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; ahaghdoost@kmu.ac.ir

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: 32,000km 2, 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 Year199719981999 Population235297249448251315 Positive slides137834071924 Annual parasitic index5.8613.667.66

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 (1975-2003) 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- R 2

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

14. Results (2) The fitted values of models based on seasonality, time trend and meteorological variables The optimum temperature and humidity 32%27.3% humidity 31.1°C35°C temperature P.fP.v

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

16. Results (4) Model number and Explanatory variables Pseudo R 2 P. falciparumP. vivax All species M1Sine transform of time0.20.430.35 M2M1 & linear effect of year0.760.490.6 M3M2 and all meteorological variables0.640.62 M4Only the number of cases in last three months0.610.640.63 M5M3 and M40.880.740.8

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. Time for your comments Thanks for you kind attention