The LLNL FMD Decision Support System: Concise Description of Features and Output

1. The LLNL FMD Decision Support System: Concise Description of Features and Output Tanya Kostova T. Bates, C. Melius, S. Smith, A. Robertson, S. Hazlett, P. Hullinger, Lawrence Livermore National Laboratory DIMACS Workshop March 2006 “Data Mining and Epidemiological Modeling”

2. LLNL is developing a decision support system for evaluation of the economic impact of FMD epidemics • Effort funded by the Department of Homeland Security • DHS has numerous S&T investments in research projects for agriculture security countermeasures and requires tools to help evaluate future investments

3. LLNL is developing a decision support system for evaluation of the economic impact of FMD epidemics • Effort funded by the Department of Homeland Security • DHS has numerous S&T investments in research projects for agriculture security countermeasures and requires tools to help evaluate future investments • Numerous FMD epidemiological models exist but… • They are not national in scale • Current models target natural or accidental introduction not an intentional act • Epidemiological and economic models are not coupled

4. GENERAL FEATURES OF THE EPIDEMIC MODEL Agent-based spatially-explicit discrete-time computational model Time progresses in increments of 1 unit (=1 day)

5. GENERAL FEATURES Agent-based spatially-explicit discrete-time computational model Time progresses in increments of 1 unit (=1 day) In a time stepping agent based model, at each time increment some of the agents change some of their attributes depending on their previous state and on the previous states of some of the other agents.

6. GENERAL FEATURES Agent-based spatially-explicit discrete-time computational model Time progresses in increments of 1 unit (=1 day) In a time stepping agent based model, at each time increment some of the agents change some of their attributes depending on their previous state and on the previous states of some of the other agents. The FMD model agents are the animal facilities.

7. GENERAL FEATURES Agent-based spatially-explicit discrete-time computational model Time progresses in increments of 1 unit (=1 day) In a time stepping agent based model, at each time increment some of the agents change some of their attributes depending on their previous state and on the previous states of some of the other agents. The FMD model agents are the animal facilities. Facilities are groups of animals managed in a specific manner. Farms, Markets, Feedlots, Slaughter houses …

8. THE ATTRIBUTES OF THE FACILITY AGENT Type (incl. species, size and operation) Spatial coordinates Static Average Number of Contacts (to and from), Method of disease spread – specific network of contacts Dynamic Disease states Change due to interaction Availability Seasonal factors Change externally and independently of interaction

9. THE ATTRIBUTES OF THE FACILITY AGENT Type The current model version deals with 34 types of animal facilities: Beef(B), Dairy(S), Dairy(M), Dairy(L), Dairy(B), Grazing(S), Grazing(L), Feedlot(S), Feedlot(L), Stocker(S), Stocker(L) Swine(B), SwineFWean(S), SwineFWean(L), SwineFinish(S), SwineFinish(L), SwineNursery(S), SwineNursery(L), SwineFFeeder(S), SwineFFeeder(L), SwineFarFin(S), SwineFarFin(L), Sheep(S), Sheep(L), Sheep(B), Goats, Goats(B), Market, Market(Cattle), Market(Swine), Market(Other), Market(L), Market(C-L), DCalfHeifer(L)

10. Beef (S) Swine (S) Dairy (S) THE ATTRIBUTES OF THE FACILITY AGENT The spatial coordinates of each facility are exact “up to the county level” The NASS data supplies the numbers of different facility types in each county There are 1.2M facilities (according to NASS data) with 160M animals. These do not include markets which come from another database. Thus, we model 1.2M+ facilities and their contacts.

