Modelling of malaria variations using time series methods Ali-Akbar Haghdoost MD, Ph.D. in...
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Modelling of malaria Modelling of malaria variations using time variations using time series methods 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; [email protected]
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Transcript of Modelling of malaria variations using time series methods Ali-Akbar Haghdoost MD, Ph.D. in...
Modelling of malaria variations Modelling of malaria variations using time series methodsusing 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;[email protected]
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
0
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1987 1989 1991 1993 1995 1997 1999 2001
year
AP
I/AF
I (pe
r 10
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01234567
SP
R (
per
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API AFI SPR
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ann
ual
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k per
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any species P.falciparum P.vivax
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)
Mediterraneanclimate
Hot and dry summer and snow -boundedwinter
Mountainousarea
Tropicalclimate
(4)
(2)
(1)
(3)
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
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%[N
EW
S
10 0 10 20 Miles
population density(per square kilometer)
0 - 1010.1 - 30>30
road types
paved (asfalt)
gravel
dirt
"8 villages
"8 Sub-district centres
%[ District centre
Research setting(6) Malaria In Kahnooj
Annual risk of malaria per 100,000 population between 1994 and 2001
64 - 286287 - 537538 - 839840 - 31293130 - 5019
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)0
100
200
300
Jun 00Jun 98Jun 96Jun 94date
fitted value ppv
0100
200
Jun 00Jun 98Jun 96Jun 94date
fitted value ppf
0200
400
Jun 00Jun 98Jun 96Jun 94date
fitted value all species
se
as
on
alit
y
0100
200
300
Jun 00Jun 98Jun 96Jun 94date
fitted value ppv
0100
200
Jun 00Jun 98Jun 96Jun 94date
fitted value ppf
0200
400
Jun 00Jun 98Jun 96Jun 94date
fitted value all species
se
as
on
alit
y a
nd t
ime
tre
nd
nu
mbe
r o
f ca
se
s
The seasonality and time trend of malaria classified by species
Results (2)0
100
200
300
Jun 00Jun 98Jun 96Jun 94date
fitted value ppv
0100
200
300
Jun 00Jun 98Jun 96Jun 94date
fitted value ppf
0500
Jun 00Jun 98Jun 96Jun 94date
fitted value all species
num
ber of cases
P. vivax P. falciparum
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°Ctemperature
P.fP.v
Results (3)
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rr
ela
ti
on
0 10 20 30 40Lag
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rt
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fa
lcip
aru
m
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ax
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to
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ela
ti
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0 10 20 30 40Lag
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0 10 20 30 40Laga
ll
sp
ec
iesR
es
idu
als
Autocorrelations and partial autocorrelations between the residuals of models, which estimated risks, based on climate, seasonality and time trend
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 0.62
M4Only the number of cases in last three
months0.61 0.64 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
Time for your comments
Thanks for you kind attention
