from observation to simulation, and return · from observation to simulation, and return...

FROM OBSERVATION TO SIMULATION,

AND RETURN PERSPECTIVES FOR DENGUE RESEARCHES

Daudé É.1, Lefebvre B.2, Telle O. 3, Maneerat S.1, Vaguet A.2, Vaguet Y.2, Cebeillac A.1, Misslin R.1 1 UMIFRE CNRS-MAE, CSH, New Delhi, Inde 2 UMR IDEES, Rouen, France 2 Institut Pasteur Paris, France / CSH, New Delhi, Inde

European Colloquium in Theoretical and Quantitative Geography (ECTQG’13), Dourdan, France, 5th-9th sept. 2013

2

Aedes

albopictus

.

200 millions individuals infected each year (Hay et al, 2013).

Vector Borne disease (Aedes Aegypti et Aedes Albopictus).

Unkown interaction of risk factors in cities (unlike rural areas)

ECTQG2013, - Daudé É., Lefebvre B., Telle O., Maneerat S., Vaguet A., Vaguet Y., Cebeillac A., Misslin R.

3


4

Environment

Aedes

Host

Virus

Micro

- Gravid

- Age

- Predation behavior

Meso

- Biting rate

- Survival rate

- Mobility behavior

Macro

- Aedes density

- Contamination rate

- Population age

Micro

- Virulence

Meso

- Serotype

Macro

- Strain Mutation

Micro Scale Meso Macro scale

- Genetic - Antibodies (Herd immunity) - Population density

- Immunity - Asymptomatic - Mobility

-Age - Dengue Risk KAP (kowledge) - Socio-economical

Micro Meso Macro

•Container - Water storage - Temperature

•House condition - Landuse - Precipitation

•Humidity - Vectorial managment - Urban planning

6

1

2

3

4

8

7

9

10

11

5

1

3

Figure 1: Dengue system and its main

components


5

Relation De type Porte sur Surveillance Contrôle

R1

E Ve

Influence Breeding site Breeding site checking Control of breeding site

Constrain Low temperature (< 20°) Meteorological survey -

R3

H Ve Transmission Virus Dengue surveillance system

Fumigation of house being of

infected individuals

R4

Ve H Transmission Virus Contaminated bitting rate

Individual prophilaxy to avoid

Mosquito bitting

R5

H E

Urban Planning

(or not)

Landuse, environment

fragmentation GIS system Environment control

R6

E H Constrain Diffusion system Population mobility

Prevent dengue to reach nodes

of the system

R7

Vi H Infect Cells NS1 virus isolation Fever, platelet surveillance

R8

H Vi Replication Antibodies Seroprevalence Vaccin

R9

Vi Ve Infect Cells % of infected mosquitoes Genetic modification of mosquito

R10

Ve Vi Replication virus

R11

E Vi Influence Température Virus virulence Temperature control

Environment

Aedes

Host

Virus

Micro

- Gravid

- Age

- Predation behavior

Meso

- Biting rate

- Survival rate

- Mobility behavior

Macro

- Aedes density

- Contamination rate

- Population age

Micro

- Virulence

Meso

- Serotype

Macro

- Strain Mutation

Micro Scale Meso Macro scale

- Genetic - Antibodies (Herd immunity) - Population

density

- Immunity - Asymptomatic - Mobility

-Age - Dengue Risk KAP (kowledge) - Socio-

economical

Micro Meso Macro

•Container - Water storage - Temperature

•House condition - Landuse - Precipitation

•Humidity - Vectorial managment - Urban planning

6

1

2

3

4

8

7

9

1

0

11

5

1

3


6

Aedes

aegypti Aedes

albopictus

ECTQG’13 - Daudé É., Lefebvre B., Telle O., Maneerat S., Vaguet A., Vaguet Y., Cebeillac A., Misslin A.

1 – Relations between dengue cases and Socio-Environmental factors:

- socio-economical contexts: wealth, employment, prevention …

-physico-environmental contexts: local temperatures, building density, land use …

- human mobilities : perimeter, frequency, places …

2 – Produce a dynamic model, spatially explicit, of Vector / Host Behaviours (Individual-based model):

- To test hypothesis on Aedes aegypti population dynamic and on human mobilities

- To explore scenarii (on environment / mosquito) for dengue control

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Aedes

aegypti Aedes

albopictus


Remote sensing

Survey, Census,

Retrospective Data,

Satellite images

Spatial analysis

Epidemiological Data

Entomological Data

Environmental Data

Monitoring System

Agent-Based Models

Territorial Diagnosis

Decision Support Tool

vector host

environment

Parameters / Input Data

Simulation Social Data

Sp

ati

al

Inte

gra

tion

(G

IS)

