas R, Ferreira MU, Gomes MGM (2012) Modeling the effects of relapse in the
transmission dynamics of malaria parasites. J Parasitol Res:921715.
4. Gomes MGM, Águas R, Lopes JS, Nunes MC, Rebelo C, Rodrigues P, Struchiner CJ
(2012) How host selection governs tuberculosis reinfection. Proc R Soc Lond B. (In press).
5. Givan O, Schwartz N, Cygelberg A, Stone L. Predicting epidemic thresholds on complex
networks: Limitations of mean-field approaches. J Theor Biol. 2011 Nov 7;288:21-8.
6. Katriel G. The size of epidemics in populations with heterogeneous susceptibility.J Math
Biol. 2011 Aug
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 2
3
7. Barnea O, Yaari R, Katriel G, Stone L. Modelling seasonal influenza in Israel. Math Biosci
Eng. 2011 Apr;8(2):561-73.
8. Roll U, Yaari R, Katriel G, Barnea O, Stone L, Mendelson E, Mandelboim M, Huppert A.
Onset of a pandemic: characterizing the initial phase of the swine flu (H1N1) epidemic in
Israel. BMC Infect Dis. 2011 Apr 14;11:92.
9. Katriel G, Yaari R, Huppert A, Roll U, Stone L. Modelling the initial phase of an epidemic
using incidence and infection network data: 2009 H1N1 pandemic in Israel as a case study.
J R Soc Interface. 2011 Jun 6;8(59):856-67.
10.
Katriel G Note on 'Age, influenza pandemics and disease dynamics' by Greer et al.
(2010). Epidemiol Infect. 2011 Sep;139(9):1440-1;
11.
Stone L., Hilker F., & Katriel H. SIR epidemic models Sourcebook in Theoretical
Ecology, Alan Hastings and Louis Gross, Editors , University of California Press 2011.
12.
Stone L. Waltz of the weevil. Nature. 2011 Feb 3;470(7332):47-9.
13.
Eames KTD, Tilston NL, Brooks-Pollock E, Edmunds WJ Measured Dynamic Social
Contact Patterns Explain the Spread of H1N1v Influenza PLoS Comput Biol 8(3):
e1002425. doi:10.1371/journal.pcbi.1002425
14.
Brooks-Pollock E, Tilston N, Edmunds WJ, Eames KT. Using an online survey of
healthcare-seeking behaviour to estimate the magnitude and severity of the 2009 H1N1v
influenza epidemic in England. BMC Infect Dis. 2011 Mar 16;11:68.
15.
Eames KT, Brooks-Pollock E, Paolotti D, Perosa M, Gioannini C, Edmunds WJ.
Rapid assessment of influenza vaccine effectiveness: analysis of an internet-based cohort.
Epidemiol Infect. 2011 Sep 12:1-7. DOI: 10.1017/S0950268811001804
16.
H. J. Herrmann, C. M. Schneider, A. A. Moreira, J. S. Andrade Jr, S. Havlin, Onionlike network topology enhances robustness against malicious attacks, J. Stat. Mech. 2011,
01027 (2011)
17.
C. Lagorio, M. Dickison, F. Vazquez, L. A. Braunstein, P. A. Macri, M. V. Migueles,
S. Havlin, H. E. Stanley, Quarantine-generated phase transition in epidemic spreading,
Phys. Rev. E 83, 026102 (2011)
24 EPIWORK D7.7
18.
A. A. Moreira, J. S. Andrade Jr., S. Havlin, H. J. Herrmann, C. M. Schneider,
Mitigation of malicious attacks on networks, PNAS 108, 3838 (2011)
19.
Rybski D, Buldyrev SV ,Havlin S, Liljeros F, Makse HA, Communication activity in
social networks: growth and correlations, European Physical Journal B 84, 147-159
(2011)
20.
Schneider CM, Mihaljev T, Havlin S, Herrmann HJ, Suppressing epidemics with a
limited amount of immunization units, Phys. Rev. E 84, (2011)
21.
Li W, Wang FZ ,Havlin S, Stanley HE, Financial factor influence on scaling and
memory of trading volume in stock market, Phys. Rev. E 84, (2011)
22.
