Disease Transmission

Question 1

What is the overall aim of public health surveillance?

Answer 1

The overall aim of public health surveillance is to ensure that the right information is available at the right time to make informed public health decisions

Question 2

Why is it important for public health surveillance data collection to be adaptable?

Answer 2

Public health surveillance data collection needs to be adaptable so that demographic and environmental driving changes in disease incidence and prevalence can be understood.

Question 3

Which organizations are responsible for carrying out public health surveillance in different countries?

Answer 3

Public health surveillance often falls under the remit of public health agencies in different countries. In the UK for example, surveillance is carried out by the United Kingdom Health Security Agency (UKHSA) and performed by clinics, GPs, and hospitals among others.

Question 4

Define public health surveillance data

Answer 4

the ongoing, systematic collection, analysis, and interpretation of health-related data essential to planning, implementation, and evaluation of public health practice.

Question 5

Name four common disease surveillance data

Answer 5

Syndromic surveillance
Genomic surveillance
Serological surveillance
Wastewater surveillance

Question 6

What are some of the common challenges with public health surveillance at the
beginning of disease outbreaks? (x4)

Answer 6

• Speed of collection, cleaning and sharing of data
• Non-representativeness (That the sample you take from the population is not representativeofthewhole)
• Changing disease case definitions
• Testing inaccuracies (false negatives / positives)

Question 7

Describe the key data sources necessary to estimate the time-varying reproduction number Rt

Answer 7

• Date of onset of cases (if daily data are reported)
• Number of cases
• Serial interval distribution (the time between the onset of symptoms in a primary case and
  the onset of symptoms of secondary cases)

Question 8

What R package does this cheeky prac use?

Answer 8

“EpiEstim”

Question 9

What is the most used visualisation package in R

Answer 9

ggplot2

Question 10

what data is in the EpiEstim package?

Answer 10

Flu incidence data

Question 11

Define the time-varying reproduction number Rt (effective R number)
CHECK

Answer 11

reproduction number at time t since the start of the
epidemic. As more individuals are infected or immunised,
Rt captures the number of secondary infections generated
from a population consisting of both naïve/susceptible and
exposed/immune individuals and therefore it both changes
in value over time and will always be less than R0.

Question 12

Define R0 (basic/initial R number)
CHECK

Answer 12

the number of secondary infections generated
from an initial case at the beginning of an epidemic,
in an entirely susceptible population.
The definition assumes that no other individuals are infected or immunized.

dimensionless number - NOT A RATE

Question 13

Values of Rt corresponding with case dynamics:

Answer 13

Rt>1 - cases increase
Rt=1 - ???????????????????
Rt<1- cases decline

Question 14

Q9 describe the epidemiological dynamics of the outbreak
by referencing the change in the time-varying reproduction number, Rt.

Answer 14

Sporadic cases detected in the first week and half of the outbreak when cases started rising quickly from the 7-11th of May.
Cases plateaued and declined.
Rt dropped below 1 around 14/15 of May.

Question 15

What did we do in Q10?

Answer 15

We estimated Rt with (using) a non parametric serial interval distribution.
We had the full distribution of the serial interval, not just a mean and standard deviation, so we fed this full distribution into estimate_r.

This plotted a serial interval distribution and helped us to estimate transmission.

Question 16

What is meant by a non-parametric distribution?

Answer 16

A non-parametric distribution implies that the distribution of serial interval (or generation time) is “distribution-free”, meaning that no assumptions need to be made about the shape or form of the distribution.

Question 17

What is the serial interval?

Answer 17

The serial interval is the time between onset of symptoms of a case and onset of symptoms of his/her secondary cases.

Question 18

What is the ideal situation for the estimation method used in this work (q10 abt serial distribution)?

Answer 18

the ideal situation is when exact times of infection are known and the infectivity profile may be approximated by the distribution of the generation time.

Question 19

Why is it difficult to measure the generation time distribution?

Answer 19

The generation time distribution is difficult to measure because the time that an individual is infected is rarely observed.

Question 20

What type of data is used in practice to estimate the distribution of the infectivity profile?

Answer 20

In practice, we apply our method to data consisting of daily counts of onset of symptoms where the infectivity
profile is approximated by the distribution of the serial interval.

Question 21

Can empirical data on the serial interval or generation time distribution be used instead of specifying a mean and standard deviation?

Answer 21

Yes, when empirical data on the serial interval or generation time distribution is available, it can be used instead of specifying a mean and standard deviation.

Question 22

Define generation time distribution

Answer 22

x

Question 23

Why might the serial interval distribution be poorly specified early in outbreaks?

Answer 23

because there may not be enough data available to accurately estimate the distribution.

Question 24

What is the purpose of integrating results over various distributions of the serial interval in estimate_R?

Answer 24

to account for uncertainty in the distribution of the serial interval, which can affect estimates of the effective reproduction number.
(incorporating various possible serial distributions in results when u don’t have enough data to specify a true distribution”

Question 25

What distributions are used to model the mean and standard deviation of the serial interval in estimate_R?

Answer 25

Truncated normal distributions are used to model the mean and standard deviation of the serial interval in estimate_R.

Question 26

Why would a sliding window size of 30 days not be realistic for estimating Rt?

Answer 26

we estimate one R per time frame and assuming a constant reproduction number for 30 days would not be realistic in the case of a fast spreading pathogen such as Influenzasince there may be significant changes in transmission dynamics in this time.

Question 27

How does the estimate of Rt once we changed the sliding windows compared to previous estimates of Rt?

Answer 27

Estimates are more smooth due to aggregation of cases through time. Small variations in case
numbers do not impact Rt estimation.