A spatial microsimulation model to forecast health needs of elderlies in 2030 (work in progress):

VirtualBelgium In HealthMorgane Dumont, NaXys, University of Namur

Supervisors : Timoteo Carletti and Eric Cornélis (Unamur)SMART, University of Wollongong, 1 July 2016

Outline of the presentation1. Context and aim

2. General methodology

3. Data available

4. Synthetic population for Belgium in 2011

5. Addition of health attributes

6. Dynamical evolution

7. Conclusion

8. Next steps

2

1. Context and aim

Context : Simple future scenario’s analysis

1. Medium hypothesis : Proportion of people in retirement home stays the

same (per age, gender and district).

2. Low hypothesis : Each person with only a little dependency will be kept

at home and nobody under 75 years old will enter the retirement home.

4

Simple future scenario’s analysis - 2030(using the projections of the Bureau Federal du Plan)

5

VBIH* = Develop a tool enabling the Health Observatory of Wallonia to forecast health needs of elderlies

for Belgium in 2030.

• At the municipality level

• Thanks to micro-simulation

• By developing an agent based modelling

Aim

*VirtualBelgium In Health, in comparison with VirtualBelgium (J. Barthélemy, PhD Thesis : ”A parallelized micro-simulation platform for population and mobility behaviour - Application to Belgium”, Unamur, 2014)

2. General methodology

Availability of data

Create a synthetic population

Evolution of this population

Analysis of the simulated population (2030)

7

3. Data available

8

National Register: Dynamical database following each declared resident in Belgium with their :

• ID• Gender • Date of birth• Nationality • Civil status• Location of residence• Other individuals in the household

New births, marriages, divorces, moves, size and type of households, …

3. Data available

9

Health data:

• Hospital and retirement home report

• Health insurances data

• Surveys about the health of the population

• …

3. Data available

10

Census2011:• Access on their website• Only 4 variables simultaneously

Data to create the synthetic population

11

Data to create the synthetic population

12

• J. Barthélemy and P. Toint (2013), Synthetic Population Generation Without a Sample, Transportation science

• Guo, J.Y., and C.R. Bhat (2007), Population Synthesis for Microsimulating Travel Behavior, Transportation Research Record

• M. Lenormand, G. Deffuant (2015), Generating a synthetic population of individuals in households : Sample-free vs sample-based methods, Journal of Artificial Societies and Social Simulation

Data ‘too’ precise.

Set of indiviuals : IPFP* followed by a simple probabilistic method

13

* Thanks to the « mipfp » R package, Barthélemy J.

Mipfp

Set of indiviuals : IPFP* followed by a simple probabilistic method

14

* Thanks to the « mipfp » R package, Barthélemy J. et al.

Mipfp

Set of indiviuals : Quality

15

Set of indiviuals : Quality

16

17

Group individuals into households: couples with a male head

18

Group individuals into households: couples with a male head

Data: • Couples :

• Municipality• Age of head• Age of partner• Gender of head• Gender of partner

• Children : • Municipality• Type of HH• Age head• Age child

Group individuals into household: a combinatorial optimization method : Simulated annealing

1. Start with a « random » grouping

2. Try exchanges

3. ( in [0,1] )

4.

5.

6. Return to step 2 if stopping criterion not reached19

20

If fitness = 1, the grouping is « perfect »

21

Group individuals into households: couples with a male head

Remove H of HM

The people without an household are grouped without respecting the size and then type of household, but by respecting the age distributions.

Quality of households

22

Non assigned individuals

23

Individuals with a wrong size of household

24

25

Simulated annealing Probabilistic grouping

Respects all sizes of households

Respects all types of households

Respects 90% of the age table Respects the age table

Each individual is assigned to a household

Simulated annealing vs probabilistic grouping

5. Addition of health attributesCollaboration with the Wallonia Health Observatory and the UCL-Mont-Gondinne

• Determine pertinent diseases to add (high prevalence for elderlies)

• Diabetes• Chronic pain• Parkinson• Osteoporosis• Chronic pulmonary diseases

• Identification of data (source : Pharmanet)

• Addition of these diseases in the synthetic population

26

27

6. Dynamical evolution – agent based modelling

• Aging

• Divorces (With Johan Barthélemy)

• Marriages

• Death

• Birth

• Moves

• Getting a new diploma

• Changes in activity status and diseases

6. Dynamical evolution – agent based modeling

• Ages

• Divorces (With Johan Barthélemy)

• Marriages

• Death

• Birth

• Moves

• Getting a new diploma

• Changes in activity status and diseases

Need to model each dynamical process

Need to determine the more coherent order of the procedures (With Johan Barthélemy and Nam Huynh)

29

Literature• N. Huynh, P. Perez, M. Berryman and J. Barthélemy (2015), Simulating Transport and Land Use Interdependencies for Strategic Urban Planning – An Agent Based Modelling Approach, Systems

• J. Barthélemy and P. Toint (2015), A Stochastic and Flexible Activity Based Model for Large Population. Application to Belgium, Journal of Artificial Societies and Social Simulation

• J. Barthélemy, T. Carletti, L. Collier, V. Hallet, M. Moriamé (2016), Interaction prediction between groundwater and quarry extension using discrete choice models and artificial neural networks, Earth Environmental Science

• …

Divorces(Modelled with Johan Barthelemy (SMART) during my stay at SMART)

Two methods: 1. Neural networks

2. Discrete choice modelling

Data concerning divorcesFor each couple still married in 2001:

• Are they still together in 2002?

