Assessing the performance of remotely-sensed flooding indicators and their potential contribution to early warning for leptospirosis in Cambodia

Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease

Background

Chagas disease is a long-lasting disease with a prolonged asymptomatic period. Cumulative indices of infection such as prevalence do not shed light on the current epidemiological situation, as they integrate infection over long periods. Instead, metrics such as the Force-of-Infection (FoI) provide information about the rate at which susceptible people become infected and permit sharper inference about temporal changes in infection rates. FoI is estimated by fitting (catalytic) models to available age-stratified serological (ground-truth) data. Predictive FoI modelling frameworks are then used to understand spatial and temporal trends indicative of heterogeneity in transmission and changes effected by control interventions. Ideally, these frameworks should be able to propagate uncertainty and handle spatiotemporal issues.

Methodology/principal findings

We compare three methods in their ability to propagate uncertainty and provide reliable estimates of FoI for Chagas disease in Colombia as a case study: two Machine Learning (ML) methods (Boosted Regression Trees (BRT) and Random Forest (RF)), and a Linear Model (LM) framework that we had developed previously. Our analyses show consistent results between the three modelling methods under scrutiny. The predictors (explanatory variables) selected, as well as the location of the most uncertain FoI values, were coherent across frameworks. RF was faster than BRT and LM, and provided estimates with fewer extreme values when extrapolating to areas where no ground-truth data were available. However, BRT and RF were less efficient at propagating uncertainty.

Conclusions/significance

The choice of FoI predictive models will depend on the objectives of the analysis. ML methods will help characterise the mean behaviour of the estimates, while LM will provide insight into the uncertainty surrounding such estimates. Our approach can be extended to the modelling of FoI patterns in other Chagas disease-endemic countries and to other infectious diseases for which serosurveys are regularly conducted for surveillance.

Metrics such as the per susceptible rate of infection acquisition (Force-of-Infection) are crucial to understand the current epidemiological situation and the impact of control interventions for long-lasting diseases in which the infection event might have occurred many years previously, such as Chagas disease. FoI values are estimated from serological age profiles, often obtained in a few locations. However, when using predictive models to estimate the FoI over time and space (including areas where serosurveys had not been conducted), methods able to handle and propagate uncertainty must be implemented; otherwise, overconfident predictions may be obtained. Although Machine Learning (ML) methods are powerful tools, they may not be able to entirely handle this challenge. Therefore, the use of ML must be considered in relation to the aims of the analyses. ML will be more relevant to characterise the central trends of the estimates while Linear Models will help identify areas where further serosurveys should be conducted to improve the reliability of the predictions. Our approaches can be used to generate FoI predictions in other Chagas disease-endemic countries as well as in other diseases for which serological surveillance data are collected.

Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.

Unraveling the invisible leptospirosis in mainland Southeast Asia and its fate under climate change

Leptospirosis is a neglected waterborne zoonosis of growing concern in tropical and low-income regions. Endemic in Southeast Asia, its distribution and environmental factors such as climate controlling its dynamics remain poorly documented. In this paper, we investigate for the first time the current and future leptospirosis burden at a local scale in mainland Southeast Asia. We adjusted machine-learning models on incidence reports from the Thai surveillance system to identify environmental determinants of leptospirosis. The explanatory variables tested in our models included climate, topographic, land cover and soil variables. The model performing the best in cross-validation was used to estimate the current incidence regionally in Thailand, Myanmar, Cambodia, Vietnam and Laos. It then allowed to predict the spatial distribution of leptospirosis future burden from 2021 to 2100 based on an ensemble of CMIP6 climate model projections and 4 Shared Socio-economics Pathways ranging from the most optimistic to the no-climate policy outcomes (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). Leptospirosis incidence was best estimated by 10 environmental variables: four landscape-, four rainfall-, two temperature-related variables. Of all tested scenario, the worst-case scenario of climate change (SSP5-8.5) surprisingly appeared as the best-case scenario for the future of leptospirosis since it would induce a significant global decline in disease incidence in Southeast Asia mainly driven by the increasing temperatures. These global patterns are however contrasted regionally with some regions showing increased incidence in the future. Our work highlights climate and the environment as major drivers of leptospirosis incidence in Southeast Asia. Applying our model to regions where leptospirosis is not routinely monitored suggests an overlooked burden in the region. As our model focuses on leptospirosis responses to environmental drivers only, some other factors, such as poverty, lifestyle or behavioral changes, could further influence these estimated future patterns.

