Projected changes in reported campylobacteriosis and cryptosporidiosis rates as a function of climate change: a New Zealand study

Two pathogens whose reported incidence rates may alter under climate change and variability were selected for study: the bacterium Campylobacter and the protozoan oocyst Cryptosporidium. Both are of particular importance in New Zealand, given its extensive and intensive agricultural farming systems, and therefore to other agriculturally-based economies. Local and international studies have indicated that rates of illnesses associated with these pathogens (campylobacteriosis and cryptosporidiosis) may increase as temperature rises and as rainfall becomes more intense. An existing calibrated linear SIR (Susceptible-Ill-Recovered) model was used to make predictions of the proportional change in the reported rates of these two zoonoses. This method uses analytical solutions of the SIR model and a simple exponential approach to describe the temporal changes in pathogen contact rates—and hence of reported disease rates. These changes reflect climate change impacts only and do not consider adaptation or mitigation measures. Projections cannot be made of the actual-but-unknown-illness rates because of under-reporting throughout the country. The SIR model outputs provide projected changes in reported disease incidence as a function of temperature and rainfall for the years 2015, 2040 and 2090. These are calculated for three climate change scenarios: low (B1), medium (A1B) and high (A2) emissions of greenhouse gases and for four seasons. Projections show the potential for substantial changes in reported rates by the year 2090 across New Zealand, with children most at-risk. Maximum increases in reported illness rates tend to occur in summer when pathogen contact rates are greatest. Average annual rates of increase of reported campylobacteriosis are predicted to rise by as much as 20 % and by 36 % for cryptosporidiosis (children, A2 scenario, 2090). To our knowledge, this is the first time that SIR modelling has been coupled with climate change projections.