Environmental vulnerability of the municipality of Rio de Janeiro to leptospirosis cases due to extreme hydrological events

Environmental vulnerability of the municipality of Rio de Janeiro to leptospirosis cases due to extreme hydrological events | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental vulnerability of the municipality of Rio de Janeiro to leptospirosis cases due to extreme hydrological events Davi Souza de Paula, Patricia Raquel da Silva Sottoriva, Kátia Eliane Santos Avelar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8001843/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We developed a statistical model for analyzing environmental vulnerability on urban infrastructure to the risk of leptospirosis due to the recurrence of intense hydrological events (floods and inundations). The SARIMA technique (Seasonal AutoRegressive Integrated Moving Average) was used to build forecasts, observing the seasonality and specific temporal dynamics of the data on city of Rio de Janeiro, where the National Reference Laboratory for Leptospirosis of the Oswaldo Cruz Foundation is located. An incidence matrix was constructed to identify patterns and frequencies of variables associated with leptospirosis. We founded that rainfall intensity and seasonality has a direct impact in leptospirosis cases (each increase of 1 mm in precipitation can raise the number of cases by 0.31%). Intense rainfall contributes to the spread the Leptospira bacteria in areas with poor sanitation infrastructure, insufficient drainage, and inadequate land use. In the face of intense hydrological events due to climate change, an increasing number of disease cases is expected in economically and socially vulnerable areas. climate change mathematical modeling seasonality environmental variables public health Figures Figure 1 Figure 2 Highlights For each 1 mm increase in precipitation, an approximate 0.31% increase in leptospirosis cases is expected in vulnerably areas. Intense hydrological events due to climate change are expected to lead to a growing number of disease cases in economically and socially vulnerable areas. The SARIMA model allowed a more accurate and detailed understanding of the temporal and seasonal events that influence the incidence of leptospirosis. Public policies should include health education campaigns to inform the population about the risks and preventive measures against leptospirosis. 1. Introduction Mathematical models applied to the context of public health serve as tools for understanding, predicting, and managing diseases, especially those influenced by environmental and socioeconomic factors. Epidemiological models allow for the simulation of disease transmission dynamics, the effectiveness of public health interventions, and the impact of environmental variables on disease patterns. Mathematical modeling can range from simple regression models to complex computational simulations that integrate multiple layers of data (Teles et al., 2023 ; Rees et al., 2023 ; Gracie et al., 2014 ). Additionally, these models enable hypothesis testing in controlled environments, outbreak prediction, and a better understanding of disease transmission mechanisms. However, they present significant challenges, such as the availability of accurate data, the complexity of integrating heterogeneous variables, temporal data clustering, and the application of modeled results to effective health policies (Douchet et al., 2024 ; Paula et al., 2024 ; Gracie et al., 2021 ; Gracie et al., 2014 ). Generalized additive models and SARIMA (Seasonal Autoregressive Integrated Moving Average) can predict leptospirosis incidence and identify high-incidence clusters, demonstrating how the temporal and spatial variation of the disease can be mapped to support more effective public health strategies (Teles et al., 2024 ). Models based on climate data have highlighted the significant influence of climate on disease seasonality, although predicting interannual outbreaks remains a challenge (Douchet L et al., 2024 ). The need to combine environmental and socioeconomic data is further emphasized in studies on flooding and leptospirosis in Brazil, which relate the increase in floods to leptospirosis incidence, underscoring the importance of contextual variables in health risk modeling (Warnasekara et al., 2021 ). Time series models applied in Sri Lanka have shown how leptospirosis predictions can vary significantly across different climatic zones, highlighting the need for local adaptation in forecasting and prevention strategies (Barcellos et al., 2023 ). These examples demonstrate the versatility and challenges of mathematical models in capturing the complexity of leptospirosis epidemiology, reinforcing the importance of accurate data and the integration of multiple variables for a comprehensive understanding and effective application in health policies. In this context, the objective of this article is to propose a model for analyzing the vulnerability of the municipality of Rio de Janeiro to leptospirosis cases, considering urban infrastructure and environmental indicators. The study’s results provide valuable insights to support public health planning and municipal preventive measures. 2. Materials and Methods This study is an applied and exploratory research that employs descriptive, correlational, and predictive methods to examine the relationships between environmental variables, urban infrastructure, and the incidence of leptospirosis. 2.1 Stage 1: Identification of Relevant Variables In this stage, key environmental indicators related to leptospirosis cases were identified through an extensive literature review using databases such as CAFe, Scielo, Web of Science, and PubMed. The variables indicating the causality of leptospirosis were extracted and organized into an occurrence matrix. 2.2 Stage 2: Data Collection Data for the selected variables (identified in Stage 1) were collected for the urban area of the municipality of Rio de Janeiro, covering the period from 2010 to 2022. Data sources included: the National Sanitation Information System (SNIS) for infrastructure data (treated water supply volume, treated sewage volume, and collected solid waste volume); the Information System for Notifiable Diseases (SINAN-DATASUS) for the number of leptospirosis cases; the Digital Disaster Atlas of Brazil for records of hydrological disasters; the National Institute of Meteorology (INMET) for rainfall data. Rio de Janeiro was chosen as the study area due to the availability of annual data for the defined period and its status as the host city of the National Reference Laboratory for Leptospirosis at the Oswaldo Cruz Foundation (Fiocruz). 2.3 Stage 3: Data Analysis To analyze the influence of the selected variables (from Stage 2) on leptospirosis incidence, a multiple linear regression model was initially applied using JASP software (version 0.18.3.0). The Shapiro-Wilk test was conducted to check for normal distribution of the data (p > 0.05), and statistical significance tests (α = 0.05, p < 0.05) were performed to assess the robustness of correlations and the validity of the model. 2.4 Stage 4: Model Development A SARIMA (Seasonal Autoregressive Integrated Moving Average) model was used to evaluate the influence of selected variables (from Stage 2) on leptospirosis incidence. This technique is particularly recommended for analyzing time series with seasonal and temporal dynamics, such as leptospirosis incidence (Teles et al., 2023 ), allowing for more accurate predictive analysis. 3. Results and Discussion 3.1 Analysis of Relevant Variables Table 1 presents the incidence matrix of the variables, grouped into categories: epidemiological, demographic, climatic, and others—allowing for the identification of patterns and frequencies associated with leptospirosis. Table 1 Incidence Matrix of Variables. RESEARCH VARIABLES A B C D E F G H I J K L M N O P Q R S T U V Spatial distribution of leptospirosis in Rio Grande do Sul, Brazil: recovering the ecology of ecological studies (Barcellos et al., 2023 ) x x x x x x x Quantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study (Rees et al., 2023 ) x x x x x x x Socio-geographical factors and vulnerability to leptospirosis in South Brazil (Teles et al., 2024 ) x x x x x x x x x x Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis (Teles et al., 2023 ) x x x x x x x x x x x x x Spatial study of leptospirosis risks in the municipality of Rio de Janeiro (RJ) (Chaiblich et al., 2017 ) x x x x x x Retrospective analysis of leptospirosis in the Metropolitan Region I of Rio de Janeiro from 2015 to 2019 (Santos, 2021 ) x x x x x x x Floods and leptospirosis in Brazilian municipalities from 2003 to 2013: use of data mining techniques (Gracie et al., 2021 ) x x x Human leptospirosis as a doubly neglected disease in Brazil (Martins; Spink, 2020 ) x x x x x x x x x x x Geoprocessing and spatial analysis for identifying leptospirosis risk areas: a systematic review (Souza et al., 2020 ) x x x x Space-time distribution of leptospirosis and risk factors in Belém, Pará, Brazil (Gonçalves et al., 2016 ) x x x x x x x x x x x x Brazilian overview of the relationship between leptospirosis and floods (Portela et al., 2020 ) x x x x x x x Leptospirosis in the municipality of Campinas, São Paulo, Brazil: 2007 to 2014 (Lara, 2019) x x x x x x x Epidemiology of human leptospirosis in urban and rural areas of Brazil, 2000–2015 (Galan et al., 2021 ) x x x x x x x x x x x Temporal trend of leptospirosis and its association with climatic and environmental variables in Santa Catarina (Silva et al., 2022 ) x x x x Time series models for prediction of leptospirosis in different climate zones in Sri Lanka (Warnasekara et al., 2021 ) x x x x x Climate-driven models of leptospirosis dynamics in tropical islands from three oceanic basins (Douchet et al., 2024 ) x x x x Climatic variables, living conditions, and public health: leptospirosis in the municipality of Rio de Janeiro from 1996 to 2009 (Oliveira et al., 2012 ) x x x x x x x x Spatial analysis of socio-environmental determinants for leptospirosis in the municipality of Itaboraí-RJ (Caldas et