Risk Stratification to Guide Prevention and Control Strategies for Arboviruses Transmitted by Aedes aegypti

Abstract

Strategies for the prevention of arboviral diseases transmitted by Aedes aegypti have traditionally focused on vector control. This remains the same to this day, despite a lack of documented evidence on its efficacy due to a lack of coverage and sustainability. The continuous growth of urban areas and generally unplanned urbanization, which favor the presence of Ae. aegypti, demand resources, both material and human, as well as logistics to effectively lower the population's risk of infection. These considerations have motivated the development of tools to identify areas with a recurrent concentration of arboviral cases during an outbreak to be able to prioritize preventive actions and optimize available resources. This study explores the existence of spatial patterns of dengue incidence in the locality of Tartagal, in northeastern Argentina, during the outbreaks that occurred between 2010 and 2020. Approximately half (50.8%) of the cases recorded during this period were concentrated in 35.9% of the urban area. Additionally, an important overlap was found between hotspot areas of dengue and chikungunya (Kendall's W = 0.92; p-value < 0.001) during the 2016 outbreak. Moreover, 65.9% of the cases recorded in 2022 were geolocalized within the hotspot areas detected between 2010 and 2020. These results can be used to generate a risk map to implement timely preventive control strategies that prioritize these areas to reduce their vulnerability while optimizing the available resources and increasing the scope of action.

Keywords: Aedes aegypti; Argentina; dengue; hotspots; risk stratification.

Figure 1

Geographic location of the city of Tartagal, Salta province (Argentina) with respect to the region and the province on the left and the urban map of the city on the right.

Figure 2

Number of dengue cases per year during the study period (2010–2020) in Tartagal, Salta (Argentina) (A) and the cumulative epidemic curve showing the registered epidemic curve during the same period (B).

Figure 3

Census tracts (CTs) in the city of Tartagal, Salta (Argentina) and the yearly dengue hotspots obtained using Getis-Ord indexes, 2010 to 2020. Maps for the years 2010 and 2015 are not included given the lack of outbreaks.

Figure 4

Evolution of hotspots throughout the study period (2010–2020) in Tartagal, Salta (Argentina). (A) Recurrent dengue hotspots per census tract (CT), (B) number of consecutive years that the same hotspot was identified as such, (C) dengue hotspot areas detected from 2010 to 2015, (D) dengue hotspot areas detected from 2016 to 2020, and (E) accumulated dengue hotspots per CT during the entire study period.

Figure 5

Mapped distribution of confirmed dengue cases during the 2022 outbreak for Tartagal, Salta (Argentina) with respect to the hotspot areas detected during the period 2010–2020.