Cautioning the use of degree‐day models for climate change projections in the presence of parametric uncertainty

Abstract

Developmental models, such as degree-day models, are commonly used to predict the impact of future climate change on the intensity, distribution, and timing of the transmission of infectious diseases, particularly those caused by pathogens carried by vectors or intermediate hosts. Resulting projections can be useful in policy discussions concerning regional or national responses to future distributions of important infectious diseases. Though the simplicity of degree-day models is appealing, little work has been done to analyze their ability to make reliable projections of the distribution of important pathogens, vectors, or intermediate hosts in the presence of the often considerable parametric uncertainty common to such models. Here, a population model of Oncomelania hupensis, the intermediate host of Schistosoma japonicum, was used to investigate the sensitivity of host range predictions in Sichuan Province, China, to uncertainty in two key degree-day model parameters (δmin and K). The intent was to examine the consequences of parametric uncertainty in a plausible biological model, rather than to generate the definitive model. Results indicate that model output, the seasonality of population dynamics, and range predictions, particularly along the edge of the range, are highly sensitive to changes in model parameters, even at levels of parametric uncertainty common to such applications. Caution should be used when interpreting the results of degree-day models used to generate predictions of disease distribution and risk under scenarios of future climate change, and predictions should be considered most reliable when the temperature ranges used in projections resemble those used to estimate model parameters. Given the potential for substantial changes in output with modest changes in parameter values, particular concern is warranted when results will be used to inform policy and management decisions.

Developmental Models for Estimating Ecological Responses to Environmental Variability: Structural, Parametric, and Experimental Issues

“…Hartley et al (2010); Ogden et al (2005); Liang et al (2005); Remais et al (2007)). Because they account for the interplay of development, mortality, reproduction and other temperature-sensitive processes, such population dynamics models can generate predictions about population viability that differ from models based solely on the degree-day model framework (Moore et al 2012). …”

“…Thus, even if model assumptions are met and results validated under current conditions, these same assumptions might not hold when applied to future conditions. This is especially important to consider when degree-day models are used to make projections far into the future, where uncertainties in degree-day model parameter estimates (Bonhomme 2000; Bergant and Trdan 2006; Moore et al 2012) are compounded with those inherent in climate forecasting (Hawkins and Sutton 2009). …”

“…Previous work has examined degree-day model assumptions and their limitations (Allen 1976; Pruess 1983; Higley et al 1986; Roltsch et al 1999; Bonhomme 2000; Bergant and Trdan 2006; Moore et al 2012), and it is well established that the degree-day model is a simplistic representation of a potentially complex developmental process. Bonhomme (2000) discusses various limits to the degree-day approach in an agricultural context, including the influence of linearizing non-linear development.…”

Genetic Evidence of Contemporary Dispersal of the Intermediate Snail Host of Schistosoma japonicum: Movement of an NTD Host Is Facilitated by Land Use and Landscape Connectivity

“…Meanwhile, the detection of Oncomelania spp. populations in new areas suggests the range of the intermediate snail host is expanding within China [31], and rising temperatures due to global climate change may further this expansion [33–35]. Understanding the dispersal characteristics of this intermediate host can provide new insights into the spatial dynamics of transmission, and can assist public health officials in limiting the geographic spread of infection.…”

Sporogonic Cycles Calculated Using Degree-Days, as a Basis for Comparison of Malaria Parasite Development in Different Eco-Epidemiological Settings in India

“…Degree-Days and Malaria prediction of disease based on the degree-day model in view of climate change should be considered most reliable when the temperature ranges used in a projection resemble those used to estimate model parameters (18). It is important to point out here that the micro-niche of mosquitoes is strictly not outdoors, while climate change projections are based on sensors installed outdoors (19).…”