IMED 2018: Mapping Monkeypox risk in the Congo Basin using Remote Sensing and Ecological Niche Models.
- 1. National Center for Emerging and Zoonotic Infectious Diseases Mapping Monkeypox risk in the Congo basin using Remote Sensing and Ecological Niche Models Yoshinori Nakazawa, Andrea McCollum, Christine Hughes, Benjamin Monroe, Whitni Davidson, Kimberly Wilkins, Joelle Kabamba, Okitolonda Wemakoy, Beatrice Nguete, Jean-Jacques Muyembe Tamfum, Victoria Olson, Mary Reynolds International Meeting on Emerging Diseases and Surveillance November 2018
- 2. Described in 1958 In humans in 1970 Poxviridae, Orthopoxvirus Smallpox-like illness – Easily confused with varicella No treatment or cure Mortality rate ~10% Attack rate of ~5 per 10,000 population, although it has been reported to be as high as 14 per 10,000. Monkeypox Damon, Roth, & Chowdhary 2006. NEJM 355(9):962-963 Rimoin et al. 2010. PNAS 107(7):16262-16267
- 3. Monkeypox Two recognized clades – Central African – Western African More pronounced morbidity, mortality, human-to-human transmission and viremia in Congo Basin clade.
- 4. Monkeypox Associated with densely forested areas of central and west Africa – Potential difference between dense and seasonally or permanently flooded forest Fuller et al. 2011. EcoHealth 8(1):14-25. Doty et al. 2017. Viruses 9(10):283; doi:10.3390/v9100283.
- 5. Monkeypox Sus scrofa Domestic pig Lophuromys sikapusi Tawny bellied rat Cricetomys emini Gambian rat Petrodromus tetradactylus Elephant shrew Protoxerus strangeri Giant Squirrel Funisciurus anerythrus Thomas’s rope squirrel Funisciurus congicus Kuhl’s rope squirrel Heliosciurus rufobrachium Sun squirrel Summary 1970’s-2011 Central and Western Africa Cephalophus monicola Duiker Graphiurus sp. African Dormice Mastomys couchi Multi-mammate mouse Sun squirrels Heliosciurus sp. Striped mice Hybomys trivirgatus Brush-tail porcupine Atherurus africanus Dwarf dormice Graphiurus murinus Gambian giant pouched rats Cricetomys gambianus Rope squirrels Funisciurus sp.
- 6. Monkeypox
- 7. Monkeypox 1982-1986 2002-2006 Difference Built a model based on human cases and environmental conditions during the 80’s and project it into environmental conditions during 2000’s. • Northward shift • Possible expansion
- 8. Objectives Create models using recent disease reports and contemporary high (spatial/temporal) resolution environmental layers (RSIF and EVI). Create/update disease transmission risk maps for central Africa. Evaluate predictability of models based on two environmental datasets.
- 9. Ecological Niche Models G2 G1 E1 G-space E-space Peterson et al.2006 J Mammal.87(3):427-432 BIOCLIM (Envelope Model) BRT (Boosted Decision Trees) BRUTO (Regression) GARP (Genetic Algorithm) DOMAIN (Multivariate Distance) GAM (Generalized Additive Model) GDM (Generalized Dissimilarity Modelling) GLIM (Generalized Linear Model) LIVES (Multivariate Distance) MARS (Multivariate Adaptive Regression Splines) MAXENT (Maximum Entropy)
- 10. Disease data Monkeypox surveillance in Tshuapa District Case localities are reported and geocoded to the village – Gazetteers, maps and geographic data collected in the field Unique localities were extracted Test for predictivity using diagonals Random subsets to reduce effects of spatial autocorrelation P1 17 P2 18 P3 35 P4 25 P5 28 P6 15 P7 14 P8 13 P9 25 P10 12 P11 13 P12 6
- 11. Environmental data Reconstructed Solar-Induced Fluorescence (RSIF) – 2002-2016 – 8-day composites – 4-week summary statistics (maximum, minimum, mean, range) – Spatial resolution ~5km Enhanced Vegetation Index (EVI) – MODIS – 2011-2014 – monthly maximum value composites – Summary statistics calculated (max, min, mean, range) – Spatial resolution ~1km
- 12. Maxent Presence-only algorithm – Pseudo-absences selected from background. Uses the maximum entropy concept to estimate probabilities based on environmental conditions at the occurrence localities • The core of idea of maxent is: • Find the probability distribution that: – 1) Have the same means of features as the observed means – 2) It is as flat as possible (maximizes entropy)
- 13. ENMs Occurrence data – Diagonals ON (training) and OFF (testing); using median latitude and median longitude – Unique occurrences 25 subsets based on 10Km radius Environmental data – Clipped using a 500km buffer from the localities – A combination of maximum, median minimum and range values if RSIF and EVI Threshold: 5% omission allowed 25 maps aggregated into final map
- 14. Results ENM EVI ON AUC = 0.95572 ON AUC test = 0.92248 ENM RSIF ON AUC = 0.884388 ON AUC test = 0.857688 ON Diagonal = Training (blue triangles) Environmental data = mean, min, max, range throughout the year
- 15. Results ENM EVI (max) ON AUC = 0.931164 ON AUC test = 0.891428 ENM RSIF (max) ON AUC = 0.943276 ON AUC test = 0.784952 ON Diagonal = Training (blue triangles) Environmental data = maximum values of 4week periods
- 16. Results ENM_RSIF (max) - Period 7 - Period 1 - Period 12 - Period 8 - Period 11 Training – 25 subsets of occurrences Environmental data = maximum values of 4week periods
- 17. Results ENM_EVI (max) - December - October - May - July - March Training – 25 subsets of occurrences Environmental data = maximum values of 4week periods
- 18. Conclusions and Future Work Better predictive ability for cases outside of Tshuapa when using RSIF; possibly related to spatial resolution Possible associations with environmental conditions at specific time periods Explore temporal/seasonal associations between MPX cases and climatic/environmental variables.
- 19. For more information, contact CDC 1-800-CDC-INFO (232-4636) TTY: 1-888-232-6348 www.cdc.gov The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Thank you!