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Word-of-Mouth Modelling Word-of-Mouth Modelling

Word-of-Mouth Modelling - PowerPoint Presentation

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Word-of-Mouth Modelling - PPT Presentation

Introduction and technical overview The need for a model How can we demonstrate program feasibility A timestepping model of program delivery is needed to demonstrate program feasibility t o test program coverage and quantify impact ID: 501460

model advice day spread advice model spread day state rumour spreading community program people parameters number month individual idea

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Slide1

Word-of-Mouth Modelling

Introduction and technical overviewSlide2

The need for a model

How can we demonstrate program feasibility?

A time-stepping model of program delivery is needed:

to demonstrate program feasibility

t

o test program coverage and quantify impact

to answer difficult questions on how to deliver the program (e.g. how many educators per community, what size of community should we target)

to maximize operational cost-effectivenessSlide3

Modelling

Key question: how many people can we reach with this program in a given number of years?

Given:

certain sociological and cultural factors governing the dissemination of such introduced knowledge

t

he size of the target community

t

he number of educators that we train to actively spread knowledge in the communitySlide4

Models for related phenomena

Models are commonly used to understand the spread of diseases

A modification of the model was made to simulate the spread of rumours. (Daley-Kendall model, 1964)

Modelling the spread of disease

University of Colorado Mathematical Biology GroupSlide5

Daley-Kendall Model

State

Has not heard of rumour

 

State

Actively spreading rumour

 

State

Knows rumour, but no longer spreads it

 

Attempted to spread rumour to a person who already knows it

 rumour no longer “news”

Told rumour by someone in state

 Slide6

Proposing a new model

Spreading advice has very different dynamics to both of the above.

Rumours are spread as long as the rumour is considered “news”.

Advice on maternal care follow an opposite model.

Cultural and sociological factors as well as reservations on believing the advice is good and valuable may slow or stop the spread of advice.

In a smaller community, the advice is likely to gain widespread acceptance, fueling its own spread.Slide7

Our model

State

Has not heard of advice

 

State

Spreading advice, slowly and with strong reservations

 

State

Aggressively spreading advice

 

Attempted to spread advice to a person who already knows it

 increases advice acceptance

Told rumour by community educator or someone else in state

 

Stochastic decay of advice value (advice loses credibility, forgotten, etc.)Slide8

Model completeness

The model is “complete” in that it models all the key factors causing word-of-mouth propagation.

An arbitrary number of

states can be added.

Parameters are to be adjusted.

Model approaches the real phenomena as

and parameters are corrected!

Model is reducible to Daley-Kendall (simply set

to 0% spread and remove

)

 Slide9

Key parameters and assumptions

5 educators

gets around to

teaching about 48 women a month, through one means or another (pamphlets, attending births,

etc

)

An individual will only spread the idea for about

two

months unless the idea becomes

“accepted”

Based on the average births per female and the average female life expectancy, along with an estimate of how many individual females that a person will know on average, we calculate how likely individuals will engage in maternal health conversation.

The estimate is about once every two months, but increases dramatically if the idea becomes accepted.Slide10

Parameters

The parameters were set on a per-day basis. Simple math was used to extrapolate these variables to monthly figures which are more informative.

Per month, 48 individuals are converted from

to

. (1.6 per day)

Each individual in

has a 1.8% chance of spreading the advice in a given day. (0.55 people a month)

Each individual in

has a 7.3% chance of spreading the advice in a given day. (2.2 people a month)

Each day, a number of people in

decreases to

at 0.5% a day.

Each day, a number of people in

decreases to

at 3% a day.