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Modelling of malaria variations using time series methods. Ali-Akbar Haghdoost MD, Ph.D. in epidemiology and biostatistics faculty of Medicine, and Physiology research center, Kerman University of Medical Sciences, Iran; ahaghdoost@kmu.ac.ir. Main objectives.

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modelling of malaria variations using time series methods

Modelling of malaria variations using time series methods

Ali-Akbar Haghdoost

MD, Ph.D. in epidemiology and biostatistics

faculty of Medicine, and Physiology research center,

Kerman University of Medical Sciences, Iran;

ahaghdoost@kmu.ac.ir

main objectives
Main objectives

Assessment of the feasibility of an early warning system based on ground climate and time series analysis

slide3

Research setting (1) Malaria In Iran

Annual number of malaria cases dropped from around 100,000 to 15,000 between 1985 and 2002

More than 80% of cases are infected by P.vivax in recent years

slide6
Arid and semiarid

Around 230,000 population in 800 villages and 5 cities

Area: 32,000km2, less than 8% of area is used for agriculture purposes

Research setting (4): Kahnooj District

research setting 7 health system
Research setting (7) Health System
  • Rural health centres
    • Trained health workers
    • Microscopists
    • GPs
  • Malaria Surveillance system
    • Active: follow-up of cases up to one year, febrile people and their families
    • Passive: case finding in all rural and urban health centres free of charge
    • Private sector does not have access to malaria drugs, it refers all cases to public sector
  • Reporting system: weekly report to the district centre
  • Supervision: An external quality control scheme is in place
data collection 1
Data Collection (1)

Surveillance malaria data between 1994 and 2002

  • Age
  • Sex
  • Village
  • Date of taking blood slides
  • Plasmodium species
data collection 2
Data Collection (2)

The ground climate data (1975-2003) from the synoptic centre in Kahnooj City

  • Daily temperature
  • Relative humidity
  • Rainfall
statistical methods 1
Statistical methods (1)
  • Poisson method was used to model the risk of disease
  • The time trend was model by using parametric method (sine and cos)
  • The autocorrelations between the number of cases in consecutive time bands were taken into account
  • The data were allocated into modelling (75%) and checking parts (25%)
  • Using forward method the significant variables were entered in the model. The significance of variables were assessed by likelihood ratio test and pseudo-R2
results 1
Results (1)

The seasonality and time trend of malaria classified by species

results 2

The optimum temperature and humidity

P.v

P.f

temperature

35°C

31.1°C

humidity

27.3%

32%

Results (2)

The fitted values of models based on seasonality, time trend and meteorological variables

results 3
Results (3)

Autocorrelations and partial autocorrelations between the residuals of models, which estimated risks, based on climate, seasonality and time trend

why is there an autocorrelation
Why is there an autocorrelation?
  • Autocorrelation in meteorological variables
  • Transmission cycle between human, mosquito and human
  • Relapse
  • The impact of control programs
conclusion
conclusion
  • Models based on time series analysis and ground climate data (which are available free of charge) can predict more than 70% of malaria variations. Therefore, it seems that an early warning system based on these models is feasible
time for your comments
Time for your comments

Thanks for you kind attention