EA, Reis MG, Diggle PJ, Ko AI. Rainfall and other meteorological factors as drivers of urban transmission of leptospirosis

BACKGROUND

Leptospirosis is an important public health problem affecting vulnerable urban slum populations in developing country settings. However, the complex interaction of meteorological factors driving the temporal trends of leptospirosis remain incompletely understood.

METHODS AND FINDINGS

From March 1996-March 2010, we investigated the association between the weekly incidence of leptospirosis and meteorological anomalies in the city of Salvador, Brazil by using a dynamic generalized linear model that accounted for time lags, overall trend, and seasonal variation. Our model showed an increase of leptospirosis cases associated with higher than expected rainfall, lower than expected temperature and higher than expected humidity. There was a lag of one-to-two weeks between weekly values for significant meteorological variables and leptospirosis incidence. Independent of the season, a weekly cumulative rainfall anomaly of 20 mm increased the risk of leptospirosis by 12% compared to a week following the expected seasonal pattern. Finally, over the 14-year study period, the annual incidence of leptospirosis decreased significantly by a factor of 2.7 (8.3 versus 3.0 per 100,000 people), independently of variations in climate.

CONCLUSIONS

Strategies to control leptospirosis should focus on avoiding contact with contaminated sources of Leptospira as well as on increasing awareness in the population and health professionals within the short time window after low-level or extreme high-level rainfall events. Increased leptospirosis incidence was restricted to one-to-two weeks after those events suggesting that infectious Leptospira survival may be limited to short time intervals.

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

Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the Global Moran's I index was calculated for incidences per 100,000 population at the province level during 2012-2018, using the monthly and annual data. The high-risk and low-risk provinces were identified using the local indicators of spatial association (LISA). The risk factors for leptospirosis were evaluated using a generalized linear mixed model (GLMM) with zero-inflation. We also added spatial and temporal correlation terms to take into account the spatial and temporal structures. The Global Moran's I index showed significant positive values. It did not demonstrate a random distribution throughout the period of study. The high-risk provinces were almost all in the lower north-east and south parts of Thailand. For yearly reported cases, the significant risk factors from the final best-fitted model were population density, elevation, and primary rice crop arable areas. Interestingly, our study showed that leptospirosis cases were associated with large areas of rice production but were less prevalent in areas of high rice productivity. For monthly reported cases, the model using temperature range was found to be a better fit than using percentage of flooded area. The significant risk factors from the model using temperature range were temporal correlation, average soil moisture, normalized difference vegetation index, and temperature range. Temperature range, which has strongly negative correlation to percentage of flooded area was a significant risk factor for monthly data. Flood exposure controls should be used to reduce the risk of leptospirosis infection. These results could be used to develop a leptospirosis warning system to support public health organizations in Thailand.

Eco-epidemiological scaling of Leptospirosis: Vulnerability mapping and early warning forecasts

Infectious disease epidemics are plaguing the world and a lot of research is focused on the development of models to reproduce disease dynamics for eco-environmental and biological investigation, and disease management. Leptospirosis is an example of a neglected zoonosis strongly mediated by ecohydrological dynamics with emerging endemic and epidemic patterns worldwide in both animal and human populations. By accounting for large heterogeneities of affected areas we show how exponential endemics and scale-free epidemics are largely predictable and linked to common socio-environmental features via scaling laws with different exponents that inform about vulnerability factors. This led to the development of a novel pattern-oriented integrated model that can be used as an early-warning signal (EWS) tool for endemic-epidemic regime classification, risk determinant attribution, and near real-time forecast of outbreaks. Forecasts are grounded on expected outbreak recurrence time dependent on exceedance probabilities and statistical EWS that sense outbreak onset. A stochastic spatially-explicit model is shown to comprehensively predict outbreak dynamics (early sensing, timing, magnitude, decay, and eco-environmental determinants) and derive a spreading factor characterizing endemics and epidemics, where average over maximum rainfall is the critical factor characterizing disease transitions. Dynamically, case cross-correlation considering neighboring communities senses 2-weeks in advance outbreaks. Eco-environmental scaling relationships highlight how predicted host suitability and topographic index can be used as epidemiological footprints to effectively distinguish and control Leptospirosis regimes and areas dependent on hydro-climatological dynamics as the main trigger. The spatio-temporal scale-invariance of epidemics - underpinning persistent criticality and neutrality or independence among areas - is emphasized by the high accuracy in reproducing sequence and magnitude of cases via reliable surveillance. Further investigations of robustness and universality of eco-environmental determinants are required; nonetheless a comprehensive and computationally simple EWS method for the full characterization of Leptospirosis is provided. The tool is extendable to other climate-sensitive zoonoses to define vulnerability factors and predict outbreaks useful for optimal disease risk prevention and control.

