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

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

Research setting (2)

Research setting (3)

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

Research setting (5) Kahnooj

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

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) Surveillance malaria data between 1994 and 2002 Age Sex Village Date of taking blood slides Plasmodium species

Data Collection (2) The ground climate data (1975-2003) from the synoptic centre in Kahnooj City Daily temperature Relative humidity Rainfall

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) The seasonality and time trend of malaria classified by species

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

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

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

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

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

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