Climate, climate change and vector-borne diseasesnas-sites.org/emergingscience/files/2014/11/Ogden.pdf · Climate, climate change and vector-borne diseases ... » Louse-borne:

Climate, climate change and vector-borne diseases Dr Nick Ogden, Zoonoses Division, Public Health Agency of Canada

Talk Outline

• Introduction to climate change and vector-borne disease (VBD)

• Climate, climate change and VBD: The biology

• Climate, climate change and VBD: The socio-economics

• Mathematical and simulation models

• Ecological niche models: statistical and other “pattern matching” methods

• Examples

• Summary

2

INTRODUCTION CC & VBD

3

4

Climate change drives emergence/re-emergence of infectious diseases

1. Human awareness (Lyme, SARS)

2. Introduction of exotic pathogens/vectors into existing suitable host/vector/human-contact

ecosystem (SARS, West Nile)

3. Geographic spread from neighbouring endemic areas (Lyme, Rabies)

4. Ecological/environmental change causing endemic disease to increase in

abundance/transmission and (for zoonoses) ‘spill-over’ into humans (Hendra, Nipah,

Hantavirus, RVF)

5. True ‘emergence’: evolution and fixation of new, pathogenic genetic variants of previously

benign microorganisms (Pathogenic Zoonotic Influenzas)

5 VBD types and drivers for their occurrence and emergence/re-emergence • Vector-borne diseases comprise two types:

» Human-vector-human transmitted VBDs » Animal-vector-human transmitted VBDs = vector-borne zoonoses

• Most emerging infectious diseases are zoonoses • Diverse range of VBD

» Fly-borne (mosquitoes): Malaria, dengue, onchocerciasis » Tick-borne: Lyme, Anaplasma, CCHF, RMSF » Flea-borne: Bartonella, Plague » Louse-borne: Relapsing fevers, Typhus

• Climate change is only one potential driver: » Habitat » Hydrology » Landuse » Agriculture » Urbanisation » Globalisation

Source: Thehero

Information public health needs from models

6

Where?

Who?

When?

How many?

How much? $

Eggs

QuestingAdults

QuestingLarvae

FeedingLarvae

EngorgedLarvae

EngorgedAdults

FeedingNymphs

QuestingNymphs

Egg-layingAdults

POP (x)

PEP (q) L to N (s)

N to A (v)

EngorgedNymphs

No. eggsper adult (e)

Rodent Nos.

RodentNos.Temperature

Basic HFR:Immature ticks

Basic HFR:Adult ticks

Deer Nos.

λqa

µqeµql

µqn

µqa

µfl

µfn

µel

µelµel

λqn

λql

FeedingAdults

µfa

HardeningLarvae

µhl

z r

w

u

y

Eggs

QuestingAdults

QuestingLarvae

FeedingLarvae

EngorgedLarvae

EngorgedAdults

FeedingNymphs

QuestingNymphs

Egg-layingAdults

POP (x)POP (x)

PEP (q)PEP (q) L to N (s)L to N (s)

N to A (v)N to A (v)

EngorgedNymphs


Rodent Nos.Rodent Nos.

RodentNos.

RodentNos.TemperatureTemperature



Deer Nos.

λqa

µqeµql

µqn

µqa

µfl

µfn

µel

µelµel

λqn

λql

FeedingAdults

µfa

FeedingAdults

µfa

HardeningLarvae

µhl

zz rr

ww

u

yy

Who for? • General biological/epidemiological

principles: Public health policy • Risk, impact (BOI) cost-benefit

assessments: Public health policy • Targeting surveillance/interventions:

Public health programs

What methods? • Projection – where and when in the next

decade/century? • Prediction – where at the present? • Forecasting – where and when next

week/month?