Huang XQ, Vodenska I, Wang FZ, Havlin S, Stanley HE, Identifying influential
directors in the United States corporate governance network Phys. Rev. E 84, (2011)
23.
Y. Hu, Y. Wang, D. Li, S. Havlin, Z. Di, Possible Origin of Efficient Navigation in
Small-Worlds, Phys. Rev. Lett. 106, 108701 (2011)
24.
D. Li, G. Li, K. Kosmidis, H.E. Stanley, A. Bunde, S. Havlin, Percolation of spatially
constraints networks, Europhys. Lett. 93, 68004 (2011)
25.
D. Li, K. Kosmidis, A. Bunde, S. Havlin, Dimension of spatially embedded networks,
Nature Physics 7, 481-484 (2011)
26.
J. Shao, S. V. Buldyrev, S. Havlin, H. E. Stanley, Cascade of failures in coupled
network systems with multiple support-dependence relations, Phys. Rev. E 83, 036116
(2011)
27.
A. Bashan, R. Parshani, S. Havlin, Percolation in networks composed of connectivity
and dependency links, Phys, Rev. E 83, 051127 (2011)
28.
Gao Jianxi, Buldyrev Sergey V., Stanley H. Eugene, Havlin S., Networks formed from
interdependent networks, Nature Physics 8, 40-48 (2011)
29.
A.Bashan and S.Havlin, The Combined Effect of Connectivity and Dependency Links
on Percolation of Networks, J Stat Phys 145, (2011)
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 2
5
30.
Hu Y. Q., Ksherim B., Cohen R., Havlin S., Percolation in interdependent and
interconnected networks: Abrupt change from second- to first-order transitions, Phys. Rev.
E 84, (2011)
31.
C. Rozenblat, D. Ietri, C. Ducruet, S. Havlin, , R. Parshani, Inter-similarity between
coupled networks Europhys. Lett. 92, 68002 (2011)
32.
Merler S, Ajelli M, Pugliese A, Ferguson NM, 2011 Determinants of the
Spatiotemporal Dynamics of the 2009 H1N1 Pandemic in Europe: Implications for RealTime Modelling. PLoS Comput Biol 7(9): e1002205. 2011.
33.
G. Guzzetta, M. Ajelli, Z. Yang, S. Merler, C. Furlanello, D. Kirschner. Modeling
socio-demography to capture tuberculosis transmission dynamics in a low burden setting.
Journal of Theoretical Biology 289: 197-205. 2011.
34.
P. Poletti et al. The effect of risk perception on the 2009 H1N1 pandemic influenza
dynamics. PLoS ONE, 6(2): e16460, 2011.
35.
I. Carreras, A. Matic, P. Saar, V. Osmani, "Comm2Sense: Detecting Proximity
Through Smartphones", in Proc. of perMoby – PERCOM Workshop 2012.
36.
C Pesquita, F Couto, Where GO is going and what it means for ontology extension.
Proceedings of ICBO 2011, International Conference on Biomedical Ontology, July 2011.
37.
Bruno Tavares, Hugo Bastos, Daniel Faria, Joao D. Ferreira, Tiago Grego, Catia
Pesquita, Francisco Couto, The Biomedical Ontology Applications (BOA) framework.
Proceedings of ICBO 2011, International Conference on Biomedical Ontology, July 2011.
38.
João D. Ferreira, Francisco Couto, Generic semantic relatedness measure for
biomedical ontologies. Proceedings of ICBO 2011, International Conference on
Biomedical Ontology, July 2011.
39.
Francisco Couto, Mário J. Silva. Disjunctive Shared Information between Ontology
Concepts: application to Gene Ontology. Journal of Biomedical Semantics. Vol 2(5).
doi:10.1186/2041-1480-2-5.
40.
J. Zamite, F. Silva, F. Couto, M. Silva, MED Collector: Multisource epidemic data
collector. Transactions on Large-scale Data-and Knowledge- centered Systems IV: Special
26 EPIWORK D7.7
Issue on Database Systems for Biomedical Applications (6990), pp. 40-72. SpringerVerlag New York, Inc. ISBN 978-3-642-23739-3 (2011)
41.