Calib

ratio

n / v

alid

atio

n

Inte

gra

tion

virus

SIMULATION SPATIAL ANALYSIS DATA COLLECTION

8


Retrospective study in Bangkok and Delhi

10

0

10

20

30

40

50

0

20

40

60

80

100

7/6

10/6

13/6

16/6

19/6

22

/62

5/6

28

/61/74

/77

/710

/713

/716

/719

/72

2/7

25

/72

8/7

31/7

3/8

6/8

9/8

12/8

15/8

18/8

21/8

24

/82

7/8

30

/82

/95

/98

/911/914

/917

/92

0/9

23

/92

6/9

29

/92

/105

/108

/1011/1014

/1017

/102

0/10

23

/102

6/10

29

/101/114

/117

/1110

/1113

/1116

/1119

/112

2/11

25

/112

8/11

1/124

/127

/1210

/1213

/1216

/1219

/122

2/12

25

/122

8/12

Nb

of

ca

se

s

PP

an

d °

C

2009 Number of cases PP °C

11

0

10

20

30

40

50

0102030405060708090

100

7/6

10

/61

3/6

16

/61

9/6

22

/62

5/6

28

/61

/74

/77

/71

0/7

13

/71

6/7

19

/72

2/7

25

/72

8/7

31

/73

/86

/89

/81

2/8

15

/81

8/8

21

/82

4/8

27

/83

0/8

2/9

5/9

8/9

11

/91

4/9

17

/92

0/9

23

/92

6/9

29

/92

/105

/108

/101

1/1

01

4/1

01

7/1

02

0/1

02

3/1

02

6/1

02

9/1

01

/114

/117

/111

0/1

11

3/1

11

6/1

11

9/1

12

2/1

12

5/1

12

8/1

11

/124

/127

/121

0/1

21

3/1

21

6/1

21

9/1

22

2/1

22

5/1

22

8/1

2

Nb

of

case

s

PP

an

d °

C

2008 Number of cases PP °C

Relation between Vectorial Data and

Environment

• 175 Colonies are sampled by 2500 municipal employees between July and October 2009.

• Data of 2009 are analysed in the thesis (HI, CI, BI) and reveal strong relation between environment and abundance of aedes larvae (HI = House Index, CI= Container Index)

Sampled

colonies

Min of

HI

Max

of HI

HI

moyen stdev Min CI

Max

CI CI mean stdev

Industrial areas 2 - - - - 1,8 4 2,86 1,54

Old planified 29 2,6 8,89 3,88 2,07 1,3 9,09 3,23 2,01

Poor 33 2,5 14,81 5,12 3,44 1,6 8,4 4,23 2,00

Spontaneous 44 2,1 15,63 5,04 3,23 2,3 5,4 4,00 3,89

Historical Center 6 1,9 8,89 4,03 3,18 1,8 9,5 3,11 2,99

Planified recent 19 1,6 8,04 3,65 1,80 1,2 8,52 3,36 1,81

Rich 9 0,9 4,44 2,71 1,33 1,5 3,57 2,54 0,64

NDMC 1 2,90 3,14

Grand Total 175 0,90 15,6 4,21 3,73 1,30 9% 3,65 2,57


Relation environment and dengue incidence

• Despite a strong relation with larvae index, geopgraphy of dengue case is not depend of environment of population. Case per KM² (of build up) are in 2008 located in Poor areas, while in 2009, cases are much more located in NDMC and rich areas of MCD.

0,000,501,001,502,002,503,003,504,004,505,00

Dengue Cases Density (2008-2009)

2008 2009


Vectorial Index in different places

of Delhi

Semaine Hospital

CI

Train

station

CI

JJ

cluster

HI

Institution

CI

CPWD

CI

education

CI

House

Index

28/06/2009 -

01/08/2009 3 1,6 2,4 5 5 6,32 3,4

02/09/2009 -

29/09/2009 8,5 9,78 8,62 13 6,53 12,4 6,7

30/09/2009 -

03/10/2009 10,3 7 5 7 6,5 17,6 7,2

04/10/2009 -

01/11/2009 5,6 7,32 4 4 4,2 2,22 4

8,8 6,425 5 7,25 5,5 11,77 5,125


Conclusion: • Geography of dengue is complex, depending of

environment, vector behavior, virus (ie. asymptomatic cases) as well as individuals behavior.

• Intra urban mobilities as well as the role of public spaces need to be understood in the diffusion of dengue Virus.

• Vectorial Data shows that Index are higher in public spaces such as hospitals, train station, ect. This suggest that contamination could occurs in those space rather than in domestic area.

• However, in the absence of effective vaccine, role of environment and gouvernance need to be understand, since environement is the only determinant on which we can act to prevent population from dengue. Some research are currently in progress in Delhi and Bangkok.


