Controlled vocabularies &

Ontologies- DSS -

Malaria Decision Support System

Integration of a number of data sets that will allow for informed choices, decisions and policy.

MDSS

SWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILANDSWAZILAND

0

0

kilometers

15 30

MOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUEMOZAMBIQUE

EmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeni

Richards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards Bay

Kosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi Bay

IngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavuma

JoziniJoziniJoziniJoziniJoziniJoziniJoziniJoziniJozini

Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay

MkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuze

HluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluwe

HlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisa

St LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt Lucia

Malaria IncidencePer 1000 Population

50 to 15025 to 500.01 to 250 to 0.01

SWAZILAND

0

0 15 30

kilometers

MOZAMBIQUE

Kosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi BayKosi Bay

IngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavumaIngwavuma

JoziniJoziniJoziniJoziniJoziniJoziniJoziniJoziniJozini

Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay Sodwana Bay

MkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuzeMkhuze

HluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluweHluhluwe

HlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisaHlabisa

St LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt LuciaSt Lucia

Richards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards BayRichards Bay

EmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeniEmpangeni

Malaria Incidenceper 1000 population

350 to 600150 to 35050 to 15025 to 505 to 250.01 to 5

Nature Reserves

Water Bodies

Main Tow ns

Magisterial Districts

Sub-District Areas

Malaria incidence per 1000 people: July 1999 to June 2000 Malaria incidence per 1000 people: July 2003 to June 2004

Nature Reserves

Water Bodies

Main Tow ns

Magisterial Districts

Sub-District Areas

W E E K 2 W E E K 8W E E K 6W E E K 4

W E E K 1 0 W E E K 1 6W E E K 1 4W E E K 1 2

Spraying Progress During Round 301 October 2003 to 16 J anuary 2004

80 to 10060 to 8040 to 6020 to 40

0 to 20Zone 2A (Boane)

Malaria surveillance in southern Africa

50s Eradication - Paper

Early 90s Dbase + EpiInfo - fragmented

Mid 90s Ms. Access - fragmented

Early 2000 – SQL - integrated

NOW – Open Souce -integrated

What it is:PostgreSQL, is a highly scalable, SQL compliant, open source object-relational database management system.

Why we decided on using it?• Free• Open source• Easily spatially enabled

Software

What it is:PHP is a widely-used general-purpose scripting language that is especially suited for Web development and can be embedded into HTML.

Why we decided on using it?• Free• Open source

• What it is:Is an extension to the PostgreSQL object-relational database system.

• Allows GIS (Geographic Information System) objects to be stored in the database.

Why we decided on using it?• Free• Open source

MA

LA

RIA

DE

CIS

ION

SU

PP

OR

T S

YS

TE

M

Health Information System

malariapatient / case

data

Entomology Surveillance System

mosquitopopulation

data

Indicator Survey

malariaprevalence

surveys

Spatial Data

GIS data

Intervention Monitoring System

malariacontrol

interventions

SYSTEM

Passive surveillance

Active Surveillance

Species identification and infectivity

Insecticide Resistance

Parasitemia & Anaemia

Household Indicators

Specific spatial data

Backdrop spatial data

Insecticide Treated Bed-nets

Indoor Residual Spraying:

Other interventions

MODULED

EN

GU

E D

EC

ISIO

N S

UP

PO

RT

SY

ST

EM

SPATIAL BACKBONE

EDUCATION

VECTOR CONTROL

VECTOR SURVEILLANCE

CLINICAL

DENGUE VIRUS SURVEILLANCE

DISEASE SURVEILLANCE

(passive or active)

CATEGORYTYPE

- Geographical boundaries- Location of hospitals, health clinics,

schools, cemeteries etc- Socioeconomic characteristics

- Environmental factors (climate, elevation, vegetation)

Knowledge, Attitude and Practice among population

Immatures: Mechanical source reduction

Vector presence & abundance(larval, pupal, adult indices)

Insecticide resistance (larvae, adults)

Dengue virus in vector populations

Dengue cases

Fever of Unknown Origin

Symptomology, Onset date, Diagnostic tests, Serotype, Outcome etc

Dengue virus in human population

Immatures: Biological control

Immatures: Chemical control

Adults: Chemical spray control

Adults: Chemical ITM-based control

residual indoor spraying

thermal fog

space spraying

outdoor

cold fogs

romanomermis iyengari

rectangular net

long lasting nets

CDC light trap

self supporting net

wedge shaped net

circular net

growth regulator

pathogens

protista

entomopathogenic nematodes

romanomermis culicivorax

MALARIA DENGUEMIRO GAZETTEER

Relationships

Definitions

Terms

Complexity

CV ONTOLOGY

IDO

Vector surveillance ontology Vector management ontology

.

X

Conceptualize

Indentify terms

Add definitions

Add relationships

Separate CV terms Formalize ontology

Incorporate into database schema

Annotate data

Analyze data

Interpret

Decision making

Disseminate

Process

X

De

velo

pmen

tU

se

Implementation

Process

MIRO, available for downloading at:http://obo.cvs.sourceforge.net/*checkout*/obo/obo/ontology/phenotype/mosquito_insecticide_resistance.obo

Participants will be able to: 1) propose new terms 2) propose modifications to existing

terms and/or their definitions3) comment on structures and undefined

relationships. No restrictions will apply to participation

and all contributions will be validated.

CV web assistant

Planning and constructing an ontology is a process that requires participation of and consensus among the expert community from the start!

IR Base

Insecticide resistant components feeds IR Base.

IR base a global database of insecticide resistance.

Entomology database components. Operational.

Entomology Database

InsecticideResistance

SpeciesDensity

SporozoiteRate

MDSS Malaria Control Programme

Linkage of systemsData sharing

InsecticideResistance

IR Base MERG WARN Malaria Atlas

MARA

MDSS

ITEGRATION OF DATA

Malaria Control Programme

Linkage of systemsGlobal databases

Challenges & Opportunities

• Time• Financial resources• Priorities/commitment• Advocacy• Community Participation & Contributions• Roles & Responsibilities• Ownership• Sustainability

• Provide opportunity to contribute• Initiate collaborative efforts• Provide standardization-annotation• Assist software development process• Provide better quality data• Provide improved comparison of data• Support contributions to global warehouses + interoperability• The use supports better decisions • Bigger picture – indirectly save lives