From prediction to practice: Integrating forecasting

models into public health education and response

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

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

Transmission risk in a given

geographic area

Transmission potential

Detection and Response infrastructure

Human Environment: Mobility patterns, Behavior and behavior

change, Social and political infrastructure

Human – Natural Environment interactions: Land use change, human-vector-zoonotic

host interactions

Baseline Natural Environmental Suitability – vectors, pathogens, zoonotic hosts

Some key concepts identified from: Arthur RF, Gurley ES, Salje H, Bloomfield LS, Jones JH. Philos Trans R Soc Lond B Biol Sci. 2017 May 5;372(1719).

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

Projecting long-term epidemic potential

Understand the system What is the process that

climate/weather influences the infectious disease

potential?

Determine patterns of seasonality

Conduct correlations across differing geographies

Laboratory experimentation

Mitigating factors

If all are exposed, who are the 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

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

Map shows the range of the Aedes aegypti mosquito for present-day (1950-2000) and future (2061-2080; RCP8.5) conditions.

Larger cities have higher potential for travel-related virus introduction and local virus transmission. Adapted from:

Andrew J. Monaghan, K. M. Sampson, D. F. Steinhoff, K. C. Ernst K. L. Eb B. Jones, M. H. Hayden, Climatic Change (2016)

Present-DaySuitabilityforAedesaegyp (Types1-3)

FutureSuitabilityforAedesaegyp (Types1-3)

Coun eswithrecentlocaldengueorchikungunyatransmission

Philadelphia

NewYork

Washington

Miami

TampaOrlando

Houston

Dallas

LosAngelesSanDiego

Denver

Sacramento

Atlanta Charleston

SanAntonio

Phoenix

St.Louis

MetroPopula on

5,000,000

20,000,000

Ae. aegypti virus transmission suitability

Predicting risk must account for

changing human factors

0

20

40

60

80

100

120

140

160

180

200

0

100000

200000

300000

400000

500000

600000

700000

800000

2000 2005 2010 2015

Num

be

r o

f bed n

ets

dis

trib

ute

d

(in m

illio

ns)

Ma

laria d

eath

s u

nder

age 5

years

Malaria deaths and bed net distribution

Malaria Deaths Under Age 5 years Bednets distributed

Linear (Bednets distributed) Data from WHO

Malaria deaths

under age 5 years

Bednets

distributed

Risk influenced by socially-determined

behaviors

0

0.5

1

1.5

2

2.5

3

3.5

4

Primary School Secondary School More than secondarySchool

Odds tha

t a w

om

an w

ill o

wn a

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t com

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se

w

ith

no

ed

ucation

Women with an education are more likely to own a bed net in the highlands of Kenya

Developing Early Warning systems

for infectious disease transmission Frameworks for early warning systems have been developed that include stages

for:

Watch: Developing prior assessment of risk of emergence event

Warning: Human disease has been detected

Emergency: Epidemic or outbreak is underway

Key challenges

Integration of data streams

Changing landscapes of risk

Human: behaviour and available control strategies, response capacity

Biological: phylodynamics, environmental conditions

Communication and adoption of early warning systems by key stakeholders

Investment in ongoing monitoring systems

Lack of integration of natural system risk with impact of human and social factors on transmission

Some concepts adapted from BA Han and JM Drake EMBO Reports 2016

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

Examples from other

fields

Sustainable forecasting has been actualized in other disciplines

Drought monitoring

Food security

Typically more directly related to environmental measurements readily available

Temperature

Rainfall

Do not include projects of health, economic or other downstream impacts in which social science has a stronger role

http://www.fews.net/

https://www.drought.gov/drought/

Early detection

Goal of early detection

Reduce the reproductive

number to minimize

transmission

Methods:

Reduce contacts

Reduce duration of

infectiousness

Target interventions

Maximize uptake

Components of Basic Ro:

