1. The OpenMalaria Microsimulation Platform
Thomas Smith.
On behalf of the Swiss TPH malaria modelling team
IDM 6th Annual Modeling Symposium
16 April 2018

2. Key steps in malaria modeling in the 20th century
Ross (1911): First model of malaria in humans
Macdonald (1956) : Extended to include mosquitoes
Compartments for both humans and mosquitoes
Dietz (1974) Garki model:
Extension to include immunity without including refractory compartment(s)
Calibration with field data
Provided the theoretical framework for IRS-based programmes
Supported the contention that IRS-based programmes were not enough (even with mass drug administration)

3. Why are malaria models wanted now?
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……………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………
(Filling in data gaps)
Predicting the impact of intervention packages in specific places

4. Geographically specific Global Includes the butterflies
Image adapted from ‘Vintage Globe’

5. OpenMalaria: the origins
Nicolas Maire
Computing group
leader
Amanda Ross
Senior statistician
- Individual-based model
- Fitted to data
- Includes only known biology

6. OpenMalaria approach
Discrete time stochastic individual-based simulations of malaria in humans
Discrete time population model of vector life cycle and malaria in mosquitoes
Includes dynamics of transmission, acquired immunity & human demography
Models of natural history with modular components
Multiple model variants capturing structural uncertainty (e.g. in duration of immunity)
Periodic forcing to capture effects of seasonality
Calibrated by formal fitting to data from field studies
Include effects of case management and other concomitant interventions
Considers heterogeneity in exposure, susceptibility, immune response (but is a single site model, not spatially explicit)
Programmed in C++ (xml configuration files)
OpenSource

7. Infectivity to vectors Morbidity and mortality
Central role of parasite densities
Parasite density
Malariatherapy 50
100
150
Days since inoculation
Infectivity to vectors
Immunity
Parasite density is a main determinant of the acquisition of immunity
The primary effect of immunity is on parasite densities
Single hosts can have multiple concurrent super-infections
Morbidity and mortality 8

8. Essentials for validity and reproducibility
Severe disease
Formal description of models
Calibration with field data
Out-of-sample validation
Initially calibrated by fitting to the datasets covering ten different epidemiological quantities (objectives). Many other datasets used to calibrate entomology and health system parameters
Initial validation using data of meta-analysis of prevalence- transmission relationships. Many other datasets used to evaluate local fit.
Calibration and validation can be improved with new data

9. OpenSource and documentation
Thorough documentation essential
Significant learning curve
In complex software bugs happen
Proper versioning etc. needed
Diggory Hardy
Main software
developer
- Individual-based model
- Fitted to data
- Includes only known biology

10. Typical use of OpenMalaria
Start of test & treat
Prevalence
▬ MDA
▬ no MDA
Year
Elimination threshold
Parameterise transmission setting with available entomological data
Run the system to an endemic steady state to approximate no-intervention
at least 1 human lifetime
Simulate forward with required interventions, varying as required:
Start of test & treat
Example question:
How does on-going test-and-treat affect the impact of Mass Drug Administration?

11. High dimension simulation experiment
Parameterise transmission setting with available entomological data
Run the system to an endemic steady state to approximate no-intervention
at least 1 human lifetime
Simulate forward with required interventions, varying as required:
Diagnostic performance
Drug/insecticide efficacy
Target group(s)
Deployment strategy
Spray/treatment timing
Adherence/use
Coverage
Replenishment (e.g. nets)
Decay(s) of effect (insecticides, PK/PD of drugs, vaccine decay)

12. Example simulation experiment
Start of test & treat
Results of model averaging
Factorially designed experiment
16,200 simulations
Parameterise transmission setting with available entomological data
Run the system to an endemic steady state to approximate no-intervention
at least 1 human lifetime
Simulate forward with required interventions, varying as required:
Percentage of scenarios
Start of test & treat
Example question:
How does on-going test-and-treat affect the impact of Mass Drug Administration?
Minimal change to stable state
New endemic stable state
New endemic stable state achieved sooner with MDA
Elimination only with MDA
Elimination with minimal speed-up
Elimination is achieved sooner with MDA

13. PSA of cost effectiveness of RTS,S
Endemicity map
Nicolas Maire
Computing group
leader
Probability density
0.0
0.2
0.4
0.6
0.8
1.0
EIR (ibpa) 10
100 1
0.1
Estimated Distribution of EIR
for malaria endemic parts of
Africa
Uncertainty in endemicity map
Maire et al, Value Health Dec;14(8):

