Data-driven computational intelligence applied to dengue outbreak forecasting: a case study at the scale of the city of Natal, RN-Brazil.

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.

Entomo-virological surveillance strategy for dengue, Zika and chikungunya arboviruses in field-caught Aedes mosquitoes in an endemic urban area of the Northeast of Brazil.

Aedes spp. are considered the main vectors of dengue (DENV), Zika (ZIKV) and chikungunya (CHIKV) viruses in the world. Arbovirus detection in Aedes mosquitoes can alert authorities to possible outbreaks, reducing the impact of these diseases. The purpose of this study was to perform an operational strategy for virological surveillance of DENV, ZIKV and CHIKV in adult Aedes aegypti and Aedes albopictus mosquitoes captured at different key-sites in an endemic urban area of the Northeast Region of Brazil, with the prospect of discussing its role as part of an alert system for outbreaks in critical areas. Residential and non-residential premises located in areas of recent of transmission of these arboviruses were selected for adult mosquito collection in the rainy season (July) of 2018. A total of 1068 adult mosquitoes were collected: 946 Culex quinquefasciatus (88.6%), 118 Ae. aegypti (11.0%), two Ae. albopictus (0.2%) and two Aedes taeniorhynchus (0.2%). Among the premises surveyed, recycling points (N = 48, 40.7%), municipal schools (N = 36, 30.5%) and junkyards (N = 31, 26.2%) were the places with the highest frequency of adult Ae. aegypti. Health units (including primary health care facilities and one hospital) (N = 23; 19.5%) together with residential premises (N = 11; 9.3%) presented the lowest frequencies. Total RNAs of the samples were extracted from Aedes mosquitoes and a nested reverse transcription (RT) polymerase chain reaction (PCR) assay for detecting and typing DENV, ZIKV and CHIKV was performed. From the 37 Aedes spp. pools analyzed (35 Ae. aegypti, one Ae. albopictus and one Ae. taeniorhynchus), seven were positive for DENV-3, including three pools containing Ae. aegypti females, one containing an Ae. aegypti engorged female and three comprised of Ae. aegypti males. The positive pools were composed of mosquitoes collected in public schools, health units, junkyards, recycling points and residential premises. Our findings reinforce the importance of continuous virological surveillance in Aedes mosquitoes, as a useful tool for detecting arboviruses circulation in vulnerable areas, even in low infestation seasons.