Aguiar, M., Ballesteros, S., Kooi, B. W., Stollenwerk, N. (2011). The role of
seasonality and import in a minimalistic multi-strain dengue model capturing differences
between primary and secondary infections: complex dynamics and its implications for data
analysis. Journal of Theoretical Biology, 289, 181–196.
42.
Stollenwerk, N., Aguiar, M., Ballesteros, S., Boto, J., Kooi, W. B., Mateus, L. (2012).
Dynamic noise, chaos and parameter estimation in population biology, accepted for
publication in Roy. Soc. Interface Focus.
43.
Aguiar, M., Stollenwerk, N. and Kooi, W. B. (2012). Scaling of stochasticity in
dengue hemorrhagic fever epidemics, accepted for publication in Math. Modelling Nat.
Phenom. (MMNP)
44.
Peyman Ghaffari, Vincent Jansen & Nico Stollenwerk (2011). Evolution towards
critical fluctuations in a system of accidental pathogens, (Numerical Analysis and Applied
Mathematics ICNAAM 2011 AIP Conf. Proc. 1389, 1224-1227 (2011); doi:
10.1063/1.3637837 Copyright 2011 American Institute of Physics 978-0-7354-09569/$30.00)
45.
Martins, J., Aguiar, M., Pinto, A., Stollenwerk, N. (2011). On the series expansion of
the spatial SIS evolution operator. Journal of Difference Equations and Applications, 17,
1107–1118.
46.
Aguiar, M., Stollenwerk, N. and Kooi, W. B. (2011). The stochastic multi-strain
dengue model: analysis of the dynamics. AIP Conference Proceedings, 1389, 1224–1227
47.
Aguiar, M., Ballesteros, S., Boto, J.P., Kooi, B.W., Mateus, L., Stollenwerk, N.
(2011). Parameter estimation in epidemiology: from simple to complex dynamics. AIP
Conference Proceedings, 1389, 1248–1251.
48.
Gerrish, P., and Sniegowski, P. (2011). Adding Dynamical Sufficiency to Fishers
Fundamental Theorem of Natural Selection. AIP Conference Proceedings, 1389, 1260–
1262.
49.
D Grady, R. Brune, C. Thiemann, F. Theis and D. Brockmann Modularity
maximization and tree clustering: Novel ways to determine effective geographic borders in
Handbook of Optimization in Complex Networks, Thai, My T., Pardalos, Panos M(eds.),
Springer (2012).
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 2
7
50.
V. Belik, T. Geisel and D. Brockmann Recurrent host mobility in spatial epidemics:
beyond reaction-diffusion European Physical Journal B, 84, 579–587 (2011).
51.
V. Belik, T. Geisel and D. Brockmann Natural human mobility patterns and spatial
spread of infectious diseases Phys. Rev. X 1, 011001 (2011).
52.
G. O'Danleyman, J. J. Lee, H. Seebens, B. Blasius and D. Brockmann Complexity in
human transportation networks: A comparative analysis of worldwide air transportation
and global cargo ship movements European Physical Journal B, 84, 589–600 (2011)
(2011).
53.
AC Singer, V Colizza, H Schmitt, J Andrews, D Balcan, WE Huang, VDJ Keller, A
Vespignani, RJ Williams, Environmental Health Perspectives 119, 1084 (2011)
International Conferences and seminars
1. Measures of immunity across scales. Talk at symposium "Infection Dynamics:
Bridging the Gap between Theory and Application", Utrecht Centre for Infection
Dynamics, The Netherlands, March 2011.
2. Heterogeneity in host-microparasite systems. Talk at workshop "Phylodynamics",
NESCent - National Evolutionary Synthesis Center, Durham, USA, May 2011.
3. Heterogeneity in antibody range and the antigenic drift of influenza A viruses. Talk at
"VIII European Conference on Mathematical and Theoretical Biology", Krakow,
Poland, June 2011.
4. Heterogeneity in antibody range and the antigenic drift of influenza viruses. Talk at
workshop "From Chaos to Complexity", Mathematics Interdisciplinary Research,
University of Warwick, UK, July 2011.
