Assessment of seasonal and climatic effects on the incidence and species composition of malaria by using GIS methods Ali-Akbar Haghdoost Neal Alexander (supervisor)

Main objectives 1.Assessment of the feasibility of an early warning system based on ground climate and remote sensing data 2.Assessment of the interaction between Plasmodium spp from different points of view: meta-analysis, modelling, and extended analysis of a large epidemiological dataset

Climate effects on malaria 1.The rate at which mosquitoes develop into adults 2.Frequency of blood feeding 3.Adult mosquito survival 4.The incubation time of parasites in the mosquito

Other considerations related to climate 1.Deforestation 2.Migration and urbanisation 3.Changing human behaviour 4.Natural disaster and conflict

GIS and malaria Sipe (2003) reviewed the GIS and malaria literature and divided the publications into the five categories outlined below: 1.Mapping malaria incidence/prevalence 2.Mapping the relationships between malaria incidence/prevalence and other potential related variables 3.Using innovative methods of collecting data such as remote sensing (e.g., GIS) 4.Modelling malaria risks 5.General commentary and reviews of GIS used in malaria control and research

Modelling of malaria (1) 1.Modelling of the abundance of vectors 2.Modelling of the frequency of malaria cases/infections

Research setting (1)

Research setting (2)

Research setting (3): 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

Research setting (4) Kahnooj

Research setting (5) 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(6) Malaria In Kahnooj Annual risk of malaria per 100,000 population between 1994 and 2001 Year Population Positive slides Annual parasitic index

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

Research setting (8) Treatment Of Malaria GPs Prescribe medicine –P.falciparum: chloroquine (3 days) + primaquine (with the second dose of chloroquine) –P.vivax: chloroquine (3 days) + primaquine (weekly does for eight weeks, or daily dose for two weeks) Health works supervise that patients take drugs completely, also take follow-up slides

Objective Assessment of the feasibility of an early warning system based on ground climate and remote sensing data

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 ( ) from the synoptic centre in Kahnooj City – Daily temperature –Relative humidity –Rainfall

Data Collection (3) GIS maps and RS data: –Electronic maps of Kahnooj contain the borders, roads, villages and cities. The map scale was 1:50,000 in Arcview format –Landsat data with 30x30m spatial resolution in January 2001, contained NDVI –NOAA-AVHRR data with 8x8km spatial resolution and 10 day temporal resolution from 1990 to 2001, contained NDVI and LST – DEM images with 1x1km resolution (National Imagery and Mapping Agency of United State of America,

Statistical methods (1) The risk of disease was estimated per village per dekad (10 days) Using mean-median smoothing method the temporal variations were explored Poisson method was used to model the risk of disease Fractional polynomial method was used to maximise the accuracy of models The time trend was model by using parametric method (sine and cos)

Statistical methods (2) Models predicted the risk of malaria in three distinct spatial levels: district, sub-sub- district (SSD) and village Using sensitivity analysis the best gap between the predictors and malaria risk was estimated 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

Statistical methods (3) Using sensitivity analysis the best buffer zone around each village was defined The number of under and over-estimations and percentages in the final model were computed The feasibility of models were assessed by comparing the over and under-estimations of models with their corresponding values based on the extrapolation from the previous month

Results (1) number of malaria cases Population Risk ratio (95% CI) Sex Male Female 9,932 8,326 98,330 97, ( ) Nationality Iran Afghanistan 17, , ( ) Age < >=30 2,972 7,436 5,001 2,84 28,571 66,316 48,498 50, ( ) 0.99( ) 0.56( ) malaria risk factors

Results (2) Pearson correlation coefficients between the annual risk of malaria and meteorological variables in Kahnooj Meteorological factorAPIAFIAVI Minimum temperature Maximum temperature Mean temperature Humidity Rainfall0.45*0.54*0.40*

Results (3) Temporal variations of malaria over a year; the observed numbers classified by species, based on 8-year data

Results (4) The seasonality and time trend of malaria classified by species

Results (5) 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

Results (6) Autocorrelations and partial autocorrelations between the residuals of models, which estimated risks, based on climate, seasonality and time trend

Results (7) Model number and Explanatory variables Pseudo R 2 P. falciparumP. vivax All species M1Sine transform of time M2M1 & linear effect of year M3M1 & quadratic effect of year M4M2 & mean daily min temperatures in last 6 dekads M5M2 & mean daily max temperatures in last 6 dekads M6M2 & mean daily mean temperatures in last 6 dekads M7M2 & mean daily relative humidity in last 6 dekads M8M2 & mean daily min temperatures in last 2 dekads M9M2 & mean daily max temperatures in last 2 dekads M10M2 & mean daily mean temperatures in last 2 dekads M11M2 & mean daily relative humidity in last 2 dekads M12M8 & M9 & M10 & M M13M8 & M9 & M M14M13 & rainfall M15 M14 & quadratic effect of min 1, max 2 of temperature and humidity in last 2 dekads M16 M15 and the sum of cases in last dekad, and periods with these dekad lags:2-4, 5-16, 17-24, and M17M15 and the sum of cases in last dekad M18M15 and the sum of cases in 2-4 dekad ago