7

Smaller spatial and temporal scales

Great complexity and parameterisation

Modelling objectives and process in the context of climate change

8

Current knowledge of

climate/weather influences on

VBD risk

Quantitative relationship

between climate/weather

and VBD risk

Model

Model/ algorithm

Model/ algorithm

Projected future risk GCM/RCM output

Forecasting risk to drive interventions

Weather data

Design surveillance to drive interventions Assessing future risk

Adaptation

Climate, climate change and VBD: The biology

Vector abundance • Temperature affects mortality rates • Temperature effects development rates/capacity for reproduction • Rainfall affects availability of areas for immature mosquito survival and development

y = 1300,1x-1,4278

R2 = 0,6582

0

50

100

150

200

250

0 5 10 15 20 25 30

Temperature

Days

to ov

iposit

ion

y = 34234x-2,2709

R2 = 0,8283

0

20

40

60

80

100

120

140

160

180

0 5 10 15 20 25 30

Temperature

Days

to ec

losion

y = 101179x-2,5468

R2 = 0,8833

0

50

100

150

200

250

0 5 10 15 20 25 30

Temperature

Days

to m

oult

y = 1595,8x-1,2082

R2 = 0,8268

0102030405060708090

100

0 5 10 15 20 25 30

Temperature

Days

to m

oult

I. scapularis development: Ogden et al. J. Med. Entomol. 2004 Tsetse mortality: Randolph

& Rogers Nature Rev Micro 2003

Vector activity • Temperature affects activity • Increased humidity increases activity • Heavy rainfall decreases activity

00.10.20.30.40.50.60.70.80.9

1

0 5 10 15 20 25 30

Temperature (oC)

Act

ivity

pro

porti

on ImmaturesAdults

Vail & Smith J. Med. Entomol. 1998 Ogden et al. Int. J. Parasitol. 2005

Extrinsic incubation period and vector survival

12

Temperature

Complex effects on VBD ecology: e.g. host communities, vector and host seasonality

• Vector-borne zoonoses are mostly maintained by wildlife: humans are irrelevant to their ecology

• Host communities indirectly affected by climate • Vector seasonality due to temperature effects on development and activity • Host demographic processes (reproduction, birth and mortality rates) are

seasonal and affected directly indirectly (via resource availability) by climate

In Quebec: White-footed mouse range expanding, Deer mouse range contracting

Simon et al. Evol Appl 2014

0

0.1

0.2

0.3

0.4

0.5

0.6

1 2 3 4 5 6 7 8 9 10 11 12

Month

Prop

ortio

n of

ann

ual n

umbe

r of t

icks

+

2050

Changing climate alters tick seasonality and affects pathogen transmission

Ogden et al., J. Theor Biol. 2008; Kurtenbach et al. Nature Rev. Microbiol. 2006

Climate, climate change and VBD: The socio-economics

Effect of climate change on VBD exotic to North America

• Internationally the VBDs most important for public health are human-vector-human transmitted diseases: malaria, dengue, chikungunya

• Climate change may theoretically increase transmission by effects on vectors and pathogen development in vectors (extrinsic incubation period)

• Practically:

» Climate has historically had only a small role in the occurrence of these diseases compared to -

» human-induced control efforts (habitat alteration, eradication, treatment, bed nets), which have often been the main drivers

• But climate change will impact capacity of developing countries to control VBDs

Climate change and the four horsemen of the apocalypse

16

Burke et al. 2009 PNAS

Famine

Climate Change

War Pestilence

Death in developing countries

Increased infected economic/refugee migration

Increased rates of immigration and import of exotic VBD into Canada

Malaria, Dengue, Chikungunya

Mathematical and simulation models

17

Model 3: SIS

0102030405060708090

100

1 366 731 1096 1461 1826 2191 2556 2921

Days of simulation

Num

ber o

f hos

ts

SusceptibleInfected

Simulation models: doing the sums – putting together quantitative knowledge of the biology of VBD transmission cycles

