Download presentation

Presentation is loading. Please wait.

Published byBrooks Bee Modified over 2 years ago

1
A VERY IMPORTANT CONCEPT Disease epidemiology is first and foremost a population biology problem Important proponents: Anderson, May, Ewald, Day, Grenfell

2
S.I.R. MODELS Susceptible, Infected, Recovered Assign each individual to one of the mutually exclusive categories and then apply equations to describe rates of transition to the different categories

3
TWO KINDS OF TRANSMISSION Direct - through physical contact or through production of free-living stages (e.g. measles) Indirect - through one or more obligatory intermediate hosts (e.g. malaria)

4
DIRECT TRANSMISSION BETWEEN HOSTS ASSUMPTIONS: Hosts do NOT recover Hosts do NOT die Host population is stable (static)

5
DIRECT TRANSMISSION BETWEEN HOSTS X = density of susceptibles Y = density of infecteds N = X + Y = transmission coefficient

6
TRANSMISSION COEFFICIENT Product of 2 constants: –C = contact rate –T = probability of transmission when a susceptible contacts an infected (0,1)

7
DIRECT TRANSMISSION BETWEEN HOSTS X = density of susceptibles Y = density of infecteds N = X + Y = transmission coefficient

9
DISEASE DYNAMICS

10
TWO STABLE EQUILIBRIA When Y = 0 When Y = N

11
BASIC RATE OF INFECTION R 0 Def’n - the rate of acquisition of susceptible individuals that get converted to infecteds (2º infections) If R 0 > 1 disease is growing

12
MEASLES Virus-caused disease Transmission is primarily by large respiratory droplets. The disease typically consists of a high fever, cough, runny nose and a generalized maculo- papular rash. Infants under one year of age have the highest case fatality rates reaching as high as 20% in epidemic situations.

13
INCIDENCE TRENDS

14
MEASLES ASSUMPTIONS Human population is stable. Deaths immediately lead to birth of a healthy susceptible individual. No age structure. Death can arise from natural causes (b) or from measles ( ) Immunity once gained is never lost No incubation time

15
BASIC MEASLES MODEL X = number of susceptibles Y = number of infecteds Z = number of immunes N = X + Y + Z β = transmission coefficient b = death due to natural causes α= death due to disease = immunity gain rate

16
BASIC MEASLES SIR MODEL d X /dt = b N + Y - β XY -bX dY /dt = β XY -( + b + ) Y dZ /dt = Y - bZ

17
WHAT IS R 0 R 0 = β N/ ( + b + )

18
TWO STABLE EQUILIBRIA If β N/ ( + b + ) > 1 the Y* adopts non-zero values If β N/ ( + b + ) < 1 then X* = 1, Y* = 0 and Z* = 0

19
MINIMUM HOST POP? Nt > ( + b + )/ β for R > 1

20
ADD REALISM Add an incubation class Q that includes individuals that are infected but not yet infectious (12 day period) Let vary according to season

21
PHASE PLANE INFECTEDS SUSCEPTIBLES

22
WITH AGE STRUCTURE INFECTEDS SUSCEPTIBLES

23
THE KEYS The susceptible class is exhausted by infection and replenished by birth of new susceptibles. Latency period accentuates oscillatory behavior. Seasonally changing β values accentuates oscillatory behavior.

24
DYNAMICS

25
A CRITICAL ASSUMPTION We assumed that decisions to vaccinate are tactical i.e. a rationale decision was made based upon costs and benefits independent of the actions of others in the population

26
WHY THAT MIGHT BE A PROBLEM The payoffs from vaccination could be dependent upon the vaccination decisions of others e.g. if everyone else vaccinated then the risk to you from not vaccinating should be small and vice versa

27
WHAT IS GAME THEORY? Def’n – the study of strategic decision making In Biology (Evolutionary Game Theory), the principle utility is fitness

28
WHAT IS GAME THEORY? Example from economics: The Prisoners’ Dilemma: Two prisoners are given the option to betray or remain silent If A and B both betray, each gets a 2 year sentence If A betrays and B remains silent, and B gets 3 years and vice versa If A and B remain silent, each receives 1 year Result: both betray

29
EXAMPLES IN BIOLOGY Def’n – the study of strategic decision making In Biology (Evolutionary Game Theory), the principle utility is fitness In Biology, we solve for the Evolutionary Stable Strategy (ESS), which is a Nash Equilibrium, wherein no one player can gain fitness by unilaterally changing his/her strategy unilaterally

30
PARAMETERS P – an individual’s strategy that he/she will vaccinate with probability P p - proportion of the population that is vaccinated r v – morbidity risk from vaccination r i - morbidity risk from infection π p - risk to an unvaccinated individual of eventual infection given vaccination coverage p

31
AN IMPORTANT TRADEOFF There are two key risks, (i) the morbidity risk from the infection and (ii) the morbidity risk from the vaccination r i vs r v

32
PAYOFF FROM VACCINATION This will always depend upon the proportion of the population that is vaccinated, p E(P, p) = P (- r v ) + (1 – P) (-r i π p ) Set r (relative risk) to r v /r i This gives: E(P, p) = -rP - π p (1 – P)

33
TO SOLVE THE ESS You need to calculate the risk of infection π p This requires that you build an S.I.R. model

34
TWO IMPORTANT PARAMETERS Growth rate of the disease: Infectious period:

35
THE BIG INSIGHT It is impossible to eradicate a disease through voluntary vaccination whenever the perceived risk from vaccination r v > 0 For any perceived risk from vaccination (i.e. r v > 0), p* < p crit If r v > π p then P* = 0 otherwise 0 < P* < 1 Recall, π p depends upon R 0 If r > 1, parents will not vaccinate

36
STABLE VACCINATION ESS coverage as a function of vaccination risk and risk of infection

37
RESPONSE TO A VACCINATION SCARE The change in vaccine uptake ▵ P depends upon the new perceived risk from the vaccine and risk from infection

38
ANOTHER INSIGHT A vaccination scare can cause vaccination rate to drop (relatively easily) however it will be relatively more difficult to restore vaccination rates to pre-scare levels

39
WHAT’S MISSING? Seasonality Transient dynamics Variance in risk perception Social dynamics

Similar presentations

OK

Predicting the Future To Predict the Future, “all we have to have is a knowledge of how things are and an understanding of the rules that govern the changes.

Predicting the Future To Predict the Future, “all we have to have is a knowledge of how things are and an understanding of the rules that govern the changes.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on waxes Ppt on image compression standards Ppt on indian unity in diversity Ppt on joints of human body Ppt on surface chemistry Ppt on event driven programming in java Ppt on sri lanka tourism Ppt on game theory raleigh Ppt on blood stain pattern analysis training Ppt on acid-base titration problems and solutions