14. Acceptability curves for RTS,S
Probability density
0.0
0.2
0.4
0.6
0.8
1.0
EIR (ibpa) 10
100 1
0.1
Estimated Distribution of
endemicity
ICER (I$ per DALY)
100 10
1000
10000
0.1 1
EIR (ibpa)
Measure of net benefit by
Endemicity (including uncertainty) 10
Ceiling ratio (I$/DALY)
100
1000
10000
Probability cost effective
0.0
0.2
0.4
0.6
0.8
1.0
I$207
I$2008
Probability you want to go ahead
Maire et al, Value Health Dec;14(8):

15. Acceptability curves for RTS,S
ICER (I$ per DALY)
100 10
1000
10000
0.1 1
The intervention effects depend on the transmission setting.
We need estimates of the distribution of exposures to complete the prediction of the net benefit of intervention.
Gray envelope encloses
95% of parameterizations
1.0
0.8
2008
0.6
1000
Probability cost effectuve
500
0.4
207
0.2
100 50
0.0
Gray boxes
Indicate ceiling ratios
in international $
EIR (ibpa)
100
1000
0.1 1 10
Maire et al, Value Health Dec;14(8):

16. Analysis of model uncertaintyX V h * b v
Cost-effectiveness of a PEV-EPI program
Tornado plot
Interval between the incremental cost-effectiveness ratio predicted from the regression at the 2.5 and 97.5 percentiles of the sampled distribution of the parameters t Q
1/2 a D x F * a 3 F E * Y Y * a 2 * S m * j Y 1 * 1 X p * i i V R vd C x 1 Y * u P B 1 P h V u N hc V i
X*h Critical value of cumulative number of
infections
Vv Vaccine purchase price
b Vaccine homogeneity
t1/2 Vaccine halflife
QD Co-morbidity intercept relevant to indirect
mortality V q x h p 1 v
rfo R V x 2 X vc v * P S r V
imm p d X
rfi * V y s a 2 V C vt g r x p T 2 a m Q m s 2 n R p q P t s
ICER (I$ per DALY averted)
Maire et al, Value Health Dec;14(8):

17. Simulations: Future Use Case
Model averaging and fitting to data for trial sites
Melissa
Penny
Group head
RTS,S/AS01: Phase 3 clinical trial in infants and children
Moderately efficacious vaccine, with waning protection
The W.H.O.: Where will the vaccine have greatest public health impact and be cost-effective?
Detailed Phase 3 vaccine efficacy
Clinical Trial Data
Simulations: Future Use Case
Effectiveness: future public health impact
Beyond trial settings
Impact on mortality
Economic analysis
Target settings for use
Models
models informed by trial data and historical data on malaria dynamics
Parasite prevalence (%)
Penny, Verity, Bever et al. Lancet 2016, 387:367

18. Country specific CEA for RTS,S
Averaging over:
Transmisssion surfaces (5 km pixels)
Model uncertainty
Parameter uncertainty (vaccine properties)
C: DALYs averted
D: cost per DALY averted
Katya Galactionova
Health Economist
Galactionova et al, Vaccine (2017)

19. Deciding which new tools to invest in
Determine key factors in achieving interruption of transmission and prevalence reduction
Explore minimal profile of malaria tools that interrupt transmission for different settings. 
coverage
of tool 1 2
efficacy
half-life
Random sampling over all parameters
Machine learning and
Gaussian process (GP)
Database of multidimensional computer simulations
Melissa
Penny

20. Sweeps and factor levels included Designed for use by National
Spectrum Malaria
Sweeps and factor levels included
Designed for use by National
Malaria Control Programs
for projecting impact
Interface linking model predictions to UN demographs, WHO and MAP data on malaria.
Built by Avenir Health
Uses a regression-based emulation of OM estimated from a simulation experiment comprising 100,000 scenarios
Korenromp et al, 2016

21. Spectrum Malaria
Hamilton et al, Malaria Journal, 2017, 16:68

22. Used as a teaching
resource

23. Open Source
Other groups can, and do, use OpenMalaria without necessarily informing Swiss TPH
We are happy to advise……..

24. Our Current Team Main Funding Diggory Hardy Software developer
Katya Galactionova
Health economics
Nakul Chitnis
Group Head
Mathematical epidemiology
Melissa Penny
Group head
Emilie Pothin
Project Leader
Malaria modelling
Flavia Camponovo
PhD student
Malaria vaccine development
Tamsin Lee:
Post-doc
Malaria drug resistance
Lydia Burgert
PhD student
Malaria drug development
New Jan
2018
Diggory Hardy
Software
developer
Thomas Smith
Unit head
Group head
Manuela Runge
PhD student
Malaria program impact
Adrian Denz
Phd student
Mosquito movement
Guoying Yang
Senior Scientific collaborator
Malaria elimination product development
New April
2018
Monica Golumbeanu
Post-doc
Olivier Briet
Project leader
Malariology
Main Funding