Climate, Health and Migration

KACEY C. ERNST, ASSOCIATE PROFESSOR OF EPIDEMIOLOGY AND BIOSTATISTICS, UNIVERSITY OF ARIZONA, TUCSON, AZ

CRO ASSEMBLY: ZURICH, SWITZERLAND NOV. 29TH, 2017

KERNST@EMAIL.ARIZONA.EDU 520-626-7374

Fourth OrderImpacts on

social and political systems

The impacts of increased atmospheric CO2

First Order

Geophysical impacts on climate

SecondOrderImpacts on

biophysical systems (forests, oceans,

grasslands)

ThirdOrderImpacts on

vector-borne disease,

extreme-heat deaths, famines

405.71 ppm

Latest CO2 reading at Mauna Loa Observatory

November 16, 2017

RCP8.5

No mitigation

High emissions Benefit: Avoided

impacts

RCP4.5

Mitigation

Lower emissions

Representative Concentration Pathways (RCPs)

RCP8.5

CESM 40-member

“Large Ensemble”

Kay et al., 2014

RCP4.5

CESM 15-member

“Medium Ensemble”

Sanderson et al.

2015

Adapted from B. O’Neill, NCAR

July 2017 – Climate scientists annouce

By end of the century:

95% chance for 2°C warming

1% chance for <1.5 °C

Work done before the US pulled out of the

Paris Climate Agreements

Nature Climate Change 2017

Does not account for decreasing price of

solar

The two components of climate

changeChanging patterns of climatic

suitability

Increasing extreme weather

events

Retreat of Arctic Sea Ice: NASA Flash flooding in Toowoomba

Health impacts of

climate change

Clear links: extreme

heat and mortality

Impacts of extreme heat

are not evenly spread

Relative Risk of Heat Related Mortality

Houston Texas

Urban Heat Island Heat Health Outcomes911 Heat Distress Calls

Race and Ethnicity Use of Air ConditioningIncome

7.9

2.3

1

0.4

0

Infections and Climate ChangeRELATIONSHIP STATUS: ITS COMPLICATED

Projecting long-term epidemic potential

Understand the systemWhat is the process that climate/weather intersect with social dynamics to

influence infectious disease potential?

Determine patternsof seasonality

Conduct correlations across differing geographies

Laboratory experimentation

Mitigating factors

Given exposure, who arethe vulnerable?

Examine risk factors for the transmission

Determine the current and projected distribution of

these risk factors

Compile into projections

If we drive process models with projected climate

data coupled with data on the human dimension, what

happens?

Determine complex interactions among all the

determinants of the process and the potentially mitigating

risk factors

Susceptibility and mode of

transmission

Sexuallytransmitted

Respiratory

Water-borne and Vector-borne

Infections linked to extreme weather events

DROUGHTS

Concentrating

pathogens

Cholera

Typhoid

West Nile

Increased

Susceptibility

Undernutrition

Measles

Respiratory

Infx.

DROUGHTS FLOODS

Dissemination

via water

Cholera

Shigellosis

Leptospirosis

FLOODS

Vector-

Reservoir

Habitat

Malaria

Schistosomiasis

West Nile

FLOODS-HURRICANE

S

Crowding/ Low

infrastructure

Respiratory

Infections

Meningitis

TB

CASE STUDY: HURRICANE KATRINA

August 29, 2005

1833 deaths

$41.1 billion in claims

Infections

22 Vibrio cases

20 clusters of

diarrheal illness in

evacuation centers

West Nile Virus 2x

higher

Case Study: Hurricane Matthew Haiti

October 4, 2016

>1000 killed directly

$1.1 billion in estimated damage

Infection

Over 50% increase in

cholera cases

Others? Unknown

Transmission risk in a given

geographic area

Risk ofTransmission

Detection/ Response

infrastructure

Human Environment: MobilityBehavior infrastructure

Human – Natural Environmentinteractions:

Environmental Suitability

Aedes aegypti aka

“The Yellow Fever Mosquito” Highly adaptable

Human commensal

Day-biter (bednets less useful)

