Panel presentation on integrative disease modelling given at Ecohealth 2014 conference. Panel members included: Delia Grace, Pete Atkinson, Gianni Lo Iacono, Johanna Lindahl and Catherine Grant.

1. Integrative Approaches to Disease
Modelling
Overview and introduction to Dynamic
Drivers of Disease in Africa Consortium
Delia Grace, Pete Atkinson, Gianni Lo Iacono, Johanna Lindahl and Catherine Grant

2. Context
Emerging zoonotic disease events 1940-2012

3. Overall hypothesis
Disease regulation as an ecosystem
service is affected by changes in
biodiversity, climate and land use, with
differential impacts on people’s health and
wellbeing.

4. Conceptual framework
Interdisciplinary science
Untangling interactions

5. Country case studies
• Kenya: Rift Valley
fever
• Zambia and
Zimbabwe:
trypanosomiasis

6. Country case studies
• Ghana: henipavirus
• Sierra Leone: Lassa fever

7. Why these diseases?
• Commonalities
– Poverty impacts: direct, indirect and potential
– Often under-recognised and under-reported
– Require animal hosts to sustain infection in human populations
• Comparisons
– Different ecosystems: humid, semi-arid, arid
– Different hosts and transmission pathways
– Different political-economic and social drivers

8. Big drivers, big impacts, big
questions
• Urbanisation
• Irrigation
• Climate change
• Population movement
• Conflict
• Wildlife-livestock interaction
• Commercial farming
Disease
Dynamics
Demography &
Development

9. Practical and policy impacts
Disease
management
Integrated
policy
interventions
Surveillance
approaches
Capacity
building

10. How different modelling approaches
contribute to answers
• Epidemiology and disease burden- Johanna Lindahl
• Process-based modelling for RVF- Gianni Lo Iacono
• Agent based modelling- Pete Atkinson
• Integration of participatory research- Catherine Grant

11. Consortium partners
• ESRC STEPS Centre, UK
• University of Cambridge, UK
• Institute of Zoology, UK
• University of Edinburgh, UK
• University College London (UCL), UK
• Wildlife Division of the Forestry Commission, Ghana
• University of Ghana, Ghana
• Department of Veterinary Services, Kenya
• International Livestock Research Institute (ILRI), Kenya
• Kenya Medical Research Institute (KEMRI), Kenya
• University of Nairobi, Kenya
• Kenema Government Hospital, Sierra Leone
• Njala University, Sierra Leone
• Ministry of Livestock and Fisheries Development, Zambia
• University of Zambia, Zambia
• Ministry of Agriculture, Mechanisation and Irrigation Development, Zimbabwe
• University of Zimbabwe, Zimbabwe
• Stockholm Resilience Centre, Sweden
• Tulane University, USA
• University of Southampton, UK

12. Dynamic Drivers of Disease in Africa
Integrating our understandings of zoonoses, ecosystems and wellbeing
Epidemiology and disease burden
Johanna Lindahl, Alexandra Shaw, Delia Grace

13. Epidemiology
• The patterns, causes, and effects of health
and disease conditions in populations
• For many diseases we lack knowledge
• Not much research
• Much research but still not understanding
• Lack information on how to prevent

14. Close contact
between
different
species
Governmental
finances and
priorities
S E I R
Global trade and
travelling
New population
at risk
Increased
contact with
wildlife
Transfer or
recruitment of
new vectors
New habits,
new cultures
Migration of
people or
animals to new
areas
New species
at risk / host transfer
Decreased
immunization
and immunity
Markets
Urbanization
Environmental
land degradation
Poverty
Undernutrition,
starvation
Ageing
population
Compartmental epidemiological models

15. Disrupted social
systems
Poverty
Water
scarcity
Irrigation
Increased risk of
exposure
Habitat
fragmentation
Deforestation
Decreased
biodiversity
Increased number
of vectors
High density
Lack of knowledge
Less dilution from
alternate hosts
Reduced
food safety
Urbanization
Markets
Industrialization of
Littering animal production
Fertilisers
Agricultural
intensification and
development
Climate
changes
S E I R

16. Most drivers are desired- and not
constantly leading to disease
Anthropogenic action:
Increased irrigation
Effect on ecosystem:
Creates more larval habitats
Vector consequence:
More infected vectors
Epidemiologic
consequence:
More individuals
exposed
Increased
disease

