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A climate based dengue early warning system for Pune, India
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Dengue incidence has grown dramatically in recent decades, with about half of the world’s population now at risk. Climate plays a significant role in the incidence of dengue. However, the climate-dengue association needs to be clearly understood at regional levels due to the high spatial variability in weather conditions and the non-linear relationship between climate and dengue. The current study evaluates the impacts of weather on dengue mortality in the Pune district of India, for a 15-year period, from 2001 to 2015. To effectively resolve the complexity involved in the weather-dengue association, a new dengue metric is defined that includes temperature, relative humidity, and rainfall-dependent variables such as intraseasonal variability of monsoon (wet and dry spells), wet-week counts, flushing events, and weekly cumulative rains. We find that high dengue mortality years in Pune are comparatively dry, with fewer monsoon rains and flush events (rainfall > 150 mm), but they have more wet weeks and optimal humid days (days with relative humidity between 60–78%) than low dengue mortality years. These years also do not have heavy rains during the early monsoon days of June, and the temperatures mostly range between 27–35°C during the summer monsoon season (June–September).  Further, our analysis shows that dengue mortality over Pune occurs with a 2-5 months lag following the occurrence of favourable climatic conditions. Based on these weather-dengue associations, an early warning prediction model is built using the machine learning algorithm random forest regression. It provides a reasonable forecast accuracy with root mean square error (RMSE) = 1.01. To assess the future of dengue mortality over Pune under a global warming scenario, the dengue model is used in conjunction with climate change simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6). Future projections show that dengue mortality over Pune will increase in the future by up to 86 percent (relative to the reference period 1980–2014) by the end of the 21st century under the high emission scenario SSP5-8.5, primarily due to an increase in mean temperature (3°C increase relative to the reference period). The projected increase in dengue mortality due to climate change is a serious concern that necessitates effective prevention strategies and policy-making to control the disease spread.
Title: A climate based dengue early warning system for Pune, India
Description:
Dengue incidence has grown dramatically in recent decades, with about half of the world’s population now at risk.
Climate plays a significant role in the incidence of dengue.
However, the climate-dengue association needs to be clearly understood at regional levels due to the high spatial variability in weather conditions and the non-linear relationship between climate and dengue.
The current study evaluates the impacts of weather on dengue mortality in the Pune district of India, for a 15-year period, from 2001 to 2015.
To effectively resolve the complexity involved in the weather-dengue association, a new dengue metric is defined that includes temperature, relative humidity, and rainfall-dependent variables such as intraseasonal variability of monsoon (wet and dry spells), wet-week counts, flushing events, and weekly cumulative rains.
We find that high dengue mortality years in Pune are comparatively dry, with fewer monsoon rains and flush events (rainfall > 150 mm), but they have more wet weeks and optimal humid days (days with relative humidity between 60–78%) than low dengue mortality years.
These years also do not have heavy rains during the early monsoon days of June, and the temperatures mostly range between 27–35°C during the summer monsoon season (June–September).
  Further, our analysis shows that dengue mortality over Pune occurs with a 2-5 months lag following the occurrence of favourable climatic conditions.
Based on these weather-dengue associations, an early warning prediction model is built using the machine learning algorithm random forest regression.
It provides a reasonable forecast accuracy with root mean square error (RMSE) = 1.
01.
To assess the future of dengue mortality over Pune under a global warming scenario, the dengue model is used in conjunction with climate change simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6).
Future projections show that dengue mortality over Pune will increase in the future by up to 86 percent (relative to the reference period 1980–2014) by the end of the 21st century under the high emission scenario SSP5-8.
5, primarily due to an increase in mean temperature (3°C increase relative to the reference period).
The projected increase in dengue mortality due to climate change is a serious concern that necessitates effective prevention strategies and policy-making to control the disease spread.
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