5. The role of weather on the relation between influenza and influenza-like illness. Talk
at "Second Workshop "Facing the Challenge of Infectious Diseases: Integrating
mathematical modeling, computational thinking and ICT applications". Pré-SaintDidier, Aosta, Italy, January 2012.
6. Platform to support a cartoon scholar contest as well as the communication and
dissemination of flu information among the youth (http://bandadesenhada.gripenet.pt).
7. Banff conference: Modelling and analysis of options for controlling persistent
infectious diseases (attendance and invited talk)
8. Invited talk at Princeton University (Bryan Grenfell’s group). Nov. 2011
9. Collaboration with Sebastien Ballesteros and visit.
10. Invited speaker at EE2 conference Aosta, Italy. Poster.
11. Robustness of Complex Networks: Nov. 14 - 16, 2010, Delft, Netherland,
``Chatastrophic Cascades of Failures of Interdependent Networks''
12. Workshop on "Applications of statistical physics to complex systems" : Jan 11-13,
2011, Budapest, Hungary , ``Robustness of Networks''
13. Horizons in Emergence & Scaling, March 19-20, 2011, Boston, USA, "Applications
of statistical physics to complex systems"
28 EPIWORK D7.7
14. APS March Meeting, Focus Session " The Dynamics of Co-evolving and
Interdependent Networks": March 21-25, 2011, Dallas, Texas USA, ``Robustness of
Interacting Networks''''
15. European Geophysics Union Meeting: April 3-8, 2011, Vienna, AustriaTitle: ``The
Emergence of El-Nino Basin as an Autonomous Component''
16. Complexity Workshop: May 11-12, 2011, Tel Aviv University, Israel, Title:
``Catastrophic Cascade of Failures in spatial interdependent networks''''
17. International Conference on Econophysics: June 4-6, 2011, Shanghai, China Title:
“Theory of spreading in Network of Networks''
18. Statistical Mechanics Day IV: June 23, 2011, Weizmann Institute of Science, Israel
Title: ``Percolation and Epidemics in Network of Networks''
19. Statistical Physics and Complex Networks: Nov. 15-17, 2011, Potsdam, Germany
Title: ``Using Real World Data in Socio-economic modeling ''
20. K Eames. "The mathematics of plagues"; Maths and science week, North London
Collegiate School, Jan 2012 http://www.nlcs.org.uk/Latest_News/Dr-Ken-Eames.php
21. K Eames. "Measuring and modelling changing social contact networks"; European
Conference on Mathematical and Theoretical Biology, Krakow June 2011
22. K Eames. "Caught in the SCaMPiE net"; Social Contacts and Mixing Patterns in
Epidemiology workshop, LSHTM April 2011
23. K Eames. "Networks in epidemiology: human mixing patterns and models of
infection" Seminar, Durham University March 2011
24. S Funk "Pitfalls in analysing biased data" at "Biases in reporting and detection of rare
events.", Columbia University. Nov 17-18, 2011.
25. S Funk "Modelling the influence of human behaviour on the spread of infectious
diseases" Contributed talk at European Conference of Mathematical and Theoretical
Biology, Kraków. June 28 - Jul 2, 2011
26. J Edmunds, invited talk on the flusurvey at the HPA Annual Conference in September
2011
27. J Edmunds, invited talk on the use of models for public health decision-making at the
Epidemics3 conference in Boston December 2012, in which he highlighted the
importance of the use of community surveillance using the internet or other methods.
28. J Edmunds, invited talk on surveillance of influenza at the EE2 talk at Courmayeur in
January 2012.
29. F. Couto, Exploring the semantics of biomedical ontologies, in External Seminar at
European Bioinformatics Institute, Hinxton, UK, April 2011.
30. Mário J. Silva. Privacy and Crowdsensing: Can't we just be friends? FuturICT's Ethics
Meeting, Zurich, June 2011.
31. Catia Pesquita, Francisco Couto, Where GO is going and what it means for ontology
extension. Proceedings of ICBO 2011, International Conference on Biomedical
Ontology, July 2011.
32. Bruno Tavares, Hugo Bastos, Daniel Faria, Joao D. Ferreira, Tiago Grego, Catia
Pesquita, Francisco Couto, The Biomedical Ontology Applications (BOA) framework.
