The effects of climate on the epidemiology of plague in

Madagascar Kathy Kreppel

Matthew Baylis; Sandra Telfer; Lila Rahalison; Andy Morse

Presently 38 countries in Asia, Africa and America report human plague cases

Most cases annually reported from the African continent

Within the African countries 60% of all cases reported from Madagascar and

Tanzania

Human Plague

Epidemiology of plague...

... in Madagascar

Climate

Humidity • Precipitation • Temperature

Flea vector density Flea vector infection rate

Rodent host parasite burden

Proportion of infected rodents

Flea vector habitat

&

Pathogen environment

Micro-Climate

Desiccation • Flooding • Temperature Fluctuation

Pathogen within vector

• Pathogen survival • Pathogen multiplication

Flea Vector

• Reproduction rate • Development rate • Adult longevity

16

17

18

19

20

21

22

23

24

25

0 10 20 30 40 50 60 70 80 90

Jan

Feb

Mar

Ap

r M

ay Ju

n

Jul

Au

g Sep

O

ct N

ov

Dec

Cas

es

Average monthly plague cases and average monthly temperature °C

Tem

per

atu

re

C°

Wet and Hot

Wet and Hot

Dry and Cool

Climate and plague Climate effects on plague are not obvious and direct as with malaria

Copyright: Jamstec

Madagascar is affected by:

- El Niño Southern Oscillation (ENSO)

- Indian Ocean Dipole (IOD)

- Frequent cyclones

Climate analysis El Niño event => drier and warmer conditions than usual 12 months later

(the hot season gets hotter)

Positive IOD => warmer conditions than usual 1-2 months later (the cold

season gets warmer)

Significant correlations in time frequency

space

- ENSO and plague

- IOD and plague

El Nino => decreased plague incidence

9-12 months later

Positive IOD => increased plague

incidence 1-2 months later

Interplay can result in plague epidemics

ENSO and plague incidence

Figure: Phase angle evolution of JMA and incidence anomalies with 2-5 year periodicity. The

red line represents incidence anomalies, the blue line the ENSO index. The x-axis is the

wavelet location in time. The y-axis denotes the phase angles.

Summary of climate analysis

Global climate:

- El Niño Southern Oscillation affects human

plague in Madagscar

- The Indian Ocean Dipole affects plague

incidence from the 1990s

-There is a non-stationarity in the

relationship

Districts reporting

plague from 1975 to

2008

1. Absence – Presence

Maximum Entropy model

2. Magnitude of incidence

Linear regression model

Spatial analysis of plague incidence and

environmental variables

MODIS variables:

-NDVI

-EVI

-MIR

-dLST

-nLST

Altitude

Maximum Entropy

Response curve of the NDVI to plague presence Response curve of dLST to plague presence

Model performance Response curve of altitude to plague presence

Absence – Presence

Altitude is positively correlated with

plague presence in districts

NDVI is negatively correlated (peak

timing of triannual cycle)

dLST is positively correlated (peak

timing of biannual cycle and

variation )

nLST is positively correlated (peak

timing of biannual cycle)

Magnitude of incidence

nLST is negatively correlated

dLST is negatively correlated

EVI is positively correlated

MIR (amplitude) is positively

correlated

Spatial analysis of plague incidence and environmental variables

Effects on....

Vector

- mortality

- survival

- development

Rodent Host

- Mortality

- survival

- Habitat

Pathogen

- Temperature

Human host

- migration

- poverty

- crop choice

- housing conditions

Study location

Step 1

Micro-climate

9

11

13

15

17

19

21

23

25

Jan Apr Jul Sep Nov

Tem

pe

ratu

re *

C

Average burrow temperatures

House burrow temperature

Outside burrow temperature

• It is warmer and less humid indoors • Temperature values are very similar between house burrows and outside burrows • Humidity values show large differences

70

75

80

85

90

95

100

Jan Apr Jul Sep Nov

Re

lati

ve H

um

idit

y %

Average burrow humidities

House burrow humidity

Outside burrow humidity

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Jan-0

9

Ap

r-09

Jul-0

9

Sep-0

9

No

v-09

Feb-1

0

Fle

a in

dex

Fieldtrip

Number of fleas per trapped rat

Xch

Sf

Sf is more abundant in the cold months July and September, while Xch is most abundant in November... is the endemic Sf adapted to the colder highlands?

