Data-driven computational intelligence applied to dengue outbreak forecasting: a case study at the scale of the city of Natal, RN-Brazil.
Sci Rep
; 12(1): 6550, 2022 04 21.
Article
in English
| MEDLINE | ID: covidwho-1815594
ABSTRACT
Dengue is recognized as a health problem that causes significant socioeconomic impacts throughout the world, affecting millions of people each year. A commonly used method for monitoring the dengue vector is to count the eggs that Aedes aegypti mosquitoes have laid in spatially distributed ovitraps. Given this approach, the present study uses a database collected from 397 ovitraps allocated across the city of Natal, RN-Brazil. The Egg Density Index for each neighborhood was computed weekly, over four complete years (from 2016 to 2019), and simultaneously analyzed with the dengue case incidence. Our results illustrate that the incidence of dengue is related to the socioeconomic level of the neighborhoods in the city of Natal. A deep learning algorithm was used to predict future dengue case incidence, either based on the previous weeks of dengue incidence or the number of eggs present in the ovitraps. The analysis reveals that ovitrap data allows earlier prediction (four to six weeks) compared to dengue incidence itself (one week). Therefore, the results validate that the quantification of Aedes aegypti eggs can be valuable for the early planning of public health interventions.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Aedes
/
Dengue
Type of study:
Case report
/
Observational study
/
Prognostic study
Limits:
Animals
/
Humans
Country/Region as subject:
South America
/
Brazil
Language:
English
Journal:
Sci Rep
Year:
2022
Document Type:
Article
Affiliation country:
S41598-022-10512-5
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