Proceedings of ICBO 2011, International Conference on Biomedical Ontology, July
2011.
33. João D. Ferreira, Francisco Couto, Generic semantic relatedness measure for
biomedical ontologies. Proceedings of ICBO 2011, International Conference on
Biomedical Ontology, July 2011.
34. Presentation of EPIWORK to students of the Master/PhD in Epidemiology at the
Faculty of Medicine of the University of Lisbon, in an invited 3 hours seminar in July
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 2
9
2011, by Mário J. Silva.
35. Mário J. Silva. Challenges in Societal Data Management. Keynote presentation at
IDEAS'11: 15th International Database Engineering & Applications Symposium.
Lisbon, September, 2011.
36. Juliana Duque, Mediação Dados-Informação: Design de Informação para a Epidemic
Marketplace Master Thesis, University of Lisbon, School of Fine Arts, November
2011 (in Portuguese).
37. Carla Patrícia Freitas Sousa, Epidemic Marketplace: Repositório e Web Services,
Master Thesis, University of Lisbon, Faculty of Sciences, January 2012.
38. Zamite, J., Silva, F., Couto, F., Silva, M. 2011: MEDCollector: Multisource epidemic
data collector. Transactions on Large-scale Data-and Knowledge- centered Systems
IV: Special Issue on Database Systems for Biomedical Applications (6990), pp. 40-72.
Springer-Verlag New York, Inc. ISBN 978-3-642-23739-3
39. Corrado Gioannini, João Zamite, Integrating the Gleamviz Simulator Tool with the
Epidemic Marketplace Platform. Poster presented at EE2, Epiwork/Epifor 2nd
International Workshop: Facing the Challenge of Infectious Diseases. 2012.
40. João Zamite, Dulce Domingos, Mário J. Silva, Owner-Centred Group- Based Access
Control for Epidemic Resources. Poster presented at EE2, Epiwork/Epifor 2nd
International Workshop: Facing the Challenge of Infectious Diseases. 2012.
http://xldb.di.fc.ul.pt/xldb/publications/Zamite.etal:EpidemicGroupBasedAccessContr
ol: 2012_poster.pdf
41. João D. Ferreira, Francisco M. Couto, Mário J. Silva, Ontologies in the
Epidemiological Domain. Poster presented at EE2, Epiwork/Epifor 2nd International
Workshop: Facing the Challenge of Infectious Diseases. 2012.
http://xldb.di.fc.ul.pt/xldb/publications/Ferreira.etal:OntologiesInThe:2012_poster.pdf
42. F. Couto, Untangling Biomedical Ontologies, Practical workshop: Bioinformatics and
Systems Modelling, Faculty of Sciences, University of Lisbon, 2011
43. M. Aguiar, Talk “Modelling dengue fever epidemi- ology: complex dynamics and its
implication for data analysis”, at 9th International Conference of Numerical Analysis
and Applied Mathematics (ICNAAM), Halkidiki, Greece, September 2011.
44. M. Aguiar, Talk “Modelling dengue fever epidemiology: complex dynamics and its
implication for data analysis”, at 8th European Conference on Mathematical and
Theoretical Biology (ECMTB), Krakow, Poland, July 2011.
45. M. Aguiar, talk “The role of seasonality and im- port rate in the two strain dengue
model: complex dynamics and its implications for data analysis”, at Second Workshop
Dynam- ical Systems Applied to Biology and Natural Sciences (DSABNS), Centro de
Matemática e Aplicações Fundamentais (CMAF), Lisbon University, Portugal,
February 2011
46. Bob W. Kooi, plenary talk ”Complex dynamics and its regulation in a predator-prey
model with predator suffering from an infectious disease”, at Second Workshop
Dynamical Systems Applied to Biology and Natural Sciences (DSABNS), Centro de
Matemática e Aplicações Fundamentais (CMAF), Lisbon University, Portugal
February 2011.
