Computational epidemiology is a form of spatiotemporal

reasoning in which social link structures

are employed, and spatially explicit models

are specified and executed. We point to issues thus

far addressed neither by engineers, nor scientists, in

the light of a use case focusing on catastrophic scenarios

that assume the emergence of a highly unlikely

but lethal and contagious strain of influenza.

Our conclusion is that important perspectives are

missing when dealing with policy issues resulting

from scenario execution and analyses in computational

epidemiology

Boman, Magnus

English

Swedish Institute of Computer Science Publications Database

On Understanding Catastrophe: The case of highly severe influenza-like illness
(Position Paper)
Magnus Boman
SICS and KTH/ICT/SCS
Kista, Sweden
mab@kth.se
Abstract
Computational epidemiology is a form of spa-
tiotemporal reasoning in which social link struc-
tures are employed, and spatially explicit models
are specified and executed. We point to issues thus
far addressed neither by engineers, nor scientists, in
the light of a use case focusing on catastrophic sce-
narios that assume the emergence of a highly un-
likely but lethal and contagious strain of influenza.
Our conclusion is that important perspectives are
missing when dealing with policy issues resulting
from scenario execution and analyses in computa-
tional epidemiology.
1 Introduction
In the summer of 2010, we were asked to participate in the
filming of a Swedish TV series, scheduled for nationwide
broadcast and intended for subsequent international broad-
casting, on the end of humanity. The threat to humanity at
hand was that of communicable disease. Our roles were those
of computational epidemiologists: experts on all aspects epi-
demiological except the medical ones. The producers knew
that we had developed one of the largest simulation platforms
for epidemiological studies in the world [Brouwers et al.,
2010], and that we had built it on registry data on the entire
Swedish population, not on samples (cf., e.g., [Eubank, 2004;
Ferguson et al., 2005]). We agreed, on two conditions. First,
the specification of the actual disease had to be made by an
epidemiologist, to guarantee the plausibility of the pathogen,
i.e., the morbidity and mortality had to be realistic even if the
occurrence of the pathogen could have a miniscule probabil-
ity. Second, our work on running simulations of more realis-
tic scenarios— in which the contact network of all Swedes
help calculate the spread of diseases with observable and
measurable characteristics— should not be disturbed.
Because of the recent H1N1 pandemic, and because our
platform had just been used for calculating its cost [Brouw-
ers et al., 2009], it was agreed that the pathogen should be
influenza, with properties only the Spanish Flu could com-
pare to among real pandemics. The haemagglutinin and neu-
raminidase combination (the H and the N of the specified in-
fluenza virus) was to be novel. Such a combination was in the
end specified, by Bjo¨rn Olsen, one of the researchers that dis-
covered H16; the latest haemagglutinin to be found [Fouch-
ier et al., 2005]. While the properties of this influenza—
harrowing as they were—ultimately proved insufficent to, by
themselves, cause the end of humanity, our experiments sug-
gested to us a new line of applications for the social sciences
within the realm of epidemiology. While network theory
[Newman, 2003], mathematical modelling [Hufnagel et al.,
2004], and social mechanisms [Hedstro¨m, 2005] may help
understand the processes of spread of communicable disease
in society [Epstein, 1999], the sheer impact of a disease more
deadly than anything experienced in history propels the re-
sults into a different scientific territory, viz. practical ethics
and social psychology. In fact, our second proviso could not
be met, because running these simulations provided us with
some unexpected lessons and questions that affected the way
we reason about computational epidemiology. The purpose of
this position paper is to point to those issues, and thereby as-
sist in hastening methodological development in this rapidly
expanding area.
2 Outbreak scenario
The influenza strain specified had roughly the following char-
acteristics (see, e.g., [Giesecke, 2002] for basics on epidemi-
ology). On day zero, replication of the new virus sets in after
a few hours, in the infected cells. When the first symptoms
(sore throat or eyes, beginning headaches) appear, after 24
hours, virus levels are very high. On days 1-3 after infection,
they peak and coincide with a cough. The virus levels slowly
diminish, until they disappear 6-7 days later. The ability for
the virus to replicate is counter-balanced by either existing
antibodies or by the production of new antibodies. That pro-
cess starts after a few days, leading to zero infectiousness af-
ter little more than a week, even if the cough persists. From
the time of infection to the first symptoms occur, infectious-
ness is lower than on days 1-3, in spite of active virus repli-
cation, since the cough is absent.
The scenario was built around an airplane arriving to Ar-
landa airport, Stockholm, on day zero. No passenger had
symptoms when boarding the plane, in South-east Asia, but
one of the passengers were infected two days before boarding.
On the plane, the passenger develops a cough, and infects five
more passengers before the plane lands. Since the virus strain
is new, there is no herd immunity to take into account. The
Figure 1: The number of infected in one typical run of the
science fiction scenario, out of several dozens. Since this is
merely for illustration, we include neither averages nor sig-
nificance tests. Weeks are on the x axis and the number of
infected individuals on the y axis.
Figure 2: The number of deceased from the same run, axes as
before.
attack rate is very high (around 50 per cent), and out of those
infected 1-5 per cent need hospital care. Out of those with se-
vere infections, all of which are in need of hospital care, 1-5
per cent die. In accordance with our usual workflow [Cakici
and Boman, 2011], this scenario was detailed through several
rounds of iteration, which provided more parameter values
for input into our executable model. The latency period was
18 hours, for instance. Severely infected individuals received
the highest probability of going to hospitals: 50 per cent went
to primary care, 10 per cent to hospitals, 1 per cent to work,
and the rest stayed home.
3 Experiments
Getting an epidemic to take off is often a problem in realistic
simulation environments, since for most realistic parameter
settings, there is no serious outbreak. In this case, however,
the attack rate and general properties of the pathogen caused
