Geospatial modelling of dry season habitats of the malaria vector, Anopheles funestus, in south-eastern Tanzania

Kahamba, Najat F.; Okumu, Fredros O.; Jumanne, Mohammed; Kifungo, Khamisi; Odero, Joel O.; Baldini, Francesco; Ferguson, Heather M.; Nelli, Luca

doi:10.1186/s13071-024-06119-6

Cited by 6 publications

(1 citation statement)

References 49 publications

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“…The Ulanga distric, in south-eastern Tanzania, experiences its rainy season from March to May. The dominant vector is Anopheles funestus and the breeding sites are river streams, ground pools and dug pits [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

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

Trujillano,

Jimenez,

Manrique

et al. 2024

Int J Health Geogr

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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.

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Section: Methodsmentioning

confidence: 99%

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

Trujillano,

Jimenez,

Manrique

et al. 2024

Int J Health Geogr

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New directions for malaria vector control using geography and geospatial analysis

Hardy

2024

Advances in Parasitology

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Societal Uses of the Main Water Bodies Inhabited by Malaria Vectors and Implications for Larval Source Management

Kahamba,

Tarimo,

Kifungo

et al. 2024

Preprint

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IntroductionLarval source management (LSM) can effectively suppress mosquito populations at source and provides an opportunity to address major challenges such as insecticide resistance that undermine primary interventions like insecticide-treated nets (ITNs). While mostly implemented in urban and arid settings, emerging research indicates its potential in some rural settings in east and southern Africa, where the main malaria vector,Anopheles funestus, prefers permanent and semi-permanent water bodies that support year-round transmission. Targeting these unique habitats could amplify effectiveness of LSM but requires careful considerations of local societal practices and expectations - particularly since mosquito breeding sites often also serve as community water resources. The aim of this study was therefore to explore how the societal uses of aquatic habitats by local communities in rural south-eastern Tanzania might influence LSM strategies, focusing on habitats frequented byAn. funestus.MethodsThis study was conducted in three villages in the Ulanga and Malinyi districts of southeastern Tanzania using a mixed-methods approach. Quantitative data were collected through a cross-sectional surveillance of all aquatic habitats, while qualitative data were gathered via a combination of individual unstructured interviews, focus group discussions with various community groups and field observations of community practices and activities. Data analysis employed weaving and inferencing techniques to integrate findings from both quantitative and qualitative components, thereby developing a comprehensive understanding from the respondents’ perspectives.ResultsA survey of 931 aquatic habitats revealed that 73% contained mosquito larvae, with late instar An. funestus identified in 23% of these habitats. River streams segments were the most common habitat type, accounting for 41%, followed by ground pools at 4%; other types included pits, rice fields, ditches, and puddles. Community use was noted for 90% of these habitats, including 95% of those with An. funestus larvae, for activities such as domestic chores such as cooking, washing utensils, washing clothes and bathing, agriculture, livestock rearing, brickmaking, and fishing. Focus group discussions indicated community readiness to implement LSM, favoring larviciding and habitat manipulation over habitat removal. Community concerns regarding LSM centered on the safety of larvicides for animal and human health and their environmental impact. The discussions proved the need for LSM interventions to integrate seamlessly with daily activities; and for community education on LSM safety and efficacy.ConclusionThis study offers valuable insights into community perspectives on LSM for malaria control in rural settings, emphasizing the dual role of aquatic habitats as both mosquito breeding sites and community water sources. This presents a set of unique challenges and opportunities – suggesting that LSM strategies must address both the biological aspects of mosquito control and the socio-economic realities of local communities. Notably, there was a marked preference for larviciding and habitat manipulation over habitat removal, with a strong emphasis on health and environmental safety. Overall, the study highlights the critical importance of educating communities, adopting culturally sensitive approaches to LSM, and aligning LSM strategies with the needs, perspectives, and daily lives of local communities.

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Geospatial modelling of dry season habitats of the malaria vector, Anopheles funestus, in south-eastern Tanzania

Cited by 6 publications

References 49 publications

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

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

New directions for malaria vector control using geography and geospatial analysis

Societal Uses of the Main Water Bodies Inhabited by Malaria Vectors and Implications for Larval Source Management

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