Climate modelling and Dengue modelling

Transcription

1 Climate modelling and Dengue modelling Graham Mackereth and David Slaney Specialist Science Solutions Manaaki Tangata Taiao Hoki protecting people and their environment through science

2 Acknowledgements Sharleen Harper, Aroon Parshotam, Graham McBride Neil de Wet Simon Hales Dan Tompkins Alistair Woodward David Slaney

3 1 Climate change projections Outline Global - Regional Local Microclimate. What does climate change mean locally on the ground? 2 Modelling Dengue or Dengue vectors at the edge of the climatic envelope 3 Dengue disease modelling and climate Dengue potential, Transmission risk, Biological and Statistical models

4 1 Climate Modelling Global World Climate Research programme 24 Atmosphere ocean general circulation models Intergovernmental Panel on Climate Change - Fourth Assessment Report (AR4). To down scale to regional level need to include regional climate processes

5 Climate Modelling Pacific Region Climate processes Easterly trade winds, Southern Hemisphere high pressure belt, Intertropical Convergence Zone (ITCZ) and South Pacific Convergence Zone (SPCZ). Climatic variability is very strongly affected by the El Niño-Southern Oscillation (ENSO). Global models for the Pacific are satisfactory for temperature, but variable in regards to precipitation. Need to downscale these to represent the small islands, shape and topography.

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8 Pacific Climate Change Science Program (PCCSP) Provide information about past, present and future climate: seasonal cycles, climate variability, observed annual trends, and projections for atmospheric and oceanic variables. - temperature, rainfall, extreme events, sea-surface temperature, ocean acidification, and sea-level rise. - for three future 20-year periods centred on 2030, 2055 and 2090, and - for three different scenarios B1 (low), A1B (medium) and A2 (high).

9 PCCSP Regional temperature and rainfall projections Temperature 70% of global average 2030, +0.5 to 1.0 o C, 2055, +1.0 to 1.5 o C 2090, 1.5 to 3 More frequent extreme events - hot days and warm nights projected. Rainfall Increase in annual mean rainfall projected prominent near the SPCZ and ITCZ, with little change in the remainder of the region. Increase in the number of heavy rain days (20 50 mm). Extreme rainfall events (20 year events) projected four times per year (2055) and seven times per year, (2090) A2 emissions scenario. Droughts are projected to occur less often.

10 Why such a large increase in frequency of extreme events? 1 in 20 year Change made up of mean change and increased variation Variation is a huge challenge for modelling 4 x per year

11 PCCSP Downscaled country reports example - Yap

12 Downscaling climate to microclimate Biophysical models for translation of coarse climate data(air temp, wind, rainfall, and solar radiatio to an organism s microclimate. Thermal sensitivity of Aedes aegypti has been incorporated into a processed based container inhabiting mosquito simulation model (CIMSIM) Looked at water depth and daily temp cycles in containers. Aedes aegypti limited by water availability and egg desiccation resistance in the North and Inland Australia and limited by adult and larval cold tolerance in the South. Watch out for increased desiccation resistance.

13 2 Modelling Dengue potential at the edge of climatic envelope Statistical approaches to potential distribution based on relationship between empirical disease surveillance data and climatic data. Rogers and Hay 2012, Oxford University: Europe The climatic suitability for dengue transmission in continental Europe. Neil de Wet et al, 2005, HOTSPOTS uses both statistical and biological approaches to model potential distribution of six exotic mosquitos in New Zealand. Slaney et al 2012, HAIFA Presenting influence of climate change on disease in an interactive map based web browser for uptake by stakeholders New Zealand

14 Rogers and Hay 2012 Potential albopictus distribution

15 HOTSPOTS Ae. Aegypti potential distribution Now Potential Temperature suitability index 2 Climatic limits: (mid-winter temp; Degree-days, Cold stress, Min and max rainfall) 3 Habitat suitability: (Land-cover index; Topography index; Elevation exclusion index). (de Wet et al., 2001, 2005a, 2005b, 2005c).

16 Health info for action - HAIFA aegypti, albopictus polynesiensis. 2015, 2040, 2090 A1B, A2, B1 Website

17 Health HAIFA info for web action based - HAIFA browsing Potential distribution of Dengue December 2012 David Slaney (ESR), Aroon Parshotam (NIWA), Wei Ye (University of Waikato), Dan Tompkins (Landcare),

18 3 Dengue modelling Dengue transmission potential is easily established from presence of component causes (virus, vector, host, suitable habitat and climate) Dengue transmission risk is complex - associated with Vectors Virus interaction Vectorial capacity Vector Host interaction - Reproductive rate, SIER, movements Host Virus interaction. - Compartmental models, Virus serotypes, To be predictive about Dengue disease should really have some indicators from each of these interactions

19 Dengue modelling - biological Vectorial capacity Vector host interaction - basic reproduction rate host numbers and host recovery Transmission efficiency between vector and host and vector SIER Compartmental models Different serotypes Population age demographics Effect of climate change (increased temperature or rainfall) Direct and indirect changes in vectorial capacity Change interactions between vector-host-virus.