47. Sebastien Ballesteros, talk ”On the predictability of dengue epidemics: a sequential
Monte Carlo approach”, at Second Workshop Dynamical Systems Applied to Biology
and Natural Sciences (DSABNS), Centro de Matemática e Aplicações
Fundamentais(CMAF), Lisbon University, Portugal February 2011.
48. Jose Martins, talk “Creation and annihilation operators in the spatial SIS model.’, at
Second Workshop Dynamical Systems Applied to Biology and Natural Sciences
30 EPIWORK D7.7
(DSABNS), Centro de Matemática e Aplicações Fundamentais(CMAF), Lisbon
University, Portugal February 2011..
49. Nico Stollenwerk, plenary Talk “The interplay be- tween nonlinearity and noise in
epidemiological systems’, at Second Workshop Dynamical Systems Applied to
Biology and Natural Sci- ences (DSABNS), Centro de Matemática e Aplicações
Fundamentais(CMAF), Lisbon University, Portugal February 2011.
50. Nico Stollenwerk, talk “On the origin of the irregularity of DHF epidemics”, at the
ECMTB 2011, Krakow, Poland, July 2011.
51. Nico Stollenwerk, talk “Chaos and noise in pop- ulation biology” , 2nd talk at the
ECMTB 2011, Krakow, Poland, July 2011.
52. Philip Gerrish, talk “Genomic mutation rates that cause extinction: general
evolutionary predictions” , 2nd talk at the ECMTB 2011, Krakow, Poland July 2011.
53. Nico Stollenwerk, talk “Parameter estimation in epidemiology: from simple to
complex dynamics”, at the IC- NAAM 2011, Chalkidiki, Greece, July 2011.
54. Philip Gerrish, talk “Adding Dynamical Suf- ficiency to Fishers Fundamental
Theorem of Natural Selection”, at the ICNAAM 2011, Chalkidiki, Greece, September
2011.
55. Nico Stollenwerk, invited plenary talk “Chaos and noise in population biology” with
emphasis on parameter esti- mation in epidemiological models, at MATE 2011,
Colchester, UK, September 2011.
56. Frank Hilker, talk “Evaluating the effectiveness of controlling pest species with
combined strategies” with emphasis on parameter estimation in epidemiological
models, at MATE 2011, Colchester, UK, September 2011.
57. M Aguiar, talk “Modelling dengue fever epi- demiology: complex dynamics and its
implication for data analy- sis.”, at Centro de Matema ́tica e Aplica ̧c ̃oes
Fundamentais, Faculty of Sciences, Lisbon University, Portugal, September 2011.
58. M Aguiar, talk “Modelling dengue fever epidemi- ology: complex dynamics and its
implication for data analysis.”, at Department of Theoretical Biology, Faculty of Earth
and Life Sci- ences, Vrije Universiteit Amsterdam, The Netherlands, August 2011.
59. M Aguiar, talk “Modelling dengue fever epidemiology: complex dynamics and its
implication for data analysis.”, at Central Veterinary Institute of Wageningen,
Lelystad, The Netherlands, August 2011.
60. M Aguiar, talk “Modelling dengue fever epidemiology: complex dynamics and its
implication for data analysis.”, Seminario Doutoral II at Faculty of Sciences, Lisbon
University, Portugal, June 2011.
61. M Aguiar, talk “Modelling dengue fever epidemiology: complex dynamics and its
implication for data analysis.”, at Universidade Fundacao Ezequiel Dias, Brazil, April
2011.
62. M Aguiar, talk “Modelling dengue fever epidemiology: complex dynamics and its
implication for data analysis.”, at Universidade Federal de Minas Gerais, ICEX/ICB,
Brazil, April 2011.
63. M Aguiar, Sebastien Ballesteros, Bob W. Kooi and Nico Stollenwerk. (2011). “The
role of seasonality and import in a minimalistic multi-strain dengue model capturing
differ- ences between primary and secondary infections”. (poster presented at at at 9th
International Conference of Numerical Analysis and Applied Mathematics
(ICNAAM), Halkidiki, Greece.).
64. M Aguiar, S Ballesteros, J Boto, B Kooi, L Mateus and N Stollenwerk. (2011).
“Parameter estimation in epidemiology: from simple to complex dynamics”. (poster
presented at at at 9th International Conference of Numerical Analysis and Applied
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 3
1
Mathematics (ICNAAM), Halkidiki, Greece.).
