Predicting the effect of climate change on African trypanosomiasis: integrating epidemiology with parasite and vector biology - PubMed (original) (raw)

Predicting the effect of climate change on African trypanosomiasis: integrating epidemiology with parasite and vector biology

Sean Moore et al. J R Soc Interface. 2012.

Abstract

Climate warming over the next century is expected to have a large impact on the interactions between pathogens and their animal and human hosts. Vector-borne diseases are particularly sensitive to warming because temperature changes can alter vector development rates, shift their geographical distribution and alter transmission dynamics. For this reason, African trypanosomiasis (sleeping sickness), a vector-borne disease of humans and animals, was recently identified as one of the 12 infectious diseases likely to spread owing to climate change. We combine a variety of direct effects of temperature on vector ecology, vector biology and vector-parasite interactions via a disease transmission model and extrapolate the potential compounding effects of projected warming on the epidemiology of African trypanosomiasis. The model predicts that epidemics can occur when mean temperatures are between 20.7°C and 26.1°C. Our model does not predict a large-range expansion, but rather a large shift of up to 60 per cent in the geographical extent of the range. The model also predicts that 46-77 million additional people may be at risk of exposure by 2090. Future research could expand our analysis to include other environmental factors that influence tsetse populations and disease transmission such as humidity, as well as changes to human, livestock and wildlife distributions. The modelling approach presented here provides a framework for using the climate-sensitive aspects of vector and pathogen biology to predict changes in disease prevalence and risk owing to climate change.

PubMed Disclaimer

Figures

Figure 1.

Figure 1.

Relationship between temperature and four different parameters that influence _R_0. The (a) daily mortality rate of G. m. morsitans (dashed line) and G. pallidipes (_d_V, solid line), (b) tsetse daily feeding rate a. (c) Daily rate of parasite development in tsetse (μ) and (d) G. m. morsitans and G. pallidipes abundance (_N_V) as a function of mean annual temperature.

Figure 2.

Figure 2.

Relationship between temperature and _R_0 when T. b. rhodesiense is vectored by G. m. morsitans (dashed line) or G. pallidipes (solid line). _R_0 = 1 represents a threshold for the successful invasion or persistence of the parasite into a susceptible host community.

Figure 3.

Figure 3.

Suitable geographical range for T. b. rhodesiense transmission based on range where _R_0 > 1 for G. pallidipes. The darker grey region (purple on the online version) represents the portion of the range also predicted to be the ideal habitat for Morsitans group tsetse flies. Lighter grey (blue on the online version) represents the portion of the suitable range currently believed to be unoccupied by Morsitans group tsetse flies. Circles represent locations of previous African trypanosomiasis outbreaks in East Africa [62]. (Online version in colour.)

Figure 4.

Figure 4.

Suitable geographical range for T. b. rhodesiense transmission in (a) 2055 and (b) 2090 under the A2 emissions scenario using the CCSM3 global circulation model. The predicted range is in black (light blue on the online version), with the white stippled (dark blue on the online version) region representing the existing portion of the predicted range and the unstippled area representing the newly expanded part of the range. Grey (red on the online version) areas on the map represent currently suitable areas predicted to be too hot under future conditions. Circles represent locations of previous outbreaks in East Africa [62]. (Online version in colour.)

Similar articles

Cited by

References

    1. Colwell R. R. 1996. Global climate and infectious disease: the cholera paradigm. Science 274, 2025–203110.1126/science.274.5295.2025 (doi:10.1126/science.274.5295.2025) - DOI - DOI - PubMed
    1. Pascual M., Rodo X., Ellner S. P., Colwell R., Bouma M. J. 2000. Cholera dynamics and El Niño-southern oscillation. Science 289, 1766–176910.1126/science.289.5485.1766 (doi:10.1126/science.289.5485.1766) - DOI - DOI - PubMed
    1. Daszak P., Cunningham A. A., Hyatt A. D. 2000. Emerging infectious diseases of wildlife: threats to biodiversity and human health. Science 287, 443–44910.1126/science.287.5452.443 (doi:10.1126/science.287.5452.443) - DOI - DOI - PubMed
    1. Hay S. I., Cox J., Rogers D. J., Randolph S. E., Stern D. I., Shanks G. D., Myers M. F., Snow R. W. 2002. Climate change and the resurgence of malaria in the East African highlands. Nature 415, 905–90910.1038/415905a (doi:10.1038/415905a) - DOI - DOI - PMC - PubMed
    1. Pascual M., Ahumada J. A., Chaves L. F., Rodo X., Bouma M. J. 2006. Malaria resurgence in the East African highlands: temperature trends revisited. Proc. Natl Acad. Sci. USA. 103, 5829–583410.1073/pnas.0508929103 (doi:10.1073/pnas.0508929103) - DOI - DOI - PMC - PubMed

MeSH terms

LinkOut - more resources