Symptoms non-sustainable development

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1 Health as an integrated sustainability index Climate change and vector-borne diseases: modeling past, present and future risks Ecosystems Natural systems Geophysical systems SocioEconomic developments Pim Martens Health Maastricht University Culture Maastricht Annual Symposium Global and European Health 9 October 2014 obesity AIDS malnutrition Source: WHO Healthy life expectancy Health: tuberculosis Social environment Politics Symptoms non-sustainable development malaria Population Society SARS Natural environment Sustaining health Receding pandemics Medical technology Emerging infectious diseases Climate Change Future? time 1

Bluetongue Impact of climate on Bluetongue in Europe. VIRUS Malaria modelling.

disease in sheep & some deer Reoviridae: Orbivirus, 24 serotypes VECTORS

imicola AFRICA, ASIA, S. EUROPE C. sonorensis N.

dispersal Transmit >50 viruses Mapping of BT s past, present and future R0

AIM: assess spa2al & temporal varia2ons in BT R0 under climate change

induced mortality rate (C,S) b: Prob. transmission of vector to host β: Prob.

ratio vectors to host (C: cattle, S: sheep) n: 1/extrinsic incubation period

recent climate High BT risk over Spain, Portugal, south western France,

Constant in time and space Constant in time, varying in space Varying in time

outbreaks in Corsica Unrealistic values over mountains and Eastern Europe

change in R0 Multimodel spread Sign consistency The BT risk increases over

2 Bluetongue Impact of climate on Bluetongue in Europe. VIRUS Malaria modelling. Bluetongue in Europe: recent history Infectious to all ruminants; severe disease in sheep & some deer Reoviridae: Orbivirus, 24 serotypes VECTORS Mapping Asian tiger mosquito suitability. RUMINANT C. obsoletus EUROPE C. imicola AFRICA, ASIA, S. EUROPE C. sonorensis N. AMERICA Culicoides biting midges Highly seasonal Very abundant Long range dispersal Transmit >50 viruses Mapping of BT s past, present and future R0 Two host species R0 = bβa² n mcφ 2 ms (1 φ ) 2 + p p + n rc + d C rs + d S AIM: assess spa2al & temporal varia2ons in BT R0 under climate change scenarios (Gubbins 2007) r:1/duration of viraemia in host (C,S) d: disease induced mortality rate (C,S) b: Prob. transmission of vector to host β: Prob. transmission of host to vector Φ: proportion of bites on each host species m: ratio vectors to host (C: cattle, S: sheep) n: 1/extrinsic incubation period a: biting rate p: vector mortality rate Mapping Bluetongue risk in Europe: recent climate High BT risk over Spain, Portugal, south western France, Sardegna and Sicilia. Constant in time and space Constant in time, varying in space Varying in time and space Simulated future Regional R0 changes This misses out observed outbreaks in Corsica Unrealistic values over mountains and Eastern Europe Shading: Ro risk (arbitrary scaled between 0 and 1) From Guis et al, 2011 % change in R0 Multimodel spread Sign consistency The BT risk increases over UK, Southern France and North- western Spain (Galicia) Changes in Northern Europe are related to the pathogen properbes Changes in Southern Europe are associated with the spread of the Afro- Tropical vector (Imicola spp) 2

Asian tiger mosquito: an invasive specie Pathogens Asian tiger mosquito: an invasive specie Source: Gancho, wilkypedia Dengue fever Chikungunya fever West Nile virus Methods Using different vector

ERA EnvHealth board meeting, Brussels Asian tiger mosquito: Europe, 2008 Hot spots: Asian tiger mosquito: Europe 1960-1989 1990-2009 2030-2050 Source: ECDC, 2010 Italy Corsica, Sardinia Sicily

al, 2002). (Modelling) Climate change and malaria Do the malaria models reproduce the observed trends in malaria endemicity over the 20 th century?

3 Asian tiger mosquito: an invasive specie Pathogens Asian tiger mosquito: an invasive specie Source: Gancho, wilkypedia Dengue fever Chikungunya fever West Nile virus Methods Using different vector models (climatic suitability) Using an ensemble of Regional Climate Model scenario blue: original distribution, cyan: areas where introduced in the last 30 years. ERA EnvHealth board meeting, Brussels Asian tiger mosquito: Europe, 2008 Hot spots: Asian tiger mosquito: Europe Source: ECDC, 2010 Italy Corsica, Sardinia Sicily Eastern coasts of Spain South-eastern France Adriatic coasts Greece Model 1: January Temperatures > 0 C, Annual rainfall > 500mm Risk defined for different annual temperature thresholds (Kobayashi et al, 2002). (Modelling) Climate change and malaria Do the malaria models reproduce the observed trends in malaria endemicity over the 20 th century? What is the impact of climate change on malaria distribution based on a multi-model ensemble? Uncertainties? How do the different malaria models behave under a warmer climate? New results different from those 20 years ago? Vectorial Capacity Number of potentially infective contacts inflicted by the mosquito population per infectious person per day VC = m*a2 *p n -ln(p) m = mosquito density a = feeding frequency p = survival probability n = incubation period parasite in the mosquito 3

20th century trends over the globe: Simula@on versus Observa@on 1900s 2000s Changes (Δ) Pre-intervention (1900s): miasma is doing a good job obs Post-intervention (2000s):

state regions Increase in good control state regions Decrease in good control state regions lmm_ro miasma Highlands of Africa and Recent Malaria Epidemics 20th century trends

realistic picture over Africa lmm_ro Decrease in malaria endemicity over the Sahel reproduced by the malaria models driven by climate parameters only miasma (Lindsay &

) Malaria Incidence in summer: Europe Based on the Liverpool Malaria Model simulabons driven by observed Rainfall and Temperature from diﬀerent observed datasets.