al., 2019 ) x x x x Socio-environmental vulnerability to leptospirosis in the metropolitan urban cluster of Curitiba (Buffon, 2018 ) x x x x x LEGEND: Epidemiological Variables: (A) Incidence; (B) Mortality; (C) Case Fatality Rate. Environmental Variables: (D) Land Use; (E) Altitude; (F) River Basins; (G) Risk Areas; (H) Climatic Conditions. Demographic Variables: (I) Sex; (J) Age; (K) Education Level; (L) Skin Color; (M) Occupation; (N) Ethnicity. Socioeconomic Variables: (O) Infrastructure; (P) Living Conditions.Climatic Variables: (Q) Precipitation; (R) Temperature. Other Variables: (S) Geographical Distribution; (T) Animal Occurrences; (U) Modes of Transmission; (V) Sociocultural Factors. Temperature, rainfall intensity, relative humidity, and solar radiation intensity reflect the significant role that climate plays in the distribution and incidence of the disease (Douchet et al., 2024 ; Teles et al., 2024 ; Teles et al., 2023 ; Rees et al., 2023 ; Barcellos et al., 2023 ; Silva et al., 2022 ; Warnasekara et al., 2021 ; Portela et al., 2020 ; Martins and Spink, 2020 ; Gracie et al., 2014 ; Oliveira et al., 2012 ). Flood risk areas indicate a higher likelihood of leptospirosis outbreaks (Gracie et al., 2021 ; Portela et al., 2020 ; Souza et al., 2020 ; Caldas et al., 2019 ; Buffon, 2018 ; Chaiblich et al., 2017 ; Gonçalves et al., 2016 ; Oliveira et al., 2012 ). Urban infrastructure conditions, such as water supply, sanitation, drainage, and waste collection, have been recognized as key determinants in disease occurrence, as they directly influence hygiene and environmental sanitary conditions (Teles et al., 2024; Teles et al., 2023 ; Rees et al., 2023 ; Gracie et al., 2021 ; Martins and Spink, 2020 ; Chaiblich et al., 2017 ; Santos, 2021 ; Portela et al., 2020 ; Galan et al., 2020; Oliveira et al., 2012 ; Caldas et al., 2019 ; Buffon, 2018 ). Living conditions, including housing quality, population density, and access to basic services, are essential for understanding vulnerability to leptospirosis (Teles et al., 2024 ; Teles et al., 2023 ; Rees et al., 2023 ; Gracie et al., 2021 ; Martins and Spink, 2020 ; Chaiblich et al., 2017 ; Santos, 2021 ; Galan et al., 2021 ; Portela et al., 2020 ; Caldas et al., 2019 ; Buffon, 2018 ; Gonçalves et al., 2016 ; Oliveira et al., 2012 ). Improvements in these factors can play a crucial role in disease prevention. Precipitation is a climatic variable directly related to leptospirosis incidence, as it influences the disease dynamics (Douchet et al., 2024 ; Teles et al., 2023 ; Rees et al., 2023 ; Silva et al., 2022 ; Galan et al., 2021 ; Warnasekara et al., 2021 ; Souza et al., 2020 ; Portela et al., 2020 ; Lara, 2019; Oliveira et al., 2012 ). Rainfall indices should be analyzed from a temporal scale perspective (monthly or annual) and in relation to disease occurrence (endemic or epidemic) (Barcellos et al., 2023 ; Buffon, 2018 ; Carrijo, 2008 ). Geospatial analysis using Geographic Information Systems (GIS) helps identify high-risk zones, reflecting the complexity and multifactorial nature of leptospirosis vulnerability (Teles et al., 2023 ; Souza et al., 2023; Caldas et al., 2019 ; Buffon, 2018 ). Additionally, demographic variables such as age and occupation indicate that certain groups are more vulnerable due to occupational exposure or immune conditions (Teles et al., 2024 ; Teles et al., 2023 ; Santos, 2021 ; Martins and Spink, 2020 ; Gonçalves et al., 2016 ; Lara, 2019; Galan et al., 2021 ). These observations suggest that the most influential variables in determining vulnerability to leptospirosis, based on the frequency and emphasis in the reviewed studies, include adverse climatic conditions, flood-prone areas, and the quality of urban infrastructure. Table 2 presents the results of the data collection for the selected variables. Table 2 Data results (period: 2010–2022). Year Water volume (1000 m³/year) Sewage volume (1000 m³/year) Waste volume (Ton/year) Precipitation (mm) Hydrological disasters Cases Lept 2022 2,237,506.6 407,857.3 2,003,434.3 1,150.6 2 216 2021 1,034,924.7 491,026.2 2,003,442.3 1,084.1 5 76 2020 889,213.6 497,419.8 2,132,001.2 1,191.2 6 118 2019 1,069,268.0 560,996.9 2,216,294.0 1,587.9 1 183 2018 1,333,534.0 593,686.7 2,141,363.0 1,059.4 0 171 2017 1,295,179.0 587,188.1 1,989,413.4 884.5 0 128 2016 1,283,050.0 587,636.0 1,972,998.8 1,231.1 0 130 2015 1,283,007.0 608,083.2 2,066,382.2 1,040.0 0 89 2014 1,230,313.4 623,922.7 2,180,439.8 857.7 1 96 2013 1,224,118.7 615,594.8 2,285,014.6 1,122.2 0 165 2012 1,177,156.0 567,865.0 2,224,345.1 793.1 0 148 2011 1,130,281.1 538,189.8 2,087,793.2 947.2 0 142 2010 1,092,982.9 528,176.4 1,989,902.0 1,385.5 0 305 The altitude and temperature variables were excluded from the analysis, as altitude variation is minimal and unlikely to significantly impact the results, while the annual summation of temperature data reduces the relevance of its daily or monthly variations. 3.2 Data modeling using multiple linear regression The coefficient of determination (R² = 0.74) and adjusted R² (0.48) indicate that the model explains a considerable proportion of variability in leptospirosis cases. The Durbin-Watson statistic (2.33) suggests no residual autocorrelation. However, the high RMSEA (Root Mean Square Error of Approximation = 0.43) indicates a poor model fit, which may limit its applicability in predicting leptospirosis incidence. Additionally, joint statistical significance was not observed (p = 0.11), meaning there is no strong evidence that the proposed regression model significantly influences leptospirosis cases. The coefficients for WATER SUPPLY VOLUME (-0.33) and SEWAGE VOLUME (-0.52) suggest an inverse relationship with leptospirosis cases, indicating that improvements in sanitation could contribute to reducing disease incidence. However, the p-value for SEWAGE VOLUME (p = 0.19) suggests that this correlation is not statistically significant within the model. The PRECIPITATION variable proved relevant, showing a strong partial correlation (0.63) and a p-value of 0.09. Although this does not meet the conventional significance threshold (p < 0.05), it still suggests a trend where higher rainfall volumes may contribute to an increase in leptospirosis cases. (i) Length of the Historical Series Despite the challenges associated with the limited sample size over twelve years, this study provided valuable insights into data modeling in public health. The RMSEA value (0.43) reflects the complexity of fitting models with multiple variables in a limited dataset (Carrijo, 2008 ), highlighting the risk of overfitting (i.e., excessive adaptation to the specificities of the data, failing to generalize to other contexts or periods). This underscores the importance of simplifying models, such as focusing on rainfall, which could reduce complexity and enhance result robustness. These findings emphasize the need for improved data collection frequency and variability, especially in long-term studies. Additionally, they encourage the exploration of new methodological approaches and analytical techniques for more precise and generalizable insights in future studies. (ii) Water, Sewage, and Waste Data The annual collection of data for variables such as water supply, sewage, and waste collection may fail to capture significant short-term variations essential for understanding leptospirosis outbreak dynamics. Seasonal changes or specific events can influence disease occurrence, yet the lack of data granularity may exacerbate collinearity issues, leading to high p-values and limiting the ability to detect statistically significant associations. (iii) Low Data Variability The analysis revealed that variables based on population data (water, sewage, and waste) exhibit little variation over time, restricting their predictive power in regression models. This suggests that these variables are less useful as robust predictors in the studied models. In contrast, rainfall shows more direct and measurable variations, making it a better candidate for correlation with leptospirosis outbreaks. (iv) Limited Records of Hydrological Disasters The low number of recorded hydrological disasters poses a significant challenge in evaluating their actual impact on leptospirosis cases. Data from the Atlas Digital de Desastres do Brasil are often collected primarily to request federal funds for response and reconstruction, rather than to provide an accurate record of disaster frequency and severity (Paula et al., 2024 ). Consequently, these records may not fully reflect the actual incidence of hydrological disasters, leading to unstable statistical coefficients and non-significant p-values in epidemiological analyses. This highlights the need for a critical approach when interpreting disaster data. In contrast, rainfall data, being continuously and systematically recorded, provides a more reliable and comprehensive dataset for analysis. It enables a more accurate understanding of how meteorological conditions influence disease incidence. The results indicate a significant association between higher rainfall levels and increased leptospirosis cases, aligning with existing literature that suggests wetter periods favor disease transmission (Douchet L et al., 2024 ; Teles A J et al., 2023; Rees EM et al., 2023; Silva et al., 2022 ; Galan DI et al., 2021; Warnasekara et al., 2021 ; Souza et al., 2020 ; Portela et al., 2020 ; Lara, 2019; Oliveira et al., 2019). Given this, the findings reinforce the importance of simplifying the model by focusing on rainfall as the primary variable. This approach offers a more effective way to explore environmental determinants of leptospirosis cases. 