serious outbreaks in almost every run. We ran our simula-
tions with various random seeds, affecting the stochastic pa-
rameters of the model in various ways. We also checked the
stability of the results through sensitivity analyses in which
we varied an important variable representing context infec-
tions. In short, the probability of infection is highest in the
family home and at work places, and lower in the neighbour-
hood and when travelling. These probabilities, the so-called
beta values of the disease, were adapted to match the attack
rate numbers provided. Then the sensitivity analyses varied
the number of colleagues an individual had an opportunity
to infect on a particular day, the number of people encoun-
tered in the neighbourhood, the likelihood of travel, and other
stochastic parameters in our model. The reason for including
results here is more pedagogical than an attempt to convey
actual results, since the entire scenario is pure science fiction.
That said, we ran a number of scenarios, and also varied the
index cases geographically, e.g., by assuming the first six peo-
ple arrived to other airports than Arlanda, and even appeared
in random places in the country. In all runs, the pandemic
took off when it reached Stockholm, where approximately
one sixth of Sweden’s population of about nine million re-
sides.
4 Results
The number of infected in our simulations were about 6.5
million, corresponding to approximately 73 per cent of the
Swedish population of 8.861.388. (The current population
is over nine million, but our population data is not quite up
to date, hence the difference.) Out of those, 7.978.105 indi-
viduals belong to the giant component, i.e. just over 90 per
cent. The number of infected is shown in Figure 1 in cumula-
tive form. The graph representing the deceased is extremely
peaked, see Figure 2. In week 13 in this particular run, almost
115.000 people died, and almost 1.600.000 were infected.
The sharp decline after the peak is to a large extent due to
the network properties: Sweden is a sparsely populated coun-
try and of the 90 per cent part of the giant component, very
few had not been exposed by then. Already in week 13, half
the entire population were infected. By week 18, the number
of infected passed 6.1 million. When we ended the simula-
tion run after week 43, less than 1500 new cases ocurred each
week, chiefly due to the fact that the population had been ex-
hausted.
5 Discussion and conclusion
First, we must note that in this short paper, we have left out
almost all details of how our model is specified and imple-
mented. This has been published elsewhere, and the platform
was open-sourced in 2010 under a GPL license [SMI, 2010].
The scientific track record of our platform is, however, not
pivotal to the points we want to make. Instead, we would like
to focus on the fact that we thought that the cross-disciplinary
team we have been a part of for the last seven years or so—
constructing and implementing the software, as well as ap-
plying it to a number of policy problems—sufficed for inter-
preting the output of pretty much any simulation run, in any
scenario. This time, though, it was different.
Upon completion of our sets of runs, and the usual post-
processing of output (graphing the distributions and looking
at the logs, etc.), we presented our results to the epidemiol-
ogist and the producers of the TV series. We discussed the
outcome, and in the process also discussed the same techni-
cal issues as for an ordinary scenario: the geographical spread
of the disease, the effects of travel, possible policy measures
(shutting down schools, reducing operations at airports and
train stations, vaccination), and the limitations (no reservoirs
modelled, for instance). Quickly, however, the discussion
turned to different matters, which were more of a societal na-
ture.
We had previously been aware of cultural differences be-
tween Sweden and other countries in the face of pandemics.
Naturally, the computational epidemiologist would like to re-
alistically model all forms of social interaction (cf. [Boman
et al., 2006]). In the case considered here, potentially awk-
ward cultural and societal questions surfaced, such as how
and where to bury the dead. Just like the number of peo-
ple capable of distributing vaccine—and the geographical lo-
cation of vaccine storage facilities—constitute logistic con-
straints on efficient vaccine distribution, the procedures for
handling the deceased may constrain efforts to diminish the
effects of the disease. The number of infected is one thing,
but what about the number of affected? With so many fami-
lies affected by ill health, and even death, what chance does a
public health infrastructure have to withstand the pressure of
the disease? Even a resilient society could face a phase tran-
sition of sorts, in which important societal functions ceased
to operate. And even if this is not the case, even if soci-
ety withstands the horror of this kind of virus and quickly
bounces back, as after the Spanish flu (and the added horror
of WWI in that case), which is the proper scientific forum for
this kind of discussion? And how does the results of such
discussions propagate back into computational epidemiology
and into policy measures, and ultimately politics? How does
one prevent unnecessary concern, fear, or even panic?
The short answer is perhaps ethics, and practical ethics in
particular. A broader perspective would include social psy-
chology and philosophy (decision theory and value theory
being included). These perspectives are then added to those
that already are covered by the areas of epidemiology, com-
puter science, sociology, statistics, mathematics, economics,
and political science; all of which constitute parts in most
of the largest modelling and policy projects currently in ex-
istence (see, e.g., [Ferguson et al., 2006; Hall et al., 2007;
Halloran et al., 2008]).
Our conclusion must be that important perspectives are
missing when dealing with policy issues resulting from sce-
nario execution and analyses in computational epidemiology.
With many basic scientific questions already pressing (e.g.,
verification and validation, replication and docking, and se-
curity and privacy issues), this might be hard to fit on the
agenda of pandemic preparedness. We would like to open up
for discussion now, before anything even remotely like what
we have just simulated is encountered by a human population.
Acknowledgements
The ASSETS European Union Best Practice Network helped
finance the link structure aspects of this research, and we
thank our colleagues in S-GEM [SGE, 2011]. In particular,
Baki Cakici provided important technical assistance.
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A workflow for software development within computational epidemiology.

