Epidemiological Surveillance (spatial)

Question 1

Pathogens move between different geographical units because of

Answer 1

movements of animals or humans or other hosts.

Question 2

Spatial patterns arise from

Answer 2

• heterogeneity in the landscape
• large and dense cities v rural transmission

Question 3

Spatial dispersal approximated by

Answer 3

exp(-d/a)

Question 4

What determines the spatial spread of plant infectious diseases?

Answer 4

• contiguous spatial kernel

Question 5

Triphragmium ulmariae

Answer 5

• rust fungus (Sphaerophragmiaceae) - meadowsweet rust gall
• chemically induces swelling on the lower surface of Filipendula ulmaria leaves
• implicated in survival

Question 6

dots represent

Answer 6

• infection
• how many are there?

Question 7

distance

Answer 7

(√(x2–x1)2+(y2–y1)2)

Question 8

calculating force of spatial invasion

Answer 8

distances between infected and non-infected locations

Question 9

Force of infection at any given location is defined by

Answer 9

• how close infected locations are to non-infected locations
• by increasing a the force of infection also increases.

Question 10

compare how a spatial model estimating dispersal compares to a aspatial nullmodel

Answer 10

• create second model: infection risk uniform across all locations
• compare using anova
• outcome = infection status
• which is the better predictor via logistic regression: foi v nullmod?

Question 11

when can you use logistic regression?

Answer 11

binary outcomes

Question 12

likelihood of becoming infected

Answer 12

connectivity of an uninfected plant to infected plants

Question 13

nullmod

Answer 13

No information about connectivity between uninfected and infected plants

Question 14

Calculate log likelihood for Gaussian kernel and compare both models using AIC and

Answer 14

visualise their kernels.

Question 15

AIC is a function of

Answer 15

• Model complexity
• Likelihood (how well the model reproduces the data)

Question 16

better model according to AIC is the one that

Answer 16

explains the greatest amount of variation in the outcome using the fewest possible predictors

Question 17

AIC =

Answer 17

• 2(number of parameters)- 2log(Likelihood)
• lower = better

Question 18

human mobility patterns

Answer 18

not strictly determined by distance.

Question 19

Models approximating mobility data usually take into account

Answer 19

• population of origin and destination location
• distance or travel time between them
• other variables that may influence travel patterns: attractiveness of a population (high degree of shopping/work opportunities)

Question 20

Gravity model

Answer 20

movement volume between two communities depends inversely on distance, but bilinearly on size

Question 21

Gravity model explanation

Answer 21

• assumes number of individuals travelling per unit time proportional to some power the source and destination populations
• decays with distance
• reflects transport infrastructure between locations

Question 22

Assumptions of SIR:

Answer 22

• susceptible and infected individuals mix at random
• infectiousness does not change during course of infection in an individual
• no latent period

Question 23

Spatial interaction matrix

Answer 23

relative connectivity (G)

Question 24

Estimating the spatial interaction matrix

Answer 24

• Population per location
• Distances between them

Question 25

Epidemic size depends on

Answer 25

• population size

Question 26

Epidemic timing depends on

Answer 26

connectivity to focal regions

Question 27

Factors that explain flu asynchrony

Answer 27

• Behavioural factors
• Household size
• Immunity
• Vaccination
• Age distribution
• Climatic factors

Question 28

a

Answer 28

shape parameter/Gaussian kernel

Question 29

Human infectious diseases disperse along

Answer 29

routes of human mobility

Question 30

invasion predictors rely on

Answer 30

measure of closeness (distance)

Question 31

“a” governs

Answer 31

how fast the foi decays with distance