3.3 Data Analysis Using the Seasonal Autoregressive Method (SARIMA) Given the results from the multiple linear regression analysis, the SARIMA model was applied to assess the influence of rainfall on disease incidence. The data were aggregated monthly (Table 3 ) and transformed using the logarithm of leptospirosis cases, with a constant added to stabilize variance and normalize the distribution. Table 3 Rainfall and disease cases aggregated by monthly sum (period 2010–2022). Months Sum of average precipitation (mm.month − 1 ) Sum of lept cases (month − 1 ) January 1,403.0 240 February 1,212.4 217 March 1,537.2 210 April 1,922.0 239 May 1,163.4 158 June 736.6 126 Lujy 744.4 116 August 620.8 109 September 855.2 155 October 945.6 97 November 1,439.4 135 December 1,531.4 165 The Fig. 1 shows the temporal decomposition of the monthly leptospirosis cases, including: observed data (data), trend, seasonality, and residuals (remainder). "Data" shows the original temporal distribution of leptospirosis cases over time. Significant variation in cases is noted, with peaks in specific years (2010, 2018, and 2022). "Trend" indicates a decrease in leptospirosis cases until 2015, followed by a gradual increase until 2022. The reduction in cases in 2020 and 2021 may be associated with changes in social patterns during the COVID-19 pandemic period (Paula et al., 2024 ). The strong seasonality observed suggests that there are periods of the year with higher incidence of cases (from December to April). Recent studies conducted in other parts of Brazil and the world found similar results, such as the spatial and temporal dynamics of leptospirosis in Rio Grande do Sul, with case peaks in 2011, 2014, and 2019, and marked seasonality between January and April (Teles et al., 2023 ). The strong influence of rainfall on leptospirosis incidence in tropical regions of several oceanic basins in Sri Lanka (Douchet et al., 2024 ), and the higher occurrence of the disease in humid zones compared to dry and high zones, is also evident due to the frequent rains and soil moisture, which favor the survival of Leptospira (Warnasekara et al., 2021 ). "Remainder" shows variations not explained by the trend or seasonality. These peaks may be due to local outbreaks, unforeseen environmental changes (Warnasekara et al., 2021 ), and extreme weather events, such as El Niño (Douchet et al., 2024 ), considered outliers. These phenomena can cause anomalies in the model's pattern, making it more challenging to predict future cases. Including detailed, localized climatic indicators could enhance prediction accuracy, emphasizing the importance of microclimatic variables (Teles et al., 2023 ). These studies highlight the complexity of leptospirosis modeling and the need to integrate residual analysis to fully understand deviations from predictions based on historical trends and seasonality patterns. (i) Preliminary Tests in SARIMA Analysis To evaluate stationarity, the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) (Kwiatkowski et al., 1992 ) tests were used. The ADF test result indicates that the null hypothesis of non-stationarity can be rejected (p-value = 0.01) (Dickey and Fuller, 1979 ). The KPSS test result suggests not rejecting the null hypothesis of stationarity at a significance level of 0.1 (p-value = 0.1) (Kwiatkowski et al., 1992 ). In both tests, the data can be considered stationary, meaning they can be used without differentiation for time series models. (ii) Interpretation of the SARIMA Model ar1 (0.3840) First-order autoregressive coefficient. It indicates a linear dependence between the current value of the series and the previously observed value, in the transformed data scale. In other words, recent values in the series have a strong relationship with past values; sar1 (0.1082) First-order seasonal autoregressive coefficient. It indicates seasonal relationships, showing that annual patterns repeat consistently every 12 months; intercept (2.1669) Intercept of the model in the transformed data scale. It represents the average of the series after the logarithmic transformation and shift, serving as the starting point for predictions; xreg (0.0031) Exogenous variable coefficient. It shows how precipitation is associated with the variability of leptospirosis cases in the transformed series. In the original scale, for each 1 mm increase in precipitation, a ~ 0.31% increase in leptospirosis cases is expected; s.e. (Standard Error) Standard error associated with the estimated coefficients. Smaller values indicate more precise estimates of the coefficients; Sigma^2 (0.2265) Variance of the errors in the adjusted model. A lower value indicates a more accurate model fit. The log-likelihood is a measure of how well the model fits the data. Higher values indicate a better fit. The AIC (217.32), AICc (217.72), and BIC (232.57) are comparative measures against other models. Smaller values indicate a more efficient model. To test for autocorrelation in the residuals, the Box-Ljung test (Box and Pierce, 1970 ) was used (p-value = 0.7349). Thus, the residuals appear to be random and do not show significant autocorrelation. Additionally, the Shapiro-Wilk test (p-value = 0.0869) suggests that there is not enough evidence to conclude that the residuals are not normally distributed. This indicates that the time series model has captured the temporal structure of the data well. (iii) Projection of Disease Cases Based on Original Data (iv) Model validation The proposed model is validated by other research employing autoregressive time series forecasting models across various geographical contexts. This consistency highlights the relevance of the predictions for the planning of preventive public health measures, underscoring their strategic importance. Studies using the SARIMA model in Sri Lanka have shown how different microclimates impact disease incidence, suggesting that the model can provide accurate forecasts even in regions with diverse climates (Warnasekara et al., 2021 ). The influence of extreme climatic conditions (such as El Niño ) on outbreak predictions emphasizes the need to prepare for phenomena that intensify leptospirosis transmission (Douchet et al., 2024 ). In this sense, forecasts can anticipate higher-risk periods, enabling preventive interventions that help mitigate the disease's impact (Rees et al., 2023 ). Therefore, the SARIMA analysis revealed that leptospirosis exhibits significant linear dependence on the time series values and strong annual seasonality, as evidenced by the autoregressive coefficients (ar1 and sar1). Additionally, it was found that for each 1 mm increase in precipitation (in vulnerable areas), the number of cases is expected to rise by 0.31%. Thus, precipitation is recognized as a critical factor in the dynamics of this disease within predictive models and highlights the need to adapt public health strategies to account for seasonal variations in cases. 4. Conclusion This study proposes an advanced model to analyze vulnerability to leptospirosis risk in the city of Rio de Janeiro, focusing on the combination of environmental and infrastructural variables. The proposition of a theoretical model provided a clear framework for understanding the interrelationships between variables and leptospirosis incidence. It guided the data collection and analysis, proving flexible and adaptable to accommodate emerging insights from the research, and made a valuable contribution to epidemiological modeling. The transition to the SARIMA model better adjusted to the data characteristics, providing an accurate understanding of the relationship between precipitation and leptospirosis cases. This model confirmed clear patterns of seasonality, indicating an expected increase in leptospirosis cases between December and April, and showed that for each 1 mm increase in precipitation, an approximate 0.31% rise in cases is expected, making this variable critical in outbreak forecasting. Intense rainfall contributes to the spread of waterborne diseases, such as the Leptospira bacteria, particularly in urban areas with poor sanitation infrastructure, insufficient drainage, and inadequate land occupation. Therefore, following intense hydrological events, an increasing number of disease records in economically and socially vulnerable areas is expected. The transition from the linear regression model to the SARIMA model is highlighted as a critical step in response to the limitations encountered, allowing for a more precise and detailed understanding of the temporal and seasonal dynamics influencing leptospirosis incidence. This methodological approach not only reinforces the connection between objectives and results but also highlights the evolution of the study in response to emerging challenges during the research. This methodological path is a positive finding, demonstrating the importance of flexibility and adaptability in developing epidemiological models. It provides perspectives on how iterative and adaptive approaches can lead to more accurate and informative results, which are essential for public health planning. Although the presented SARIMA model has demonstrated strong forecasting capacity, some intrinsic limitations of the study are recognized. The reliance on quality and complete data is one of these, as inconsistencies in databases can significantly affect results. Furthermore, the application of the model in a single municipality limits the generalization of results to other regions with different environmental and infrastructural conditions. These limitations are important for interpreting the results and suggest caution when extrapolating the conclusions. Future research is recommended to expand the application of the model to include multiple locations, enabling broader validation of the findings and enhancing the model's robustness in different epidemiological scenarios. It is suggested to explore the impact of infrastructural interventions and delve deeper into the investigation of interactions between climate change and disease incidence, aiming to support more effective and proactive public health planning. Studies integrating predictive climate models into leptospirosis epidemiology, supporting public health planning, especially in the context of climate change scenarios, are necessary, as precipitation patterns may change, worsening the risk of disease outbreaks. Additionally, it is imperative that public policies focusing on improving urban infrastructure and sanitation systems be implemented to mitigate the risks associated with leptospirosis in the city of Rio de Janeiro, given the discrepancy found between disease records and the high coverage of sanitation services reported by the National Sanitation Information System (where higher coverage should correlate with fewer cases). It is also recommended to continue and expand the collection of environmental and health data, integrating them into monitoring systems and early warning systems, which could be crucial for preventing future outbreaks. Effective public policies should also include health education campaigns to inform the population about risks and preventive measures against leptospirosis, emphasizing the importance of avoiding contact with potentially contaminated water, especially after intense rainfall periods. This research contributes to a better understanding of the factors influencing leptospirosis in the city of Rio de Janeiro and offers a robust methodology that can be adapted for future epidemiological studies, supporting strategic decision-making in the fight against the disease. Declarations 5. Funding sources No. 6. Ethical aspects In accordance with the institutional policy, this research was not needed ethics approval. 