1. Dynamic Random Graph Modelling and Applications in the UK 2001 Foot-and-Mouth Epidemic Christopher G. Small Joint work with Yasaman Hosseinkashi, Shoja Chenouri (Thanks to Rob Deardon for supplying the data.)

2. Outline Foot-and-mouth disease outbreak in the UK, 2001 and dynamic random graphs (DRG) Introducing a Markov model for DRGs Inferring the missing edges and some epidemiological factors Application to the UK 2001 foot-and-mouth (FMD) epidemic Simulation results and detecting the high risk farms

3. At day 4 At day 5 Foot-and-mouth outbreak, UK 2001 All farms involved in the epidemic Farm known to be infectious at the current day New infection discovered Edges show possible disease pathways

4. At day 44 At day 184 Foot-and-mouth outbreak, UK 2001

5. Date (day) Cumulative number of infectious farms over time Changes in the diameter of the infectious network over time Date (day) Foot-and-mouth outbreak, UK 2001

6. Infectious disease outbreak as a dynamic random graph The development of an infectious disease outbreak can be modeled as a sequence of graphs with the following vertex and edge sets: Vertices: individuals (patients, susceptible). Edges: disease pathways (directed). Individuals Connecting two individuals if one has infected another

7. A Markov model for dynamic random graphs is a sequence of random directed graphs over a continues time domain T where Model assumptions: Every infectious farm i may infect a susceptible farm j with exponential waiting time. Simultaneous transmissions occur with very small probability. Transmission hazard can be parameterized as a function of local characteristics of both farms and their Euclidean distance.

8. Embedded in discrete time domain: Waiting time to get into k’th state All farms with their active transmission pathways The composition of infectious/susceptible/removed farms changes over time by new infections and culling the previously infected farms. The k’th transition occurs when the k’th farm is infected. Culling modeled as deterministic process. A Markov model for dynamic random graphs

9. At state 3: 1 2 3 4 5 6 7 At state 4: 1 2 3 4 5 6 7 Edges are usually missing in the actual epidemic data! A Markov model for dynamic random graphs

10. Transition probabilities and likelihood:

11. Euclidean distance Density of livestock Model parameters : A Markov model for dynamic random graphs

12. Inference on missing edges and epidemiological factors Probability distribution for the k’th transmission pathway: : The farm which is infected at k’th transition, : Infectious and susceptible farms at state k

13. Inference on missing edges and epidemiological factors The basic reproduction number ( ) is defined as the expected number of secondary cases produced by a typical infected individual during its entire infectious period, in a population consisting of susceptibles only (Heesterbeek & Dietz, 1996): Expected infectivity of an individual with infection age. The density of population at the start of the epidemic when every individual is susceptible.

14. The basic reproduction number can be estimated by the expected out degree of a vertex, summed over all transitions (which is equivalent to sum over its infectious period). Expected outdegree of the vertex i during the epidemic, which can be considered as the specific basic reproductive number for vertex i. Inference on missing edges and epidemiological factors Assuming homogenous infectivity over the population!

15. Inference on missing edges and epidemiological factors 2 3 4 5 6 7 1 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Transition oneTransition twoTransition three Transition four Example:

16. Inference on missing edges and epidemiological factors Considering all potential disease pathways at each transition: 2 3 4 5 6 7 1 The cumulative risk that a farm has tolerated before becoming infected is summarized by the expected potential indegree over the time interval that it belongs to the susceptible subgraph. The cumulative threat that a farm has caused is computed by its expected potential outdegree over the time interval that it belongs to the infectious subgraph.

17. The data A very intensive region All farms who involved the epidemic according to our data: 2026 farms, 235 days Information available for each farm includes: - Report date, Infectious data, Cull date - Amount and density of livestock (sheep and cattle) - Region (shown by colors in this plot) - X – Y coordinates - Diagnosis type The data has been polished by removing farms with missing information.

18. Normal test statistics (H0: parameter = 0) ML estimatesparametershazard 12.5873576 0.2141929 12.01374599 0.18493832 0.10886620 0.04010013 31.208301 19.552151 -1.359490 -1.275928 38.4376552 22.2245189 ML estimation

19. Simulation and effects of control policies InfPr ~ Infectious Period Infection Date Number of infectious farms Date (day) Reducing the infectious period for each farm Decreasing the number of infectious (active) farms at each time point

20. Actual epidemic Simulation After 50 transmissions After 1000 transmissions After 1500 transmissions Simulation results

21. Data Cumulative number of infectious farms Date (day) Simulation results

22. Highly infectious farms Highly resistant farms How infectious and resistance each farm was during the epidemic? All in region 2! Density of livestock is 78.57 on average with 47.6 standard deviation. Mostly in regions: 2, 6, 4, and 1 Density of livestock is 137.30 on average with 113.9 standard deviation.

23. Comparing the two groups Highly infectious farms Highly resistant farms The kernel density estimate of sheep ratio in the livestock Epidemic data

24. Thanks…