Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.12.10.627826

Flood-related cases of leptospirosis in Campinas, Brazil: the interplay between drainage, impermeable areas and social vulnerability

Abstract

Leptospirosis is an epidemic disease caused by bacteria of the Leptospira genus. Its risk is closely associated with inadequate sanitation and flooding, a common public health challenge in large urban centers together with urban environmental modifications, and socio-economic factors. This retrospective observational research investigated the association between the distribution of leptospirosis cases and three contextual factors --drainage, soil impermeability and social vulnerability--in Campinas city, Sao Paulo, Brazil. We hypothesized that the number of cases will increase in areas that are impermeable and in proximity to drainage systems as well as where social vulnerability is high. We investigated the associations based on 83 autochthonous cases, comparing cases where infection risk was linked to contact with floodwater or mud (n=54) to cases associated with other exposures (n=29). Spatial statistics were used to map disease distribution and investigate the relationship between leptospirosis cases and contextual factors. Our results indicate that the density of leptospirosis increases near drainage systems with risk peaking at 200 m, in areas of greater social vulnerability with increased risk due to floodwater or mud exposure in highly vulnerable regions, and in highly waterproofed urban areas. This study demonstrated that leptospirosis risk remains highly determined by living and working conditions. These findings support targeted strategies to deliver effective prevention, treatment and control interventions in highly populated urban areas of the Global South and similar contexts. Furthermore, combining local contextual environmental information with spatial analysis produces relevant evidence for guiding health public policy and spatial planning and provides precise parameters for future epidemiological models and prevention actions. Author summaryThis study explores the links between environmental and contextual factors that influence the risk of leptospirosis transmission to humans in Campinas, Sao Paulo, Brazil. Leptospirosis is an infectious disease caused by the Leptospira bacteria. We investigated how drainage systems, impermeable soil area, and social vulnerability contribute to disease distribution, using spatial statistics to map spatial conditions for 83 cases associated with water and mud contact and other risks. Our findings highlight how environmental and socio-economic factors intersect to influence public health, shedding light on the role of urban planning and drainage infrastructure in the transmission risk of infectious diseases. This interdisciplinary approach underscores the importance of considering social and environmental contexts when developing public health strategies, aligning with broader global efforts to address diseases linked to urbanization and environmental changes. Our study advances the understanding of how spatial data and environmental factors can guide more precise parameters for epidemiological models, offering insights into disease control interventions. We discuss the role of prevention, flood management, and equitable infrastructure in safeguarding public health, emphasizing how the environment shapes health risks. This research provides practical recommendations for helping decision-makers prioritize areas for intervention to reduce the burden of leptospirosis, particularly in vulnerable communities.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

de Azevedo, T. S., Nisa, S., Littlejohn, S., Muylaert, R. L.. 2024-12-16. Flood-related cases of leptospirosis in Campinas, Brazil: the interplay between drainage, impermeable areas and social vulnerability. https://doi.org/10.1101/2024.12.10.627826

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Floristic composition, phenology, and conservation value of four peat bogs in Bucovina, with the presence of Betula nana

This paper presents a comparative analysis of the floristic composition and site characteristics of four peat bogs in Bucovina, Romania: Poiana Stampei, Romanesti, Saru Dornei, and Gaina-Lucina. The research was based on phytosociological releves on 25 msq plots and direct field phenological observations, on six field visits from May to August 2026. Vegetation was characterised using the Braun Blanquet method, and floristic similarity between sites was assessed with the Sorensen and Bray Curtis indices. All four plots shared a common core of taxa characteristic of peatland vegetation: Sphagnum spp., Carex rostrata, Drosera rotundifolia, Eriophorum vaginatum, and Vaccinium species. Species richness was 13 taxa at Poiana Stampei, Romanesti, and Saru Dornei, and 12 at Gaina-Lucina. Romanesti and Saru Dornei showed the highest floristic similarity (descriptive values, not statistically tested, given a single releve per site), while Gaina-Lucina differed most markedly, not through species richness, which was similar across sites, but through species identity and through the presence of Betula nana, a glacial relict absent from the other sites. The results provide a descriptive basis for future research on the floristic composition and conservation of these habitats.

ecology↗

Long-Term Surveillance Reveals Establishment of Aedes albopictus in Eastern Nebraska, USA

Aedes albopictus (Skuse), the Asian tiger mosquito, is a highly competent arboviral vector whose range has expanded substantially across the United States over the past four decades. Despite predictive models placing Nebraska within the species' climatically suitable range, its establishment status in the state has remained poorly characterized. Here, we report results from a nine-year mosquito surveillance program (2017-2025) conducted across 44 Nebraska counties in collaboration with the Nebraska Department of Health and Human Services. Ae. albopictus was detected in five counties, with sustained, annually increasing populations documented in Richardson, Douglas, and Lancaster counties. Richardson County recorded continuous detections during 2017-2025, with proportional representation rising to 60.50% of collected mosquitoes by 2025. In Douglas and Lancaster counties, temporal advancement of first seasonal detection in 2024 and 2025 provide evidence consistent with successful overwintering rather than annual reintroduction. A cumulative degree-day model predicted adult emergence in mid-May across all county-year combinations, consistently preceding trap deployment by two to seven weeks and revealing a systematic early-season surveillance gap. Generalized linear mixed-effects models indicated that trap-level detection persistence, rather than urban location, was the primary predictor of yearly Ae. albopictus positivity, suggesting that current invasion dynamics are driven by focal source populations. These findings provide strong evidence for the establishment of Ae. albopictus in eastern Nebraska and highlight the need for earlier seasonal surveillance and standardized criteria to define establishment in northward-expanding vector populations.

ecology↗

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