7. 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PLoS ONE. 2021;16(3):e0247763. https://doi.org/10.1371/journal.pone.0247763 . Gonçalves NV et al. Distribuição espaço-temporal da leptospirose e fatores de risco em Belém, Pará, Brasil [Leptospirosis space-time distribution and risk factors in Belém, Pará, Brazil]. Cien Saude Colet, 2016, 21(12):3947–3955. Portuguese. Available in: https://doi.org.10.1590/1413-812320152112.07022016. Gracie R, Xavier DR, Medronho R. Cad Saúde Pública. 2021;37(5):14. https://doi.org/10.1590/0102-311X00100119 . Inundações e leptospirose nos municípios brasileiros no período de 2003 a 2013: utilização de técnicas de mineração de dados. Gracie R, et al. Geographical scale effects on the analysis of leptospirosis determinants. Int J Environ Res Public Health. 2014;11(10):10366–83. https://doi.org/10.3390/ijerph111010366 . Lara JM. Leptospirose no município de Campinas, São Paulo, Brasil: 2007 a 2014. Rev. bras. Epidemiol, 2019, 22. Available in: https://doi.org/10.1590/1980-549720190016 Kwiatkowski D, et al. Testing the null hypothesis of stationarity against the alternative of a unit root. J Econ. 1992;54:1–3. http://dx.doi.org/10.1016/0304-4076(92)90104-Y . Martins MHDM, Spink MJP. Human leptospirosis as a doubly neglected disease in Brazil. Cien Saúde Colet. 2020;25(3):919–28. https://doi.org/10.1590/1413-81232020253.16442018 . Portuguese, English. Epub 2018 Jun 27. dos Oliveira TV. S et al. Variáveis climáticas, condições de vida e saúde da população: a leptospirose no. Volume 17. Ciência & Saúde Coletiva; 2012. pp. 1569–76. https://doi.org/10.1590/S1413-81232012000600020 . município do Rio de Janeiro de 1996 a 2009. de Paula DS, Avelar KES, Bilotta P. Impacto das Mudanças Climáticas e a Pandemia na Ocorrência de Casos de Leptospirose no Estado do Rio de Janeiro. Fronteira: Journal of Social, Technological and Environmental Science, 2024, v. 13, n. 1, pp. 21–39. Available in: https://doi.org/10.21664/2238-8869.2024v13i1.p21-39 Portela FC, Kobiyama M, Goerl RF. Panorama brasileiro da relação entre leptospirose e inundações. Volume 35. Florianópolis: Geosul; 2020. pp. 711–34. https://doi.org/10.5007/1982-5153.2020v35n75p711 . Rees EM, et al. Quantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study. PLOS Glob Saúde Pública. 2023;11. https://doi.org/10.1371/journal.pgph.0002400 . Santos TA. Análise retrospectiva sobre leptospirose na região metropolitana I do Rio de Janeiro de 2015 a 2019. Pubvet, 2021, v. 15(6), 2021. Available in: https://ojs.pubvet.com.br/index.php/revista/article/view/533 Silva AEP, Latorre MDRDO, Chiaravalloti Neto F, Conceição GMS. Tendência temporal da leptospirose e sua associação com variáveis climáticas e ambientais em Santa Catarina. Cien Saude Colet. 2022;27(3):849–60. https://doi.org/10.1590/1413-81232022273.45982020 . Portuguese, English. Available. Souza I, Uberti M, Tassinari W. Geoprocessing and spatial analysis for identifying leptospirosis risk areas: a systematic review. Revista do Instituto de Medicina Tropical de São Paulo. 2020;62. https://doi.org/10.1590/S1678-9946202062035 . Teles AJ, et al. Spatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis. PLoS Negl Trop Dis. 2023;17(4):e0011239. https://doi.org/10.1371/journal.pntd.0011239 . Teles AJ, et al. Socio-geographical factors and vulnerability to leptospirosis in South Brazil. BMC Public Health. 2024;23(1):1311. https://doi.org/10.1186/s12889-023-16094-9 . Warnasekara J, Agampodi S, Abeynayake RR. Time series models for prediction of leptospirosis in different climate zones in Sri Lanka. PLoS ONE. 2021;16(5):e0248032. https://doi.org/10.1371/journal.pone.0248032 . 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14:48:34","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116118,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8001843/v1/15e0f83db20e5bd8e2bec057.html"},{"id":95845115,"identity":"51f1770c-bfe7-4819-be2c-ba25a0d747a5","added_by":"auto","created_at":"2025-11-13 14:48:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8110,"visible":true,"origin":"","legend":"\u003cp\u003eDecomposition of the time series of leptospirosis cases.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8001843/v1/2ae28bc3105cea0ea10f04a1.png"},{"id":96241495,"identity":"68fbb092-2831-47dd-b1ed-7be49ba71ed0","added_by":"auto","created_at":"2025-11-19 07:10:48","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":181968,"visible":true,"origin":"","legend":"\u003cp\u003eshows the prediction of leptospirosis cases for 2024 and 2025 based on the model constructed using the SARIMA method.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8001843/v1/5bbb727939ba745167a13693.jpeg"},{"id":96364779,"identity":"4a26dd50-6a8a-4e9a-bdc9-464304250339","added_by":"auto","created_at":"2025-11-20 10:09:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1252714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8001843/v1/0bfbfd51-e9d9-4ff4-b07f-2a119d221cbb.pdf"}],"financialInterests":"","formattedTitle":"Environmental vulnerability of the municipality of Rio de Janeiro to leptospirosis cases due to extreme hydrological events","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eFor each 1 mm increase in precipitation, an approximate 0.31% increase in leptospirosis cases is expected in vulnerably areas.\u003c/li\u003e\n \u003cli\u003eIntense hydrological events due to climate change are expected to lead to a growing number of disease cases in economically and socially vulnerable areas.\u003c/li\u003e\n \u003cli\u003eThe SARIMA model allowed a more accurate and detailed understanding of the temporal and seasonal events that influence the incidence of leptospirosis.\u003c/li\u003e\n \u003cli\u003ePublic policies should include health education campaigns to inform the population about the risks and preventive measures against leptospirosis.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eMathematical models applied to the context of public health serve as tools for understanding, predicting, and managing diseases, especially those influenced by environmental and socioeconomic factors. Epidemiological models allow for the simulation of disease transmission dynamics, the effectiveness of public health interventions, and the impact of environmental variables on disease patterns. Mathematical modeling can range from simple regression models to complex computational simulations that integrate multiple layers of data (Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rees et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gracie et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, these models enable hypothesis testing in controlled environments, outbreak prediction, and a better understanding of disease transmission mechanisms. However, they present significant challenges, such as the availability of accurate data, the complexity of integrating heterogeneous variables, temporal data clustering, and the application of modeled results to effective health policies (Douchet et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Paula et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Gracie et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gracie et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGeneralized additive models and SARIMA (Seasonal Autoregressive Integrated Moving Average) can predict leptospirosis incidence and identify high-incidence clusters, demonstrating how the temporal and spatial variation of the disease can be mapped to support more effective public health strategies (Teles et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Models based on climate data have highlighted the significant influence of climate on disease seasonality, although predicting interannual outbreaks remains a challenge (Douchet L et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The need to combine environmental and socioeconomic data is further emphasized in studies on flooding and leptospirosis in Brazil, which relate the increase in floods to leptospirosis incidence, underscoring the importance of contextual variables in health risk modeling (Warnasekara et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTime series models applied in Sri Lanka have shown how leptospirosis predictions can vary significantly across different climatic zones, highlighting the need for local adaptation in forecasting and prevention strategies (Barcellos et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These examples demonstrate the versatility and challenges of mathematical models in capturing the complexity of leptospirosis epidemiology, reinforcing the importance of accurate data and the integration of multiple variables for a comprehensive understanding and effective application in health policies.\u003c/p\u003e\u003cp\u003eIn this context, the objective of this article is to propose a model for analyzing the vulnerability of the municipality of Rio de Janeiro to leptospirosis cases, considering urban infrastructure and environmental indicators. The study\u0026rsquo;s results provide valuable insights to support public health planning and municipal preventive measures.