F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.184944.1 Systematic Review Articles Rainfall and Human Leptospirosis Incidence: A Systematic Review of Ecological Temporal Studies

[version 1; peer review: awaiting peer review]

 Ataupah Mira Kiliana Conceptualization Data Curation Formal Analysis Investigation Methodology Project Administration Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0006-8360-3617 a 1 Asa Ansgaria Febriantika Data Curation Investigation Writing – Original Draft Preparation Writing – Review & Editing 1 Siagian Angelina Apriarni Data Curation Investigation Writing – Original Draft Preparation Writing – Review & Editing 1 Ganis Roderikus Kucik Data Curation Investigation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0009-0008-3230-7978 1 Putri Febriani Data Curation Investigation Writing – Review & Editing https://orcid.org/0009-0007-9204-1983 1 Muda Ishak Israel Data Curation Investigation Writing – Review & Editing 2 Yongsi Susana Claudia Cristina Data Curation Investigation Writing – Review & Editing 3 Noviyani Maria Yosi Data Curation Investigation Writing – Review & Editing 3 Amabi Stella Sritamar Data Curation Investigation Writing – Review & Editing 3 Rumbino Lusi Natalia M.Y. Data Curation Investigation Writing – Review & Editing https://orcid.org/0009-0008-0161-5847 3 Maula Ahmad Watsiq Conceptualization Methodology Supervision Validation Writing – Review & Editing 1 Department of Biostatistics, Epidemiology and Population Health, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Special Region of Yogyakarta, Indonesia Master's Program Health Policy and Administration Specialization, Faculty of Public Health, University of Nusa Cendana, Kupang, East Nusa Tenggara, Indonesia Department Health Behaviour, Environment, and Social Medicine, Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada, Yogyakarta, Special Region of Yogyakarta, Indonesia mirakilianaataupah@mail.ugm.ac.id

No competing interests were disclosed.

 30 6 2026 2026 15 1040 22 6 2026 Copyright: © 2026 Ataupah MK et al. 2026 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background

Leptospirosis is a zoonotic disease influenced by climatic and environmental conditions. Rainfall is an important environmental factor in leptospirosis transmission; however, the strength, temporal patterns, and characteristics of this association vary across studies. This systematic review aimed to synthesize evidence on the association between rainfall and human leptospirosis occurrence and to identify the temporal characteristics of this relationship.

 Methods

This systematic review was conducted in accordance with the PRISMA 2020 guidelines, and the protocol was registered in PROSPERO (CRD420261330699). Literature searches were performed in PubMed, Scopus, ScienceDirect, and Google Scholar. Studies evaluating the association between rainfall and human leptospirosis occurrence were included. Risk of bias was assessed using the Navigation Guide Systematic Review Methodology. Due to heterogeneity across studies, findings were synthesized narratively without meta-analysis.

 Results

Twenty-two studies met the inclusion criteria. Synthesized evidence indicated that increased rainfall was generally associated with increased leptospirosis occurrence across diverse geographical and epidemiological settings. Positive associations were reported for total rainfall, extreme rainfall, cumulative rainfall, rainfall anomalies, and wetter-than-normal hydrometeorological conditions. Temporal lag patterns between rainfall exposure and leptospirosis occurrence ranged from several weeks to several months. Risk-of-bias assessment identified confounding as the most frequent source of bias.

 Conclusions

Rainfall appears to be an important environmental factor associated with leptospirosis occurrence. This relationship is influenced by rainfall characteristics, temporal dynamics, and local contextual factors. Consideration of rainfall indicators and lag patterns may support the development of leptospirosis surveillance and early warning systems.

 : Leptospirosis; Rainfall; Extreme rainfall; Time lag; Seasonality; Environmental determinants; Zoonotic diseases; Systematic review Lembaga Pengelola Dana Pendidikan 0008302/TRP/M/AF-2024 This work was supported by the Indonesia Endowment Fund for Education (LPDP) - 0008302/TRP/M/AF-2024, Ministry of Finance of the Republic of Indonesia, through the Master's Scholarship Program. The support provided by LPDP facilitated the conduct of this research and the preparation of this manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Introduction

Leptospirosis is a bacterial zoonosis that is widespread worldwide and remains a significant public health problem, particularly in tropical and subtropical regions. The disease is caused by pathogenic bacteria of the genus Leptospira and is transmitted to humans through direct or indirect contact with the urine of infected animals . 1 , 2 Globally, leptospirosis is estimated to cause more than one million cases and nearly 60,000 deaths annually, making it one of the zoonoses with the highest disease burden in the world. 3 , 4 , 5 Nevertheless, the disease is still classified as a neglected tropical disease and is often underdiagnosed or underreported due to its diverse clinical manifestations and similarity to various other infectious diseases. 1 , 4 In addition to causing high morbidity and mortality rates, leptospirosis also imposes a substantial global disease burden, reflected in the loss of disability-adjusted life years (DALYs) as well as significant social and economic impacts. Therefore, leptospirosis remains a major challenge for global public health, particularly in tropical and subtropical countries. 1 , 3

