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

2. 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

3. 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

4. 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

5. Highest Deforestation Rate in Peru Iquitos-Nauta Road Paving & Fujimori logging concessions MAJOR FLOOD Roll Back Malaria 2 nd highest increase in the Amazon to 2006, Major decline to 2011

6. Human Malaria Infection Precipitation & Climate

7. Land Use Density of Anopheles Adults Anopheles Larva Habitat Human Malaria Infection Precipitation & Climate

8. Land Use Density of Anopheles Adults Infrastructure Migration, Colonization, and Agriculture Human Exposure Anopheles Larva Habitat Human Malaria Infection Precipitation & Climate

9. Iquitos Local Scale

10. Regional Scale

11. Local: Predict Breeding Sites T min [5 day] Rain [1 day] SW net [1 day] SW net [5 day] Soil Moisture [5 day] Land Cover Variables Denis Valle, Univ. of Florida

12. Local: Predict mosquitoes 1.5 -1.5 0.0 Denis Valle, Univ. of Florida 1.5 -1.5 0.0

13. Regional: Predict cases Model for each district: MALARIA RATE (t) Annual TREND SEASONAL Cycles CLIMATE Drivers LAND COVER Characteristics == 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

14. Regional: Predict cases Model for each district: MALARIA RATE (t) Annual TREND SEASONAL Cycles CLIMATE Drivers LAND COVER Characteristics == 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

15. 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

16. Thresholds of Rainfall, Temperature, and Humidity Logistic Regression with multiple climate variables

17. 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

18. 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

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

20. 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.

21. 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.

22. 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

23. 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

24. Data Accessibility

25. Communication with users  However, with accessibility comes risk!  We want to facilitate appropriate interpretation and application of all GPM data products

26. 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

27. Thank you