17. A framework to help understand costs
and to model costs
• Aim is to collect the data necessary to make
an assessment of the multiple burden of
disease
• Using the same framework for multiple
diseases helps comparison
• Economic modelling important for policy
makers
• Money matters
• Priorities

18. Framework for assessing the
economic costs and burdens of
zoonotic disease
Alexandra Shaw, Ian Scoones, Melissa Leach, Francis
Wanyoike and Delia Grace

20. Costs of zoonotic disease
• Zoonoses sicken 2.4 billion people,
kill 2.2 million people and affect
more than 1 in 7 livestock each year
• Cost $9 billion in lost productivity;
$25 billion in animal mortality;
and$50 billion in human health

21. Benefits of controlling zoonoses in animals and
along the value chain chain
• Credible economic cost benefit studies
(n=13)
– Average benefit cost ratio 6:1
–Median 4:1
–Range 1.1-19.8
• Implies $85 billion losses could be
averted by $21 billion expenditure
21
Ex ante 5
Ex post 6.6
Developing countries 3.7
Developed countries 7.4

22. How can we model disease burden?
What do we include in the burden of disease?
• Disability-adjusted life years (DALYs)
• Economic impact
• Society/nation
• Personal
• Environmental impact?

23. Zoonoses have multiple burdens
• Disease in humans
• Economic consequences of disease in
humans
• For people and society
• Loss of incomes, and costs for treatments
• Disease in animals
• Economic consequences of disease in
animals
• For people and society
• Lost production, trade bans

24. 2 trade offs
1. Between disease control expenditure and
illness in humans and animals
2. Between ecosystem change and disease
incidence

25. The vicious cycle- for people
Missed
work/school
Less income,
lower
education
Reduced
living
standards/
reduced
nutrition
Disease
Increased
exposure to
pathogen/
reduced
immune
defense

26. How can we model disease burden?
• Simplified situation
• Assessing what we can assess
• Direct economic impacts
• Collecting more data on what we don’t know
• Creating a Framework for assessing
economic costs and burdens of zoonotic
disease

27. 2 aspects of costs of disease
1. who pays (public or private sector)?
2. how easy is it to quantify them? (availability
of information and applicability /availability of
market prices).

28. The multiple burdens of zoonotic
disease: human, animal and
ecosystem health
Actors Cost of Illness Cost of prevention
Intangible and
opportunity costs
Private
Individual and household
(1) Treatment costs
(e.g. medication)
(2) Loss of household
production
(1) Risk mitigation
such as boiling
water, buying filters
(1) Disutility of ill
health for individual
(DALY)
(2) Disutility of ill
health for friends,
family, etc.*
Livestock sector
(1) Cost of treatment,
(2) Herd slaughter,
product recall,
mortality,
morbidity, lower
production, loss of
exports
(1) Costs of increased
biosecurity,
(2) vaccination,
practices and
procedures to
control disease
along the value
chain
(1) Cost of future
emerging
diseases*
(2) Loss of animal
genetic resources.
*
Public
Health (human and
animal)
(1) Treatment costs
(hospital provision,
etc.)
(2) outbreak costs,
movement
restrictions, culling,
(3) vaccination
(1) Risk mitigation
such as water
fluoridation,
vaccination
(2) (Disease
surveillance,
research)
(1) Loss of
opportunities
occasioned by
spending on
disease prevention
and care*a
Ecosystem
(1) Spill-over into
wildlife,
(2) loss of ecosystem
services
(1) Bio-security,
avoiding wildlife
and vectors,
(2) disease
surveillance,
research
Included
in
DALYs

29. The cost of illness and burden of
disease in people- how to measure
Information needed Type of data Possible existing sources Further investigations
Reported cases of disease Record of individuals diagnosed
with disease
Hospital and clinic records, national
and provincial health statistics
May be worth visiting local hospitals
and clinics to collect data if it is not
summarised at national level
Estimate of extent of under-reporting
Compare recorded cases with
number actually found
Published/grey literature (PGL)
studies or investigations
If field work involves testing people,
or finding people with the disease
then the prevalence or incidence
can be compared to that reported.
Often test high risk groups (people
with fevers not responding to
malaria, people working/living in
close contact with relevant animals)
Burden of disease in affected
individuals
(Valued as Disability-adjusted life
years – DALYs)
Deaths Hospital and clinic records, PGL
data on death rates and DALYs –
the years of life lost (YLL)
component
Visit local hospitals and clinics to
collect data, ask about it in
household interviews
Disability PGL studies and interviews and
DALY estimates, including relevant
disability weights – the years of life
lived with disability (YLD)
component of the DALY
Interview patients and families to
find out about length of illness and
extent of disability.
Impact on household incomes
while person is ill
Estimated loss of household income
generated by the patient during their
illness
PGL studies Interview patients and families