Results (8)

Results (9)

Results (10) The pseudo R 2 between malaria risks and the average NDVI around villages in : The average NDVI around each village was computed in circles with 15m up to 6km radiuses 2: Fractional polynomial, degree two 3: Powers (1,2); 4: powers (-2,-0.5); 5: powers (-2,-0.5)) Radius 1 All species P. falciparumP. vivax LinearFR 2 LinearFR 2 LinearFR 2 15m km km km km km km

Results (11) The observed and predicted risk maps of malaria in 2001 in Kahnooj, the predicted maps were computed based on NDVI around villages (in 5km radius)

Results (12) The observed and predicted risk maps of malaria in in Kahnooj, the predicted maps were computed based on the mean of altitude three kilometres around villages by using fractional polynomial models Malaria was rare in villages with less than 450 or more than 1400 meter altitude. The maximum risks were observed in villages with 700 to 900 meters altitude.

Results (13) Pseudo R 2 P. falciparumP. vivax All species villageSSDDistrictvillageSSDDistrictvillageSSDDistrict Models based on remote sensing data Models based on time trend, seasonality and autocorrelation The final model based on time trend, seasonality, autocorrelation and remote sensing data The pseudo R 2 of Poisson models classified by the species based on village, SSD or whole district data

Results (14) Checking part 2 (%) Over estimationUnder estimation Predicted value extrapolated from previous month’s data P. falciparum P. vivax All species 372 (27.3) 438 (22.6) 613 ( 22.1 ) 303 (25.6) 441 (22.6) 743 ( 24.7 ) Seasonality, time trend and ground climate data P. falciparum P. vivax All species 321(16.3) 408(18.4) 570( 16.5 ) 296(20.1) 365(17.1) 581( 16.7 ) Seasonality, time trend and mean of LST and NDVI P. falciparum P. vivax All species 709 (45.1) 697 (20.0) 1,271 ( 25.2 ) 376 (23.9) 812 (23.3) 1,187 ( 23.5 ) Predicted value extrapolated from previous month’s data P. falciparum P. vivax All species 535 (38.4) 1,286 (40.2) 1,654 ( 36.2 ) 524 (37.6) 864 (27) 1,220( 26.7 ) Seasonality, time trend, NDVI and LST P. falciparum P. vivax All species 673 (48.3) 1,179 (36.9) 1,647 ( 36.0 ) 759 (54.5) 1,602 (50.1) 2,215 ( 48.5 ) Predicted value extrapolated from previous month’s data P. falciparum P. vivax All species 1,233 (84.9) 2,133 (71.5) 3,137 ( 70.1 ) 952 (65.6) 1,903 (63.8) 2,621 ( 59.2 ) Seasonality, time trend, NDVI and LST P. falciparum P. vivax All species 1,205 (82.9) 2,599 (87.1) 3,592 ( 81.2 ) 1,285 (88.4) 2,309 (77.4) 3,424 ( 77.4 ) District SSD Village Over and under- predictions of models based on seasonality, time trend and ground and remote sensing data

Results (15) Species-specific ROCs, they assess the relationship between sensitivity and specificity of the full models (with NDVI and LST) in predicting local transmissions in all data

Results (16) Comparing the fitted and observed risk maps of local transmission, the fitted values were computed based on seasonality, time trend, history of disease, NDVI and LST

Summary of main findings (1) 1. Ground climate data explained around 80% of P. vivax and 75% of P. falciparum variations one month ahead 2. Comparing to the extrapolation of data from previous month, ground climate data improve the accuracies around 10%; but remote sensing data does not improve 3. The ground climate data are freely available in the filed; therefore, it was concluded that the models based on ground climate data are feasible.

Summary of main findings (2) 4. Ground climate data improved predictions around 10% one month ahead in district level 5. NDVI and LST (with 8x8km resolution) did not improve the prediction 6. Elevation (with 1x1km resolution) improved predictions around 15% 7. NDVI (with 30x30m resolution) did not improve the predictions

Summary of main findings (3) 8. Elevation (with 1x1km resolution) improved predictions around 15% 9. NDVI (with 30x30m resolution) did not improve the predictions 10. P. falciparum and P. vivax models had different parameters. 11. The accuracy of temporal P. vivax variations was less than that in P. falciparum

conclusion Ground climate data (which are available free of charge) improved the model accuracies around 10% and it seems that early warning system based on these models is feasible

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