Dengue outbreak in Peru affects adults and children

Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments

BACKGROUND

In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies.

RESULTS

We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance.

CONCLUSIONS

Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Genotype of dengue virus serotype 1 in relation to severe dengue in Guangzhou, China

Guangzhou has been the city most affected by the dengue virus (DENV) in China, with a predominance of DENV serotype 1 (DENV-1). Viral factors such as dengue serotype and genotype are associated with severe dengue (SD). However, none of the studies have investigated the relationship between DENV-1 genotypes and SD. To understand the association between DENV-1 genotypes and SD, the clinical manifestations of patients infected with different genotypes were investigated. A total of 122 patients with confirmed DENV-1 genotype infection were recruited for this study. The clinical manifestations, laboratory tests, and levels of inflammatory mediator factors were statistically analyzed to investigate the characteristics of clinical manifestations and immune response on the DENV-1 genotype. In the case of DENV-1 infection, the incidence of SD with genotype V infection was significantly higher than that with genotype I infection. Meanwhile, patients infected with genotype V were more common in ostealgia and bleeding significantly. In addition, levels of inflammatory mediator factors including IFN-γ, TNF-α, IL-10, and soluble vascular cell adhesion molecule 1 were higher in patients with SD infected with genotype V. Meanwhile, the concentrations of regulated upon activation normal T-cell expressed and secreted and growth-related gene alpha were lower in patients with SD infected with genotype V. The higher incidence of SD in patients infected with DENV-1 genotype V may be attributed to elevated cytokines and adhesion molecules, along with decreased chemokines.

The 2023 extreme coastal El Niño: Atmospheric and air-sea coupling mechanisms

In the boreal spring of 2023, an extreme coastal El Niño struck the coastal regions of Peru and Ecuador, causing devastating rainfalls, flooding, and record dengue outbreaks. Observations and ocean model experiments reveal that northerly alongshore winds and westerly wind anomalies in the eastern equatorial Pacific, initially associated with a record-strong Madden-Julian Oscillation and cyclonic disturbance off Peru in March, drove the coastal warming through suppressed coastal upwelling and downwelling Kelvin waves. Atmospheric model simulations indicate that the coastal warming in turn favors the observed wind anomalies over the far eastern tropical Pacific by triggering atmospheric deep convection. This implies a positive feedback between the coastal warming and the winds, which further amplifies the coastal warming. In May, the seasonal background cooling precludes deep convection and the coastal Bjerknes feedback, leading to the weakening of the coastal El Niño. This coastal El Niño is rare but predictable at 1 month lead, which is useful to protect lives and properties.