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThis study is an applied and exploratory research that employs descriptive, correlational, and predictive methods to examine the relationships between environmental variables, urban infrastructure, and the incidence of leptospirosis.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Stage 1: Identification of Relevant Variables\u003c/h2\u003e\u003cp\u003eIn this stage, key environmental indicators related to leptospirosis cases were identified through an extensive literature review using databases such as CAFe, Scielo, Web of Science, and PubMed. The variables indicating the causality of leptospirosis were extracted and organized into an occurrence matrix.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Stage 2: Data Collection\u003c/h2\u003e\u003cp\u003eData for the selected variables (identified in Stage 1) were collected for the urban area of the municipality of Rio de Janeiro, covering the period from 2010 to 2022. Data sources included: the National Sanitation Information System (SNIS) for infrastructure data (treated water supply volume, treated sewage volume, and collected solid waste volume); the Information System for Notifiable Diseases (SINAN-DATASUS) for the number of leptospirosis cases; the Digital Disaster Atlas of Brazil for records of hydrological disasters; the National Institute of Meteorology (INMET) for rainfall data.\u003c/p\u003e\u003cp\u003eRio de Janeiro was chosen as the study area due to the availability of annual data for the defined period and its status as the host city of the National Reference Laboratory for Leptospirosis at the Oswaldo Cruz Foundation (Fiocruz).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Stage 3: Data Analysis\u003c/h2\u003e\u003cp\u003eTo analyze the influence of the selected variables (from Stage 2) on leptospirosis incidence, a multiple linear regression model was initially applied using JASP software (version 0.18.3.0). The Shapiro-Wilk test was conducted to check for normal distribution of the data (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and statistical significance tests (α\u0026thinsp;=\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were performed to assess the robustness of correlations and the validity of the model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Stage 4: Model Development\u003c/h2\u003e\u003cp\u003eA SARIMA (Seasonal Autoregressive Integrated Moving Average) model was used to evaluate the influence of selected variables (from Stage 2) on leptospirosis incidence. This technique is particularly recommended for analyzing time series with seasonal and temporal dynamics, such as leptospirosis incidence (Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), allowing for more accurate predictive analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Analysis of Relevant Variables\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the incidence matrix of the variables, grouped into categories: epidemiological, demographic, climatic, and others\u0026mdash;allowing for the identification of patterns and frequencies associated with leptospirosis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eIncidence Matrix of Variables.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"23\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRESEARCH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"22\" nameend=\"c23\" namest=\"c2\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eJ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eK\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003eO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c18\"\u003e\u003cp\u003eQ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c19\"\u003e\u003cp\u003eR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c20\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c21\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c22\"\u003e\u003cp\u003eU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c23\"\u003e\u003cp\u003eV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpatial distribution of leptospirosis in Rio Grande do Sul, Brazil: recovering the ecology of ecological studies (Barcellos et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuantifying the relationship between climatic indicators and leptospirosis incidence in Fiji: A modelling study (Rees et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocio-geographical factors and vulnerability to leptospirosis in South Brazil (Teles et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpatial and temporal dynamics of leptospirosis in South Brazil: A forecasting and nonlinear regression analysis (Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpatial study of leptospirosis risks in the municipality of Rio de Janeiro (RJ) (Chaiblich et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetrospective analysis of leptospirosis in the Metropolitan Region I of Rio de Janeiro from 2015 to 2019 (Santos, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFloods and leptospirosis in Brazilian municipalities from 2003 to 2013: use of data mining techniques (Gracie et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHuman leptospirosis as a doubly neglected disease in Brazil (Martins; Spink, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeoprocessing and spatial analysis for identifying leptospirosis risk areas: a systematic review (Souza et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpace-time distribution of leptospirosis and risk factors in Bel\u0026eacute;m, Par\u0026aacute;, Brazil (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c14\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrazilian overview of the relationship between leptospirosis and floods (Portela et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeptospirosis in the municipality of Campinas, S\u0026atilde;o Paulo, Brazil: 2007 to 2014 (Lara, 2019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpidemiology of human leptospirosis in urban and rural areas of Brazil, 2000\u0026ndash;2015 (Galan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemporal trend of leptospirosis and its association with climatic and environmental variables in Santa Catarina (Silva et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime series models for prediction of leptospirosis in different climate zones in Sri Lanka (Warnasekara et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClimate-driven models of leptospirosis dynamics in tropical islands from three oceanic basins (Douchet et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClimatic variables, living conditions, and public health: leptospirosis in the municipality of Rio de Janeiro from 1996 to 2009 (Oliveira et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpatial analysis of socio-environmental determinants for leptospirosis in the municipality of Itabora\u0026iacute;-RJ (Caldas et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocio-environmental vulnerability to leptospirosis in the metropolitan urban cluster of Curitiba (Buffon, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c20\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c21\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLEGEND: Epidemiological Variables: (A) Incidence; (B) Mortality; (C) Case Fatality Rate. Environmental Variables: (D) Land Use; (E) Altitude; (F) River Basins; (G) Risk Areas; (H) Climatic Conditions. Demographic Variables: (I) Sex; (J) Age; (K) Education Level; (L) Skin Color; (M) Occupation; (N) Ethnicity. Socioeconomic Variables: (O) Infrastructure; (P) Living Conditions.Climatic Variables: (Q) Precipitation; (R) Temperature. Other Variables: (S) Geographical Distribution; (T) Animal Occurrences; (U) Modes of Transmission; (V) Sociocultural Factors.\u003c/p\u003e\u003cp\u003eTemperature, rainfall intensity, relative humidity, and solar radiation intensity reflect the significant role that climate plays in the distribution and incidence of the disease (Douchet et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teles et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rees et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Barcellos et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Silva et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Warnasekara et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Portela et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Martins and Spink, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gracie et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Flood risk areas indicate a higher likelihood of leptospirosis outbreaks (Gracie et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Portela et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Souza et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Caldas et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Buffon, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chaiblich et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUrban infrastructure conditions, such as water supply, sanitation, drainage, and waste collection, have been recognized as key determinants in disease occurrence, as they directly influence hygiene and environmental sanitary conditions (Teles et al., 2024; Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rees et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gracie et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Martins and Spink, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chaiblich et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Santos, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Portela et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Galan et al., 2020; Oliveira et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Caldas et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Buffon, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLiving conditions, including housing quality, population density, and access to basic services, are essential for understanding vulnerability to leptospirosis (Teles et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rees et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gracie et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Martins and Spink, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chaiblich et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Santos, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Galan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Portela et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Caldas et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Buffon, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Improvements in these factors can play a crucial role in disease prevention.