Β = Probability of transmission given contact

c = Number of contacts per given time period

D = Duration of infectiousness

Can modify by: x – proportion susceptible - Targeted vaccination (measles OB) - Distribution of control measures (bed net)

Non-traditional Early Detection sources

Social listening: Using social media data to identify trends and respond (often used in marketing but can be used for infectious disease trends)

Sources of data

Twitter (predomínate source)

Facebook

Internet searches

Instagram

Key strategy

Develop algorithims for searching for key words/ phrases – machine learning (Kagashe I, Yan Z, Suheryani I J Med Internet Res 2017)

Monitor trends

Key Challenges

Biases in data (age, geography)

Noisy data – best suited for trends in high transmission diseases (influenza (n=50), dengue (some regions) etc.)

Other factors may influence discussion of a topic

Fig 2. Tweets are a useful tool for estimating Dengue activity at country level.

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

Non-traditional early detection

sources cont.

Community-based participatory surveillance

Recruited group of users report symptoms each week

Examples

FluNearYou

successfully implemented (Pearson corr. >90% adjusted)

Biased population

More females

Higher human development index

Smolinski, M. S., Crawley, A. W., Baltrusaitis, K., Chunara, R., Olsen, J. M., Wójcik, O., . . .

Brownstein, J. S. (2015). 105(10), 2124-2130.

Baltrusaitis K, Santillana M, Crawley AW, Chunara R, Smolinski M, Brownstein JS.

JMIR Public Health Surveill. 2017 Apr 7;3(2):

Kidenga – CBS for vector-borne

System:

Similar to Flu Near You

Monitoring of syndromes and mosquito activity in US-Mexico border región

Key lessons

Recruitment and retention of participants is challenging

Need to provide tailored messages to motivate action

Interest falls off after transmission wanes

Too high numbers required for identification of rare events

Current utility

Platform for educational messaging and dissemination of EWS messages

Intention – get it on everyone’s phone – periodic activitation

Kidenga 2.0: Iterate with alerts and cues to action

“relating it

to the weather”

“‘some kind

of notification

that there was activity”

‘a good

prompt for me in my

environment’

‘”check- ins”

Summary Identify the complex processes behind disease transmission – integrate human and natural processes

Parameterize models for predicting future risk – both long and short term

Invest in sustainable infrastructure to support forecasting efforts

Identify best practices to convey information to motivate action in stakeholders and public

Operationalize messaging at an appropriate and actionable spatial and temporal scale

Acknowledgements 19

University of Arizona

Dr. Mike Riehle

Dr. Kathleen Walker

Teresa Joy

Dr. Pablo Castro-Reyes

Dr. Yves Carriere

Dr. Andrew Comrie

Dr. Cory Morin

Steve Haenchen

Eileen Jeffrey

Daniel Williamson

NCAR

Dr. Mary Hayden

Dr. Andy Monaghan

Dr. Daniel Steinhoff

NIH/NIAID: Grant R56AI091843 and 1R01AI091843

and Skoll Global Threats Grant

Research was approved by

University of Arizona,

Directorate of Education, Quality and Research

Ethics Committee of the Department and

Medicine and Health Sciences of the

Universidad de Sonora

Universidad de Sonora

Dr. Gerardo Alvarez

Dra. Maria del Carmen Candia Plata

El Colegio de Sonora

Dra. Lucia Castro

Dr. Rolando Diaz Caravantes

Carmen Arellano Gálvez

Office of Border Health

Robert Guerrero

Orion McCotter

Sonoran Ministry of Health Department of

Epidemiology and Department of Health

Services and Directorate of Education

Dr. José Jesús Bernardo Campillo

García.

Dr. Francisco Javier Navarro Gálvez.

Dr. Sergio Olvera Alba.

Dr. Ariel Vázquez Gálvez.

Hospital General, Nogales

Mercedes Gameros

Centers for Disease Control

• Dr. Steve Waterman

• Alba Phippard