Agent-based computational models and generative social science.

Characterization of a novel influenza a virus hemagglutinin subtype (H16) obtained from black-headed gulls.

Dissecting the Social.

Economic consequences to society of pandemic H1N1 influenza 2009: Preliminary results for Sweden.

Forecast and control of epidemics in a globalized world.

Group for Epidemic Modeling,

Micro-simulation of a smallpox outbreak using official register data.

Modeling targeted layered containment of an influenza pandemic in the united states.

Modelling disease outbreaks in realistic urban social networks.

Modern Infectious Disease Epidemiology. Arnold Publishers, 2nd edition,

Real-time epidemic forecasting for pandemic influenza.

Social network visualization as a contact tracing tool.

Strategies for containing an emerging influenza pandemic in Southeast Asia.

Strategies for mitigating an influenza pandemic.

The structure and function of complex networks.

On Understanding Catastrophe — The Case of Highly Severe Influenza-Like Illness

Computational epidemiology is a form of spatiotemporal reasoning in which social link structures are employed, and spatially explicit models are specified and executed. We point to issues thus far addressed neither by engineers, nor scientists, in the light of a use case focusing on catastrophic scenarios that assume the emergence of a highly unlikely but lethal and contagious strain of influenza. Our conclusion is that important perspectives are missing when dealing with policy issues resulting from scenario execution and analyses in computational epidemiology.ASSET

Digitala Vetenskapliga Arkivet - Academic Archive On-line

RISE – Research Institutes of Sweden

http://soda.swedish-ict.se/5210/1/mabIJCAI2011.pdf

On Understanding Catastrophe — The Case of Highly Severe Influenza-Like Illness

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Swedish Institute of Computer Science Publications Database

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RISE – Research Institutes of Sweden