This document discusses using a system dynamics modeling approach to analyze the economic impacts of Rift Valley fever (RVF) outbreaks in Kenya. The proposed model would integrate epidemiological modeling of RVF spread with economic modeling of the livestock value chain. Preliminary results show the model can simulate the effects of an RVF outbreak on livestock populations with and without vaccination. The integrated model aims to help analyze how RVF outbreaks influence livestock productivity and demand, and how various intervention policies could change their effects. It requires epidemiological, economic and value chain data to fully parameterize.

1. 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

2. 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

3. 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)?
oSD is a computer-aided approach to policy analysis and design
oIt 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.”
oA 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

4. Conceptual framework
Financial
Performance
Policy
Risk
Production

5. 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
oWhen you have an outbreak, this influences behavior of actors (Distress selling by farmers) and this in turn influences the spread of the disease

6. 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)

7. The Epidemiology module: Culex dynamics

8. The Epidemiology module: Aedes dynamics

9. The Epidemiology module: Disease spread dynamics

10. 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)

11. Herd Dynamics

12. Market dynamics module

13. Financial costs module

14. 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.

15. Preliminary results
5:03 AM Wed, Jun 25, 2014
Disease ev olution in liv estock
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Disease evolution in the event of an outbreak with no vaccination

16. 4:57 AM Wed, Jun 25, 2014
Disease ev olution in liv estock
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1: Exposed liv estock 2: Inf ected liv estock 3: Recov ered liv estock 4: Suspectible liv estock
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Disease evolution in the event outbreak but 80% of animals
had been vaccinated

17. Total number of animals with and without vaccination
4:57 AM Wed, Jun 25, 2014UntitledPage 11.0015.7530.5045.2560.00Days1: 1: 1: 270315360Total number of livestock: 1 - 2 - 11112222

18. 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