Key data for outbreak evaluation: building on the Ebola experience
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Published version
Author(s)
Type
Journal Article
Abstract
Following the detection of
a
n infectious disease outbreak,
rapid epidemiological assessment
is
critical
to guide
an
effective
public h
ealth
response. To understand the transmission
dynamics
and potential impact of an outbreak, several types of data are necessary. Here we build on
experience
gained in
the
West African
Ebola
epidemic and prior emerging infectious disease
outbreaks
to set out a checklist of data needed to
: 1
) quantify severity a
nd transmissibility
;
2
)
characterise heterogeneities in trans
mission and their determinants
;
and
3
) assess the
effectiveness of different interventions.
We
differentiate data needs into i
ndividual
-
level
data
(e.g. a detailed
list of reported cases), exposu
re
data
(e.g
.
identifying where /
how
cases may
have been infected
) and
population
level
data
(
e.g.
size
/demographics
of the population
(s)
affected and
when/where interventions were implemented). A remarkable amount of
individual
-
level and exposure
data was collected during the
West African
Ebola
epidemic
, which allowed
the assessment of
(1) and (2)
. However,
gaps in population
-
level data
(
particularly around which
interventions were applied
when
and where)
posed challenges to the assessment of
(3)
.
Here
we
highlight
recurrent data issues
,
give practical suggestions
for
address
ing
the
se issues and
discuss priorities for improvements in data collection in future outbreaks.
a
n infectious disease outbreak,
rapid epidemiological assessment
is
critical
to guide
an
effective
public h
ealth
response. To understand the transmission
dynamics
and potential impact of an outbreak, several types of data are necessary. Here we build on
experience
gained in
the
West African
Ebola
epidemic and prior emerging infectious disease
outbreaks
to set out a checklist of data needed to
: 1
) quantify severity a
nd transmissibility
;
2
)
characterise heterogeneities in trans
mission and their determinants
;
and
3
) assess the
effectiveness of different interventions.
We
differentiate data needs into i
ndividual
-
level
data
(e.g. a detailed
list of reported cases), exposu
re
data
(e.g
.
identifying where /
how
cases may
have been infected
) and
population
level
data
(
e.g.
size
/demographics
of the population
(s)
affected and
when/where interventions were implemented). A remarkable amount of
individual
-
level and exposure
data was collected during the
West African
Ebola
epidemic
, which allowed
the assessment of
(1) and (2)
. However,
gaps in population
-
level data
(
particularly around which
interventions were applied
when
and where)
posed challenges to the assessment of
(3)
.
Here
we
highlight
recurrent data issues
,
give practical suggestions
for
address
ing
the
se issues and
discuss priorities for improvements in data collection in future outbreaks.
Date Issued
2017-04-10
Date Acceptance
2016-11-11
Citation
Philosophical Transactions of the Royal Society B: Biological Sciences, 2017, 372
ISSN
1471-2970
Publisher
Royal Society, The
Journal / Book Title
Philosophical Transactions of the Royal Society B: Biological Sciences
Volume
372
Copyright Statement
© 2017 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.
Sponsor
Medical Research Council (MRC)
National Institute for Health Research
Grant Number
MR/K010174/1B
HPRU-2012-10080
Subjects
Science & Technology
Life Sciences & Biomedicine
Biology
Life Sciences & Biomedicine - Other Topics
West African Ebola epidemic
epidemic
mathematical modelling
outbreak response
data
public health
INFECTIOUS-DISEASE TRANSMISSION
HEALTH-CARE WORKERS
VIRUS DISEASE
SIERRA-LEONE
WEST-AFRICA
MATHEMATICAL-MODELS
COST-EFFECTIVENESS
INFLUENZA A/H1N1
DATA-COLLECTION
RISK-FACTORS
Evolutionary Biology
06 Biological Sciences
11 Medical And Health Sciences
Publication Status
Published
Article Number
20160371