\u003c/p\u003e\u003cp\u003ePrecipitation is a climatic variable directly related to leptospirosis incidence, as it influences the disease dynamics (Douchet et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rees et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Silva et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Galan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Warnasekara et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Souza et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Portela et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lara, 2019; Oliveira et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Rainfall indices should be analyzed from a temporal scale perspective (monthly or annual) and in relation to disease occurrence (endemic or epidemic) (Barcellos et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Buffon, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Carrijo, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGeospatial analysis using Geographic Information Systems (GIS) helps identify high-risk zones, reflecting the complexity and multifactorial nature of leptospirosis vulnerability (Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Souza et al., 2023; Caldas et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Buffon, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, demographic variables such as age and occupation indicate that certain groups are more vulnerable due to occupational exposure or immune conditions (Teles et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Santos, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Martins and Spink, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lara, 2019; Galan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese observations suggest that the most influential variables in determining vulnerability to leptospirosis, based on the frequency and emphasis in the reviewed studies, include adverse climatic conditions, flood-prone areas, and the quality of urban infrastructure. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the data collection for the selected variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData results (period: 2010\u0026ndash;2022).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater volume\u003c/p\u003e\u003cp\u003e(1000 m\u0026sup3;/year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSewage volume (1000 m\u0026sup3;/year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWaste volume\u003c/p\u003e\u003cp\u003e(Ton/year)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrecipitation\u003c/p\u003e\u003cp\u003e(mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHydrological disasters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCases Lept\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,237,506.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e407,857.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,003,434.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,150.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e216\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,034,924.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e491,026.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,003,442.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,084.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e889,213.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e497,419.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,132,001.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,191.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,069,268.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e560,996.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,216,294.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,587.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,333,534.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e593,686.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,141,363.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,059.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,295,179.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e587,188.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,989,413.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e884.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,283,050.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e587,636.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,972,998.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,231.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,283,007.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e608,083.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,066,382.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,040.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,230,313.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e623,922.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,180,439.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e857.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,224,118.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e615,594.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,285,014.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,122.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,177,156.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e567,865.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,224,345.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e793.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,130,281.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e538,189.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,087,793.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e947.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,092,982.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e528,176.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,989,902.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1,385.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe altitude and temperature variables were excluded from the analysis, as altitude variation is minimal and unlikely to significantly impact the results, while the annual summation of temperature data reduces the relevance of its daily or monthly variations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Data modeling using multiple linear regression\u003c/h2\u003e\u003cp\u003eThe coefficient of determination (R\u0026sup2; = 0.74) and adjusted R\u0026sup2; (0.48) indicate that the model explains a considerable proportion of variability in leptospirosis cases. The Durbin-Watson statistic (2.33) suggests no residual autocorrelation. However, the high RMSEA (Root Mean Square Error of Approximation\u0026thinsp;=\u0026thinsp;0.43) indicates a poor model fit, which may limit its applicability in predicting leptospirosis incidence.\u003c/p\u003e\u003cp\u003eAdditionally, joint statistical significance was not observed (p\u0026thinsp;=\u0026thinsp;0.11), meaning there is no strong evidence that the proposed regression model significantly influences leptospirosis cases.\u003c/p\u003e\u003cp\u003eThe coefficients for WATER SUPPLY VOLUME (-0.33) and SEWAGE VOLUME (-0.52) suggest an inverse relationship with leptospirosis cases, indicating that improvements in sanitation could contribute to reducing disease incidence. However, the p-value for SEWAGE VOLUME (p\u0026thinsp;=\u0026thinsp;0.19) suggests that this correlation is not statistically significant within the model.\u003c/p\u003e\u003cp\u003eThe PRECIPITATION variable proved relevant, showing a strong partial correlation (0.63) and a p-value of 0.09. Although this does not meet the conventional significance threshold (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), it still suggests a trend where higher rainfall volumes may contribute to an increase in leptospirosis cases.\u003c/p\u003e\u003cp\u003e\u003cem\u003e(i) Length of the Historical Series\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDespite the challenges associated with the limited sample size over twelve years, this study provided valuable insights into data modeling in public health. The RMSEA value (0.43) reflects the complexity of fitting models with multiple variables in a limited dataset (Carrijo, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), highlighting the risk of overfitting (i.e., excessive adaptation to the specificities of the data, failing to generalize to other contexts or periods). This underscores the importance of simplifying models, such as focusing on rainfall, which could reduce complexity and enhance result robustness. These findings emphasize the need for improved data collection frequency and variability, especially in long-term studies. Additionally, they encourage the exploration of new methodological approaches and analytical techniques for more precise and generalizable insights in future studies.\u003c/p\u003e\u003cp\u003e\u003cem\u003e(ii) Water, Sewage, and Waste Data\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe annual collection of data for variables such as water supply, sewage, and waste collection may fail to capture significant short-term variations essential for understanding leptospirosis outbreak dynamics. Seasonal changes or specific events can influence disease occurrence, yet the lack of data granularity may exacerbate collinearity issues, leading to high p-values and limiting the ability to detect statistically significant associations.