30. The cost of illness and burden of
disease in animals- how to measure
Information needed Type of data Possible existing sources Further investigations
Reported cases (incidence) of
the disease over a certain
period or prevalence (number or
percentage with the disease at a
given point in time)
Record of animals thought to have
the disease
 Outbreak investigations
 Incidence and prevalence
studies
 Reported cases from
veterinary clinics
 Other PGL studies
Animal sampling in the field (blood
tests)
Estimate of under-reporting Extrapolation to whole animal
population. Difficult because
studies focus on high incidence
events or high prevalence sub-populations
Published/grey literature (PGL)
studies or investigations.
Local expertise
Compare results from sampling
with other, pre-existing, estimates
Burden of disease in affected
animals
(Monetary values)
Mortality PGL studies looking at individual
diseases. Sometimes records
from vet clinics and national
veterinary statistics. For many
animal diseases, the only impact
that is recorded is deaths.
Focus group discussions.
Livestock keeper surveys.
Morbidity (lowered productivity) PGL field-based studies
comparing healthy and infected
animals. There aren’t many!
Estimate and value disease impact
on fertility, output (milk, wool,
animal traction, etc.), slaughter
rates and weights (meat), etc.
Note that livestock keepers
reactions (cull sick animals) form
part of the impact.
Livestock keeper and dog-owner
surveys. These are time-consuming
and obtaining a
suitable control group to estimate
impact is difficult. Studying wildlife
and companion animals is even
trickier.

31. The cost of treatment and control in
people- how to measure
Information needed Type of data Possible existing sources Further investigations
Private costs for treatment and
hospitalisation
 Health care seeking costs
(often very high for these
uncommon conditions)
 Time spent by family
looking after patient at
home and when looking
for care of being treated
 Patient expenditure on
correct and incorrect
medication and
diagnostics
 Local clinics and medical
practitioners, hospitals
 PGL studies
Patient and patient family
interviews.
Public costs for treatment and
hospitalisation
 Cost of hospitalisation,
operations, drugs,
diagnostic
Ministry of Health, hospital
and clinic data
Interviews with care staff in
specialist units
Private costs for disease control  Patient and other
members of the public -
costs for vaccination,
quarantine, any other
disease prevention or
mitigation measures
 Local clinics and medical
practitioners, hospitals
 PGL studies
Patient and patient family
interviews. Interviews with
target populations (e.g. of
vaccination campaigns)
Public costs for disease control  Cost of surveillance
 Costs of vaccination
Ministry of Health, hospital
and clinic data
Interviews with staff involved
in this work

32. Costs of prevention-
Humans and animals
221 Kenyan households interviewed
How much did you spend last year on the following health protection (Kenyan shilling)?
Mosquito
nets
Vaccines & routine clinic
visits for kids
Boiling or other water
treatment
Insurance
(annual fee)
Other health
prevention
Mean 762 254 6.8 0.9 586
Range 0-3150 0-5000 4 households paid
between 150-600
220 households
paid nothing, one
household paid
200
0-6000
How much did you spend last year on the following health prevention for animals?
Deworming Vaccinations (to
prevent not to
treat)
Tick and fly
treatments
Insurance
(annual fee)
Mean 928 437 599 0
Range 0-11000 0-5000 0-5000 Not existing

33. Sharing resources for health delivery
• Efficiency & effectiveness gains
– Shared infrastructure; training, services
• Joined up services for zoonoses: Across a range of studies 5-15% reduction
in costs +/or improvement cover
• World Bank (2012) estimates 25% savings across a range of joint services
for AI and 7% additional costs = net savings of 18%
• Developing country health sector expenditure: 250 billion
• Developing country veterinary expenditure: 2 billion
– Amenable to joined up services: $4 billion
33

34. Increase in
people
Political decisions and economy
Increase in
livestock
Yes
No Probably low risk
of increased
disease incidence
No
Yes
Sufficient medical
care and
infrastructure?
Are circulating
diseases
Vector control known?
programs
used?
Appropriate
sanitation?
Increase in
vectors
Can they be
prevented
or cured?
High risk of increased
incidence of vector-borne,
rodent-borne,
water and food-borne
diseases
No
No
No
Yes
Yes
Yes
Yes
No
Surveillance
No
No
High risk of increased
disease
Yes
Yes No
Too late, but
good anyway
Wildlife
interface
Yes

35. In conclusion
• We need to show the multiple burdens of
disease
• We need to show the money savings
• We need to show economical consequences
Because money talks.

