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

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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
Main objectives
  • Assessment of the feasibility of an early warning system based on ground climate and remote sensing data
  • 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
Climate effects on malaria
  • The rate at which mosquitoes develop into adults
  • Frequency of blood feeding
  • Adult mosquito survival
  • The incubation time of parasites in the mosquito
other considerations related to climate
Other considerations related to climate
  • Deforestation
  • Migration and urbanisation
  • Changing human behaviour
  • Natural disaster and conflict
gis and malaria
GIS and malaria

Sipe (2003) reviewed the GIS and malaria literature and divided the publications into the five categories outlined below:

  • Mapping malaria incidence/prevalence
  • Mapping the relationships between malaria incidence/prevalence and other potential related variables
  • Using innovative methods of collecting data such as remote sensing (e.g., GIS)
  • Modelling malaria risks
  • General commentary and reviews of GIS used in malaria control and research
modelling of malaria 1
Modelling of malaria (1)
  • Modelling of the abundance of vectors
  • Modelling of the frequency of malaria cases/infections
slide9
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 (3): Kahnooj District

slide11

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 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
research setting 8 treatment of malaria
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
Objective

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

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
data collection 3
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, http://geoengine.nima.mil/)
statistical methods 1
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 polynomialmethod was used to maximise the accuracy of models
  • The time trend was model by using parametric method (sine and cos)
statistical methods 2
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-R2
statistical methods 3
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
Results (1)

malaria risk factors

results 2

Meteorological factor

API

AFI

AVI

Minimum temperature

-0.02

-0.01

-0.04

Maximum temperature

0.40

0.33

0.46

Mean temperature

0.18

0.15

0.19

Humidity

-0.12

-0.09

-0.14

Rainfall

0.45*

0.54*

0.40*

Results (2)

Pearson correlation coefficients between the annual risk of malaria and meteorological variables in Kahnooj 1887-2001

results 3
Results (3)

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

results 4
Results (4)

The seasonality and time trend of malaria classified by species

results 5

The optimum temperature and humidity

P.v

P.f

temperature

35°C

31.1°C

humidity

27.3%

32%

Results (5)

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

results 6
Results (6)

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

results 10
Results (10)

The pseudo R2 between malaria risks and the average NDVI around villages in 2001

1: 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))

results 11
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
Results (12)

The observed and predicted risk maps of malaria in 1994-2001 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
Results (13)

The pseudo R2 of Poisson models classified by the species based on village, SSD or whole district data

results 14
Results (14)

Over and under-predictions of models based on seasonality, time trend and ground and remote sensing data

District

SSD

Village

results 15
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
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
Summary of main findings (1)
  • Ground climate data explained around 80% of P. vivax and 75% of P. falciparum variations one month ahead
  • Comparing to the extrapolation of data from previous month, ground climate data improve the accuracies around 10%; but remote sensing data does not improve
  • 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
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
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
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
time for your comments
Time for your comments

Thanks for you kind attention