Fleas

Laboratory data • Larvae of the endemic Sf pupate on average 8.5 days later

• At individual temperatures humidity affects development

• No consistent effect of humidity across the range of temperatures

• Humidity seems to affect Sf more

0

5

10

15

20

25

30

35

40

18 21 25 28.5 32

Me

an d

eve

lop

me

nt

tim

e in

day

s

Temperature ˚C

Mean development time of Sf at different treatments

At 80%RH

At 90% RH

0

5

10

15

20

25

30

35

40

18 21 25 28.5 32

Me

an d

eve

lop

me

nt

tim

e in

day

s

Temperature ˚C

Mean development time of Xch at different treatments

At 80% RH

At 90% RH

Endemic Sf Ubiquitous Xch

Laboratory data

Sf larvae have a 43% higher mortality rate than Xch

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

18 21 25 28.5 32

Mo

rtal

ity

rate

Temperature ˚C

Mortality rate of Sf and Xch at different temperatures

Xch

Sf

Endemic Sf Ubiquitous Xch

0 10 20 30 40 50 60

0.0

0.2

0.4

0.6

0.8

1.0

Time to death

Su

rviv

ors

hip

Time to death for Sf. by treatment

18-80

18-90

21-80

21-90

25-80

25-90

28.5-80

28.5-90

32-80

32-90

0 10 20 30 40

0.0

0.2

0.4

0.6

0.8

1.0

Time to death

Su

rviv

ors

hip

Time to death of Xch per treatment

18-80

18-90

21-80

21-90

25-80

25-90

28.5-80

28.5-90

32-80

32-90

Time until death for Sf larvae Time until death for Xch larvae

Sf larvae which do not pupate live longer than Xch larvae which do not pupate

Combining field and laboratory data

0 5

10 15 20 25 30 35 40 45 50 55 60 65 70 75

Jan Apr Jul Sep Dec

Days to complete larval development in an outside burrow

Xch outside

Sf outside

0 5

10 15 20 25 30 35 40 45 50 55 60 65 70 75

Jan Apr Jul Sep Dec

Days to complete larval development in a house burrow

Xch inside

Sf inside

The larvae of Sf take longer to develop than Xch except in an outside burrow during July

Summary of vector study

Vector presence all year round Vectors link the exterior focus with houses – human infection Sf have a slight advantage in summer

Xenopsylla

cheopis

(ubiquitous)

Synopsyllus

fonquerniei

(endemic)

Conclusion

- Temperature and humidity affect the vectors Plague season onset

-All year round vector cover and transmission

cycle is only in the highlands Altitudinal threshold

-Host burrow climate differs between indoor

and outdoor Different habitats favoured at different times of the year

Overall conclusions • Climate does affect human plague incidence in Madagascar

Global climate drivers such as El Niño and the Indian Ocean Dipole influence the epidemiology of plague

• Spatial analysis identified altitude and environmental variables such as vegetation cover and temperature as predictors of presence and absence of plague and magnitude of incidence

• Evidence suggests that temperature and humidity

have a significant effect on both flea vectors

• This implies that climate and environmental variables have an impact and could be used as warning and forecasting tools in a country with very limited resources.

Acknowledgements

I would like to thank my supervisors

Sandra Telfer, Matthew Baylis and Andy Morse for help and support, Nohal Elissa for the opportunity to work in Entomology at the IPM, and LUCINDA

group members for moral support!

Many thanks to the technicians Corinne and Tojo for their help with the lab experiment and the

technicians of the plague unit for their hard work during each field trip.

Questions?

Environmental variables

Variable Feature Coefficient

Average altitude quadratic

hinge

9.016

-0.319

Average NDVI peak

timing of triannual cycle

quadratic -1.082

Average dLST peak

timing of biannual cycle

linear 0.668

Average dLSTd2 quadratic 0.937

Average nLSTp2 quadratic 0.875

Magnitude of plague Variable Feature Coefficient Std Error t P>|t| 95% Conf. Interval

Average MIR a2 linear -.0717949 .0194965 -3.68 0.001 -.1114164 -.0321733

Average MIR a2 quadratic .0001889 .0000496 3.81 0.001 .0000881 .0002896

Average MIR d2 linear .3156656 .121508 2.60 0.014 .0687316 .5625995

Average dLST d2 linear .3680068 .086814 4.24 0.000 .1915796 .544434

Average dLST d2 quadratic -.0074938 .0019328 -3.88 0.000 -.0114217 -.0035658

Average nLST d1 linear .1554398 .0291623 5.33 0.000 .0961749 .2147047

Average nLST d1 quadratic -.001523 .0003071 -4.96 0.000 -.0021472 -.0008989

Average EVI d3 linear -1.919053 .5244084 -3.66 0.001 -2.984779 -.8533271

Average EVI d3 quadratic .7311711 .2387242 3.06 0.004 .2460252 1.216317

Constant -3.123993 .9695764 -3.22 0.003 -5.094409 -1.153577