Transmits

Yellow fever virus

Dengue viruses

Chikungunya virus

Zika virus

Mayora virus

0

1

Distribution of dengue virus

Aedes aegypti infest urban areas

throughout the Arizona-Sonoran Desert

region

Hermosillo

Phoenix

TucsonYuma

PuertoPeñasco

Caborca

Santa Ana

Guaymas

CiudadObregón

Nogales

• Social factors play a role in differential transmission

• Arizona, Estados Unidos • Sonora, México

Transmission of dengue in Arizona and Sonora

Source: Reyes-Castro, P. et al. (2015)

Sonora – N-S gradient

Arizona – No transmission

Higher transmission

Social factors

Lower education

Higher population density

Distance from highway

Climate factors

Higher Precipitation

Higher Minimum

temperatura

Disparity in dengue transmission

Year Hermosillo Nogales

2006 22.6 1.4

2007 15.4 0.5

2008 92.0 No cases

2009 22.2 1.9

2010 504.0 1.9

2011 26.3 1.0

201212.3

No cases

201333.1

1.9

2014 120.6 6.6

2015 88.1 1.9

Incidence per year (by 100,000)

No locally acquired dengue transmissionSource: Ernst, K.C. and Walker, K.R. Aedes aegypti (Diptera: Culicidae) Longevity and Differential Emergence of Dengue Fever in Two Cities in Sonora, Mexico. J Med Entomol 2017; 54 (1): 204-211.

Abundance of

Ae. aegypti

old enough to

transmit

disease varies

by city and

year

02468

101214161820

Re

lativ

e R

isk o

f A

e.

ae

gyp

ti s

urv

ivin

g E

IP

ab

un

da

nc

e

2013 2014 2015

No

difference

Coupled factors enhancing expansion

Increasing Trade

IncreasingUrbanization

Increasing Habitat

Increasing Travel

Pathogen Introduction: Focus on

Mobility

Short-term travel

• Business travel

• Vacationers

Mid-range

• Project work

• Students

Permanent Resettlement

• Refugees

• Immigration

Likelihood of traveler/ migrant contracting pathogen

Likelihood of infected traveler/ migrant introducing spread to home country

a)1950-2000(Reference)

b)2061-2080(RCP4.5minusReference)

c)2061-2080(RCP8.5minusReference)

UnsuitableType1

Type2

Type3

Type4

U->44->33->22->1N/A1->22->33->44->U

U->44->33->22->1N/A1->22->33->44->U

Case Study: Chinese Construction

Sites in Sri Lanka

• Colombo, Sri Lanka, November 6th, 2017

• 3500 Chinese laborers

• Risk factors

• Previously unexposed to dengue

• Construction created breeding sites Increasing habitat

Suitability to

Ae. aegypti

in coming decades

for southern China

Coming soon: Port City

Long term predictions to early

warning and early detection

Time

Ca

ses

50yrs 20yrs 10yrs 5yrs 1yr 6mo 3mo 1mo onset peak end

Early

detection

systems

Early warning

systems

Long-term

prediction

Uncertainty

Driven by:

-climate

change

scenarios

-Population

projections

Driven by:

-seasonal

forecasts

-current

census

information

Sources:

-syndromic

- data

mining

- HC-based

- CBP

Traditional

surveillance

Use in public health response and planning

Morin, C. W., A. J. Monaghan, M. H. Hayden, R. Barrera, and K. C. Ernst, 2015:. PLoS Neg. Trop. Dis

Long-term Predictions: Projected global range of Ae. aegypti, 2061-2080

(Monaghan et al. 2016, Climatic Change)

a)1950-2000(Reference)

b)2061-2080(RCP4.5minusReference)

c)2061-2080(RCP8.5minusReference)

UnsuitableType1

Type2

Type3

Type4

U->44->33->22->1N/A1->22->33->44->U

U->44->33->22->1N/A1->22->33->44->U

Monaghan AJ, Morin CW,….Ernst

K. PLOS Currents (March 2016)

Zika Risk in CONUS

• Weather-driven

mosquito models

with

• travel,

• socioeconomic

conditions

• virus history

• Required rapid

analysis

• Designed for

widespread

dissemination to

stakeholders and the

public.