65. M Aguiar, S Ballesteros, B Kooi and N stollenwerk. (2011). “The role of seasonality
and import in a minimalistic multi-strain dengue model capturing differences between
primary and secondary infections”. (poster presented at Summer School Dynamical
Models in Life Sciences , E ́vora, Portugal).
66. M Aguiar, S Ballesteros, J Boto, B Kooi, L Mateus and N Stollenwerk. (2011).
“Parameter estimation in epidemiology: from simple to complex dynamics”. (poster
presented at Summer School Dynamical Models in Life Sci- ences , E ́vora, Portugal).
67. Ghaffari, P., Jansen, V., & Stollenwerk, N. (2011). “Evolution towards critical
fluctuations in a system of accidental pathogens”. (poster presented at ESMTB-EMS
summer school, Evora, July 2011).
68. Stollenwerk, N., & Boto, J.P. (2011). “Super-diffusion in epidemiology”. (poster
presented at ESMTB-EMS summer school, Evora, July 2011).
69. D. Paolotti, I. Carreras, C. Gioannini, P. Saar, D. Miorandi, V. Colizza and A.
Vespignani, "Influweb: an internet-based and mobile platform for influenza
surveillance", in Demo Session - eHealth 2011 [demo]
70. P. Saar, A. Matic, I. Carreras, V. Osmani, “Comm2Sense: inferring social interactions
via smartphones”, in Demo Session - eHealth 2011. [demo]
71. P. Saar, A. Matic, I. Carreras, V. Osmani, “Comm2Sense: inferring social interactions
via smartphones”, in Proc. of Demo Session - eHealth 2011. [demo]
72. M Tizzoni, Poster. Title: Human mobility networks, travel restrictions, and the global
spread of 2009 H1N1 pandemic. Venue: Epidemics3, Boston, MA, USA.
73. M Tizzoni, Talk. Title: GLEaM, a global stochastic simulation model for influenza
epidemics: its application to the 2009 H1N1 pandemic.cVenue: Workshop "Why
should one trust your model?", SIMUL 2011, Barcelona, Spain.
74. M Tizzoni Talk. Title: GLEaM, a global stochastic simulation model for influenza
epidemics: its application to the 2009 H1N1 pandemic. Venue: ECCS'11, Wien,
Austria.
75. M Tizzoni Poster. Title: GLEaM, a global stochastic simulation model for influenza
epidemics: its application to the 2009 A/H1N1 pandemic. Venue: NetSci2011,
Budapest, Hungary.
76. D Balcan, Talk, Role of large scale computational models in forecasting spreading
patterns of emerging infectious diseases: Experiences from the 2009 H1N1 influenza
pandemic, Mathematics in Emerging Infectious Disease Management, Centro
Internacional de Ciencias, Cuernavaca, Mexico – January 2011
77. C. Poletto, SIAM Conference on Application of Dynamics Systems, Snowbird, Utah,
USA, May 22-26, 2011. Title: Multiscale Networks and the Spatial Spread of
Infectious Diseases
78. C. Poletto, NetSci2011, Budapest, Hungary, June 6-10, 2011. Title: Human travel
and time spent at destination: impact on the epidemic invasion dynamics
79. C. Poletto, Dagstuhl Seminar “Data Mining, Networks and Dynamics”, Dagstuhl
School, Germany, November 6 – 11, 2011. Title: Multiscale Networks and
the Spatial Spread of Infectious Diseases
80. A. Vespignani, EE² - EPIWORK/EPIFOR 2nd International workshop “Facing the
challenge of infectious diseases, Courmayeur, Italy, January 18 - 20, 2012.
81. A. Vespignani, Topix Conference, Turin, Italy December 6, 2011. "Frontiers in
multiscale computational modeling for zoonotic epidemics", Kansas City, October 1012, 2011.
32 EPIWORK D7.7
82. A. Vespignani, Workshop "Data Science and Epidemiology", Center for Infectious
Disease Dynamics (CIDD) at Penn State University, October 6 and 7, 2011.