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9 10 11 12

Month

Num

ber o

f birt

hs p

er d

ay

P. leucopus Canada

P. leucopus southern NJ

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

1 2 3 4 5 6 7 8 9 10 11 12

Month

Num

ber o

f dea

ths p

er d

ay

P. leucopus Canada

P. leucopus southern NJ

Ogden et al. 2007 Parasitology

Reservoir host dynamics

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 10 20 30 40 50 60 70 80 90

Day

Prev

alen

ce o

f inf

ectio

n in

xen

odia

gnos

tic ti

cks

C3H mice infected with strain BL206

C3H mice infected with strain B348

P. leucopus mice infected with strain BL206

P. leucopus mice infected with strain B348

Hanincova et al. 2008 AEM

Host infection and transmission dynamics

y = 1300,1x-1,4278

R2 = 0,6582

0

50

100

150

200

250

0 5 10 15 20 25 30

Temperature

Days

to ov

ipositi

on

y = 34234x-2,2709

R2 = 0,8283

0

20

40

60

80

100

120

140

160

180

0 5 10 15 20 25 30

Temperature

Days

to ecl

osion

y = 101179x-2,5468

R2 = 0,8833

0

50

100

150

200

250

0 5 10 15 20 25 30

Temperature

Days

to mo

ult

y = 1595,8x-1,2082

R2 = 0,8268

0102030405060708090

100

0 5 10 15 20 25 30

Temperature

Days

to mo

ult

I. scapularis development: Ogden et al. J. Med. Entomol. 2004

Vector biology

Climate drivers

Ravel et al. Int J Hygiene Env Health 2004

Agriculture dynamics

Levels of complexity

• Indices of climatic limits for survival using laboratory or field-obtained data:

• Simple mathematical models:

• Complex simulation models

19

)ln)((

2

0 phrHpNaR

nVIIV

−+= −− ββ

Uninfected rodents

Acutely-infected rodents

Carrier rodents

Juvenile females Subadult females

Pregnant females

Litter-bearing femalesJuvenile males Subadult males Adult males

Adult females


Pregnant females


Adult females


Pregnant females


Adult females

Uninfected rodents


Carrier rodents


Pregnant females


Adult femalesJuvenile females Subadult females

Pregnant females


Adult females


Pregnant females



Pregnant females


Adult females


Pregnant females



Pregnant females


Adult females

Eggs

Questing Adults

Questing Larvae

Larvae feedingon infected rodents

Infected engorged larvae

Engorged Adults

Infected nymphs feedingon rodents

Infected questing nymphs

Egg-laying Adults

Engorged NymphsFeeding

Adults

Hardening Larvae

Larvae feedingon uninfected rodents

Larvae feeding on deer

Uninfected engorged larvae

Uninfected questing nymphs

Nymphs feedingon deer

Uninfected nymphs feeding on rodents

Eggs

Questing Adults

Questing Larvae



Engorged Adults



Egg-laying Adults


Adults

Hardening Larvae







Ixodes scapularis population model

Population and SIR model of Peromyscus leucopus

Uninfected rodents


Carrier rodents


Pregnant females


Adult females


Pregnant females


Adult females


Pregnant females


Adult females

Uninfected rodents


Carrier rodents


Pregnant females



Pregnant females


Adult females


Pregnant females



Pregnant females


Adult females


Pregnant females



Pregnant females


Adult females

Eggs

Questing Adults

Questing Larvae



Engorged Adults



Egg-laying Adults


Adults

Hardening Larvae







Eggs

Questing Adults

Questing Larvae



Engorged Adults



Egg-laying Adults


Adults

Hardening Larvae







Ixodes scapularis population model

Population and SIR model of Peromyscus leucopus

Data synthesised in silico to quantify effects of climate on (e.g.) a vector

20

Development rates, mortality rates and effects of temperature from lab/field studies

Host densities from field studies

Climate input variable

Vector/pathogen abundance or R0 output variables

Climate VBD relationship

Global and local sensitivity analyses conducted to check and measure uncertainty of model outputs