37. Integrative Approaches to Disease
Modelling
Agent Based Modelling
Pete Atkinson, University of Southampton, UK

38. Modelling background
• Epidemiological models are traditionally created using dynamic,
compartmentalised approaches…
• Sleeping sickness is represented by the – –
(SIS) model due to the absence of immunity.
38
Population sizes
Infection rate (contact rate)
Space (?!)
The theoretical number of
people in each compartment
at a given time.
Homogeneous mixing.
Differential equations.

39. Agent-based model
• Models the movements of individual agents:
– Humans
– Animals
– Vectors
• Need to know:
– Landscape
– Agents
– Rules
39

40. A simple model for Trypanosomiasis
• Only one previous ABM of Sleeping Sickness
• Spatially abstract simulation backdrop represents
– a river (blue), with banks (green) and pasture
• Three agents:
– Human, Cow, Tsetse fly
• Humans are divided into
– cattle farmers and non-farmers
• Black icons represent home settlements
40

41. 1st iteration of the model
Tow
n
Human
Uninfected Fly
Cattl
e
Infected
Fly
River and
banks

42. 42
2nd iteration of the model
• Short video simulating one day of real
time
• As before (orange),
(blue), (green)
• Walking speed = 5 km hr-1
• N.B. Frequency of trips to water
increased for demonstration

43. PRM

46. Acknowledgements
Neil Anderson
Joanna Kuleszo
Simon Alderton
Kathrin Schaten
Noreen Machilla
Alex Shaw

47. Thank you
pma@soton.ac.uk

48. Dynamic Drivers of Disease in Africa
Integrating our understandings of zoonoses, ecosystems and wellbeing
Integration of Participatory Research
Professor Peter Atkinson, Dr Gianni Lo Iacono, Catherine Grant, Dr Bernard
Bett, Professor Vupenyu Dzingirai, Tom Winnebah and other members of the
Dynamic Drivers of Disease in Africa Consortium

49. Our conceptual framework

50. Our rationale for integration of
participatory work
• Models can provide characterisations and predictions to
advance knowledge and evidence for policy but often they
are constructed by single disciplines representing a
selective view of the world.
• Researchers can be influenced by perspective and the
political and funding arena and, often not considering views
of those actually living with the disease.
• Infectious diseases need to be studied using a
multidisciplinary perspective, including involving local
people to potentially improve model selection and accuracy.

51. Aims of our work
• Explain the benefits to using participatory approaches to
improve model design and facilitating multidisciplinary
research in this area- overcoming disciplinary hurdles
• Proposing practical examples of effective integration
• Models can create tangible information from uncertainty
which leads them to be given an authority which may be
unjustified in a decision-making or policy context.
• This work aims to make models and their predictions more
useful for decision-making and policy formulation and
include information such as predicted behavior change.

52. Participatory work in action

54. The benefits of participatory research
1. Removal of ignorance
2. Confirmation
3. Removal of irrelevance
4. Addition of knowledge
5. Removal of error
Acknowledgement: Pete Atkinson

55. Application of this to our case studies
Participatory research as a tool to:
1. Structure a model: population-based mathematical modelling
2. Structure a model: geographically explicit ABM (previous
presentation)
3. Select the most relevant parameters of the system
4. Identify the most relevant regime of the system
5. Mathematical modelling as a tool to better structure participatory
research
6. Diversity of modelling approaches challenge the conclusions of
other types of modelling

56. 1. A tool to structure a model: population-based
mathematical modelling
Examples from Sierra Leone
• Provide information on patterns of mobility- increasing
model accuracy
• Provide new data on seasonal activities- allowing the
inclusion of a periodically varying rate of contact with
humans
• Interpreting the reliability of hospital data e.g. seasonal
hospital attendance

57. Examples from Kenya
RVF Agent Based Model (Bett et al.)
resource maps for a
village
proportional
piling on
livestock
species
kept
livelihood
activities by
gender
Modelling Exposure
Model Input of
relative
proportion of
hosts
Modelling Risk in
Spatial Models
Acknowledgement: Gianni Lo Iacono

58. Immigration of
infected animals in
RVF free site
Frequency of such
movements
Can the site
become
endemic?
Conditions for endemicity
Acknowledgment: Gianni Lo Iacono

59. 2. As a tool to structure a model: geographically
explicit ABM
As described in the previous presentation

60. 3. A tool to select the most relevant parameters
of the system
Hunting
Bats
Economic
factors
Bushmeat
culture

61. 4. A tool to identify the most relevant regime of
the system
Participatory modelling can assist in determining whether or not a
system has reached equilibrium, identifying the possible causes
leading to a disruption of the equilibrium, and it can direct the
mathematical approach towards the relevant regime, that is, transient
regime rather than equilibrium.
For example:
• In Ghana, changing farming and hunting patterns and varying
pesiticide use, information gathered from participatory research,
shows that the environment is changing and not in equilibrium.
• In Sierra Leone land use change affects rodent habitats, affecting
population size and where they live.

62. 5. Mathematical modelling as a tool to better
structure participatory research
Using the results from other modelling can help provide new
questions and sources of investigation for participatory research.
For example:
• Mathematical modelling found that human transmission has a
relatively high impact due to the prescence of living virus in
urine. Therefore participatory research could focus on new
areas such as hygiene and potential contact points.
• Focus on movement to inform ABM could lead participatory
research to focus on the politics of who moves where and when.

63. 6. Diversity of modelling approaches challenges the
conclusions of other types of modelling
Reality is too complex to model in full and no model can capture
everything. Different models highlight different issues and are based
on different assumptions, world views and sources of information,
leading to different conclusions about disease risk and the
appropriate actions and policy decisions to take (Leach and Scoones
2013).
Interdisciplinary working can address these issues, embracing
multiple sources of evidence. This can lead to an enriched
interpretation of research findings, integrating perspectives from
those coming from different disciplinary outlooks, and wider-ranging
translation of research. This also means that there is more
opportunity for wider dissemination and that the integrated models
will be more useful in practice and policy.

64. Conclusion
• This paper shows that reality is too complex to be modelled by one
modelling approach from one discipline.
• The use of the One Health approach, working together to embrace
multiple sources of evidence, can provide more realistic models to
assist with policy decisions that reduce disease and benefit local
people.
• Participatory research, in particular, can help to explain who gets
sick, where and why as well as provide explanations for health
seeking behaviour.
• Participatory research can help illuminate new areas. It is not about
challenging other approaches, but helping provide new ways of
thinking and alternative methods.
• There is lots we don’t know and participatory research can augment
standard modelling and help us move interdisciplinary science
forward, adding nuance and complexity to already useful areas of
enquiry.
• However, there are, of course, challenges to integrating models and
data, due to researchers’ different perspectives on approaches.

65. Dynamic Drivers of Disease in Africa Consortium

66. Overview
• Introduction to DDDAC (Ecohealth/One Health context)
• Agent Based Modelling (trypanosomiasis in Zambia)
• Process-based modelling (RVF –ecological &
environmental modelling, local knowledge).
• Epidemiology and disease burden
• Interdisciplinarity and Participatory research

68. This work is of crucial importance globally
because…..
• More than 60% of emerging infectious diseases over the last few decades
have been zoonotic
• Zoonoses have the potential to result in global disease outbreaks (e.g.
Avian influenza)
• Many zoonoses affect disenfranchised communities, quietly decimating
poor people’s lives and livelihoods
• Diseases of poverty, including zoonoses, are often under-measured and
therefore under-prioritised in national and international health systems
• If unchecked, emerging zoonoses create dangerous future threats
• This is of particular concern for zoonotic diseases with complex
connections to a wider set of ecosystem changes, such as
land use change, habitat loss and climate change.

69. Importance of our One Health approach…..
• One Health involves the environmental, human and animal
health sectors crossing professional, disciplinary and
institutional boundaries to work, challenging as this may be, in
a more integrated fashion.
• Zoonotic diseases provide an archetypal illustration of the utility
of the One Health approach as they are shaped by complex
interactions amongst humans, animals and the environment
and, thus, between epidemiological, ecological, social and
technological processes which affect vulnerabilities to, and
risks of, transmission influenced by wider socioeconomic and
environmental drivers (Leach and Scoones 2013).

70. Thank you
For more information on our Consortium:
www.driversofdisease.org
@DDDAC_org
contact@driversofdisease.org