\u003c/p\u003e\u003cp\u003e\u003cem\u003e(iii) Low Data Variability\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe analysis revealed that variables based on population data (water, sewage, and waste) exhibit little variation over time, restricting their predictive power in regression models. This suggests that these variables are less useful as robust predictors in the studied models. In contrast, rainfall shows more direct and measurable variations, making it a better candidate for correlation with leptospirosis outbreaks.\u003c/p\u003e\u003cp\u003e\u003cem\u003e(iv) Limited Records of Hydrological Disasters\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe low number of recorded hydrological disasters poses a significant challenge in evaluating their actual impact on leptospirosis cases. Data from the Atlas Digital de Desastres do Brasil are often collected primarily to request federal funds for response and reconstruction, rather than to provide an accurate record of disaster frequency and severity (Paula et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, these records may not fully reflect the actual incidence of hydrological disasters, leading to unstable statistical coefficients and non-significant p-values in epidemiological analyses. This highlights the need for a critical approach when interpreting disaster data.\u003c/p\u003e\u003cp\u003eIn contrast, rainfall data, being continuously and systematically recorded, provides a more reliable and comprehensive dataset for analysis. It enables a more accurate understanding of how meteorological conditions influence disease incidence.\u003c/p\u003e\u003cp\u003eThe results indicate a significant association between higher rainfall levels and increased leptospirosis cases, aligning with existing literature that suggests wetter periods favor disease transmission (Douchet L et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Teles A J et al., 2023; Rees EM et al., 2023; Silva et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Galan DI et al., 2021; Warnasekara et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Souza et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Portela et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lara, 2019; Oliveira et al., 2019). Given this, the findings reinforce the importance of simplifying the model by focusing on rainfall as the primary variable. This approach offers a more effective way to explore environmental determinants of leptospirosis cases.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Data Analysis Using the Seasonal Autoregressive Method (SARIMA)\u003c/h2\u003e\u003cp\u003eGiven the results from the multiple linear regression analysis, the SARIMA model was applied to assess the influence of rainfall on disease incidence. The data were aggregated monthly (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and transformed using the logarithm of leptospirosis cases, with a constant added to stabilize variance and normalize the distribution.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRainfall and disease cases aggregated by monthly sum (period 2010\u0026ndash;2022).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonths\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of average precipitation (mm.month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSum of lept cases (month\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJanuary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,403.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFebruary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,212.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,537.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApril\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,922.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMay\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,163.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJune\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e736.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLujy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e744.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAugust\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e620.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeptember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e855.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOctober\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e945.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNovember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,439.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDecember\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,531.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the temporal decomposition of the monthly leptospirosis cases, including: observed data (data), trend, seasonality, and residuals (remainder).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\"Data\" shows the original temporal distribution of leptospirosis cases over time. Significant variation in cases is noted, with peaks in specific years (2010, 2018, and 2022). \"Trend\" indicates a decrease in leptospirosis cases until 2015, followed by a gradual increase until 2022. The reduction in cases in 2020 and 2021 may be associated with changes in social patterns during the COVID-19 pandemic period (Paula et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe strong seasonality observed suggests that there are periods of the year with higher incidence of cases (from December to April). Recent studies conducted in other parts of Brazil and the world found similar results, such as the spatial and temporal dynamics of leptospirosis in Rio Grande do Sul, with case peaks in 2011, 2014, and 2019, and marked seasonality between January and April (Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The strong influence of rainfall on leptospirosis incidence in tropical regions of several oceanic basins in Sri Lanka (Douchet et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and the higher occurrence of the disease in humid zones compared to dry and high zones, is also evident due to the frequent rains and soil moisture, which favor the survival of Leptospira (Warnasekara et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\"Remainder\" shows variations not explained by the trend or seasonality. These peaks may be due to local outbreaks, unforeseen environmental changes (Warnasekara et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and extreme weather events, such as El Ni\u0026ntilde;o (Douchet et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), considered outliers. These phenomena can cause anomalies in the model's pattern, making it more challenging to predict future cases. Including detailed, localized climatic indicators could enhance prediction accuracy, emphasizing the importance of microclimatic variables (Teles et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These studies highlight the complexity of leptospirosis modeling and the need to integrate residual analysis to fully understand deviations from predictions based on historical trends and seasonality patterns.\u003c/p\u003e\u003cp\u003e\u003cem\u003e(i) Preliminary Tests in SARIMA Analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo evaluate stationarity, the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) (Kwiatkowski et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) tests were used. The ADF test result indicates that the null hypothesis of non-stationarity can be rejected (p-value\u0026thinsp;=\u0026thinsp;0.01) (Dickey and Fuller, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). The KPSS test result suggests not rejecting the null hypothesis of stationarity at a significance level of 0.1 (p-value\u0026thinsp;=\u0026thinsp;0.1) (Kwiatkowski et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). In both tests, the data can be considered stationary, meaning they can be used without differentiation for time series models.\u003c/p\u003e\u003cp\u003e\u003cem\u003e(ii) Interpretation of the SARIMA Model\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ear1 (0.3840)\u003c/strong\u003e\u003cp\u003eFirst-order autoregressive coefficient. It indicates a linear dependence between the current value of the series and the previously observed value, in the transformed data scale. In other words, recent values in the series have a strong relationship with past values;\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003esar1 (0.1082)\u003c/strong\u003e\u003cp\u003eFirst-order seasonal autoregressive coefficient. It indicates seasonal relationships, showing that annual patterns repeat consistently every 12 months;\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eintercept (2.1669)\u003c/strong\u003e\u003cp\u003eIntercept of the model in the transformed data scale. It represents the average of the series after the logarithmic transformation and shift, serving as the starting point for predictions;\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003exreg (0.0031)\u003c/strong\u003e\u003cp\u003eExogenous variable coefficient. It shows how precipitation is associated with the variability of leptospirosis cases in the transformed series. In the original scale, for each 1 mm increase in precipitation, a\u0026thinsp;~\u0026thinsp;0.31% increase in leptospirosis cases is expected;\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003es.e. (Standard Error)\u003c/strong\u003e\u003cp\u003eStandard error associated with the estimated coefficients. Smaller values indicate more precise estimates of the coefficients;\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSigma^2 (0.2265)\u003c/strong\u003e\u003cp\u003eVariance of the errors in the adjusted model. A lower value indicates a more accurate model fit.