Application of mathematical modelling

• Mathematical models addressing fundamental principles = high level policy » Country-level risk assessment (current and future projections) » Country level risk, impact (BOI) cost-benefit assessments (current and future

projections)

• Simulation models = programmatic activities » Local-level risk, impact (BOI) cost-benefit assessments » Risk mapping using environmental drivers to targeting surveillance and

intervention » Identifying risk populations/locations to targeting surveillance and interventions » Forecasting for heightened surveillance or directing intervention » Dynamically modelling/predicting trajectories of spread » Predicting evolution of new pathogenic variants

Pros and cons Pros • Biological precision of associations between climate/environment and

vector/pathogen occurrence/survival - Use real, demonstrated associations and values

Cons • Require detailed knowledge of/data on ecology and epidemiology • Data frequently not available – need laboratory and field studies • Conflict between precision and parameterisation:

» The more spatio-temporal precision is needed – the more highly parameterised they need to be

» The more highly parameterised, the greater likelihood of erroneous parameter values • Prospective studies are needed for validation

22

Ecological niche models: statistical and other “pattern matching” methods

23

cMeanTXMinSVPRMinTMP ++++≈ 4321 ββββ

Salmonella Case Count and Mean Temperature per Week from 1992 to 2000

0

10

20

30

40

50

60

1 101 201 301 401

Week

Coun

t

-40

-30

-20

-10

0

10

20

30

Tem

pera

ture

Identifying associations between climate and occurrence/abundance

• Used where detailed, sound knowledge of the biology, sufficient to develop

simulation models is lacking

• Seek associations between possible explanatory variables (usually

environmental) and occurrence/abundance of vectors/pathogens using

environmental/ecological niche models:

• Presence-absence data – regression models

• Presence only data – machine learning/algorithm selection methods (MAXENT,

neural networks, GARP, BIOCLIM)

Combined GIS and statistical modelling

I am an Aedes albopictus and I was found here

Associated with: Climate Altitude Aspect Land use Agriculture Wildlife habitat Wildlife species Wildlife abundance Farm animal abundance

Here

Here

Here

Not Here

cMeanTXMinSVPRMinTMP ++++≈ 4321 ββββ

Climate VBD relationship Uncertainty expressed in errors, confidence intervals etc.

26 Application of ecological niche modelling

• High level policy » Country-level risk assessment (current and future projections) » Country level risk, impact (BOI) cost-benefit assessments (current and future

projections)

• Programmatic activities » Local-level risk, impact (BOI) cost-benefit assessments » Risk mapping using environmental drivers to targeting surveillance and

intervention » Identifying risk populations/locations to targeting surveillance and interventions » Forecasting for heightened surveillance or directing intervention

Pros and Cons

Pros • Presence-absence data usually readily available (e.g. surveillance) • Explanatory variable data very available (weather stations, remote-sensed

data, habitat maps, digital elevation models etc.) • Presence-absence data act as validating data (leave-one-out analyses)

Cons • Type I and II statistical errors – missing key environmental drivers,

identification of spurious associations • Frequently use false negative data:

» Realised niche rarely the full climatological niche » Vectors/pathogen range limited more by climate independent factors

27

x x x x x x

x x

EXAMPLES

28

Indices of climatic limits for survival: Assessing the risk of Chikungunya emergence in Canada

29

Key determinants of risk of Chikungunya establishment are:

• Immigrating infected people: now increasing in Canada due to infected holiday makers returning from the Caribbean

• Presence of vectors: simple lab/field-generated climatic indicators for persistence of populations of the temperate vector Aedes albopictus (the Asian tiger mosquito)

• Validation of climate indicators against surveillance data for Ae. albopictus in the US

