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Home , Brazilian purpuric fever, Louping ill, Pogosta disease, Spotted fever, Tanapox

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 - 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 Monkey pox

Vaccinia and and Mayaro

Typhus - epidemic - P.falciparum

Tungiasis Dengue

Tropical sprue Tropical P. vivax

Tropical pulmonary eosinophilia pulmonary Tropical Lassa

Tropical phagedenic ulcer phagedenic Tropical Viliuisk encephalomyelitis

Trichostrongyliasis Schistosomiasis - japonicum

Thogoto Echinococcosis - unilocular virus disease virus Tanapox

Spondweni Gnathostomiasis

Sparganosis Barmah Forest disease

Sennetsu neorickettsiosis Sennetsu 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 group (-borne) - Rickettsioses, New World

• Option 2: map occurrence Paracoccidioidomycosis Spotted fever group (tick-borne) - Rickettsioses, Old World

P. ovale P.

P. malariae P. Typhus - scrub (mite-borne)

O'nyong nyong O'nyong felis infection

Old World phleboviruses World Old H5N1 avian Oesophagostomiasis Venezuelen hemorrhagic fever

North Asian tick typhus tick Asian North Yaws New World phleboviruses World New Eastern equine encephalitis

– (n=64, ) 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 • Option 3: map maximum potential range Bussuquara and Ilheus Mansonelliasis - M. perstans

Histoplasmosis - African - Histoplasmosis

Histoplasmosis

Heterophyid Heterophyid Sindbis

Group C viral viral C Group Dracunculiasis (Guinea worm)

Gongylonemiasis Nipah and Nipah-like virus disease – (n=30, Plasmodium ovale) Powassan

Gastrodiscoidiasis Flinders Island spotted fever spotted Island Flinders

Filariasis - Brugia timori Brugia - Filariasis

Fasciolopsiasis HIV infection - initial illness Entomophthoramycosis Venezuelan equine encephalitis

Enteritis necroticans Enteritis Chandipura and Vesicular stomatitis

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 disease

Dicrocoeliasis Rocky Mountain spotted fever

Coenurosis Ebola Chromomycosis

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

Brainerd diarrhea Brainerd Onchocerciasis Bertiella and Inermicapsifer and Bertiella Oropouche virus

Baylisascariasis

Bartonellosis - South American South -

Balantidiasis

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 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

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 - 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, , 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 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) uncertainty • Statistically robust and geographically plausible (definitive extent; ++occurrence data) • Biologically plausible: covariates selected have epidemiological relevance: precip. (37%), temp. suitability (20%), G-econ (9%) Cohort studies

• N = 57 • Inclusion criteria = serological testing of paired blood samples taken at least one year apart • For some studies: active fever surveillance and questionnaires identify the proportion of symptomatic/apparent infections From dengue risk to burden

• Pair probability of occurrence with cohort studies to infer inapparent (n=54) and apparent (n=39) incidence per pixel • Then pair with population surfaces for 2010 to sum up global totals • Consistent global estimates for B&MGF, GAVI and for WHO Previous burden estimates

• In broad accordance with other metrics but – much finer spatial scale – full uncertainty estimates – to be endorsed by WHO for WHA 2015 Automating for scale-up and emergencies Atlas of Baseline Risk Assessment for Infectious Disease

B&MGF: OPP1093011 ABRAID concept

Hay, S.I. et al. (2013). PLoS Med., 10(4): e1001413. ABRAID details

• Automate data retrieval – Machine learning (provenance and geo-positioning) • Automate mapping – BRT and derivatives in a feed-back loop • Extensive validation – Crowd-source checking – Geo-wiki integrated with Global Health Network user groups – Additional expert panel of arbiters • Potential audiences – Standard map users: (i) atlas; (ii) spatial audit; (iii) advocacy; (iv) travel health – Biosurveillance: (i) geo-spatial triage of outbreak risk; (ii) abraid = to awaken – GBD: (i) supply infectious disease map covariates; (ii) help improve timeliness and spatial resolution ABRAID now live and working Social media inputs

• New low provenance data sources – Twitter as an example – There are and will be others i.e. Google, Wikipedia, Facebook etc. • High volume – >150 million tweets daily and increasing – Increasing, 5% are geopositioned – Tweetping movie – every minute for 7 hours • Assumption lots of occurrence data – future challenges change from obtaining data to extracting signal from noise The Ebolavirus family

• Four species known to infect humans – (1976) – Zaire ebolavirus (1976) – Tai Forest ebolavirus (1994)

– (2007) National Institute of Allergy and Infectious Diseases (NIAID) Niche mapping - option 4 - EVD

• Not better, but faster and cheaper and thus useful for scaling and emergencies EVD niche mapping

Ebola index cases Background samples Environmental correlates

• Maps the zoonotic niche not secondary transmission • Marburg, Lassa fever and CCHF also a major concern Pigott et al. (2014). eLife, 3: e04395. Changing PAR in EVD niche A wider range of VHFs

• Methodology equally applicable to a range of IDs • Various VHFs including Marburg, Lassa fever and CCHF are our first priority • These conditions are VHFs of greatest concern due to the potential for secondary human-to-human transmission and thus outbreaks that precipitate PHEs • Do we know their geography? Thank you

We hope to encourage the use of maps to communicate that disease burden reductions are possible, ongoing and with the help of model inferences, cost-effective and sustainable.