Phillip Stroud, Sara Del Valle, Stephen Sydoriak, Jane Riese and Susan Mniszewski (2007)
Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society
Journal of Artificial Societies and Social Simulation
vol. 10, no. 4 9
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Received: 13-Apr-2007 Accepted: 14-Jun-2007 Published: 31-Oct-2007
|Table 1: Selected attributes that characterize the state of each individual (i.e. the members of the EPerson class)|
|fID||long||Unique identifier for each individual|
|Class that holds::|
Unique household id
household's half-block id
Person's age (years)
male or female
|A person's disease-related history and status.|
|fContacts||list of long||List of all other persons contacted by this person so far|
|fSchedule||Linked list of scheduleComponents, each having:|
|sequence of person's daily activities|
building or block id
activity start time
enum of 8 activity types
list of float
list of float
|for each activity, the fraction of a person's contacts that would be traced, and the fraction of time the person would wear a mask|
|fGeneration||int||one plus the fGeneration of the person that infected this person.|
|indicators of whether this person is prodromal, symptomatic, or incapacitated, and their infectiousness and susceptibility levels.|
|fTreatmentSet||list of int||the set of treatments that are currently in effect for this person|
|fDeliveredTreatmentSet||list of int||the set of treatments that have ever been delivered to this person|
|fTreatmentInfectivityFactor||float||an infectivity reduction factor that corresponds to the treatments currently in effect for this person|
|fCurrentRoom||long||ID of room that person currently occupies|
|fNextDepartTime||time||time of person's next disease state change|
|fUsingMask||bool||True if person is currently wearing a mask|
|fFamilyMemberSickAtHome||bool||True if person has sick household members|
|Figure 1. Households are geo-located to city half-blocks to match relevant demographic statistics in each of 12,226 block groups (top left). Business locations are geo-located by their business address (top right). Each individual is assigned an activity schedule (bottom left). Activity patterns are drawn from actual household activity surveys (bottom right)|
where tij is the time that susceptible person j was in the same sublocation as infectious person i, and the sum extends over all infectious persons that co-occupied the sublocation with individual j. EpiSimS computes the co-occupation times for all overlapping pairs of individuals as they enter and leave sublocations.
|Figure 2. The pandemic influenza disease progression model. Each individual is initially in the uninfected stage. Upon becoming infected, all untreated individuals transition to the incubating-1 stage (pre-symptomatic incubating)|
ESimulator::main Initialize local parameters from the config file Register to receive messages from other processors Create and open a local event logger Read in the disease manifestation (parameters giving fig. 2) IF master Read in the partition file (sublocations per location) Read in disease states (initial health of each person) Send each location to the appropriate processor. Read in schedule file (activities for each person) Send each individual to the appropriate processor. Until endOfSimulation receive and log results ELSE Receive locations Receive individuals Place scheduled events on event queue Until endOfSimulation Handle next event
101 1 52 1 1 603389 24177686 202000 24177686This record parses as: Person 101 resides in household 1, is a 52 year old, male, worker. His household is located on city half-block 603389, which is on NAVTEQ road segment 24,177,686. His household income is $202,000. He begins the simulation at his home location.
00:00:00 24177686 101 5 0 08:15:00 24177686 101 1 23914209 08:45:00 23914209 101 0 1 18:30:00 23914209 101 1 24177686 18:49:59 24177686 101 0 0 21:19:59 24177686 101 1 23937362 21:28:59 23937362 101 0 6 21:30:00 23937362 101 1 24177686 21:45:00 24177686 101 0 0The first field specifies the time (HH:MM:SS). The second field gives the road segment where the activity occurs. The third field specifies the person. Values for the 4th field specify the event type: 5-activity at start of simulation; 1- depart from activity; 0- arrive at activity. For departure events, the 5th field gives the location of the next event. For start-simulation and arrival events, the 5th field specifies the activity type: 0-home; 1-work; 2-shop; 3-visit; 4-social recreation; 5-other -an activity category allowed on the NHTS survey; 6-car pool; 7-school; 8-college.
EDiseaseTransmitter::SpreadDisease GET list of room occupants and time since last update Instantiate working variables FOR each pair of room occupants Increment their contact time Make empty lists of infectious and susceptible people FOR each person in list of room occupants infectivity = GetInfectivity of person IF (infectivity > 0) add person to list of infectiousPersons; susceptibility = GetSusceptibility of person IF (susceptibility > 0) add person to list of susceptiblePersons IF (person is CurrentlyExposed) THEN add person to list of exposedPersons ELSE add person to list of unexposedPersons END FOR IF no one in room is infectious MARK every person in room as unexposed return MARK every person in room as exposed IF no one in room is susceptible THEN return FOR each susceptible person in room susceptibility = GetSusceptibility of susceptiblePerson IF susceptiblePerson is wearing a mask, reduce susceptiblity sumOfLogs = 0 FOR all infectious persons in room infectivity = GetInfectivity of infectiousPerson reduce infectivity if infectiousPerson is wearing mask spreadability = susceptibility * infectivity * seasonalVarFactor * TransmissionFactor sumOfLogs += Math.log(1 - spreadability) END FOR prob=(1.-exp(deltaTMinutes * sumOfLogs)) rannum <- random uniform variate in (0,1) if (rannum < prob) then susceptableperson becomes infected end for
|Figure 3. The degree distribution, showing how many people have a given number of contacts per day, for the synthetic population of southern California|
|Figure 4. The distribution of contact duration among people in southern California|
|Figure 5. The distribution of contact duration for each activity type|
|Table 2: Average duration per contact by activity category|
|Activity category||Average Duration||Standard Deviation|
|Home||8 hrs 49 min||4 hrs 28 min|
|School||5 hrs 2 min||2 hrs 14 min|
|Work||4 hrs 13 min||2 hrs 55 min|
|College||2 hrs 6 min||1 hr 40 min|
|Visit||1 hr 43 min||1 hr 39 min|
|Other||1 hr 19 min||1 hr 45 min|
|Social Recreation||1 hr 12 min||1 hr 14 min|
|Shop||30 min||55 min|
|Carpool||18 min||43 min|
|Figure 6. Average number of total and household contacts hours per person per representative day, as a function of the age of the person|
|Table 3: Daily average number of contact hours by age group|
|average contact hours per day with preschoolers||average contact hours per day with students||average contact hours per day with adults||average contact hours per day with seniors|
|Table 4: Daily average number of home-related contact hours by age group|
|average household contact hours per day with preschoolers||average household contact hours per day with students||average household contact hours per day with adults||average household contact hours per day with seniors|
|Figure 7. The base case EpiSimS simulation run, showing the percentage of the population that becomes infected or symptomatic per day|
|Table 5: Breakout of infections by activity category|
|Activity category||Fraction of cumulative infections|
|Figure 8. Total attack rate (symptomatic and subclinical) by age cohort|
|Figure 9. Clinical attack rate by census tract, ranging from mild (green) to severe (red)|
|Figure 10. The clinical attack rate plotted against the average household size (i.e. the residential population divided by the number of households) for each of the 3978 census tracts in southern California, as simulated by EpiSimS|
|Figure 11. The clinical attack rate plotted against the ratio of students to non-students, for each of the 3978 census tracts in southern California, as simulated by EpiSimS|
|Figure 12. The clinical attack rate plotted against the logarithm of the per capita income, for each of the 3978 census tracts in southern California, as simulated by EpiSimS|
|Figure 13. The clinical attack rate plotted against the population density, for each of the 3978 census tracts in southern California, as simulated by EpiSimS|
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