ROC AUC = 0.92

Ogden et al. 2014 Parasites Vectors submitted

Predicted Observed

0 1 2 3 4 0 1 2 3 4

65 66 73 68 72 70 75 77 79 81 84 82 86 88 89 91 93 95 96 98 100

0 1 2 3 4

2011-2040 RCP4.5 2041-2070 RCP4.5 2041-2070 RCP8.5

Overwintering + annual mean

temperature

Jan temp + summer temp

+ annual rainfall

Climatic indicator

30

Projected distributions of Ae. albopictus

• Uncertainty associated with selection of climatic indicator for Ae. albopictus

• But can even so can feed risk assessments

Ogden et al. 2014 Parasites Vectors submitted

Patz et al. EHP 1998

VC = mbca 2 p n / -Ln ( p )

Simple mathematical model: assessing risk of Dengue with climate change

Assessing the risk of Lyme disease emergence in Canada: emergence by geographic spread from the US

32

Eggs

QuestingAdults

QuestingLarvae

FeedingLarvae

EngorgedLarvae

EngorgedAdults

FeedingNymphs

QuestingNymphs

Egg-layingAdults

POP (x)

PEP (q) L to N (s)

N to A (v)

EngorgedNymphs


Rodent Nos.

RodentNos.Temperature



Deer Nos.

λqa

µqeµql

µqn

µqa

µfl

µfn

µel

µelµel

λqn

λql

FeedingAdults

µfa

HardeningLarvae

µhl

z r

w

u

y

Eggs

QuestingAdults

QuestingLarvae

FeedingLarvae

EngorgedLarvae

EngorgedAdults

FeedingNymphs

QuestingNymphs

Egg-layingAdults

POP (x)POP (x)

PEP (q)PEP (q) L to N (s)L to N (s)

N to A (v)N to A (v)

EngorgedNymphs


Rodent Nos.Rodent Nos.

RodentNos.

RodentNos.TemperatureTemperature



Deer Nos.

λqa

µqeµql

µqn

µqa

µfl

µfn

µel

µelµel

λqn

λql

FeedingAdults

µfa

FeedingAdults

µfa

HardeningLarvae

µhl

zz rr

ww

u

yy

Key determinants of Lyme disease risk are:

• Suitable habitat for ticks: assessed by field study (Ogden et al. JME 2006a)

• Suitable host densities: assessed previous field studies

• Dispersal of population-seeding ticks into Canada by migratory birds: assessed by surveillance/field study (Ogden et al. JME 2006b, AEM 2008)

• Temperature threshold for tick population persistence: obtained by simulation modelling (Ogden et al. 2005)

• Algorithm using temperature from GCMs and tick dispersion developed and mapped

• Produces information needed for public health risk assessment

Photo by Bill Hilton Jr (www.hiltonpond.org)

High risk Low risk Risk of bird-borne ticks

year 2000

Moderate risk

Projected distributions of Ixodes scapularis

Ogden et al. Int. J. Health Geogr. 2008


year 2020

Moderate risk




year 2050

Moderate risk



year 2080



Moderate risk


Montreal ON

NY VT NH

Validation of blacklegged tick modelling in field studies and analyses of surveillance data

