Aronrag Meeyai, Mahidol University, Thailand: aronrag.coo@mahidol.ac.th

Kraichat Tantrakarnapa, Mahidol University: kraichat.tan@mahidol.ac.th

Climate change and infectious diseases: predictive modelling

EMISSIONS & Land-use Change

IMPACTS

Source: The Fifth Assessment Report. Intergovernmental Panel on Climate Change (IPCC).

Impacts assessment

Aronrag Meeyai, Mahidol University, Thailand: aronrag.coo@mahidol.ac.th

Kraichat Tantrakarnapa, Mahidol University: kraichat.tan@mahidol.ac.th

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EMISSIONS & Land-use Change

IMPACTS

Source: The Fifth Assessment Report. Intergovernmental Panel on Climate Change (IPCC).

Impacts assessment

EMISSIONS & Land-use Change

IMPACTS

Source: ECLAC, UN 2011. An assessment of the economic impact of climate change on the health sector in Saint Lucia.

Impacts assessment

Dengue as a case study

Climate change and infectious diseases: predictive modelling

Source: Cory W. Morin, Andrew C. Comrie, and Kacey Ernst. Environmental Health Perspectives, 2013.

Interaction between environment, vectors, and DENV

Source: Cory W. Morin, Andrew C. Comrie, and Kacey Ernst. Environmental Health Perspectives, 2013.

Interaction between environment, vectors, and DENV

Dengue distribution models and projections

The main types of models used to forecast future climatic influences on infectious diseases (each type of model addresses somewhat different questions) include:

- Statistical models, and landscape-based models - Process-based models (mechanistic models)

Types of models:

The derivation of a statistical relationship between the distribution of the disease and the climatic conditions. This describes the climatic influence on the actual distribution of the disease, given current levels of human intervention (disease control, environmental management, etc.). Then applying this statistical equation to future climate scenarios, the actual distribution of the disease in future is estimated. The effects of both climatic and other environmental factors (e.g. different vegetation types: landscape-based models) can be included in the models.

Statistical models & landscape-based models:

Linear regression models Colon-Gonzalez et al. used multiple linear regression models to examine the associations between changes in the climate variability and dengue incidence in the warm and humid regions of Mexico for the years 1985–2007.

Time series/wavelet time series models Gharbi et al. fitted a SARIMA model of dengue incidence and climate variables including temperature, rainfall and relative humidity in French West Indies for the period 2000–2006.

Hu et al. used a SARIMA model to examine the impact of El-Niño on dengue in Queensland, Australia for the period 1993–2005.

Johansson et al. used wavelet time series analysis has been applied to examine the associations between El-Niño Southern Oscillation (ENSO), local weather, and dengue incidence in Puerto Rico, Mexico, and Thailand, with the aim of identifying time- and frequency-specific associations.

Castells et al. used a wavelet time series analysis to demonstrate a strong non-stationary association between dengue incidence and El-Niño in Thailand for the years 1986 to 1992.

Thai et al. used a wavelet time series analysis to investigate the associations between climate variables including mean temperature, humidity and rainfall, and ENSO indices and dengue incidence in Vietnam during the period 1994 to 2009.

Chowell et al. used wavelet time series analysis to determine the relationship between climatic factors including mean, maximum and minimum temperature and rainfall and dengue incidence for the period 1994–2008 in jungle and coastal regions of Peru.

Poisson regression models Earnest et al. used Poisson regression model to determine the association between climate variables (temperature, humidity, rainfall), ENSO indices and dengue in Singapore.

Pinto et al. used Poisson regression model to determine the impact of climate variables (temperature, rainfall and relative humidity) on dengue cases in Singapore.

Chen et al. applied Poisson regression using a GAM model to examine the relationship between precipitation and dengue in Taiwan for the period 1994–2008.

Bayesian models Hu et al. applied Bayesian spatial conditional autoregressive modelling to demonstrate the impact of climatic, social and ecological factors on dengue in Queensland, Australia.

Non-linear models Descloux et al. developed an early warning system using a long-term data set (39 years) including dengue cases and meteorological data (mean temp, min and max temperature, relative humidity, precipitation and ENSO indices) in New Caledonia, using multivariate non-linear models.

The statistical models & landscape-based models employed for evaluating the relationship between climate variables and dengue have been typically different with respect to the distributional assumptions (e.g., normal, Poisson), the nature of the relationship (linear and non-linear) and the spatial and/or temporal dynamics of the response. Overall, the models reveal variability in the relationship between dengue and climate variables, related to country, but the methods identified an association with temperature followed by rainfall in the majority of research. Source: Naish S et al. BMC Infectious Diseases 2014, 14:167

Projections using statistical & landscape-based models:

Source: Hales S et al. Potential effect of population and climate changes on global distribution of dengue fever: an empirical model. Lancet 360, 830–834 (2002).

Estimated population at risk of dengue in 1990 (baseline) and 2050 (projection). [using logistic regression model with humidity as the predictor]

Source: Astrom, C. et al. Potential distribution of dengue fever under scenarios of climate change and economic development. Ecohealth 9, 448–454 (2012).

Estimated population at risk of dengue in 2050 (projection). [based on climate change and socioeconomic development (GDPpc)]

Process-based models use equations that express the scientifically relationship between climatic variables and biological parameters – e.g., vector breeding, survival, and biting rates. In their simplest form, such models express, via a set of equations, how climate variables would affect vector and, therefore, disease transmission. The conditioning effects of human interventions and social contexts can also be incorporated.

Process based (mechanistic) models:

Process based (mechanistic) models:

Source: Patz, J. A et al. Dengue fever epidemic potential as projected by general circulation models of global climate change. Environ. Health Perspect. 106, 147–153 (1998).

The specific GCMs yielded the following increases: GFDL89, 45% (35-69%); ECHAM 1-A, 47% (37-74%); and UKTR, 31% (24-47%). Globally, the largest change would occur in temperate regions. Tropical and subtropical regions would experience an increase in epidemic potential to a lesser extent or would remain unchanged. For developing countries, these maps indicate upward changes in potential infectious disease transmission. On aggregate, this increase in potential risk varied between 31 and 47% for these regions.

Projected global distribution of dengue in 2050. [under scenarios of a 1.16 °C temperature increase from 3 GCMs]

The areas where the potential transmission intensity is greater than 1 correspond fairly well to areas where recent dengue activity has been observed or areas susceptible to dengue transmission (top: baseline).

At a global temperature increase of 2°C, the model shows possible transmission of dengue in some parts of southern Europe and of southern US. But the changes in areas where endemic dengue already occurs are limited.

Source: Jetten, T. H. & Focks, D. A. Potential changes in the distribution of dengue transmission under climate warming. Am. J. Trop. Med. Hyg. 57, 285–297 (1997).

Projected transmission intensity of dengue for simulated 2°C rises in temperature.

Lessons learned from model projections: All of the studies conducted to date project an increase in the overall global extent of dengue transmission. The results do not agree with regard to the specific geographical areas where expansion or intensification is likely to occur. As projecting the global distribution of dengue involves the inclusion of multiple covariates, the comparison of new projections would require projecting changes in population, climate and economic factors on an agreed set of future dates. A minimum spatial resolution would also be require for such a comparison.

Source: Messina et al. NATURE REVIEWS . MICROBIOLOGY. VOLUME 13 , APRIL 2015.

Source: Messina et al. NATURE REVIEWS . MICROBIOLOGY. VOLUME 13 , APRIL 2015.