4 20th century trends over the globe: versus 1900s 2000s Changes (Δ) Pre-intervention (1900s): miasma is doing a good job obs Post-intervention (2000s): mara provides a realistic picture at global scale mara Change of vulnerability by 2080 (compared to baseline) Increase in poor control state regions Decrease in poor control state regions Increase in good control state regions Decrease in good control state regions lmm_ro miasma Highlands of Africa and Recent Malaria Epidemics 20th century trends over Africa: Simula@on versus Observa@on 1900s obs 2000s Changes (Δ) Pre-intervention (1900s): miasma is doing a good job mara Post-intervention (2000s): mara provides a realistic picture over Africa lmm_ro Decrease in malaria endemicity over the Sahel reproduced by the malaria models driven by climate parameters only miasma (Lindsay & Martens) Climate may have partly contributed to the observed changes over the Sahel and the highlands (no causality!) Malaria Incidence in summer: Europe Based on the Liverpool Malaria Model simulabons driven by observed Rainfall and Temperature from diﬀerent observed datasets. Northern Italy, some parts of Galicia in Spain and the Landes region in France are climabcally at risk The incidence values are relabvely low in magnitude (20-50%) compared to what can be expected in Africa. The observed decline of malaria endemicity over the 20th century at global scale more related to control measure than climate change > 1500m Epidemic Future: A market world Developed countries Medical technology Business as usual Economic growth Technological development pandemics PopulationReceding growth Environmental problems Climate change Developing countries Emerging infectious diseases 4

emerging pathogens Future: A fragmented world Stagnation globalisation Decreasing economic growth Regionalism Future of Receding inequity pandemics Decline social infrastructures Environmental

Developed countries Sustaining health Future: A sustainable world Developing countries Increased social and ecological consciousness Economic growth within limits of sustainable development Receding

5 emerging pathogens Future: A fragmented world Stagnation globalisation Decreasing economic growth Regionalism Future of Receding inequity pandemics Decline social infrastructures Environmental problems Rapid climate changes Developing and developed countries Emerging infectious diseases Taylor et al., 2001 Phil. Trans. R. Soc. Lond. Developed countries Sustaining health Future: A sustainable world Developing countries Increased social and ecological consciousness Economic growth within limits of sustainable development Receding pandemics Decreased inequity Stabilisation population growth Improve quality of the environment Stabilising climate Medical technology Global Change and Human Health: A Change of Scale and Content Conventional Epidemiology Toxicological Estimation of risks from past realities Short time horizon Estimation of more local risks Statistical models Static cause-effect Reductionistic approach Eco-Epidemiology Ecological Assessment of future health risks Long time horizon Estimation of global/ regional risks Mathematical models System dynamic, nonlinear models Holistic Approach Various sources of uncertain@es: MIM vs GCMs vs RCPs Layers of Uncertainty Underlying the Health Impacts Assessment of Global Changes social and economic uncertainties Social responses (economical, political, and technological) Impacts on ecological determinants of human health Impacts upon human health scientific uncertainties Atmospheric changes Largest uncertainties related to the method (malaria model) Uncertainties related to the GCMs huge over the northern epidemic fringe (consistent with the spread in GCM rainfall projections over this region). Emission Scenario uncertainties increase as a function of time (about 40% over the highlands in 2080s, consistent with a Temperature effect on malaria transmission for those regions). 5

6 Different temporal and spatial scales Multiple Scales millenium Log temporal scale Global Modelling century ENSO year month Adult mosquito day Decades Global warming decade Larval development Human Field Survey migration weather hour Days Log spatial scale International Centre for Integrated assessment and Sustainable 1 cm m 1 km 100km 1000development km the Earth Estimate Change Malaria Incidence Zimbabwe 50 Highland Malaria in Kericho C C, +20% P % change base incidence (/1,000) GIS / Local le sca le Modelling al l sca i t a a Sp por m Months Te +2 C, -20% P base H arare <600mS 6-900mS mS >1200m mN 6-900mN <600mN altitude Bulaw ayo A sustainable health transition NO PREDICTIONS Micro and macro approach macro e.g. poverty reduction, education, macro-economic developments, reduction greenhouse gas emission Climate change interferes with all levels e.g. lifestyle, housing, food- and water provision micro 6

Using climate models to project the future distributions of climate-sensitive infectious diseases

Liverpool Marine Symposium, 17 Jan 2011 Using climate models to project the future distributions of climate-sensitive infectious diseases Prof. Matthew Baylis Liverpool University Climate and Infectious