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Each Individual-mosquito is a computer model of Aedes aegypti:

- defined by stage and transitions (egg, larva, pupae, adult, virgin female, non-ovipositing, first gonotrophic, ovipositing, normal gonotrophic, dead), by behaviours related to stage (mating, feeding, ovipositing, hatching, biting, flying) and parameters

- A population is a set of Individuals with the same behaviours (competences), differentiated according to their life stage and according to specific parameters

category sub-category parameters values unity

AEDES'S ACTIVITIES

mating MATING_PROB 0,95 mating probabilities for a new emergent female

feeding

MAX_ENERGY 4320 minutes the value is based on the number of days that a mosquito can survive without feeding. It's 3 days

BLOOD_DETECT_DIST 3 meters

BLOOD_GAIN_ENERGY_UL 1080 minutes

1080 mn gain per 1 ul blood feeding. This value is based on the fact that Aedes take minimum 3 days from the last blood meal (4 ul max/meal) to finish gonotrophic cycle, so 1 ul blood is approximately equivalent to 1080 mn gain (4320mn/4ul).

NECTAR_GAIN_ENERGY 1080? minutes

To confirm!!! In average, Aedes takes about 1mn for nectar feeding. If it can take blood meal for one minutes, it can earn 1080mn (1 ul), this value can also apply to nectar feeding?

oviposition MIN_TEMP_FOR_OVIPOSITION 18 °C

MAX_STCKEGGS 120 eggs 120 = max nb of eggs to lay per a gonotrophic cycle

MIN_STCKEGGS 100 eggs 100 = min nb of eggs to lay per a gonotrophic cycle

MAX_SPEED_LAYEGG 0,05 eggs 0.05 eggs/s = 3eggs/mn (cf: enquête auprès des entomologues)

MIN_SPEED_LAYEGG 0,016 eggs 0.016 eggs/s = 1egg/mn

egg hatching EGGS_HATCH_NO_FLOODING_PROB 0,197

EMBRYONATED_EGG_HATCH_PROB 0,596

MIN_TEMP_EGGHATCH 22

Biting MAX_QUANT_BLOOD 3 mg

MAX_TIME_BITING 15 minutes a revérifier!!!!

BITING_SUCC_RATE 0,8 including host defence rate

flight TARGET_MAXDIST_TOLERENCE ?? target reached tolerent distance

PERCEPTION_TARGET_RADIUS 10 meters the percepetion distance by Aedes is assumed here to 10 meters for all kind of targets

MAX_SPEED 1 meters/sec 1m/s

MIN_SPEED 0,5 meters/sec 0,53/s

MAX_VERSPEED 0,8

MIN_VERSPEED 0,2

FLIGHT_ENERGY_LOST ?? mn/s

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category data parameters values

LAND USE

building

fHeight {0,1,..,n} m

fPorosity [0,1]

fBloodAttractDay [0,1]

fBloodAttractNight [0,1]

fRestAttractDay [0,1]

fRestAttractNight [0,1]

fOvipositAttract [0,1]

vegetation

fNectarAttract [0,1]






fSunExpo [0,1]

thoroughfare

fPorosity [0,1]






fSunExpo [0,1]

water surface


fPorosity [0,1]

empty space

fPorosity [0,1]






construction site

fHeight {0,1,..,n}

fPorosity [0,1]






fSunExpo [0,1]

METEOROLOGY

weather

DATE DD/MM/YYYY

DAILY_MAX_TEMP x °C

DAILY_MIN_TEMP x °C

DAILY_MAX_WIND x km/h

DAILY_TOT_RAIN_MM x mm

fAirSaturationDeficit (humidity) %

fHourlyTemp x °C

sun

DATE DD/MM/YYYY

SUNRISE H.MN

SUNSET H.MN

Female adult Aedes aegypti’s activities decision diagram


20


Spatial

distribution

Stages’

evolution Mosquitoe

s Activities

Global monitoring

Geosimulation of Dengue vector population dynamic

Scenarii that can be tested :

• Spatial evolution of the disease according to localisation of first cases, temperature, rainfall and centrality of the area / mobilities of individuals.

• Local probability of dengue virus diffusion once a case is register

• Impact of fumigation during an epidemic

• Impact of environment management during inter-epidemic period (eradication of ecological niches for virus).

• Urban central nodes managment vs local managment of spaces

• Etc….

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THANK YOU!

Partners:

Daudé Eric

Vaguet Alain

Telle Olivier

Lefebvre Bertrand

Cebeillac Alexandre

Maneerat Somsakun

Misslin Renaud

Taillandier Patrick

Vaguet Yvette

Contact: [email protected]

This research was founded by the EU project DENFREE:

Dengue Research Framework for Resisting Epidemics in

Europe (grant agreement: 282 378), funded by the European

Commission’s Seventh Framework Research Programme and

by the French project AEDESS: Analyse de l’Emergence de la

Dengue Et Simulation Spatiale, funded by the Agence

Nationale de la Recherche, ANR 10 CEPL 004-01.

from observation to simulation, and return · from observation to simulation, and return...

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