20 Temperature increase Increases biting activity Increases reproductive rate therefore abundance Extends or intensifies transmission season impacts age groups exposed EIP 7 days at 32 o, 21 days at 13 o Potentially decreases longevity? Rainfall changes Increased rainfall can increase mosquito abundance Decreased rainfall can also increase abundance through water storage behaviors

21 Climate change timeframes Bio-physical dengue models could be used to estimate the future change in transmission potential associated with climate scenarios. BUT Other factors change in the same timescale and affect transmission risk Human population size and age structure, urban living conditions; Globalization of vectors and serotypes; evolution Control factors - Changes in GDP, Improved barriers, vaccines, Insecticides, Genetic modification - Intervention and Economic models

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23 Dengue modelling fit with bigger picture Understanding what we need to know to act Driving force Developmental Dengue example Actions Population growth, Country development Population Policies urbanisation, tourismgrowth plans and policies Social & cultural beliefs Eco socia Pressure Distal cause State Proximal cause Exposure Physical/pathophysiological cause Effect Health outcome Production & consumption patterns Local climate change Global climate change Climate change, sea level rise Local climate, Ecosystem aquaitic disruption habitat, abundance Exposure of susceptable people Access to safe & exposure to contaminated environments, food, water Dengue infection and Ecological change Pathogen behaviour changes Food- & water-borne disease burden (incidence, disease outbreaks, morbidity, mortality) Land-cover changes Emissions reduction Monitoring and control Barriers, control, Behavioural education changes Treatment Quality of environment, food, water H man Mon c prog Ed Miti ad str Tre

24 Global climate change Global Dengue virus circulation Regional climate change Control measures Local climate Regional cycles Impact on human settings Adaptive strategies Micro climate Habitat suitability Population distribution, density and susceptibility Vector abundance Transmission and epidemics

25 References 1. Sharleen Harper, Aroon Parshotam & Graham McBride, 2010, Mathematical Modelling Approaches for the Impact of Climate Change and/or Variability on Infectious Diseases HAIFA report. May Hales, S., de Wet, N., Maindonald, J., Woodward, A., Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. The Lancet. 3. WHO. (2003). Methods of assessing human health vulnerability and public health adaptation to climate change. Health and Global environmental Change. Series 4. Lafferty, Kevin D The ecology of climate change and infectious diseases. Ecology 90: Supriatna, A.K., Soewono, E., van Gils, S.A. A two-age-classes dengue transmission model (2008) Mathematical Biosciences, 216 (1), pp doi: /j.mbs Rogers and Hay European Centre for Disease Prevention and Control. The climatic suitability for dengue transmission in continental Europe. Stockholm: ECDC. 7. N De Wet, D Slaney, W Ye, S Hales, Exotic Mosquito Risk Profiles for New Zealand. - waikato.ac.nz 8. de Wet N, Ye W, Hales S, Warrick R, Woodward A, Weinstein P 2001,. Use of a computer model to identify potential hotspots for dengue fever in New Zealand. The New Zealand Medical Journal 114(1140): M Derouich 1, A Boutayeb 1,2* and EH Twizell 2 A model of dengue fever BioMedical Engineering OnLine 2003, 2:4 10. de Wet, N., Ye, W., Slaney, D., Hales, S. and Warrick, R. (2005). HOTSPOTS capacity for the analysis of mosquito-borne disease risks in New Zealand. System description and users guide. International Global Change Institute, University of Waikato, Hamilton and Ecology and Health Research Centre, Wellington School of Medicine and Health Sciences, University of Otago, Wellington. 11. Burattini, M.N., Chen, M., Chow, A., Coutinho, F.A.B., Goh, K.T., Lopez, L.F., Ma, S., and Massad, E. 2008, Modelling the control strategies against dengue in Singapore. Epidemiol. Infect. 136: Michael Kearney, Warren P. Porter, Craig Williams, Scott Ritchie, Ary A. Hoffmann Integrating biophysical models and evolutionary theory to predict climatic impacts on species ranges: the dengue mosquito Aedes aegypti in Australia. Functional Ecology Volume 23, Issue 3, pages , June Kelly Richardson, Ary A. Hoffmann, Petrina Johnson, Scott Ritchie, and Michael R. Kearney Thermal Sensitivity of Aedes aegypti from Australia: Empirical Data and Prediction of Effects on Distribution. Journal of Medical Entomology, 48(4): Australian Bureau of Meteorology and CSIRO, Climate Change in the Pacific: Scientific Assessment and New Research. Volume 1: Regional Overview. Volume 1: Regional overview 15. Australian Bureau of Meteorology and CSIRO, Climate Change in the Pacific: Scientific Assessment and New Research. Volume 1: Regional Overview. Volume 2: Country Reports. 16. Meehl, G.A., Covey, C., Taylor, K.E., Delworth, T., Stouffer, R.J., Latif, M., McAvaney, B., Mitchell, J.F. (more), 2007: The WCRP CMIP3 Multimodel Dataset: A new era in climate change research. Bulletin of the American Meteorological Society, 88, Louis Lambrechts et al, Impact of daily temperature fluctuations on dengue virus transmission by Aedes aegypti. April 18, 2011, doi: /pnas