Spatial-temporal patterns and risk factors for human leptospirosis in Thailand, 2012-2018

Geospatial Analysis of Abiotic and Biotic Conditions Associated with Leptospirosis in the Klaten Regency, Central Java, Indonesia

The Klaten Regency, Central Java Province, Indonesia, is a leptospirosis endemic area. The purpose of this study is to spatially describe the abiotic and biotic environmental factors that contributed to the incidence of leptospirosis in the Klaten Regency in 2018. This was a descriptive observational with a cross-sectional approach conducted in the Klaten Regency, Central Java, in 2019 with 59 respondents. The results revealed that the percentage of abiotic environmental factors such as poor waste disposal facilities, poor gutter conditions, rivers < 200 m, and flooding history, namely 35.6%, 41.2%, 54.2%, and 6.8%, respectively. The highest leptospirosis cases occurred in April 2018, with 325 mm of rainfall, an average temperature of 27 °C, an average humidity of 82.3%, and an altitude of 100-200 MASL (79.7%). Meanwhile, biotic factors included rat nest existence (100%), having pets at risk (32.2%), and ≥three types of vegetation (79.7%). The main result confirmed that all leptospirosis cases had rat nests throughout the respondent's house. This finding emphasizes the importance of rat pest control programs by establishing cross-sectoral collaboration with the Department of Agriculture and educating the public to also play a role in environmental cleanliness in controlling rats.

Epidemiological Features of Leptospirosis and Identification of Leptospira wolffii as a Persistently Prevailing Species in North-Central Bangladesh

Leptospirosis is considered to be the most widespread, yet neglected, re-emerging zoonotic disease caused by infection with a pathogenic species of the genus Leptospira. Although this disease is prevalent in Bangladesh, the recent epidemiological status has not yet been well documented. In this study, we aimed to determine the prevalence of leptospirosis among febrile patients using different diagnostic methods and to characterize the epidemiological features and species of Leptospira in Mymensingh, north-central Bangladesh. Among the blood samples of 186 patients with suspected leptospirosis who met the inclusion criteria, including having a fever for more than 5 days (November 2021-June 2022), 88 samples (47%) were Leptospira-positive according to IgM LAT, IgM ELISA, or nested PCR (positivity rates: 38%, 37%, and 42%, respectively). Nested PCR showed a significantly higher positivity rate (54%) in patients with a short fever (5-10 day) than the other methods did, with lower rates among those with a longer fever. Leptospirosis cases were more common in males (68%), those 16-45 years of age (70%), residents of rural areas (81%), and farmers (41%). In addition to a fever, myalgia and jaundice were found in more than 70% of the patients, while variable symptoms were observed. The 16S rRNA sequencing analysis revealed that the Leptospira species in all the 22 samples tested were L. wolffii, belonging to the pathogenic subclade P2. This study showed the recent epidemiological features of leptospirosis in Bangladesh, indicating the presumptive predominance of L. wolffii since 2019.

Analysing the outbreaks of leptospirosis after floods in Kerala, India

A growing number of studies have linked the incidence of leptospirosis with the occurrence of flood events. Nevertheless, the interaction between flood and leptospirosis has not been extensively studied to understand the influence of flood attributes in inducing new cases. This study reviews leptospirosis cases in relation to multiple flood occurrences in Kerala, India. Leptospirosis data were obtained for three years: 2017 (non-flood year) and two years with flooding-2018 (heavy flooding) and 2019 (moderate flooding). We considered the severity of flood events using the discharge, duration and extent of each flooding event and compared them with the leptospirosis cases. The distribution of cases regarding flood discharge and duration was assessed through descriptive and spatiotemporal analyses, respectively. Furthermore, cluster analyses and spatial regression were completed to ascertain the relationship between flood extent and the postflood cases. This study found that postflood cases of leptospirosis can be associated with flood events in space and time. The total cases in both 2018 and 2019 increased in the post-flood phase, with the increase in 2018 being more evident. Unlike the 2019 flood, the flood of 2018 is a significant spatial indicator for postflood cases. Our study shows that flooding leads to an increase in leptospirosis cases, and there is stronger evidence for increased leptospirosis cases after a heavy flood event than after a moderate flooding event. Flood duration may be the most important factor in determining the increase in leptospirosis infections.

Spatio-temporal dynamics and distributional trend analysis of African swine fever outbreaks (2020-2021) in North-East India

African swine fever (ASF) is a highly contagious, notifiable, and fatal hemorrhagic viral disease affecting domestic and wild pigs. The disease was reported for the first time in India during 2020, resulted in serious outbreaks and economic loss in North-Eastern (NE) parts, since 47% of the Indian pig population is distributed in the NE region. The present study focused on analyzing the spatial autocorrelation, spatio-temporal patterns, and directional trend of the disease in NE India during 2020-2021. The ASF outbreak data (2020-2021) were collected from the offices of the Department of Animal Husbandry and Veterinary Services in seven NE states of India to identify the potential clusters, spatio-temporal aggregation, temporal distribution, disease spread, density maps, and risk zones. Between 2020 and 2021, a total of 321 ASF outbreaks were recorded, resulting in 59,377 deaths. The spatial pattern analysis of the outbreak data (2020-2021) revealed that ASF outbreaks were clustered in 2020 (z score = 2.20, p < .01) and 2021 (z score = 4.89, p < .01). Spatial autocorrelation and Moran's I value (0.05-0.06 in 2020 and 2021) revealed the spatial clustering and spatial relationship between the outbreaks. The hotspot analysis identified districts of Arunachal Pradesh, Assam and districts of Mizoram, Tripura as significant hotspots in 2020 and 2021, respectively. The spatial-scan statistics with a purely spatial and purely temporal analysis revealed six and one significant clusters, respectively. Retrospective unadjusted, temporal, and spatially adjusted space-time analysis detected five, five, and two statistically significant (p < .01) clusters, respectively. The directional trend analysis identified the direction of disease distribution as northeast-southwest (2020) and north-south (2021), indicate the possibility of ASF introduction to India from China. The high-risk zones and spatio-temporal pattern of ASF outbreaks identified in the present study can be used as a guide for deploying proper prevention, optimizing resource allocation and disease control measures in NE Indian states.

Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model

BACKGROUND

Leptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its incidence rate has been estimated at approximately 2.33 cases per 10,000 individuals. Our research focused on analyzing the spatiotemporal clustering of Leptospirosis and developing a disease prevalence model as an essential focal point for public health policymakers, urging targeted interventions and strategies.

METHODS

The SaTScan and Maximum Entropy (MaxEnt) modeling methods were used to find the spatiotemporal clusters of the Leptospirosis and model the disease prevalence in Iran. We incorporated nine environmental covariates by employing a spatial resolution of 1 km x 1 km, the finest resolution ever implemented for modeling Human Leptospirosis in Iran. These covariates encompassed the Digital Elevation Model (DEM), slope, displacement areas, water bodies, and land cover, monthly recorded Normalized Difference Vegetation Index (NDVI), monthly recorded precipitation, monthly recorded mean and maximum temperature, contributing significantly to our disease modeling approach. The analysis using MaxEnt yielded the Area Under the Receiver Operating Characteristic Curve (AUC) metrics for the training and test data, to evaluate the accuracy of the implemented model.

RESULTS

The findings reveal a highly significant primary cluster (p-value < 0.05) located in the western regions of the Gilan province, spanning from July 2013 to July 2015 (p-value < 0.05). Moreover, there were four more clusters (p-value < 0.05) identified near Someh Sara, Neka, Gorgan and Rudbar. Furthermore, the risk mapping effectively illustrates the potential expansion of the disease into the western and northwestern regions. The AUC metrics of 0.956 and 0.952 for the training and test data, respectively, underscoring the robust accuracy of the implemented model. Interestingly, among the variables considered, the influence of slope and distance from water bodies appears to be minimal. However, altitude and precipitation stand out as the primary determinants that significantly contribute to the prevalence of the disease.

CONCLUSIONS

The risk map generated through this study carries significant potential to enhance public awareness and inform the formulation of impactful policies to combat Leptospirosis. These maps also play a crucial role in tracking disease incidents and strategically directing interventions toward the regions most susceptible.

The combination of RPA-CRISPR/Cas12a and Leptospira IgM RDT enhances the early detection of leptospirosis

BACKGROUND

Lack of available sensitive point-of-care testing is one of the primary obstacles to the rapid diagnosis of leptospirosis. The purpose of this study was to test the performance of two point-of-care tests, a clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 12a (CRISPR/Cas12a) fluorescence-based diagnostic assay (FBDA), a Leptospira immunoglobulin M (IgM) rapid diagnostic test (RDT), and the two tests combined.

METHODOLOGY/PRINCIPAL FINDINGS

For the diagnosis of 171 clinical samples, a recombinase polymerase amplification (RPA)-CRISPR/Cas12a FBDA for whole blood and Leptospira IgM RDT (Medical Science Public Health, Thailand) for serum were used. The confirmed cases were determined by using any positive qPCR, microscopic agglutination test (MAT), and culture results. Diagnostic accuracy was assessed on the first day of enrollment and stratified by the day after symptom onset. The overall sensitivity of the Leptospira IgM RDT and RPA-CRISPR/Cas12a FBDA was 55.66% and 60.38%, respectively. When the two tests were combined, the sensitivity rose to 84.91%. The specificity of each test was 63.08% and 100%, respectively, and 63.08% when combined. The sensitivity of the Leptospira IgM RDT rose on days 4-6 after the onset of fever, while the RPA-CRISPR/Cas12a FBDA continued to decrease. When the two tests were combined, the sensitivity was over 80% at different days post-onset of fever.

CONCLUSIONS/SIGNIFICANCE

The combination of Leptospira IgM RDT and RPA-CRISPR/Cas12 FBDA exhibited significant sensitivity for the detection of leptospires at various days after the onset of fever, thereby reducing the likelihood of misdiagnosis. The combination of these assays may be suitable for early leptospirosis screening in situations with limited resources.

A global one health perspective on leptospirosis in humans and animals

Leptospirosis is a quintessential one health disease of humans and animals caused by pathogenic spirochetes of the genus Leptospira. Intra- and interspecies transmission is dependent on 1) reservoir host animals in which organisms replicate and are shed in urine over long periods of time, 2) the persistence of spirochetes in the environment, and 3) subsequent human-animal-environmental interactions. The combination of increased flooding events due to climate change, changes in human-animal-environmental interactions as a result of the pandemic that favor a rise in the incidence of leptospirosis, and under-recognition of leptospirosis because of nonspecific clinical signs and severe signs that resemble COVID-19 represents a "perfect storm" for resurgence of leptospirosis in people and domestic animals. Although often considered a disease that occurs in warm, humid climates with high annual rainfall, pathogenic Leptospira spp have recently been associated with disease in animals and humans that reside in semiarid regions like the southwestern US and have impacted humans that have a wide spectrum of socioeconomic backgrounds. Therefore, it is critical that physicians, veterinarians, and public health experts maintain a high index of suspicion for the disease regardless of geographic and socioeconomic circumstances and work together to understand outbreaks and implement appropriate control measures. Over the last decade, major strides have been made in our understanding of the disease because of improvements in diagnostic tests, molecular epidemiologic tools, educational efforts on preventive measures, and vaccines. These novel approaches are highlighted in the companion Currents in One Health by Sykes et al, AJVR, September 2022.