The Global Epidemic Simulator Wes Hinsley 1, Pavlo Minayev 1
Stephen Emmott 2, Neil Ferguson 1 1 MRC Centre for Outbreak
Analysis and Modelling, Imperial College London 2 Microsoft
Research, Cambridge
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Aims: To simulate the emergence and spread of an epidemic by
explicitly modelling the worlds 6.5 billion people. A platform for
simulating any directly transmitted pathogen e.g. influenza,
smallpox, or SARS.
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Previous Work Strategies for containing an emerging influenza
pandemic in Southeast Asia, (Ferguson et al) Nature 437, Sep 2005
Strategies for mitigating an influenza pandemic (Ferguson et al),
Nature 442, July 2006
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Challenges: Computational Performance and Memory Data
availability Algorithmic Complexity Statistical Validation or
Justification
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Design Create a synthetic population, distributed evenly across
different computational nodes. Overlay the world with a grid of
patches, and calculate probabilities that people contact each other
randomly, from patch-to-patch (rejection/acceptance algorithm)
Consider other reasons people may contact each other: households,
schools, workplaces, long range travel.
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The Landscan Dataset A grid of 43200x20880 points,
(longitude,latitude) Each point is number of people in a region of
around 1km 2 3.5 Gb, but mostly sparse so reducible to 600Mb or
less if you need
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Decomposition Tool Cutting the world into similarly sized
sections is not trivial. Writing an data decomposition application
made the process (slightly) easier.
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Decomposition Tool This is how the Europe Node sees the world.
Yellow = local (where individuals are stored).Blue = remote
patches.
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Decomposition Tool In more detail Yellow (local) squares are
always 20x20 landscan cells. Blue (remote) squares are usually
320x320 landscan cells, but
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Decomposition Tool remote squares can be divided further, if
the placing of a border requires them to be. Borders are aligned to
a grid, resolution 20x20 landscan cells.
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Decomposition Tool Different nodes see the world in
differently. But every node knows for every geographical location,
which computer has the detailed view.
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Patch k (20x20 landscan cells) Simulation Initialisation
Although we assign individuals to ~20km patches, we preserve
Landscan resolution by assigning more precise longitude and
latitude to individuals.
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Population N k D k,k Patch k Patch k For each k q k,k = F(D k,k
)N k Z k where F is kernel function Z k is normalisation term Main
loop: For each infected individual in patch k Find poisson(R 0 )
contacts, Each contact: sample q k,k Pick random individual in k
Use ratio of F(D k,k ) and F(r i,j ) to adjust for q k,k being an
over-estimate. Population N k r i,j Contact Acceptance/Rejection
Algorithm
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Early Results
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For R 0 =2.0, incubation ~2 days, infectious ~3 days. Seeded in
South America
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Early Results For R 0 =2.0, incubation ~2 days, infectious ~3
days. Seeded in South America As above, but seeded in Eastern
USA
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Early Results For R 0 =2.0, incubation ~2 days, infectious ~3
days, Kernel function adjusted to be less local
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Internode Communication When contacts are on another node:-
Acquire extra local contacts, assuming all remote contacts are
rejected. Send all requests for contact in one message (MPI).
Package message together to reduce overhead. One message per
timestep, between each node. Individual i in patch k Individual j
in patch k M CONTACT_REQUEST M ACCEPT_CONTACT If accepted, mark j
as contact of i Update contacts of i, if remote contacts made (inc.
infect info)
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Community Contacts Across Nodes
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Results of first global run Total infected 4.3 billion. (Zero
in America!) Use a less local kernel? Or, global contact-making
doesnt always follow a gravity-based model need other ways of
travelling long distances.
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Beyond Community Contacts Representing travel:- WTO Country
Border Data. Annual border crossings by origin/nationality Air and
ground travel, and some duration data. USA is one entry. Some
duplication/confusion. TFS Airline Data. Annual airport-to-airport
passenger flows No record of final destination. Others Expensive,
or sparse.
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Time spent in local community Individual is infectedInfected
individual recovers Time spent travelling Loc. Community Travel
Loc. Com. Travel Loc. Community Representing Journeys If we also
can sample journey duration, then we could construct some simple
travel plans for individuals for both infected, and susceptible.
Loc. Com. Travel Infected Individual makes contacts
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Build extendable origin-destination matrix structure for
flights, ground borders and any new data we might acquire. Add
households, workplaces and schools, as demonstrated in previous
work. Consider the properties of real diseases in more detail.
Future Work
