Potential effect of population and climate changes on global distribution of dengue fever: an empirical model

Lancet. 2002 Sep 14;360(9336):830-4. doi: 10.1016/S0140-6736(02)09964-6.


Background: Existing theoretical models of the potential effects of climate change on vector-borne diseases do not account for social factors such as population increase, or interactions between climate variables. Our aim was to investigate the potential effects of global climate change on human health, and in particular, on the transmission of vector-borne diseases.

Methods: We modelled the reported global distribution of dengue fever on the basis of vapour pressure, which is a measure of humidity. We assessed changes in the geographical limits of dengue fever transmission, and in the number of people at risk of dengue by incorporating future climate change and human population projections into our model.

Findings: We showed that the current geographical limits of dengue fever transmission can be modelled with 89% accuracy on the basis of long-term average vapour pressure. In 1990, almost 30% of the world population, 1.5 billion people, lived in regions where the estimated risk of dengue transmission was greater than 50%. With population and climate change projections for 2085, we estimate that about 5-6 billion people (50-60% of the projected global population) would be at risk of dengue transmission, compared with 3.5 billion people, or 35% of the population, if climate change did not happen.

Interpretation: We conclude that climate change is likely to increase the area of land with a climate suitable for dengue fever transmission, and that if no other contributing factors were to change, a large proportion of the human population would then be put at risk.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aedes / growth & development
  • Aedes / virology
  • Animals
  • Dengue / transmission*
  • Disease Outbreaks / statistics & numerical data*
  • Forecasting
  • Greenhouse Effect
  • Humans
  • Humidity
  • Logistic Models
  • Population Dynamics*
  • Risk
  • Topography, Medical