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“An introduction to infectious disease modelling” - Coggle Diagram
“An introduction to infectious disease modelling”
CHAPTER 1
INFECTION
Invasion by a smaller organism
Infectious agent can be harmful such as viruses like HIV virus which leads to AIDS. Unharmul as well such as bacteria in human stomach to aid in digestion.
Infectious agents can also multiply in hosts. Each agent have different location to multiply in the hosts.
TRANSMISSION
From hosts whether animal or human
Direct contant (scabies)
Insect vector (malaria,dengue)
Faecal-oral route (thypoid)
Respiratory route (influenza , tuberculosis)
Sexual contact (HIV)
Rn = R0 x S
HIT = 1- 1/R = R0 -1/R0
Outcome
Infections can be mild or fatal. A person can be immune an infection if infected by the same agent.. An example would be measels.
In other cases like HIV the person will stay infected and not acquire immunity.
3 time period
Incubation
Infectious
Pre infectious
CHAPTER 2
Setting up a model
Identify revelant facts about infection
(key feature questions) :
what is the preinfectious period ?
basic reproduction number ?
how long is the infection ?
does age groups matter ?
history of infection
time period (short term or long term transmission)
3.Choose type of modelling method
Deterministic : explain avaerage occurance , fixed input parameters and predictions.
Stochastic : Range of outcome, the porbability of occurance of disease, number of individuals recovered at time and at random.
Question must be identified
5.Set up model
Deterministic model : uses different equations , describe transition between category of diseases, different model usage.
Classes of models : compartmental, inidvidual based, transmission dynamic, static and network
uses computer programming like spreadsheet
7.Prediction and optimizatio
Detail analysis prediction. Prediction depends on input parameter. Sensitivity of model prediction must be identified.
4.Specify model input parameters
susceptibility to infection between t and t +1
propotion of infected whihc become infected from t and t +1
proportion of infection people who become immune and recover from t + t 1
6.Model validation
Needs to be validated by checking outputs agaisnt independant data.