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Mapping the Global Distribution of All Infectious Disease

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Mapping the Global Distribution of All Infectious Disease Mapping the global distribution of all infectious disease @simonihay i-sense, London Centre for Nanotechnology University College London, London, U.K. 06 February 2015 Talk overview • Infectious disease mapping opportunities • MBG and SEEG malaria activities • Niche mapping and dengue burden • Automating for scale up - ABRAID • Automating for emergencies - Ebola Infectious Diseases • How many infectious diseases are there? – ~1400 (ever recorded) and 350 on • Pre-requisites for mapping – Spatial: geographically variable occurrence – Aetiology: understand pathogen life history – Measurement: information available to map – Utility: of public health interest • What ID can we map? • What ID have we mapped? – Systematic review of all IDs to score maps available vs. maps possible – Hay et al. (2013). Phil. Trans. Roy. Soc. B., 368(1614): 20120250 “Case law” for ID mapping • Option 1: Unsuitable for occurrence mapping – (n=176, EBV) Whitewater Arroyo virus infection Monkey pox Vaccinia and cowpox and Vaccinia Mayaro Typhus - epidemic - Typhus P.falciparum Tungiasis Dengue Tropical sprue Tropical P. vivax Tropical pulmonary eosinophilia pulmonary Tropical Lassa fever Tropical phagedenic ulcer phagedenic Tropical Viliuisk encephalomyelitis Trichostrongyliasis Schistosomiasis - japonicum Thogoto Echinococcosis - unilocular Tanapox virus disease virus Tanapox Pogosta disease Spondweni Gnathostomiasis Sparganosis Barmah Forest disease Sennetsu neorickettsiosis Sennetsu Marburg virus disease Schistosomiasis - mattheei - Schistosomiasis Hantavirus pulmonary syndrome Rocio Coltiviruses - Old World Rickettsialpox Loiasis Rhinosporidiosis Ockelbo disease Pentastomiasis - Linguatula - Pentastomiasis Schistosomiasis - intercalatum Pentastomiasis - Armillifer - Pentastomiasis Mycobacteriosis - M. ulcerans Penicilliosis Spotted fever group (tick-borne) - Rickettsioses, New World • Option 2: map occurrence Paracoccidioidomycosis Spotted fever group (tick-borne) - Rickettsioses, Old World P. ovale P. Colorado tick fever P. malariae P. Typhus - scrub (mite-borne) O'nyong nyong O'nyong Rickettsia felis infection Old World phleboviruses World Old H5N1 avian influenza Oesophagostomiasis Venezuelen hemorrhagic fever North Asian tick typhus tick Asian North Yaws New World phleboviruses World New Eastern equine encephalitis – (n=64, Monkeypox) Nanophyetiasis Crimean-Congo hemorrhagic fever Murray Valley encephalitis Valley Murray Endemic syphilis (bejel) Metorchiasis Leishmaniasis - cutaneous/mucosal, New World Mansonelliasis - M. streptocerca M. - Mansonelliasis Leishmaniasis - cutaneous/mucosal, Old World Mansonelliasis - M. ozzardi M. - Mansonelliasis Trypanosomiasis - African Mammomonogamiasis Zika Lagochilascariasis Echinococcosis - multilocular Kyasanur Forest disease Forest Kyasanur Tick-borne encephalitis Israeli spotted fever spotted Israeli Chikungunya • Option 3: map maximum potential range Bussuquara and Ilheus Mansonelliasis - M. perstans Histoplasmosis - African - Histoplasmosis Tularemia Histoplasmosis Melioidosis Heterophyid infections Heterophyid Sindbis Group C viral fevers viral C Group Dracunculiasis (Guinea worm) Gongylonemiasis Nipah and Nipah-like virus disease – (n=30, Plasmodium ovale) Glanders Powassan Gastrodiscoidiasis Rift Valley fever Flinders Island spotted fever spotted Island Flinders Brazilian purpuric fever Filariasis - Brugia timori Brugia - Filariasis Cholera Fasciolopsiasis HIV infection - initial illness Entomophthoramycosis Venezuelan equine encephalitis Enteritis necroticans Enteritis Chandipura and Vesicular stomatitis viruses Entamoeba polecki infection polecki Entamoeba Opisthorchiasis • Option 4: niche / occurrence mapping Echinostomiasis Anthrax Echinococcosis - American polycystic American - Echinococcosis Argentine hemorrhagic fever (Junin virus) Dioctophyme renalis infection renalis Dioctophyme Hendra virus disease Dicrocoeliasis Rocky Mountain spotted fever Coenurosis Ebola Chromomycosis Japanese encephalitis Cercarial dermatitis Cercarial Schistosomiasis - mansoni – (n=68, ebola) extraintestinal - Capillariasis Clonorchiasis Bunyaviridae infections - misc. - infections Bunyaviridae Leprosy Brazilian hemorrhagic fever (Sabia virus) (Sabia fever hemorrhagic Brazilian Omsk hemorrhagic fever Brainerd diarrhea Brainerd Onchocerciasis Bertiella and Inermicapsifer and Bertiella Oropouche virus Baylisascariasis Rabies Bartonellosis - South American South - Bartonellosis Plague Balantidiasis West Nile fever Babesiosis (Babesia microti, ducani, divergens, EU1, bigemina) EU1, divergens, ducani, microti, (Babesia Babesiosis Bolivian hemorrhagic fever (Machupo virus) Angiostrongyliasis - abdominal - Angiostrongyliasis Pinta • Option 5: model-based geo-statistics Anaplasmosis Trypanosomiasis- American African tick bite fever bite tick African Hookworm Aeromonas & marine Vibrio infx. Vibrio marine & Aeromonas Karelian fever Tick-borne encephalitis: Russian spring-summer Russian encephalitis: Tick-borne Leishmaniasis- visceral Rickettsia sibirica mongolotimonae infection mongolotimonae sibirica Rickettsia Poliomyelitis Queensland tick typhus tick Queensland Ross River virus Paragonimiasis Filariasis - Bancroftian Metagonimiasis Filariasis - Brugia malayi Sandfly fever Sandfly Alkhurma hemorrhagic fever Lobomycosis Relapsing fever Capillariasis - intestinal - Capillariasis St. Louis encephalitis Japanese spotted fever spotted Japanese Hantavirus infection - Old World Pythiosis Coccidioidomycosis Western equine encephalitis equine Western Schistosomiasis - haematobium Lyme disease Lyme Astrakhan fever Louping ill Louping Ehrlichiosis - human monocytic California encephalitis group encephalitis California Schistosomiasis - mekongi Blastomycosis Fascioliasis – (n=14, Plasmodium falciparum) fever Yellow • Results – 176 / 352 to map – Well mapped achieved 75% of maximum score – only 7 (4%) – dengue, Lassa fever, Mayaro, monkey pox, coltiviruses and P. falciparum and P. vivax Model Based Geostatistics and SEEG malaria activities Why malaria maps? Guerra et al. (2008). PLoS Med. • Unstable risk: first evidence-based population at risk Hay et al. (2009). PLoS Med. • Stable risk: first model-based geostats and uncertainty MBG surfaces of P. falciparum in 2010 Gething et al. (2011). Mal. J., 10: 378. A: P.f. PR2-10 prevalence surveys (n = 22,212, 1985-2010) B: MBG P.f. PR2-10 surface Infer much more from maps with the use of models: from B to C and D C: MBG P.f. EIR surface D: MBG P.f. Rc surface Direct uptake and dissemination W.H.O. Collaborating Centre in Geospatial Disease Modelling 2014 Directed by Prof. Peter Gething: B&MGF: OPP1068048, OPP1106023 Tracking change 1000 cases/100 0/yr 0 ROAD MAP - 2014 • Repository for Open Access Data • Malaria Atlas Project = open-source cartographic information suite to inform malaria control and elimination globally • Aim – to maximise access and dissemination of MAP collated data and outputs • Themes – parasite, mosquito, host • Maps – national, regional, global • Data – explorer B&MGF: OPP1106023 Niche mapping not better, but faster and cheaper Disease occurrence mapping • “Borrows” directly from ecological theory – assume disease observations approximate the realized niche of a pathogen – correlate with covariates where known and predict extensively • It is clearly not perfect – i) sampling – do we capture enough of the realized distribution? – ii) biotic interactions, biogeography and human impact – especially important at the global scale – iii) environment – is our covariate suite representative? • Mitigated from the outset – i) comprehensive collection of occurrence data – ii) evidence-based consensus – iii) bespoke environmental covariates data Occurrence mapping Bhatt et al. (2013). Nature, 496(7446): 504-507 (a) evidence-based consensus • ITHG (WHO/CDC) disagree on endemic status of 34 territories • Systematic review and treatment of evidence of transmission (admin0 and admin1) • Brady et al. (2012). PLoS NTD, 6(8): e1760. (b) occurrence data • Huge effort – all freely available • Formal literature searches were conducted – PubMed, ISI WoS and PROMED • All papers identified and abstracts read • All occurrence data geo-positioned manually • This yielded 8309 points for mapping from 106 countries (1963-2012) • An additional 1622 points were provided by HealthMap (2007-2012) (b) occurrence data • N = 8309 from literature searches and N = 1622 from HealthMap • Africa relatively data-poor and a future interest • Messina et al. (2014). A global compendium of human dengue virus occurrence. Scientific Data, 1: 140004 (c) pseudo-absence data • All niche modelling techniques • These data are generated in proportion to definitive extent certainty • We explore the full effect through ensemble approaches (d) environmental datasets • Lots of useful raster information available from familiar climatologies to GDP • Synoptic precipitation http://www.worldclim.org • From 0 mm (yellow) to 800 mm (blue) per month (d) environmental datasets • Synoptic mean monthly temperature http://www.worldclim.org • Covariate is annual number of days suitable for dengue transmission • Converted to daily dengue transmission suitability for Ae. aegypti using VC • Thus bespoke environmental covariates to formalize entomological knowledge (d) environmental datasets • Lots of opportunity to use “epidemiological” covariates • Geographically-based economic data • Pixel-based equivalent of gross domestic product (GDP) (e) probability of occurrence • BRT mapping with (i) evidence base; (ii) modern cartography and (iii)
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