• One time assessment

• Used climate not

current weather

Early Warning

Example

Early Detection: Leveraging Social Media for Surveillance

Marques-Toledo CdA, Degener CM, Vinhal L, Coelho G, Meira W, et al. (2017) Dengue prediction by the web: Tweets are

a useful tool for estimating and forecasting Dengue at country and city level. PLOS Neglected Tropical Diseases 11(7):

e0005729. https://doi.org/10.1371/journal.pntd.0005729

http://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0005729

Dengue in Brazil

• Tweets strongly

correlated

with case reports

• County level

• City level

• Nowcasting

• Forecasting up to 8

weeks

Early Detection: Kidenga, self-reported syndromic informationUSER GENERATED

CONFIRMED HEALTH DEPARTMENT DATA

SummaryClimate change will influence many healthoutcomes

Complex relationships betweenenvironmental-human-pathogen factors willvary by region

Risk will be modified by the vulnerability ofthe populations exposed to the pathogens

Systems that can readily and reliably predictand detect transmission are needed

Mobilization of resources early in anepidemic will greatly reduce transmission

References

Bhatt S, Gething PW, Brady OJ, et al. The global distribution and burden of dengue. Nature. 2013;496(7446):504-507. doi:10.1038/nature12060.

Cai W, Borlace S, Lengaigne M, Rensch Pv, Collins M, Vecchi G, Timmermann A, Santoso A, McPhaden MJ, Wu L, et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat Clim Change 2014; 4: 111-6;

Coumou D, Robinson A, Rahmstorf S. Global increase in record-breaking monthly-mean temperatures. Clim Change 2013; 118: 771-82;

McMichael, A.J., Extreme weather events and infectious disease outbreaks.Virulence, 2015. 6(6): p. 543-547.

IPCC. Summary for Policymakers. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM, editors. Managing the risks of extreme events and disasters to advance climate change adaptation, editors Cambridge University Press, Cambridge, UK, and New York, NY, USA; 2012: pp. 1-19

Lozano-Fuentes S, Hayden MH, Welsh-Rodriguez C, et al. The Dengue Virus Mosquito Vector Aedes aegypti at High Elevation in México. The American Journal of Tropical Medicine and Hygiene. 2012;87(5):902-909. doi:10.4269/ajtmh.2012.12-0244.

References cont.

Morin, C. W., Monaghan, A. J., Hayden, M. H., Barrera, R., & Ernst, K. (2015). Meteorologically Driven Simulations of Dengue Epidemics in San Juan, PR. PLoS Neglected Tropical Diseases, 9(8), e0004002. http://doi.org/10.1371/journal.pntd.0004002

Marques-Toledo CdA, Degener CM, Vinhal L, Coelho G, Meira W, et al. (2017) Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level. PLOS Neglected Tropical Diseases 11(7): e0005729.

Monaghan, A.J., Sampson, D.F. Steinhoff, K.C. Ernst, K.L. Ebi, B. Jones, and M.H. Hayden, 2015: The potential impacts of 21st century climatic and population changes on human exposure to the virus vector mosquito Aedes aegypti. Climatic Change, (accepted).

Monaghan, A. J., Morin, C. W., Steinhoff, D. F., Wilhelmi, O., Hayden, M., Quattrochi, D. A., … Ernst, K. (2016). On the Seasonal Occurrence and Abundance of the Zika Virus Vector Mosquito AedesAegypti in the Contiguous United States. PLoS Currents, 8, ecurrents.outbreaks.50dfc7f46798675fc63e7d7da563da76

Schmidt, C., Phippard, A., Olsen, J. M., Wirt, K., Rivera, A., Crawley, A., … Ernst, K. (2017). Kidenga: Public engagement for detection and prevention of Aedes-borne viral diseases. Online Journal of Public Health Informatics, 9(1), e111. http://doi.org/10.5210/ojphi.v9i1.7694

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