83. A. Vespignani, ECCS 11, European Conference on Complex Systems 2011 Vienna,
September 12-16, 2011.
84. A. Vespignani, The 2011 Summer Institute in Statistics and Modeling in Infectious
Diseases (SISMID) , June 13-29, 2011, University of Washington in Seattle,
Washington.
85. A. Pugliese, S. Merler, M. Ajelli, N.M. Ferguson. Modelling the European spread of
H1N1 epidemic in 2009. UCID Symposium Infection Dynamics, Utrecht, The
Netherlands, March 9-11, 2011. [Oral presentation]
86. A. Pugliese, S. Merler, M. Ajelli, N.M. Ferguson. The role of epidemic modelling,
focussing on 2009 pandemic influenza. Annual meeting of the European Influenza
Surveillance Network, Lublijana, Slovenia, June 7-9, 2011. [Oral presentation]
87. M. Ajelli, S. Merler. Investigating the spatiotemporal dynamics of pandemic influenza
in Europe. SIAM Conference on Applications of Dynamical Systems, Snowbird,
Utah, USA, May 22-26, 2011. [Oral presentation]
88. Poletti, M. Ajelli, S. Merler. Risk perception and 2009 H1N1 pandemic influenza
spread in Italy. 8th European Conference on Mathematical and Theoretical Biology,
Krakow, Poland, June 28 - July 2, 2011. [Oral presentation]
89. S. Merler, M. Ajelli. Simulation models for public health prepareness. Cloud
Computing per la Sanità Digitale, Castelfranco Veneto, Italy, Oct 18, 2011 [Oral
presentation]
90. S. Merler, M. Ajelli, A. Pugliese, N.M. Ferguson, Factors determining the dynamics
of the 2009 H1N1 pandemic in Europe, Epidemics3, Boston, MA, USA, Nov 19 Dec. 2, 2011 [Poster]
91. P. Poletti, M. Ajelli, S. Merler, The influence of uncoordinated human response on the
spread of the 2009 pandemic influenza in Italy. Epidemics3, Boston, MA, USA, Nov
19 - Dec. 2, 2011 [Poster]
92. S. Merler, M. Ajelli, A. Pugliese, N.M. Ferguson. Investigating the spread of the 2009
H1N1 influenza pandemic in Europe. Mathematical Innovative Methods and Models
of BIOsciences. Trento, Italy, December 19-21, 2011. [Oral presentation]
93. L. Fumanelli, M. Ajelli, P. Manfredi, A. Vespignani, S. Merler. Synthetic mixing
patterns and infectious disease epidemiology. Mathematical Innovative Methods and
Models of BIOsciences. Trento, Italy, December 19-21, 2011. [Oral presentation]
94. L. Fumanelli, M. Ajelli, P. Manfredi, A. Vespignani, S. Merler, Computing Synthetic
contact matrices through modeling of social mixing patterns relevant to infectious
disease transmission. ESCAIDE, Stockholm, Sweden, Nov 6-8, 2011 [Oral
presentation]
95. E. Dal Fava, L. Fumanelli, S. Merler, L. Marangi, M. Ajelli, A. Melegaro, Z. Shkedy,
G. Scalia Tomba, C. Rizzo, P. Manfredi. Contact patterns and transmission of
varicella in Europe. ESCAIDE, Stockholm, Sweden, Nov 6-8, 2011 [Oral
presentation]
96. V. Colizza ECDC Infectious Disease Modeling Meeting, ECDC, Stockholm, Sweden,
November 14-15, 2011
97. V. Colizza, Workshop ‘The role of modeling in influenza pandemic planning and
response: lessons from 2009’ Venice, Italy, May 26-27, 2011.
98. V. Colizza, International Conference on Innovation and Information Technologies,
Abu Dhabi, UAE, April 25-27, 2011. [keynote
2012  Copyright lies with the respective authors and their institutions
EPIWORK D7.7 3
3
99. V. Colizza, SAMSI Workshop ‘Dynamics on networks’, RTI, NC, USA, March 2123, 2011.
100.
V. Colizza, Journee de l’Ecole Doctorale Pierre Louis de Sante Publique, Saint
Malo October 19-21, 2011.