ArticleHeliyon2024
Assessing the impact of emergency measures in varied population density areas during a large dengue outbreak.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Dengue incidence and its relationship with El Niño oceanic index, as a sensitive variable to anticipate outbreaks in the Colombian Caribbean regionBiomedica : revista del Instituto Nacional de Salud · 2025Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The patterns of dengue are affected by many factors, including population density and climate factors. Densely populated areas could play a role in dengue transmission due to increased human-mosquito contacts, the presence of more diverse and suitable vector habitats and breeding sites, and changes in land use. In addition to population densities, climatic factors such as temperature, relative humidity, and precipitation have been demonstrated to predict dengue patterns. To control dengue, emergency measures should focus on vector management. Most approaches to assessing emergency responses to dengue risks involve applying simulation models or describing emergency activities and the results of implementing those responses. Research using real-world data with analytical methods to evaluate emergency responses to dengue has been limited. This study investigated emergency control measures associated with dengue risks in areas with high and low population densities, considering their different control capacities. Methodology: Data from the 2015 dengue outbreak in Kaohsiung City, Taiwan, were utilized. The government database provided information on confirmed dengue cases, emergency control measures, and climatic data. The study employed a distributed lag non-linear model (DLNM) to assess the effect of emergency control measures and their time lags on dengue risk. Principal findings: The findings revealed that in areas with high population density, the absence of emergency measures significantly elevated the risks of dengue. However, implementing emergency measures, especially a higher number, was associated with lower risks. In contrast, in areas with low population density, the risks of dengue were only significantly elevated at the 1st week lag if no emergency control measures were implemented. When emergency activities were carried out, the risks of dengue significantly decreased only for the 1st week lag. Conclusions: Our findings reveal distinct exposure-lag-response patterns in the associations between emergency control measures and dengue in areas with high and low population density. In regions with a high population density, implementing emergency activities during a significant dengue outbreak is crucial for reducing the risk. Conversely, in areas of low population density, the necessity of applying emergency activities may be less pronounced. The implications of this study on dengue management could provide valuable insights for health authorities dealing with limited resources.
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Registered trials
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