11. THE ATTRIBUTES OF THE FACILITY AGENT The spatial coordinates of the facilities are generated using a random algorithm based on the county-based data. Hogs and pigs Cattle and cows Sheep

12. THE ATTRIBUTES OF THE FACILITY AGENT Type (incl. species, size and operation) Spatial coordinates Static Average Number of Contacts (to and from), Method of disease spread – specific network of contacts Dynamic Disease states Change due to interaction Availability Seasonal factors Change externally and independently of interaction

13. THE ATTRIBUTES OF THE FACILITY AGENT Depends on the size and type of facility and determined for each specific facility as random number drawn from a given probability distribution obtained from survey data Average Number of Contacts (to and from), Method of disease spread – specific network of contacts

14. THE ATTRIBUTES OF THE FACILITY AGENT Depends on the size and type of facility and determined for each specific facility as random number drawn from a given probability distribution obtained from survey data Average Number of Contacts (to and from) Method of disease spread – specific network of contacts Direct (regional and inter-state) Indirect (high risk and low risk)

15. THE ATTRIBUTES OF THE FACILITY AGENT Type (incl. species, size and operation) Spatial coordinates Static Average Number of Contacts (to and from), Method of disease spread – specific network of contacts Dynamic Disease states Change due to interaction Availability Seasonal factors Change externally and independently of interaction

16. THE ATTRIBUTES OF THE FACILITY AGENT Disease states Culled Confirmed Suspected S - Susceptible (healthy) L- Latent U- Subclinically infectious I- Clinically infectious W – Vaccinated and susceptible V- Vaccinated M- Immune P- Suspected F- Confirmed X - Culled Immune Waning of immunity L1 L2 L3 L Latent (infected) Subclinically infectious Susceptible Clinically infectious ? Infection Vaccinated

17. THE ATTRIBUTES OF THE FACILITY AGENT The disease state attributes of each facility are calculated by an “intra-facility model” (IFM) The intra-facility model is a “time-since infection” Reed-Frost type model Represents a discrete-time system of difference equations representing the number of animals on the facility that are in each state S, L, I, U , V, W, M

18. THE ATTRIBUTES OF THE FACILITY AGENT The disease state attributes of each facility are calculated by an “intra-facility model” (IFM) The intra-facility model is a “time-since infection” Reed-Frost type model Represents a discrete-time system of difference equations representing the number of animals on the facility that are in each state S, L, I, U , V, W, M The output of the IFM is used to calculate the probability that an infected facility will infect other facilities This is done by using a “spread model “

19. THE ATTRIBUTES OF THE FACILITY AGENT Average Number of Contacts (to and from) Method of disease spread – specific network of contacts Availability Seasonal factors These attributes are used by the Spread Model to calculate the newly infected facilities

20. The Spread Model calculates the newly infected facilities Infected agents can spread the epidemic via various methods along method-specific networks Examples of methods - direct (movement of animals) - indirect: personnel movements; - inter-state direct movements “Truck routes” network For each method, the infection can be spread within a predefined set of facilities specific to the method. Thus, an infected facility will spread the infection to the facilities within the networks to which it belongs. “Vet routes” network not infected infected

21. The epidemic spread is modeled by a random process Uses information about the Average Number of adequate Contacts ANC of the infected facility by each of the methods The daily number of adequate contactsRANCmiis obtained from a Poisson process with mean ANC STEP 1 A contact originating from a facility that can cause infection is an adequate contact. An adequate contact that actually infects a target facility is an effective contact. For each method of infection m For each infected facility i: - A probability density function Pmi(j) defined on each of the nodes j of the network Smi of m and i is calculated - For each node j ofSmi the probability Cmj is calculated STEP 2 Pmi(j)is the probability that facility j will get a contact with facility i by method m. Distance dependent Cmjis the probability that an adequate contact to facility j will cause infection. Pmi(j) is used in a roulette algorithm to determine which facilities receive an adequate contact Cmjis used to determine which of the contacted facilities become infected RANCmi, Pmi(j) and Cmj are used to trace back the cause of infection of j STEP 3

22. The Spread Model involves factors sampled from PDFs Pmi(j) depends on - the average number of m-type contacts received by j - size of the facility j - seasonal factors - control measure factors - distance between i and j - frequency of contacts between i and j Cmj depends on - the fraction of vaccinated animals on the facility - control measure factors - probability that a contact of type m would cause infection Many of these factors are uncertain or involve variability and are sampled from probability density functions.