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe log-likelihood is a measure of how well the model fits the data. Higher values indicate a better fit. The AIC (217.32), AICc (217.72), and BIC (232.57) are comparative measures against other models. Smaller values indicate a more efficient model. To test for autocorrelation in the residuals, the Box-Ljung test (Box and Pierce, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1970\u003c/span\u003e) was used (p-value\u0026thinsp;=\u0026thinsp;0.7349). Thus, the residuals appear to be random and do not show significant autocorrelation. Additionally, the Shapiro-Wilk test (p-value\u0026thinsp;=\u0026thinsp;0.0869) suggests that there is not enough evidence to conclude that the residuals are not normally distributed. This indicates that the time series model has captured the temporal structure of the data well.\u003c/p\u003e\u003cp\u003e\u003cem\u003e(iii) Projection of Disease Cases Based on Original Data\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(iv) Model validation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe proposed model is validated by other research employing autoregressive time series forecasting models across various geographical contexts. This consistency highlights the relevance of the predictions for the planning of preventive public health measures, underscoring their strategic importance. Studies using the SARIMA model in Sri Lanka have shown how different microclimates impact disease incidence, suggesting that the model can provide accurate forecasts even in regions with diverse climates (Warnasekara et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The influence of extreme climatic conditions (such as \u003cem\u003eEl Ni\u0026ntilde;o\u003c/em\u003e) on outbreak predictions emphasizes the need to prepare for phenomena that intensify leptospirosis transmission (Douchet et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this sense, forecasts can anticipate higher-risk periods, enabling preventive interventions that help mitigate the disease's impact (Rees et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTherefore, the SARIMA analysis revealed that leptospirosis exhibits significant linear dependence on the time series values and strong annual seasonality, as evidenced by the autoregressive coefficients (ar1 and sar1). Additionally, it was found that for each 1 mm increase in precipitation (in vulnerable areas), the number of cases is expected to rise by 0.31%. Thus, precipitation is recognized as a critical factor in the dynamics of this disease within predictive models and highlights the need to adapt public health strategies to account for seasonal variations in cases.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study proposes an advanced model to analyze vulnerability to leptospirosis risk in the city of Rio de Janeiro, focusing on the combination of environmental and infrastructural variables.\u003c/p\u003e\u003cp\u003eThe proposition of a theoretical model provided a clear framework for understanding the interrelationships between variables and leptospirosis incidence. It guided the data collection and analysis, proving flexible and adaptable to accommodate emerging insights from the research, and made a valuable contribution to epidemiological modeling. The transition to the SARIMA model better adjusted to the data characteristics, providing an accurate understanding of the relationship between precipitation and leptospirosis cases. This model confirmed clear patterns of seasonality, indicating an expected increase in leptospirosis cases between December and April, and showed that for each 1 mm increase in precipitation, an approximate 0.31% rise in cases is expected, making this variable critical in outbreak forecasting. Intense rainfall contributes to the spread of waterborne diseases, such as the Leptospira bacteria, particularly in urban areas with poor sanitation infrastructure, insufficient drainage, and inadequate land occupation. Therefore, following intense hydrological events, an increasing number of disease records in economically and socially vulnerable areas is expected.\u003c/p\u003e\u003cp\u003eThe transition from the linear regression model to the SARIMA model is highlighted as a critical step in response to the limitations encountered, allowing for a more precise and detailed understanding of the temporal and seasonal dynamics influencing leptospirosis incidence. This methodological approach not only reinforces the connection between objectives and results but also highlights the evolution of the study in response to emerging challenges during the research.\u003c/p\u003e\u003cp\u003eThis methodological path is a positive finding, demonstrating the importance of flexibility and adaptability in developing epidemiological models. It provides perspectives on how iterative and adaptive approaches can lead to more accurate and informative results, which are essential for public health planning.\u003c/p\u003e\u003cp\u003eAlthough the presented SARIMA model has demonstrated strong forecasting capacity, some intrinsic limitations of the study are recognized. The reliance on quality and complete data is one of these, as inconsistencies in databases can significantly affect results. Furthermore, the application of the model in a single municipality limits the generalization of results to other regions with different environmental and infrastructural conditions. These limitations are important for interpreting the results and suggest caution when extrapolating the conclusions. Future research is recommended to expand the application of the model to include multiple locations, enabling broader validation of the findings and enhancing the model's robustness in different epidemiological scenarios.\u003c/p\u003e\u003cp\u003eIt is suggested to explore the impact of infrastructural interventions and delve deeper into the investigation of interactions between climate change and disease incidence, aiming to support more effective and proactive public health planning. Studies integrating predictive climate models into leptospirosis epidemiology, supporting public health planning, especially in the context of climate change scenarios, are necessary, as precipitation patterns may change, worsening the risk of disease outbreaks.\u003c/p\u003e\u003cp\u003eAdditionally, it is imperative that public policies focusing on improving urban infrastructure and sanitation systems be implemented to mitigate the risks associated with leptospirosis in the city of Rio de Janeiro, given the discrepancy found between disease records and the high coverage of sanitation services reported by the National Sanitation Information System (where higher coverage should correlate with fewer cases). It is also recommended to continue and expand the collection of environmental and health data, integrating them into monitoring systems and early warning systems, which could be crucial for preventing future outbreaks. Effective public policies should also include health education campaigns to inform the population about risks and preventive measures against leptospirosis, emphasizing the importance of avoiding contact with potentially contaminated water, especially after intense rainfall periods.\u003c/p\u003e\u003cp\u003eThis research contributes to a better understanding of the factors influencing leptospirosis in the city of Rio de Janeiro and offers a robust methodology that can be adapted for future epidemiological studies, supporting strategic decision-making in the fight against the disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e5. Funding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Ethical aspects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn accordance with the institutional policy, this research was not needed ethics approval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7. Statements and declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest and non-financial interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBarcellos C, et al. Distribui\u0026ccedil;\u0026atilde;o espacial da leptospirose no Rio Grande do Sul, Brasil: recuperando a ecologia dos estudos ecol\u0026oacute;gicos. Cadernos de Sa\u0026uacute;de P\u0026uacute;blica. 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PLoS ONE. 2021;16(5):e0248032. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0248032\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0248032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"climate change, mathematical modeling, seasonality, environmental variables, public health","lastPublishedDoi":"10.21203/rs.3.rs-8001843/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8001843/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe developed a statistical model for analyzing environmental vulnerability on urban infrastructure to the risk of leptospirosis due to the recurrence of intense hydrological events (floods and inundations). The SARIMA technique (Seasonal AutoRegressive Integrated Moving Average) was used to build forecasts, observing the seasonality and specific temporal dynamics of the data on city of Rio de Janeiro, where the National Reference Laboratory for Leptospirosis of the Oswaldo Cruz Foundation is located. An incidence matrix was constructed to identify patterns and frequencies of variables associated with leptospirosis. We founded that rainfall intensity and seasonality has a direct impact in leptospirosis cases (each increase of 1 mm in precipitation can raise the number of cases by 0.31%). Intense rainfall contributes to the spread the Leptospira bacteria in areas with poor sanitation infrastructure, insufficient drainage, and inadequate land use. In the face of intense hydrological events due to climate change, an increasing number of disease cases is expected in economically and socially vulnerable areas.\u003c/p\u003e","manuscriptTitle":"Environmental vulnerability of the municipality of Rio de Janeiro to leptospirosis cases due to extreme hydrological events","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 14:48:29","doi":"10.21203/rs.3.rs-8001843/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca16d262-2975-4c97-beb2-ec830d2c0aab","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T16:15:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 14:48:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8001843","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8001843","identity":"rs-8001843","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}