The Total Operating Characteristic from Stratified Random Sampling with an Application to Flood Mapping

The Total Operating Characteristic (TOC) measures how the ranks of an index variable distinguish between presence and absence in a binary reference variable. Previous methods to generate the TOC required the reference data to derive from a census or a simple random sample. However, many researchers apply stratified random sampling to collect reference data because stratified random sampling is more efficient than simple random sampling for many applications. Our manuscript derives a new methodology that uses stratified random sampling to generate the TOC. An application to flood mapping illustrates how the TOC compares the abilities of three indices to diagnose water. The TOC shows visually and quantitatively each index’s diagnostic ability relative to baselines. Results show that the Modified Normalized Difference Water Index has the greatest diagnostic ability, while the Normalized Difference Vegetation Index has diagnostic ability greater than the Normalized Difference Water Index at the threshold where the Diagnosed Presence equals the Abundance of water. Some researchers consider only one accuracy metric at only one threshold, whereas the TOC allows visualization of several metrics at all thresholds. The TOC gives more information and clearer interpretation compared to the popular Relative Operating Characteristic. Our software generates the TOC from a census, simple random sample, or stratified random sample. The TOC Curve Generator is free as an executable file at a website that our manuscript gives.

Agro-Environmental Determinants of Leptospirosis: A Retrospective Spatiotemporal Analysis (2004-2014) in Mahasarakham Province (Thailand)

Leptospirosis has been recognized as a major public health concern in Thailand following dramatic outbreaks. We analyzed human leptospirosis incidence between 2004 and 2014 in Mahasarakham province, Northeastern Thailand, in order to identify the agronomical and environmental factors likely to explain incidence at the level of 133 sub-districts and 1982 villages of the province. We performed general additive modeling (GAM) in order to take the spatial-temporal epidemiological dynamics into account. The results of GAM analyses showed that the average slope, population size, pig density, cow density and flood cover were significantly associated with leptospirosis occurrence in a district. Our results stress the importance of livestock favoring leptospirosis transmission to humans and suggest that prevention and control of leptospirosis need strong intersectoral collaboration between the public health, the livestock department and local communities. More specifically, such collaboration should integrate leptospirosis surveillance in both public and animal health for a better control of diseases in livestock while promoting public health prevention as encouraged by the One Health approach.

Predicting the Presence of Leptospires in Rodents from Environmental Indicators Opens Up Opportunities for Environmental Monitoring of Human Leptospirosis

Leptospirosis, an environmental infectious disease of bacterial origin, is the infectious disease with the highest associated mortality in Seychelles. In small island territories, the occurrence of the disease is spatially heterogeneous and a better understanding of the environmental factors that contribute to the presence of the bacteria would help implement targeted control. The present study aimed at identifying the main environmental parameters correlated with animal reservoirs distribution and Leptospira infection in order to delineate habitats with highest prevalence. We used a previously published dataset produced from a large collection of rodents trapped during the dry and wet seasons in most habitats of Mahé, the main island of Seychelles. A land use/land cover analysis was realized in order to describe the various environments using SPOT-5 images by remote sensing (object-based image analysis). At each sampling site, landscape indices were calculated and combined with other geographical parameters together with rainfall records to be used in a multivariate statistical analysis. Several environmental factors were found to be associated with the carriage of leptospires in Rattus rattus and Rattus norvegicus, namely low elevations, fragmented landscapes, the proximity of urbanized areas, an increased distance from forests and, above all, increased precipitation in the three months preceding trapping. The analysis indicated that Leptospira renal carriage could be predicted using the species identification and a description of landscape fragmentation and rainfall, with infection prevalence being positively correlated with these two environmental variables. This model may help decision makers in implementing policies affecting urban landscapes and/or in balancing conservation efforts when designing pest control strategies that should also aim at reducing human contact with Leptospira-laden rats while limiting their impact on the autochthonous fauna.