23. The Control Measures Component “Control measures” include Vaccination Culling Contact restrictions Isolation Increased detection Control measures are applied regionally

24. Events during one increment of time

25. AGGREGATION ALGORITHMS Our model is of US - national scale; however to keep calculations to a minimum: - We do not calculate all facilities at all times. - Only facilities in infected and their neighboring counties are initialized - Intra-facility model calculated only for infected facilities - Counties and states that have not been yet infected are considered as aggregated entities; if a contact happens to in such a county, it gets disaggregated.

26. OUTPUTS A simulation is made of N MC runs NO(102) - O(103)

27. OUTPUTS A simulation is made of N MC runs NO(102) - O(103) A run implements M time steps MO(102), usually 200-330 days or until a certain criterion is met (epidemic comes to end)

28. OUTPUTS A simulation is made of N MC runs NO(102) - O(103) A run implements M time steps MO(102), usually 200-330 days or until a certain criterion is met (epidemic comes to end) At each time step we keep track of the number P of facilities that are currently involved in the epidemic (i.e. the ones that are infected or in the neighborhoods of infected facilities. PO(102) - O(105) ???

29. OUTPUTS A simulation is made of N MC runs NO(102) - O(103) A run implements M time steps MO(102), usually 200-330 days or until a certain criterion is met (epidemic comes to end) At each time step we keep track of the number P of facilities that are currently involved in the epidemic (i.e. the ones that are infected or in the neighborhoods of infected facilities. PO(102) - O(105) ??? For each facility the important data (current states, costs, trace-back facilities) is O(101)

30. OUTPUTS O(1010) Thus, the total output of a simulation could be in the range of or more. Naturally, we do not keep all this output although what we do not keep may be important for the analysis What do we keep currently?

31. OUTPUTS Daily Numbers of facilities of the 34 types that are in the 9 disease states L- Latent U- Subclinically infectious I- Clinically infectious W – Vaccinated and susceptible V- Vaccinated M- Immune P- Suspected F- Confirmed X - Culled Numbers of facilities that have just acquired a new state Numbers of facilities that have ever been in some disease state Total numbers of infected, vaccinated, culled facilities Daily and total numbers of infected, vaccinated, culled animals of different species

32. OUTPUTS Durations: Lengths of time for which the 34 types of facilities were in some disease state Duration of total epidemic Costs associated with epidemic and control measures

33. Duration of epidemic Cumulative Frequency Duration Days after index herd infected OUTPUTS Currently, output is in Excel spreadsheet format and is used for visualization As well as to calculate statistics (means, quantiles, skewness, kurtosis, etc.) of MC output.

34. Epidemic model outputs and data mining Question: How can modern data mining tools help in the analysis of output data generated by a large-scale epidemic model?

35. Epidemic model outputs and data mining Question: How can modern data mining tools help in the analysis of output data generated by a large-scale epidemic model? Specifically, can data mining help uncover important relations between - scope of epidemic and spatial distributions of facilities? - how control measures are applied and the cost of the epidemic?

36. Epidemic model outputs and data mining Further, can data-mining tools help … Identify sources (infected facilities), likely transmission mechanisms? Classify of outbreaks into "natural" vs. "intentional" to help policy makers develop correct response strategies?

37. Epidemic model outputs and data mining Further, can data-mining tools help … Identify sources (infected facilities), likely transmission mechanisms? Classify of outbreaks into "natural" vs. "intentional" to help policy makers develop correct response strategies? Identify key facilities/locations for surveillance? Identify which control mechanisms are having the largest impact?

38. Epidemic model outputs and data mining Further, can data-mining tools help … Identify sources (infected facilities), likely transmission mechanisms? Classify of outbreaks into "natural" vs. "intentional" to help policy makers develop correct response strategies? Identify key facilities/locations for surveillance? Identify which control mechanisms are having the largest impact? Evaluate new technologies?Evaluate vulnerability of different industries and regions of the country?

39. If the answer is “yes” to at least some of our questions, which are the recommended data mining tools? Are they available?