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Chikungunya is a viral disease transmitted by Aedes aegypti and Aedes albopictus mosquitoes. Aedes albopictus is present in Europe and is expanding its territory. Besides more global travel, climate change can cause more chikungunya cases in Europe since shifts in temperature and precipitation make the region’s climate more suitable for the transmission of the disease.
One way of measuring the health threats associated with chikungunya under the changing climate is to estimate the changes in the basic reproduction rate (R0), which estimates the expected number of secondary infections from one infectious case in a completely susceptible population. If R0 is higher than 1, outbreaks have the potential to grow. The higher R0 is, the faster the outbreak will grow.
This indicator uses a stage-structured mechanistic model to estimate the mosquito abundance and subsequently the basic reproduction rate (R0) for chikungunya by combining information on temperature, rainfall, daylight hours, mosquito abundance, and human population density.
Caveats
The predicted R0 should not be confused with actual chikungunya cases, although it is an indicator of the potential for outbreaks. Some key limitations of the model include some gaps in our knowledge on the infectivity of dengue vectors, the absence of vector densities in the estimation of vectorial capacity, a need for better scenarios for land use, urbanization, population growth, human mobility, and microscale climate variability and the limitations around developing stochastic estimates of the risk of outbreak in non-endemic areas, as the deterministic approaches are less appropriate for estimating outbreak risks and basic reproduction numbers when the incidence rate is low and subject to high stochasticity. For a full description of the model limitations and caveats please see Liu-Helmersson et al. (2014, 2016) and Rocklöv et al. (2019).
Reference information
Websites:
Source:
Publication:
van Daalen, K. R., et al., 2024, The 2024 Europe report of the Lancet Countdown on health and climate change: unprecedented warming demands unprecedented action, The Lancet Public Health. https://doi.org/10.1016/S2468-2667(24)00055-0
Data sources:
Climatic data:
- Copernicus Climate Change Service (C3S), ERA5 Land Reanalysis data
Population data: hybrid gridded data combining:
- NASA-SEDAC Gridded Population of the World (GPW) v4
- ISIMIP historic and future annual global gridded population data
Link to repository with code:
- Pratik, S., 2024, Dengue transmission suitability LCDE 2024, https://github.com/Pratik697/Dengue-transmission-suitability-LCDE2024
Additional reading:
- Colón-González, F. J., et al., 2021, Projecting the risk of mosquito-borne diseases in a warmer and more populated world: a multi-model, multi-scenario intercomparison modelling study, The Lancet Planetary Health 5(7) e404-e414. https://doi.org/10.1016/S2542-5196(21)00132-7
- Murray, K. A., et al., 2020, Tracking infectious diseases in a warming world, BMJ 371, m3086. https://doi.org/10.1136/bmj.m3086
- DiSera, L., et al., 2020, The mosquito, the virus, the climate: an unforeseen reunion in 2018, Geohealth 4(8), e2020GH000253. https://doi.org/10.1029/2020GH000253
- Rocklöv, J., et al., 2019, Using big data to monitor the introduction and spread of Chikungunya, Europe, 2017, Emerging infectious diseases 25(6), 1041. https://doi.org/10.3390/ijerph120505256
Contributor:
Lancet Countdown in EuropePublished in Climate-ADAPT: Dec 5, 2022
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