The effects of flooding and weather conditions on leptospirosis transmission in Thailand

The epidemic of leptospirosis in humans occurs annually in Thailand. In this study, we have developed mathematical models to investigate transmission dynamics between humans, animals, and a contaminated environment. We compared different leptospire transmission models involving flooding and weather conditions, shedding and multiplication rate in a contaminated environment. We found that the model in which the transmission rate depends on both flooding and temperature, best-fits the reported human data on leptospirosis in Thailand. Our results indicate that flooding strongly contributes to disease transmission, where a high degree of flooding leads to a higher number of infected individuals. Sensitivity analysis showed that the transmission rate of leptospires from a contaminated environment was the most important parameter for the total number of human cases. Our results suggest that public education should target people who work in contaminated environments to prevent Leptospira infections.

Modelling and analyzing spatial clusters of leptospirosis based on satellite-generated measurements of environmental factors in Thailand during 2013-2015

This study statistically identified the association of remotely sensed environmental factors, such as Land Surface Temperature (LST), Night Time Light (NTL), rainfall, the Normalised Difference Vegetation Index (NDVI) and elevation with the incidence of leptospirosis in Thailand based on the nationwide 7,495 confirmed cases reported during 2013-2015. This work also established prediction models based on empirical findings. Panel regression models with random-effect and fixed-effect specifications were used to investigate the association between the remotely sensed environmental factors and the leptospirosis incidence. The Local Indicators of Spatial Association (LISA) statistics were also applied to detect the spatial patterns of leptospirosis and similar results were found (the R2 values of the random-effect and fixed-effect models were 0.3686 and 0.3684, respectively). The outcome thus indicates that remotely sensed environmental factors possess statistically significant contribution in predicting this disease. The highest association in 3 years was observed in LST (random- effect coefficient = -9.787, P<0.001; fixed-effect coefficient = -10.340, P=0.005) followed by rainfall (random-effect coefficient = 1.353, P<0.001; fixed-effect coefficient = 1.347, P<0.001) and NTL density (random-effect coefficient = -0.569, P=0.004; fixed-effect coefficient = -0.564, P=0.001). All results obtained from the bivariate LISA statistics indicated the localised associations between remotely sensed environmental factors and the incidence of leptospirosis. Particularly, LISA's results showed that the border provinces in the northeast, the northern and the southern regions displayed clusters of high leptospirosis incidence. All obtained outcomes thus show that remotely sensed environmental factors can be applied to panel regression models for incidence prediction, and these indicators can also identify the spatial concentration of leptospirosis in Thailand.

Spatial distribution of leptospirosis incidence in the Upper Yangtze and Pearl River Basin, China: Tools to support intervention and elimination

BACKGROUND

Since 2011 human leptospirosis incidence in China has remained steadily low with persistent pockets of notifications reported in communities within the Upper Yangtze River Basin (UYRB) and Pearl River Basin (PRB). To help guide health authorities within these residual areas to identify communities where interventions should be targeted, this study quantified the local effect of socioeconomic and environmental factors on the spatial distribution of leptospirosis incidence and developed predictive maps of leptospirosis incidence for UYRB and PRB.

METHODS

Data on all human leptospirosis cases reported during 2005-2016 across the UYRB and PRB regions were geolocated at the county-level and included in the analysis. Bayesian conditional autoregressive (CAR) models with zero-inflated Poisson link for leptospirosis incidence were developed after adjustment of environmental and socioeconomic factors such as precipitation, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), land surface temperature (LST), elevation, slope, land cover, crop production, livestock density, gross domestic product and population density.

