Modelling and Map Portal

Aims

To identify six indicator diseases to be used as a framework for modelling the potential impacts of climate change on infectious diseases in New Zealand; construct at the 5x5 km scale disease specific models coupled to climate change projections for the three greenhouse gas emission scenarios B1 (low), A1B (medium), and A2 (high) and the three time periods 2015, 2040, and 2090; and develop a web based Geographic Information System to display the results of the disease projection models as well as the ability to carry out ‘what if’ scenarios.

Summary

Following a ranking and consultation process the six indicator diseases selected for modelling were campylobacteriosis, cryptosporidiosis, meningococcal disease, influenza, and Ross River and dengue fevers (the latter two are exotic diseases for New Zealand). Using these diseases as a framework a range of mathematical, statistical and mechanistic disease specific projection models were constructed via the analysis of health, demographic, environmental, and climate data. The results from these models were then incorporated into a purpose built web based Geographic Information System containing over 500 discrete maps displaying annual and seasonal projections for the six indicator diseases for 2015, 2040 and 2090 under climate change scenarios B1, A1B, and A2.

Using the resources in HAIFA, end-users will now be able to (i) make predictions of which infectious diseases (of the six) and contributing risk factors will be of key concern to human health in 2015, 2040 and 2090; (ii) predict changes in the occurrence levels of these infectious diseases due to climate change; (iii) identify the communities and population groups most likely at risk from these infectious diseases; and (iv) recognise the infectious diseases predicted to most threaten specified communities and population groups.

Although there are a number of inherent assumptions and uncertainties involved in the modelling (detailed in the Modelling Report) it is important to carry out such studies for informing climate change adaptation planning.

Resources

Modelling the Health Impacts of Climate Change Report  [PDF, 6 MB]    
Map Portal  
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de Wet N., Slaney D., Ye W., Hales S. & Warrick, R. (2005). Hotspots: Exotic mosquito risk profiles for New Zealand. IGCI Report. Hamilton, New Zealand: International Global Change Institute (IGCI), University of Waikato; Ecology and Health Research Centre, Wellington School of Medicine and Health Sciences. http://researchcommons.waikato.ac.nz/handle/10289/916 (external link)

de Wet N., Slaney D., Ye W., Hales S. & Warrick, R. (2005). Hotspots: Modelling capacity for vector-borne disease risk analysis in New Zealand: A case study of Ochlerotatus camptorhynchus incursions in New Zealand. IGCI Report. Hamilton, New Zealand: International Global Change Institute (IGCI), University of Waikato; Ecology and Health Research Centre, Wellington School of Medicine and Health Sciences. http://researchcommons.waikato.ac.nz/handle/10289/917  (external link)

Hambling T., & Bandaranayake D. 2012. Editorial - Climate change and waterborne diseases in New Zealand and the role of primary care in the early detection of common source waterborne disease outbreaks. New Zealand Public Health Surveillance Report 10(4):2. http://www.surv.esr.cri.nz/surveillance/NZPHSR.php?we_objectID=3193 (external link)  

Hill N. 2012. Disease Modelling and Handling the Presentation of Large Datasets. ESRI Asia Pacific User Conference, 5-7 November, Sky City Convention Centre Auckland. [Disease Modelling and Handling the Presentation of Large Datasets [PDF, 1 MB]

Mackereth G., & Slaney D. 2012. Climate modelling and dengue modelling. Dengue and climate change workshop, 3-6 September 2012, Infectious Diseases Unit, Institut Louis Malardé, Tahiti. [Climate modelling and dengue modelling [PDF, 1.4 MB]

McBride G., Slaney D., & Tait A. 2013. Predicting changes in reported notifiable disease rates for New Zealand using a SIR modelling approach. Special session on Water, Climate and Health, General Assembly of the European Geosciences Union (EGU), 7-12 April 2013, Vienna, Austria. Predicting changes in reported notifiable disease rates for New Zealand using a SIR modelling approach [PDF, 264 KB]

McBride G., Tait A., Slaney D. (2014) Projected changes in reported campylobacteriosis and cryptosporidiosis rates as a function of climate change: a New Zealand study. Stochastic Environmental Research and Risk Assessment 28(8):2133-2147. http://link.springer.com/article/10.1007%2Fs00477-014-0920-5 (external link)

Meade A., Benschop J., Jones G., French N., & Slaney D. 2011. The effect of climate variation on infectious diseases in humans in New Zealand. 1st International One Health Congress, 14-16th February 2011, Melbourne, Australia. The effect of climate variation on infectious diseases in humans in New Zealand [PDF, 2.8 MB]  

Slaney D., Parshotam A., Ye W., Tompkins D., & Mackereth G. 2012. Modelling dengue potential in New Zealand under various climate change scenarios. 11th Arbovirus Research in Australia and the 10th Mosquito Control Association of Australia Symposium on the 9 – 14 September 2012 on the Gold Coast, Queensland. [Modelling dengue potential in New Zealand under various climate change scenarios [PDF, 271 KB]

Tompkins D., Slaney D. (2014) Exploring the potential for Ross River virus emergence in New Zealand. Vector-Borne and Zoonotic Diseases 14(2):141-148. DOI: http://dx.doi.org/10.1089/vbz.2012.1215 (external link)

Wilson N., Slaney D., Baker M.G., Hales S., & Britton E. 2011. Climate change and infectious diseases in New Zealand: a brief review and tentative research agenda. Reviews on Environmental Health 26(2):93-99. DOI: http://dx.doi.org/10.1515/reveh.2011.013 (external link)

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