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a service of the UN Office for the Coordination of Humanitarian Affairs



How to: Predict vaccine coverage by modelling



lead photo
LONDON, 22 August 2012 (IRIN) - Researchers in Canada are combining
mathematics with "social learning" to predict how epidemics are
affected by fears - usually unfounded - that a vaccine can harm, so as
to improve the design of vaccination campaigns. Social learning - how
behaviours are learnt and decisions are made in group settings - has
long been examined in economics and now, increasingly, in healthcare.

"We're giving 110-120 million courses of vaccine out every year around
the globe, but how much do we know about why people take these up?"
asked John Edmunds, of the London School of Hygiene and Tropical
Medicine and author of a forthcoming book
-4614-5473-1> on modelling and disease control. "How much do we invest
in understanding what drives mothers to accept these vaccines, and
sometimes not to?"

Payoffs and penalties

Chris Bauch and colleagues at the University of Guelph in Ontario,
Canada, set out to create a mathematical model that would show how much
a person's decision to be vaccinated was influenced by disease
prevalence, and how much by peer pressure.

They tested the model using data from a 1990s measles-mumps-rubella
(MMR) vaccine scare in England, and a 1970s pertussis (whooping cough)
vaccine scare in Wales to see how well their model predicted vaccine
coverage and disease outbreaks in those instances.

The researchers grouped people into "vaccinator" and "non-vaccinator"
categories. The mathematical formula tried to calculate how people
judged a vaccine's risks and rewards. The risk was the perceived
chances of getting infected, multiplied by the cost of infection - the
cost of medicine and doctors or clinic visits, being unable to work and
perhaps losing income, and the discomfort of being ill. The perceived
"payoff" was whether a person judged the vaccine would do more good
than harm.

Social learning was included by measuring how often people switched
from one group to the other, based on observing others' vaccine
decisions and whether their health improved.

Bauch reported that their model did well
1002452> in foretelling disease outbreaks and vaccination coverage for
both MMR and pertussis.

More scares?

When enough people in a community are vaccinated against a particular
disease, outbreaks of that illness become rare, or no longer occur.
Medical evidence has shown that this "herd immunity
" makes
some people perceive certain vaccines as unnecessary or risky.

"When a disease becomes more rare, people begin to forget what it was
like. As a result, they become less scared of the disease and against
this backdrop - where the disease risk is perceived to be zero - even a
small vaccine risk suddenly looks very large," said Bauch.

His model assumes that low disease prevalence means less vaccine demand
among people who feel protected by herd immunity. As more diseases are
wiped out, the authors speculate that vaccine scares will become more
common.

In 2003, polio immunization programmes were suspended for over a year
in the northern Nigerian state of Kano
s-Kano-drops-boycott-minister> after religious leaders falsely told
people that the vaccine could cause infertility, HIV and cancer.

Polio spread from Nigeria to 20 other African countries between 2006
and 2010, said a report
olio-eradication-initiative-report-july-2011> by the Independent
Monitoring Board of the Global Polio Eradication Initiative in July
2011, which noted Kano as a "major worry" with "low routine
immunization coverage".

Limits

The model is still years away from having "predictive value" for
vaccine campaigns, said Bauch. His team notes that the model needs data
from the first years of a vaccine scare to predict what happens in
subsequent years, and that "the model cannot predict when a vaccine
scare will occur, since this presumably depends on...historical events".
"It's basic research and there are always surprises in basic research.
It's too early to base policy on [our model] in a very explicit way,"
said Bauch. Data on vaccine coverage and subsequent disease outbreaks
collected during vaccine campaigns could be useful to modellers.

"Modelling is useful in terms of trying to make public health
decisions," said Louise Ivers of the US-based international NGO,
Partners in Health (PIH). "[It's] very interesting - the addition of
social learning into a model."

PIH, in collaboration with the Haitian Ministry of Health and a local
medical NGO, GHESKIO ,
rolled out a cholera vaccine pilot project earlier this year that is
evolving by means of ongoing data collection.

If models are to inform policy and guide vaccine campaigns, consistent
real-time data collection and analysis are needed. Edmunds of the
London School of Hygiene and Tropical Medicine noted: "Otherwise
[policy decisions are] based on hunches - effectively, people doing
models in their heads - and that's how things have been traditionally
been done."

oja/pt/he


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_____

[This report does not necessarily reflect the views of the United
Nations]
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