RESULTS

The relationship of environmental and socioeconomic variables with human leptospirosis incidence varied between both regions. While across UYRB incidence of human leptospirosis was associated with MNDWI and elevation, in PRB human leptospirosis incidence was significantly associated with NDVI, livestock density and land cover. Precipitation was significantly and positively associated with the spatial variation of incidence of leptospirosis in both regions. After accounting for the effect of environmental and socioeconomic factors, the predicted distribution of residual high-incidence county is potentially more widespread both in the UYRB and PRB compared to the observed distribution. In the UYRB, the highest predicted incidence was found along the border of Chongqing and Guizhou towards Sichuan basin and northwest Yunnan. The highest predicted incidence was also identified in counties in the central and lower reaches of the PRB.

CONCLUSIONS

This study demonstrated significant geographical heterogeneity in leptospirosis incidence within UYRB and PRB, providing an evidence base for prioritising targeted interventions in counties identified with the highest predicted incidence. Furthermore, environmental drivers of leptospirosis incidence were highly specific to each of the regions, emphasizing the importance of localized control measures. The findings also suggested the need to expand interventional coverage and to support surveillance and diagnostic capacity on the predicted high-risk areas.

Spatial epidemiology of Japanese encephalitis virus and other infections of the central nervous system infections in Lao PDR (2003-2011): A retrospective analysis

BACKGROUND

Central nervous system (CNS) infections are important contributors to morbidity and mortality and the causative agents for ~50% patients are never identified. The causative agents of some CNS infections have distinct spatial and temporal patterns.

METHODOLOGY/PRINCIPAL FINDINGS

Here we present the results of a spatial epidemiological and ecological analysis of CNS infections in Lao PDR (2003-2011). The data came from hospitalizations for suspected CNS infection at Mahosot Hospital in Vientiane. Out of 1,065 patients, 450 were assigned a confirmed diagnosis. While many communities in Lao PDR are in rural and remote locations, most patients in these data came from villages along major roads. Japanese encephalitis virus ((JEV); n = 94) and Cryptococcus spp. (n = 70) were the most common infections. JEV infections peaked in the rainy season and JEV patients came from villages with higher surface flooding during the same month as admission. JEV infections were spatially dispersed throughout rural areas and were most common in children. Cryptococcus spp. infections clustered near Vientiane (an urban area) and among adults.

CONCLUSIONS/SIGNIFICANCE

The spatial and temporal patterns identified in this analysis are related to complex environmental, social, and geographic factors. For example, JEV infected patients came from locations with environmental conditions (surface water) that are suitable to support larger mosquito vector populations. Most patients in these data came from villages that are near major roads; likely the result of geographic and financial access to healthcare and also indicating that CNS diseases are underestimated in the region (especially from more remote areas). As Lao PDR is undergoing major developmental and environmental changes, the space-time distributions of the causative agents of CNS infection will also likely change. There is a major need for increased diagnostic abilities; increased access to healthcare, especially for rural populations; and for increased surveillance throughout the nation.

[Leptospirosis in Germany: current knowledge on pathogen species, reservoir hosts, and disease in humans and animals]

Leptospirosis is a zoonotic disease with a wide spectrum of clinical symptoms in humans and animals, ranging from subclinical infections to severe signs of multiorgan dysfunction. In Germany, laboratory confirmation of acute human infection is notifiable based on the Protection Against Infection Act. Disease or occurrence of the pathogen in pigs and sheep must be reported according to the regulation on reportable animal diseases. Transmission occurs via direct and indirect contact with the urine of infected animals, with rodents acting as the main reservoir. With an average annual incidence of 0.1 notified cases per 100,000 inhabitants, leptospirosis is a rare disease in Germany.This review article presents the current knowledge on leptospirosis in Germany in the framework of the project "Improving public health through a better understanding of the epidemiology of rodent-transmitted diseases" (RoBoPub) funded by the Ministry of Education and Research. In a One-Health approach, information about clinical manifestation, available prevalence data in humans and animals, knowledge of pathogen distribution, host association, mode of transmission, and survival in the environment is summarized. Preliminary findings on the influence of fluctuations in rodent populations on the occurrence of leptospirosis are also discussed. The aim of the article is to increase the awareness of this currently neglected disease in Germany.In future, higher temperatures and more frequent heavy rainfalls, which could occur due to climate change, should be taken into account.

Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches

Background

Recent reports of the National Ministry of Health and Treatment of Iran (NMHT) show that Gilan has a higher annual incidence rate of leptospirosis than other provinces across the country. Despite several efforts of the government and NMHT to eradicate leptospirosis, it remains a public health problem in this province. Modelling and Prediction of this disease may play an important role in reduction of the prevalence.

Methods

This study aims to model and predict the spatial distribution of leptospirosis utilizing Geographically Weighted Regression (GWR), Generalized Linear Model (GLM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) as capable approaches. Five environmental parameters of precipitation, temperature, humidity, elevation and vegetation are used for modelling and predicting of the disease. Data of 2009 and 2010 are used for training, and 2011 for testing and evaluating the models.

Results

Results indicate that utilized approaches in this study can model and predict leptospirosis with high significance level. To evaluate the efficiency of the approaches, MSE (GWR = 0.050, SVM = 0.137, GLM = 0.118 and ANN = 0.137), MAE (0.012, 0.063, 0.052 and 0.063), MRE (0.011, 0.018, 0.017 and 0.018) and R² (0.85, 0.80, 0.78 and 0.75) are used.

Conclusion

Results indicate the practical usefulness of approaches for spatial modelling and predicting leptospirosis. The efficiency of models is as follow: GWR > SVM > GLM > ANN. In addition, temperature and humidity are investigated as the most influential parameters. Moreover, the suitable habitat of leptospirosis is mostly within the central rural districts of the province.

Climate variability, satellite-derived physical environmental data and human leptospirosis: A retrospective ecological study in China

BACKGROUND

In the past three decades, the incidence rate of notified leptospirosis cases in China have steeply declined and are now circumscribed to discrete areas in the country. Previous research showed that climate and environmental variation may play an important role in leptospirosis transmission. However, quantitative associations between climate, environmental factors and leptospirosis in the high-risk areas in China, is still poorly understood.

OBJECTIVE

To quantify the temporal effects of climate and remotely-sensed physical environmental factors on human leptospirosis in the high-risk counties in China.

METHODS

Time series seasonal decomposition was performed to explore the seasonality pattern of leptospirosis incidence in Mengla County, Yunnan and Yilong County, Sichuan for the period 2006-2016. Time series cross-correlation analysis was carried out to examine lagged effects of rainfall, relative humidity, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and land surface temperature (LST) on leptospirosis. The associations of climatic and physical environment factors with leptospirosis in each county were assessed by using a generalized linear regression model with negative binomial link, adjusted by seasonal components.

RESULTS

Leptospirosis incidence in both counties showed strong and unique annual seasonality. Our results show that in Mengla County leptospirosis notifications exhibits a bi-modal temporal pattern while in Yilong County it follows a typical single epidemic curve. After adjusting for seasonality, the final best-fitting model for Mengla County indicated that leptospirosis notifications were significantly associated with present LST values (incidence rate ratio, IRR = 0.857, 95% confidence interval (CI):0.729-0.929) and rainfall at a lag of 6-months (IRR = 0.989; 95% CI: 0.985-0.993). The incidence of leptospirosis in Yilong was associated with rainfall at 1-month lag (IRR = 1.013, 95% CI: 1.003-1.023), LST (3-months lag) (IRR = 1.193, 95% CI: 1.095-1.301), and MNDWI (5-months lag) (IRR = 7.960, 95% CI: 1.241-47.66).

CONCLUSIONS

Our study identified lagged effects between leptospirosis incidence and climate and remotely-sensed environmental factors in the two most endemic counties in China. Rainfall in combination with satellite derived physical environment factors provided better insight of the local epidemiology as well as good predictors for leptospirosis outbreak in both counties. This would also be an avenue for the development of leptospirosis early warning systems to support leptospirosis control in China.

