Overview of Leptospirosis

Grassmann, André Alex; Cunha, C. E.; Bettin, Everton Burlamarque; McBride, Alan J. A.

doi:10.1007/978-3-319-68493-2_8

Cited by 4 publications

(2 citation statements)

References 197 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rural areas tend to present a higher risk compared to urban areas due to a larger number of animal reservoirs in agricultural and forested areas, as well as a higher level of transmission between wild and domestic animal hosts (Ellis, 2015;Mutalip et al, 2019). In contrast, urban leptospirosis is relatively easier to control by controlling the reproduction of rats due to availability of food and harborage (Grassmann et al, 2017), i.e., by proper management of dilapidated or abandoned house and public services including waste disposal. An unclean environment is always associated with the transmission of leptospirosis due to possible presence of contaminated water and soil (Schneider et al, 2013).…”

Section: Leptospirosis and Environmental Factors Influencing Transmismentioning

confidence: 99%

Exploratory Data Analysis and Artificial Neural Network for Prediction of Leptospirosis Occurrence in Seremban, Malaysia Based on Meteorological Data

et al. 2020

View full text Add to dashboard Cite

Leptospirosis outbreaks in various parts of the world have been linked to changes in the weather. Furthermore, the effects have been shown to occur at different lags of up to 10 months, affecting the performance of simulation models that predict leptospirosis occurrence. In Malaysia, the link between different weather parameters, at different time lags, has yet to be established despite an increasing number of cases in recent years. In this study, a combination of data mining and machine learning is used to analyze, capture, and predict the relation between leptospirosis occurrence and temperature, rainfall, and relative humidity using the Seremban district in Malaysia as a case study. First, the optimal time lags for rainfall were determined using graphical exploratory data analysis (EDA) while non-graphical EDA was used for temperature. Then, an artificial neural network (ANN) model is developed to classify the combination of selected features into disease occurrence and non-occurrence using back-propagation training, optimizing the number of hidden layers and hidden nodes. The success is measured using accuracy, sensitivity, and specificity of each model. EDA has shown that leptospirosis occurrence in Seremban is highly correlated with weekly average temperature at lag 16 weeks and weekly rainfall amount at lag 12-20 weeks. Using these selected features, the ANN model achieved the highest accuracy, sensitivity, and specificity at 84.00, 86.44, and 79.33%, respectively. Overall, the EDA approach has increased the accuracy of the predictive model by 13.30-31.26% from the baseline models.

show abstract

Section: Leptospirosis and Environmental Factors Influencing Transmismentioning

confidence: 99%

Exploratory Data Analysis and Artificial Neural Network for Prediction of Leptospirosis Occurrence in Seremban, Malaysia Based on Meteorological Data

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Leptospira is a diverse genus and, as such, pathogenic leptospires of varying serogroups may evade detection via the microscopic agglutination test (MAT), the “Gold Standard” and most widely used serological diagnostic test for leptospirosis, which was developed over a century ago [ 17 ]. Diagnosis by MAT relies on two separate samples taken during both the acute and convalescent stages of the disease, requires a skilled laboratorian and a diverse set of reference isolates to execute, is not widely available, and yields results that are often inconclusive [ 18 ]. In many cases, serological results from MAT are confusing or conflicting when paired with genotyping results from PCR [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

DNA Capture and Enrichment: A Culture-Independent Approach for Characterizing the Genomic Diversity of Pathogenic Leptospira Species

et al. 2023

View full text Add to dashboard Cite

Because they are difficult to culture, obtaining genomic information from Leptospira spp. is challenging, hindering the overall understanding of leptospirosis. We designed and validated a culture-independent DNA capture and enrichment system for obtaining Leptospira genomic information from complex human and animal samples. It can be utilized with a variety of complex sample types and diverse species as it was designed using the pan-genome of all known pathogenic Leptospira spp. This system significantly increases the proportion of Leptospira DNA contained within DNA extracts obtained from complex samples, oftentimes reaching >95% even when some estimated starting proportions were <1%. Sequencing enriched extracts results in genomic coverage similar to sequenced isolates, thereby enabling enriched complex extracts to be analyzed together with whole genome sequences from isolates, which facilitates robust species identification and high-resolution genotyping. The system is flexible and can be readily updated when new genomic information becomes available. Implementation of this DNA capture and enrichment system will improve efforts to obtain genomic data from unculturable Leptospira-positive human and animal samples. This, in turn, will lead to a better understanding of the overall genomic diversity and gene content of Leptospira spp. that cause leptospirosis, aiding epidemiology and the development of improved diagnostics and vaccines.

show abstract

Spatiotemporal Assessment of Pathogenic Leptospira in Subtropical Coastal Watersheds

Bridgemohan,

Deitch,

Harmon

et al. 2023

Preprint

View full text Add to dashboard Cite

The World Health Organization classifies leptospirosis as a significant public health concern, predominantly affecting impoverished and unsanitary regions. Using the Pensacola Bay System as a case study, this study examines the underappreciated susceptibility of developed subtropical coastal ecosystems such as the Pensacola Bay System to zoonotic pathogens such as Leptospira. We analyzed 132 water samples collected over 12 months from 44 distinct locations with high levels of E. coli (> 410 MPN/100 mL). Fecal indicator bacteria (FIB) concentrations were assessed using IDEXX Colilert-18 and Enterolert-18, and an analysis of water physiochemical characteristics and rainfall intensity was conducted. The LipL32 gene was used as a quantitative polymerase chain reaction (qPCR) indicator to identify the distribution of Leptospira interrogens. The results revealed 12 instances of L. interogenes presence at sites with high FIB over various land cover and aquatic ecosystem types. Independent of specific rainfall events, a seasonal relationship between precipitation and elevated rates of fecal bacteria and leptospirosis was found. These findings highlight qPCR's utility in identifying pathogens in aquatic environments and the widespread conditions where it can be found in natural and developed areas.

show abstract

Overview of Leptospirosis

Cited by 4 publications

References 197 publications

Exploratory Data Analysis and Artificial Neural Network for Prediction of Leptospirosis Occurrence in Seremban, Malaysia Based on Meteorological Data

Exploratory Data Analysis and Artificial Neural Network for Prediction of Leptospirosis Occurrence in Seremban, Malaysia Based on Meteorological Data

DNA Capture and Enrichment: A Culture-Independent Approach for Characterizing the Genomic Diversity of Pathogenic Leptospira Species

Spatiotemporal Assessment of Pathogenic Leptospira in Subtropical Coastal Watersheds

Contact Info

Product

Resources

About