1. 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;

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

3. 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

4. Research setting (2)

5. Research setting (3)

6. Research setting (4): Kahnooj District
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

7. Research setting (5) Kahnooj

8. Research setting(6) Malaria In Kahnooj
Annual risk of malaria per 100,000 population between 1994 and 2001
Year
1997
1998
1999
Population
235297
249448
251315
Positive slides
1378
3407
1924
Annual parasitic index
5.86
13.66
7.66

9. 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

10. Data Collection (1) Surveillance malaria data between 1994 and 2002
Age
Sex
Village
Date of taking blood slides
Plasmodium species

11. Data Collection (2)
The ground climate data ( ) from the synoptic centre in Kahnooj City
Daily temperature
Relative humidity
Rainfall

12. 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

13. Results (1)
The seasonality and time trend of malaria classified by species

14. Results (2) humidity temperature P.f P.v
The fitted values of models based on seasonality, time trend and meteorological variables
The optimum temperature and humidity
32%
27.3%
humidity
31.1°C
35°C
temperature
P.f
P.v

15. Results (3)
Autocorrelations and partial autocorrelations between the residuals of models, which estimated risks, based on climate, seasonality and time trend

16. Model number and Explanatory variables
Results (4)
Model number and Explanatory variables
Pseudo R2
P. falciparum
P. vivax
All species M1
Sine transform of time
0.2
0.43
0.35 M2
M1 & linear effect of year
0.76
0.49
0.6 M3
M2 and all meteorological variables
0.64
0.62 M4
Only the number of cases in last three months
0.61
0.63 M5
M3 and M4
0.88
0.74
0.8

17. Why is there an autocorrelation?
Autocorrelation in meteorological variables
Transmission cycle between human, mosquito and human
Relapse
The impact of control programs

18. 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

19. Thanks for you kind attention
Time for your comments
Thanks for you kind attention