37

Ogden et al., 2008 Int J Hlth Geogr; 2010 EHP Leighton et al. 2012 J Appl Ecol

Ogden et al., Environ Health Perspect 2011

0

1

2

3

4

5

6

7

8

9

10

0 2000 4000 6000 8000 10000 12000 14000 16000

Algorithm used in risk maps

Inde

x of

cer

tain

ty fo

r I. s

capu

laris

pop

ulat

ions Quebec: 70 sites

New Brunswick: 16 sitesStatistical model

0

1

2

3

4

5

6

7

8

9

10

0 50 100 150 200 250 300

Model-predicted temperature suitability for I. scapularis

Inde

x of

cer

tain

ty fo

r I. s

capu

laris

pop

ulat

ion Quebec: 70 sites

New Brunswick: 16 sitesStatistical model

Current status of I. scapularis and Lyme disease incidence in Canada

38

2004 2014

Logistic regression modelling of Culex pipiens occurrence

39

Description Coef. Std. Err. Z p-value

Annual mean temperature 4.959 0.887 5.59 <0.001

Annual mean precipitation -0.026 0.005 -5.29 <0.001

Max temperature of the warmest period -1.106 0.236 -4.70 <0.001

January mean minimum temperature -0.379 0.115 -3.31 0.001

May mean minimum temperature -0.606 0.195 -3.11 0.002

June mean minimum temperature 0.338 0.139 2.43 0.015

September mean minimum temperature -0.897 0.196 -4.57 <0.001

October mean minimum temperature -0.864 0.229 -3.78 <0.001

January mean maximum temperature -0.381 0.150 -2.55 0.011

April mean maximum temperature -0.498 0.147 -3.40 0.001

September mean maximum temperature 0.670 0.197 3.39 0.001

November mean maximum temperature -0.458 0.187 -2.46 0.014

December mean maximum temperature -0.416 0.135 -3.09 0.002

January mean precipitation 0.026 0.007 3.64 <0.001

February mean precipitation 0.051 0.009 5.82 <0.001

March mean precipitation 0.028 0.010 2.85 0.004

April mean precipitation 0.056 0.007 7.79 <0.001

May mean precipitation 0.044 0.006 7.38 <0.001

June mean precipitation 0.022 0.006 3.57 <0.001

July mean precipitation 0.027 0.006 4.42 <0.001

August mean precipitation 0.036 0.007 5.32 <0.001

September mean precipitation 0.031 0.007 4.62 <0.001

October mean precipitation 0.026 0.006 4.41 <0.001

November mean precipitation 0.026 0.008 3.23 0.001

Within 2 kms of Cropland 0.053 0.160 3.30 0.001

Within 2 kms of Built-up land 0.441 0.167 2.64 0.008

Intercept -15.085 4.320 -3.49 <0.001

Presence-absence data

Multiple variables explored

Description Coef. Std. Err. Z p-value

Annual mean temperature above 2°C -0.067 0.059 -1.14 0.256 Annual precipitation between 600 and 1000 mm 0.001 0.001 7.40 <0.001 Mean March minimum temperature 0.148 0.025 5.90 <0.001 April mean precipitation below 130 mm 0.025 0.002 11.49 <0.001 Mean September maximum temperature above 21°C 0.081 0.013 6.24 <0.001 Mean November minimum temperature of previous year above -4°C 0.241 0.064 3.79 <0.001 Mean minimum temperature for July above 13°C 0.067 0.016 4.18 <0.001 Mean precipitation for August below 125 mm 0.010 0.002 4.21 <0.001 Intercept -5.158 0.356 -14.49 <0.001

Multi-variable model developed = climate-vector occurrence relationship

Projections developed – using GCM output

Hongoh et al., J App Geogr 2012

Statistical forecasting of WNv risk: Wang et al. J Med Entomol 2011

Summary

• A range of modelling methods are available to: » Elucidate quantitative relationships between climate and VBD occurrence/abundance » Develop projections using GCM outputs/develop adaptation tools using weather data

• All have pros and cons • Our confidence in them depends on validation • Gaps and needs to improve modelling:

» Data and knowledge of the life-cycles/transmission cycles of vectors and pathogens for simulation models

» Surveillance data collected in systemic spatio-temporal patterns for statistical models » Long-term monitoring to calibrate and validate projections » More direct linkage of climate and VBD simulation models » Linkage of effects of climate change on wider ecological and socio-economic

determinants of VBD risk » Standards for characterising uncertainty (spatial, temporal, model output)

41

Documents

Climate, climate change and vector-borne diseasesnas-sites.org/emergingscience/files/2014/11/Ogden.pdf · Climate, climate change and vector-borne diseases ... » Louse-borne: