Application of system dynamics in the analysis of economic impacts of Rift Valley fever in Kenya
Karl M. Rich and Francis Wanyoike International Livestock Research Institute
Presented at a Dynamic Drivers of Disease in Africa Consortium (DDDAC) project workshop Naivasha, Kenya, 24 – 26 June 2014
Background
• In 2007, ILRI carried out a study of the socio-economic impacts of the 2006 Rift Valley fever (RVF) outbreak in Kenya
• Results shed light on how various value chain actors were affected by the outbreak including also the macroeconomic impacts
• The analysis we carried out was however static
• We now wish to conduct a more dynamic kind of analysis of the disease impacts and also simulate how these effects would change in the light of various interventions (policy or otherwise) that could be instituted
Use of system dynamics in disease impact analysis
• This method has been used by ILRI in Botswana to analyze the impacts of FMD outbreaks on trade and competitiveness (Hamza et al. 2014) and Cambodia (Rich and Roland-Holst 2013)
• What is system dynamics (SD)?
o SD is a computer-aided approach to policy analysis and design
o It applies to dynamic problems in complex social, managerial, economic, or ecological systems — such dynamic systems are characterized by interdependence, mutual interaction, information feedback, and circular causality.”
o A methodology for studying complex dynamic systems that include nonlinearities, delays, and feedback loops
• When used in analysis of disease impacts, SD provides a way to integrate epidemiological and economic and other impacts in one platform
Conceptual framework
Financial
Performance
Policy
Risk
Production
Motivation
• An integrated epidemiological-economic model captures 2
types of disease related impacts:
i. Disease outbreaks that influences productivity or are associated with
significant mortality, and also the control measures taken to control
them will influence stocks of animals held by farmers
ii. Animal diseases can have demand impacts, either by reducing
domestic demand due to perceived food safety concerns or
international demand through trade bans, or both
• Note that in both cases, feedback effects matter
o When you have an outbreak, this influences behavior of actors
(Distress selling by farmers) and this in turn influences the spread of
the disease
The Epidemiology module
The diseases spread is based on the RVF transmission
model of Gianni:
Model captures dynamics of mosquito populations (Aedes
and Culex) and their interactions with livestock
The idea is to program the existing model from R into
STELLA (a system dynamics software) to allow integration
with other modules.
Model has been parameterized based on R model, though a
few aspects remain problematic (e.g., endogenizing sizes of
ponds for mosquitos)
The Epidemiology module: Culex dynamics
The Epidemiology module: Aedes dynamics
The Epidemiology module: Disease spread dynamics
The economics/value chain component also has 3
modules /sectors
The economic/value chain module
Market dynamics module: Based on the supply
and demand model of Whelan and Msefer (1996).
Financial costs module
Herd dynamics module: based on the DynMod
model developed by Lesnoff et al. (2008)
Herd Dynamics
Market dynamics module
Financial costs module
Results
The model is still under construction so the results
shown below are just illustrative of how we aspire
to use it
The graphs simulate what happens to cattle
population in Ijara district in the event of an RVF
out break with and with out vaccination
Similar kinds of analysis will be done for value of
livestock, costs, sales, prices etc.
Preliminary results
5:03 AM Wed, Jun 25, 2014
Disease ev olution in liv estock
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0
45
90
0
20
40
0
150
300
0
200
400
1: Exposed liv estock 2: Inf ected liv estock 3: Recov ered liv estock 4: Suspectible liv estock
11
1 12 2 2 23
3 3 3
4
4 4 4
Disease evolution in the event of an outbreak with no vaccination
4:57 AM Wed, Jun 25, 2014
Disease ev olution in liv estock
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0
3
6
0
2
3
280
310
340
0
40
80
1: Exposed liv estock 2: Inf ected liv estock 3: Recov ered liv estock 4: Suspectible liv estock
1
1
1
12
2
2
23
3
3 34
4
4 4
Disease evolution in the event outbreak but 80% of animals
had been vaccinated
Total number of animals with and without
vaccination
4:57 AM Wed, Jun 25, 2014
Untitled
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270
315
360
Total number of liv estock: 1 - 2 -
1
1 1 1
22
2 2
Data needed • Livestock demographics & population dynamics
• Animal movements data
• Elasticities (demand, supply, income)
• Value chain process variables
– Period of time taken between farm sales and market arrivals
– Period of time taken between sales from farms and slaughter (weeks)
– Inventories of meat (weeks)
• RVF epidemiological data
• Market prices
• RVF control costs
• Draught labour parameters
Thank you
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