Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

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Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University

Transcript of Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Page 1: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Public Health and Ecological ForecastingBen ZaitchikJohns Hopkins University

Page 2: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Four Thoughts These are highly mediated and often

multi-scale systems Models have a strong empirical

component In many cases, processes of interest

unfold over weeks, months or years Data accessibility and

communication are critical

Page 3: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Four Thoughts These are highly mediated and often

multi-scale systems Models have a strong empirical

component In many cases, processes of interest

unfold over weeks, months or years Data accessibility and

communication are critical

Page 4: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Anopheles darlingi is the

dominant malaria vector in the Peruvian

Amazon

Symptoms (e.g., fever, chills, etc.) appear ~5-10 days after being bitten by

an infected mosquito.

Biting rates are influenced by both

climate and land cover

Page 5: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Highest Deforestation Rate in Peru

Iquitos-Nauta Road Paving & Fujimori logging concessions

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140Reported Malaria, Loreto Province 1990-2013

P. vivaxP. falciparum

# C

as

es

(1

00

0s

) MAJOR FLOOD

Roll Back Malaria2nd highest increase in the Amazon

to 2006, Major decline to 2011

Page 6: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Human Malaria

Infection

Precipitation &

Climate

Page 7: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Land Use

Density of Anopheles

Adults

Anopheles Larva Habitat

Human Malaria

Infection

Precipitation &

Climate

Page 8: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Land Use

Density of Anopheles

Adults

Infrastructure

Migration, Colonization,

and Agriculture

Human Exposure

Anopheles Larva Habitat

Human Malaria

Infection

Precipitation &

Climate

Page 9: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Iquitos

Local Scale

Page 10: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Regional Scale

Page 11: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Local: Predict Breeding Sites

Tmin

[5 day]

Rain[1 day]

SWnet

[1 day] SWnet

[5 day]

SoilMoisture[5 day]

Land Cover Variables

Denis Valle, Univ. of Florida

Page 12: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Local: Predict mosquitoes1.5

-1.5

0.0

Denis Valle, Univ. of Florida

1.5

-1.5

0.0

Page 13: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Regional: Predict casesModel for each district:

MALARIA RATE (t)

Annual

TREND

SEASONAL Cycles

CLIMATE Drivers

LAND COVER Characteristic

s

==

Captures the long-term

change in the mean of

malaria cases in the district

Variation in the series that is annual

in period. It is of direct interest (i.e., we do not remove seasonality, but

rather estimate it)

Influence both human exposure

(e.g., occupational labor) and

Anopheles density

Page 14: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Regional: Predict casesModel for each district:

MALARIA RATE (t)

Annual

TREND

SEASONAL Cycles

CLIMATE Drivers

LAND COVER Characteristic

s

==

TMPA rain rate is positively associated with

case count

Precipitation is negatively associated with vectors at local scale but positively associated with clinical cases at

district scale

Page 15: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Four Thoughts These are highly mediated and often

multi-scale systems Models have a strong empirical

component In many cases, processes of interest

unfold over weeks, months or years Data accessibility and

communication are critical

Page 16: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Thresholds of Rainfall, Temperature, and Humidity

Logistic Regression with multiple climate variables

Page 17: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Empirical The good news: in some applications

consistency may be as important as accuracy

The bad news: some forecasts are sensitive to thresholds and nonlinear responses

Page 18: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Four Thoughts These are highly mediated and often

multi-scale systems Models have a strong empirical

component In many cases, processes of interest

unfold over weeks, months or years Data accessibility and

communication are critical

Page 19: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Lead Time

Midekisa et al. (2012): Satellite rainfall is predictive of malaria in Ethiopia at 1-3 month lead

Page 20: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Lead Time Murray Valley Encephalitis Virus (MVEV) in Australia

has been predicted using TRMM at 2-month lead In our Amazon malaria risk model, satellite rainfall

rate is most predictive at 10 week lead. Rift Valley Fever warnings in East Africa make use

of satellite observations integrated over 3-months.

For hantavirus, seasonal rainfall anomalies can influence cases two or more years later.

Cholera dynamics have been related to precipitation at lead times from days to seasons.

Page 21: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Lead Time The good news: we can use satellite

rainfall observations to generate actionable forecasts.

The so-so news: many of these forecast horizons fall in between RT and research grade TRMM and GPM products.

Page 22: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Four Thoughts These are highly mediated and often

multi-scale systems Models have a strong empirical

component In many cases, processes of interest

unfold over weeks, months or years Data accessibility and

communication are critical

Page 23: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Data Accessibility Smooth web access, simple product

descriptions, and GIS compatibility are critical for this research community

The merged precipitation estimates are most widely used, but other products could also be valuable

Page 24: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Data Accessibility

Page 25: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Communication with users However, with accessibility comes

risk! We want to facilitate appropriate

interpretation and application of all GPM data products

Page 26: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Four Thoughts These are highly mediated and often

multi-scale systems Models have a strong empirical

component In many cases, processes of interest

unfold over weeks, months or years Data accessibility and

communication are critical

Page 27: Public Health and Ecological Forecasting Ben Zaitchik Johns Hopkins University.

Thank you


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