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MODELLING THE TRANSMISSIONOF VECTOR BORNE DISEASESUSING SVM-PSOSudheer Ch.


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Synopsis

Introduction Problem formulation

SVM

PSO SVM-PSO

Application to case study

Results and Discussions

Conclusions


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INTRODUCTION

Vector : Any agent (person or animal ormicroorganism) that carries and transmits a

disease (Lexicographic meaning)

Mosquitoes

Flies etc.

Dengue

Chickenguniy

a

Malaria

Vector

Borne

Diseas

es


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Malaria: Infection

Anophelesmosquito bites

infected host

Mosquito drinks parasite in

blood

Parasite reproduces in gut of

mosquito

Sporozoites travel to

mosquitos salivary glands Mosquito transmits disease

during next blood meal.


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Last few

decades there is

an increase invector borne

diseases.

Researchers have attributed that one

of the main reason for this isClimate change


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Malaria among all vector born

diseases is specially becoming a big

threat to human life and health in

the country, and hence in this

paper study on the malaria has

been taken up.

Climate change or global warmingis predicted to have unexpected

effects on the vector prevalence

thereby affecting malaria

incidences.


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Entomological and parasitological parameters

Parameter Value

1. Optimum Temperature for viability ofeggs

28C

2. Anthropophilic Index 0 to 4%

3. Gonotrophic cycle 78hrs-132hrs (3 days) after first blood mealAverage-96hrs

40 hrs at temp 26-32C

4. Biting Behavior/ man landing rate 0.4/man/night in plain0.95/man /night in forest

0.2/man/night in dam average 0.2-0.9

5. Longevity 4 to 8 weeks -12 to 18C12to 28 days-25

6 to 18 days-30C4 to 10 days- 35C (at 20 to 100% RH)

>24 hours-40C

With RH 60 -80%

6. Daily Survival probability 0.81 to 0.89

7. Entomological Inoculation Rate (EIR) P.v-0.06-0.12

P.f-0.06


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131900 21002000

20

15

14

16

17

18

19Earths AverageSurface Temp (OC)

Year

205019501860

Centralestimate:

2.5 oCincrease

Band o f 1200-yr historicalclimatic variabil i ty

Most of warming since1950 is due to humanactions (IPCC, 2001)

IPCC (2001)

estimate:

+ 1.4-5.8 oC by 2100

Past ClimateMean surface temperature, 1855-2004

Climate Research Unit, UEA, 2005

Temperature variation

from 1961-90 average oC

Temperature increase and

fluctuation affects the vector

and parasite life cycle. This

can cause reduced prevalenceof the disease in some areas,

while it may increase in

others [3-7].

Do you Know?

There were 243 millionmalaria cases reported

in 2008 (WHO 2009).


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How to address this problem?

If malarial incidences can be predicted in

advance then Health dept can take various

precautions .

So in this study SVM-PSO is used forforecasting the malarial outbreaks.


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Support Vector Machines

Support vector machine

constructs a hyperplane or set ofhyperplanes in a high- or infinite-

dimensional space, which can be

used for classification, regression,

or other tasks.

Intuitively, a good separation isachieved by the hyperplane that

has the largest distance to the

nearest training data point of any

class (so-called functional

margin), since in general thelarger the margin the lower

the generalization error of the

classifier.


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Unique Features of SVM SVM training always finds

global minimum

The computational

complexity of SVMs does not

depend on the dimensionalityof the input space.

SVMs use structural risk

minimization.

SVMs are less prone tooverfitting

Local minimaGlobal minima

Parameter Estimation C, epsilion, gamma,

Kernel function.- DEFECTS OF SVM


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Particle Swarm Optimization (PSO) PSO was developed in 1995 by

James Kennedy (social-

psychologist) and RussellEberhart (electrical engineer).

It uses a number of agents

(particles) that constitute a

swarm moving around in the

search space looking for thebest solution.

Each particle is treated as a

point in a N-dimensional space

which adjusts its flying

according to its own flying

experience as well as the flying

experience of other particles.


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Each particle keeps track of its coordinates in the solutionspace which are associated with the best solution (fitness)

that has achieved so far by that particle. This value is

called personal best , pbest.

Another best value that is tracked by the PSO is the bestvalue obtained so far by any particle in the neighborhood

of that particle. This value is called gbest.

The basic concept of PSO lies in accelerating each particle

toward its pbest and the gbest locations, with a randomweighted acceleration at each time step.

Particle Swarm Optimization (PSO)


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Flow Chart for computing SVM parameters

using PSO


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Determining SVM parameters (Hsu etal 2009)

Coarse search Fine search

C [10-5,105] [ 10-1,10]

[ 0,10 ] [ 10-7,10-1 ]

[ 0,10 ] [ 0,1 ]


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Case Study

Maximum and Minimum Temperatures in area of Falodi.

Relative Humidity and malarial incidences .

Falodi area of Jodhpur District

Latitude 27-06 to 27-09 north

and 72-20 to 72-23 east.

Average elevation of 303 meters.

Average temperature in summer

falling in the range of 42.2 C (max)

to 36.6 C (min).

The average temperature in thewinter season is somewhere between

27.5 C (max) to 15.5 C (min).


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Comparison of SVM-PSO with other models

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

1 2 3 4 5 6 7 8 9

MalariaIncidences

Months

Actual Malaria Incidences

SVM-PSO

SVM-GA

SVM


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Conclusions

SVM conducts structural minimization rather than the minimizationof the errors.

The PSO selects the optimal parameters for SVM to improve the

forecasting accuracy.

So this unique combination of SVM and PSO has made the proposed

SVM-PSO model to perform better compared to the other models. The performance of SVM-PSO is out performing comparing to other

models like SVM-GA and SVM

Also it is seen from the results that SVM performs poorly in case the

parameters are not chosen properly.

Furthermore this investigation demonstrates that the proposed SVM-PSO offers a valid alternative for predicting the malaria outbreaks.


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