A study, published in the journal Lancet Planetary Health, shows that the use of climatological information can help health authorities anticipate mosquito-borne diseases transmission, such as dengue, and optimize the use of resources. Mosquito-borne diseases are particularly sensitive to changes in climate, as temperature affects the proliferation and activity of the vector and the replication of the virus in it.

Thanks to a model that incorporates forecasts of rainfall, temperature and the Niño phenomenon, the monthly number of dengue cases can be predicted several months in advance, according to a study developed by ISGlobal, coordinated with the London School of Hygiene & Tropical Medicine.

Dengue fever, which number of cases worldwide has increased significantly over the last few decades (nearly 60 million in 2013, according to the World Health Organization) is particularly sensitive to climate change. For example, temperature affects the proliferation and activity of the vector (the Aedes mosquito) and virus replication therein.

However, collaboration between meteorological and health services to assist public health decision-making is still very limited. In addition, predictions on the effect of climate on dengue incidence are so made retrospectively, using data that would not have been available before the season.

This is the first work that used climatological forecasts to make long-term predictions about the incidence of dengue in the city of Machala, Ecuador, a highly endemic region to the disease.

In particular, they used real-time forecasts of temperature, precipitation, and El Niño index, to predict the number of monthly dengue cases in 2016. They compared the incidence calculated by the probabilistic model and the actual recorded incidence that year.

In addition, they used active epidemiological surveillance data to eliminate false cases (which were reported as dengue but were actually cases of chikungunya, a virus that was introduced to the region in 2014 and produces similar symptoms to dengue).

The results show that the model effectively predicted the increase in dengue incidence in the first half of 2016 (compared to the previous five years), and also that the peak of the epidemic would occur three months earlier than normal.

The big advantage of this model is that it makes predictions about the entire season since the beginning of the year,” says Rachel Lowe, the study’s first author. “This prior information on the magnitude and evolution of peak incidence can help public health authorities better invest resources, especially when they are scarce,” adds Xavier Rodo, head of the ISGlobal Climate and Health program.

The authors caution that climate predictions are particularly useful in years when El Niño or La Niña events occur. Other years, factors such as human mobility, vector control campaigns or the level of population immunity can play a role as or more important than climate. In addition, they point out that reliable, timely and adequate spatiotemporal climatological data are needed, which is not always easy in low-resource countries.


Source: SINC