Environmental health effects attributed to toxic and infectious agents following hurricanes, cyclones, flash floods and major hydrometeorological events

Extreme hydrometeorological events such as hurricanes and cyclones are increasing in frequency and intensity due to climate change and often associated with flash floods in coastal, urbanized and industrial areas. Preparedness and response measures need to concentrate on toxicological and infectious hazards, the potential impact on environmental health, and threat to human lives. The recognition of the danger of flood water after hurricanes is critical. Effective health management needs to consider the likelihood and specific risks of toxic agents present in waters contaminated by chemical spills, bio-toxins, waste, sewage, and water-borne pathogens. Despite significant progress in the ability to rapidly detect and test water for a wide range of chemicals and pathogens, there has been a lack of implementation to adapt toxicity measurements in the context of flash and hurricane-induced flooding. The aim of this review was to highlight the need to collect and analyze data on toxicity of flood waters to understand the risks and prepare vulnerable communities and first responders. It is proposed that new and routinely used technologies be employed during disaster response to rapidly assess toxicity and infectious disease threats, and subsequently take necessary remedial actions.

Risk factors for Neospora caninum, bovine viral diarrhoea virus, and Leptospira interrogans serovar Hardjo infection in smallholder cattle and buffalo in Lao PDR

Smallholder large ruminant production in Lao People's Democratic Republic (Laos) is characterised by low reproductive efficiency. To determine if common abortifacient bovid infectious diseases are involved, a serological investigation was conducted. Sera was collected from stored and fresh cattle (n = 390) and buffalo (n = 130) samples from 2016-18 from, and then examined for associations in a retrospective risk factor study of 71 herds. The sera were assayed for antibodies to Neospora caninum, bovine viral diarrhoea virus (BVDV), Leptospira interrogans serovar Hardjo and Brucella abortus using commercially available enzyme-linked immunosorbent assay kits. These pathogens were detected in buffalo samples at 78.5% (95% CI 71.4-85.6), 0%, 2.3% (95% CI 0-4.9) and 0%, respectively, and in cattle at 4.4% (95% CI 2.4-6.4), 7.7% (95% CI 3.1-12.3), 12.8% (95% CI 9.5-16.1) and 0.26% (95% CI 0-0.8), respectively. Exposure of buffalo to N. caninum was positively associated with buffalo age, with a predicted seropositivity at birth of 52.8%, increasing to 97.2% by 12 years of age (p = 0.037). Exposure of cattle to L. interrogans serovar Hardjo was more prevalent in females compared to males, was associated with higher titres of BVDV, and was more prevalent in the wet season compared to the dry season. Exposure of cattle to BVDV was more prevalent in males compared to females, the wet and dry seasons were comparable, and was associated with rising antibody titres against N. caninum and L. interrogans serovar Hardjo. The risk factor survey identified that the probability of herds being N. caninum positive increased with farmer age, if farmers believed there were rodents on farm, and if farmers weren't aware that canids or rodents could contaminate bovid feed on their farm. The probability of a herd being positive to L. interrogans serovar Hardjo increased on farms where multiple cows shared the same bull, where farmers had lower husbandry knowledge, and on farms that used water troughs. The probability of a herd being BVDV seropositive increased with increasing herd size and increasing titres to N. caninum. The benchmarking of bovid exposure to emerging abortifacient pathogens and identification of their risk factors potentially informs disease prevention strategies, supporting efforts to establish a biosecure beef supply for enhanced smallholder livestock productivity, public health and food security in Laos and surrounding countries.

Spatial epidemiological approaches to inform leptospirosis surveillance and control: A systematic review and critical appraisal of methods

Leptospirosis is a global zoonotic disease that the transmission is driven by complex geographical and temporal variation in demographics, animal hosts and socioecological factors. This results in complex challenges for the identification of high-risk areas. Spatial and temporal epidemiological tools could be used to support leptospirosis control programs, but the adequacy of its application has not been evaluated. We searched literature in six databases including PubMed, Web of Science, EMBASE, Scopus, SciELO and Zoological Record to systematically review and critically assess the use of spatial and temporal analytical tools for leptospirosis and to provide general framework for its application in future studies. We reviewed 115 articles published between 1930 and October 2018 from 41 different countries. Of these, 65 (56.52%) articles were on human leptospirosis, 39 (33.91%) on animal leptospirosis and 11 (9.5%) used data from both human and animal leptospirosis. Spatial analytical (n = 106) tools were used to describe the distribution of incidence/prevalence at various geographical scales (96.5%) and to explored spatial patterns to detect clustering and hot spots (33%). A total of 51 studies modelled the relationships of various variables on the risk of human (n = 31), animal (n = 17) and both human and animal infection (n = 3). Among those modelling studies, few studies had generated spatially structured models and predictive maps of human (n = 2/31) and animal leptospirosis (n = 1/17). In addition, nine studies applied time-series analytical tools to predict leptospirosis incidence. Spatial and temporal analytical tools have been greatly utilized to improve our understanding on leptospirosis epidemiology. Yet the quality of the epidemiological data, the selection of covariates and spatial analytical techniques should be carefully considered in future studies to improve usefulness of evidence as tools to support leptospirosis control. A general framework for the application of spatial analytical tools for leptospirosis was proposed.