• Type and size of household

• Year of marriage

• Educational level

• Subjective health

• Age group of each

≈ 1 million couples including ≈ 8000 divorces(50% for the calibration and 50% for the validation)

Divorces – Neural Network

Different tests• Variables:

• Numeric• Normalized• Boolean

Different tests• Variables:

• Numeric• Normalized• Boolean

• Data:• Addition of a noise to the output of calibration• Addition of a noise to the input of calibration• Addition of a random variable• Genetic algorithm to calibrate the N(mu,sigma) added

Different tests• Variables:

• Numeric• Normalized• Boolean

• Data:• Addition of a noize to the output of calibration• Addition of a noize to the input of calibration• Addition of a random variable• Genetic algorithm to calibrate the N(mu,sigma) added

• Structure of network:• Different numbers of layers• Different numbers of nodes per layer• Different activation functions

Neural Network: best result

25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 90-94

Age of the woman

Divorces – Discrete choice modelling (stochastic)

Probability that a person p selects the alternative a that have a utility of U(p,a):

• Upa = Vpa + ɛpa with ɛpa inobservable

• Ppa = P(Upa > Upo ∀ o ≠ a )

= P(ɛpo - ɛpa < Upo - Upa ∀ o ≠ a )

Binary logic discrete choice :

• F(ɛpa) = exp(-exp(-ɛpa))

• Ppa =

Divorces – Discrete choice modelling -0.0254 * LengthMarriage + 2.04 * Woman25-29 + 1.78 * Woman30-34 + 1.72 * Woman35-39+ 1.65 * Woman40-44 + 1.27 * Woman45-49 + 0.643 * Woman50-54 - 0.563 * Woman55-59- 1.01 * Woman60-64 - 1.67 * Woman65-69 - 1.83 * Woman70-74 - 0.41 * Woman75-79 - 0.117 * AgeMan - 0.129 * DiplMan - 0.0839 * HealthWoman - 0.135 * SizeHH

Utility_Divorce = Utility_Stay = 0

Divorces – Discrete choice modelling-0.0254 * LengthMarriage + 2.04 * Woman25-29 + 1.78 * Woman30-34 + 1.72 * Woman35-39+ 1.65 * Woman40-44 + 1.27 * Woman45-49 + 0.643 * Woman50-54 - 0.563 * Woman55-59- 1.01 * Woman60-64 - 1.67 * Woman65-69 - 1.83 * Woman70-74 - 0.41 * Woman75-79 - 0.117 * AgeMan - 0.129 * DiplMan - 0.0839 * HealthWoman - 0.135 * SizeHH

Utility_Divorce = Utility_Stay = 0

Model : Binary logitRho-square: 0.940Adjusted rho-square: 0.939

P(Divorce)=

Validation set (mean of 200 simulations)

Validation set

Validation set

Validation set

Order of the procedures(work started in SMART with Nam Huynh and Johan Barthelemy)Starting from an existing dynamical process :

N. Huynh, P. Perez, M. Berryman and J. Barthélemy (2015), Simulating Transport and Land Use Interdependencies for Strategic Urban Planning – An Agent Based Modelling Approach, Systems

Testing different orders of the processes:AgingDeathDivorcesMarriagesGiving birth

120 possible combinations (No immigration considered)

Study Area3 LGAs (dark grey)+/- 180,000 inhabitants in 201168 Km2

Study Area28 Suburbs (in grey)+/- 180,000 inhabitants in 201168 Km2

       

+/- 0,6% of the initial population

Place of death in the dynamical process

       

Three different runs give very similar results.

       

Place of aging in the dynamical process

Three different runs give very similar results.

7. Conclusion

50

• We have a complete synthetic population for Belgium for 1/1/2011 without requiring a sample;

• The discrete choice modelling looks better than the neural network for the divorces;

• The order of the dynamical process is an important feature that needs to be considered.

8. Next steps

Write a paper with Johan Barthelemy (SMART) about the use of discrete choice models and neural networks to model the divorces.

Continue the work with Johan Barthelemy and Nam Huynh (SMART) on the choice of ordering.

Make a more rigorous analysis of the robustness and sensibility of the synthetiser;

Model the other dynamical processes (with a collaboration with SMART)

Implementation of what-if scenarios

51

8. Next steps (if we really have time)

Analyse different methods of integerisation after mipfp (with Johan Barthelemy)

Compare different methods to generate a synthetic population (the one

presented here, the synthetiser of Johan Barthelemy in his PhD, the synthetiser of

Floriana Gargiulo, others?)

52

Acknowledgment

53

• SMART (Pascal, Tania and the whole staff) for the wonderful and productive time.

• This research used resources of the "Plateforme Technologique de Calcul Intensif (PTCI)“ (http://www.ptci.unamur.be) located at the University of Namur, Belgium, which is supported by the F.R.S.-FNRS under the convention No. 2.5020.11. The PTCI is member of the "Consortium des Équipements de Calcul Intensif (CÉCI)" (http://www.ceci-hpc.be).

• The different people involved including people from:• Unamur • UCL• OWS• SMART

• The DGO6 that funded this research.

Thanks for your attention.(And hope to see you soon)

54