A remotely sensed flooding indicator associated with cattle and buffalo leptospirosis cases in Thailand 2011-2013

BACKGROUND

Leptospirosis is an important zoonotic disease worldwide, caused by spirochetes bacteria of the genus Leptospira. In Thailand, cattle and buffalo used in agriculture are in close contact with human beings. During flooding, bacteria can quickly spread throughout an environment, increasing the risk of leptospirosis infection. The aim of this study was to investigate the association of several environmental factors with cattle and buffalo leptospirosis cases in Thailand, with a focus on flooding.

METHOD

A total of 3571 urine samples were collected from cattle and buffalo in 107 districts by field veterinarians from January 2011 to February 2013. All samples were examined for the presence of leptospirosis infection by loop-mediated isothermal amplification (LAMP). Environmental data, including rainfall, percentage of flooded area (estimated by remote sensing), average elevation, and human and livestock population density were used to build a generalized linear mixed model.

RESULTS

A total of 311 out of 3571 (8.43%) urine samples tested positive by the LAMP technique. Positive samples were recorded in 51 out of 107 districts (47.66%). Results showed a significant association between the percentage of the area flooded at district level and leptospirosis infection in cattle and buffalo (p = 0.023). Using this data, a map with a predicted risk of leptospirosis can be developed to help forecast leptospirosis cases in the field.

CONCLUSIONS

Our model allows the identification of areas and periods when the risk of leptospirosis infection is higher in cattle and buffalo, mainly due to a seasonal flooding. The increased risk of leptospirosis infection can also be higher in humans too. These areas and periods should be targeted for leptospirosis surveillance and control in both humans and animals.

Reproductive Disorders and Leptospirosis: A Case Study in a Mixed-Species Farm (Cattle and Swine)

Animal leptospirosis, exempt in rodents, manifests as peculiar biology where the animal can function, simultaneously or not, as a susceptible host or reservoir. In the first case, clinical symptoms are likely. In the second case, infection is subclinical and manifestations are mild or absent. Mild clinical symptoms encompass reproductive failure in production animals for host-adapted Leptospira sp. serovars. This work presents a study on Leptospira sp. infection in a mixed-species (bovine and swine) farm with documented reproductive disorders in the cattle unit. A long calving interval (above 450 days) was the hallmark observed in cows. Some cows (2/26 tested) presented a high titre of antibodies against Leptospira sp. serogroup Sejroe, but the overall within-herd prevalence was low (11.5% and 7.7% for cut-off titres of 1:30 and 1:100, respectively). The in-herd prevalence of leptospirosis in the sow unit (determined for 113/140 animals) was high when using a lowered cut-off threshold (32.7% vs. 1.8% for cut-off titre of 1:30 and 1:100, respectively). In this unit, the most prevalent serogroup was Autumnalis. The final diagnostic confirmation of Leptospira sp. maintenance within the farm was obtained through detection by PCR of Leptospira sp. DNA in an aborted swine litter. Despite the fact that a common causative infective agent was diagnosed in both species, the direct link between the two animal units was not found. Factors such as drinking from the same water source and the use of manure prepared with the swine slurry might raise suspicion of a possible cross-contamination between the two units. In conclusion, this work suggests that leptospirosis be included in the differential diagnosis of reproductive disorders and spontaneous abortions in production animals and provides data that justify the use of a lowered threshold cut-off for herd diagnosis.