In addition to the significant disease burden it causes, leptospirosis also has unique epidemiological characteristics because its transmission is strongly influenced by environmental conditions. 1 , 2 As a zoonosis whose transmission involves interactions between reservoir animals, humans, and the environment, the risk of leptospirosis is known to be influenced by various environmental factors that can affect the dynamics of disease transmission. 2 , 6 Various studies indicate that the distribution patterns of leptospirosis vary according to geographical characteristics, environmental conditions, and hydrometeorological factors in endemic regions, as reflected in the differences in the disease’s spatial, temporal, and seasonal patterns. 7 , 8 , 9 , 10 These findings indicate that environmental factors are a key component in the epidemiology of leptospirosis and play a role in shaping the dynamics of disease transmission across various geographic and epidemiological contexts.

Among the various environmental and hydrometeorological factors that have been studied, rainfall is one of the factors most frequently associated with leptospirosis outbreaks. Studies from various geographic regions report that an increase in leptospirosis cases often occurs following periods of heavy rainfall, the rainy season, or flooding events. 1 , 2 , 7 This association has been observed in various epidemiological contexts, including in South America, Asia, and other tropical regions, indicating that rainfall is a relevant environmental factor in leptospirosis epidemiology. 7 , 8 , 9 , 11 Therefore, rainfall data is frequently used as an environmental indicator in leptospirosis epidemiological research as well as in the development of disease risk prediction models. 8 , 9

Although rainfall is often associated with leptospirosis outbreaks, the nature of this relationship does not always follow a consistent pattern. 7 , 12 Existing research has used various rainfall indicators—ranging from total rainfall, cumulative rainfall, rainfall intensity, and extreme rainfall to more complex hydrometeorological indices—to assess the risk of leptospirosis outbreaks. 7 , 8 , 9 , 11 Additionally, the observed temporal patterns of the relationship vary, with some studies reporting an increased risk within a relatively short timeframe following rainfall events, while others find effects emerging after a longer lag period. 7 , 8 , 14 The magnitude of the reported rainfall effects also varies across regions, likely influenced by differences in local geographic, hydrological, environmental, and epidemiological conditions 8 , 9 , 13 This diversity of approaches and findings indicates that the relationship between rainfall and leptospirosis is complex, thus requiring a more comprehensive synthesis to fully understand the characteristics of this relationship.

Previous systematic reviews have identified rainfall as one of the environmental determinants associated with leptospirosis. However, these reviews generally evaluated environmental and climatic factors in a broad sense and have not specifically examined the nature of the relationship between rainfall and the incidence of leptospirosis 13 Since the publication of those reviews, various new studies have been conducted using a wider range of rainfall indicators, including cumulative rainfall, extreme rainfall, rainfall anomalies, and more complex hydrometeorological indices. In addition, an increasing number of studies have evaluated the temporal patterns of the relationship between rainfall and leptospirosis through various lag structures and different analytical approaches. However, this evidence has not yet been comprehensively synthesized to understand how the characteristics of the relationship between rainfall and leptospirosis incidence vary across different geographical and epidemiological contexts.

Therefore, this systematic review aims to evaluate the relationship between rainfall and the incidence of leptospirosis and to identify the characteristics of that relationship across various geographic and epidemiological contexts. Specifically, this review aims to identify patterns in the relationship between rainfall and the incidence of leptospirosis, as well as the rainfall characteristics and temporal patterns most frequently associated with an increased risk of the disease.

 Methods Study design and reporting guideline

This study was conducted as a systematic review to synthesize evidence on the relationship between rainfall and human leptospirosis incidence. The review was designed and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Statement. The review protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO; Registration No. CRD420261330699).

 Eligibility criteria

Inclusion and exclusion criteria were developed to ensure that the selected studies were aligned with the objectives of this review. Because this review aimed to evaluate the temporal relationship between rainfall and human leptospirosis incidence, including the characteristics of rainfall exposure, lag effects, and seasonal patterns, only ecological studies incorporating a temporal component were considered eligible. Studies using time-series, longitudinal ecological, spatial-temporal, or spatiotemporal designs were included, whereas ecological cross-sectional studies without temporal analysis were excluded because they do not allow assessment of temporal associations between rainfall exposure and disease occurrence ( Table 1).

Inclusion and exclusion criteria.
Inclusion criteria Exclusion criteria
Studies involving human populations of any age and geographical setting Animal studies, environmental-only studies, or laboratory-based experimental studies
Studies assessing the relationship between rainfall, precipitation, or rainfall-related indicators and human leptospirosis incidence or occurrence Studies not assessing rainfall, precipitation, or rainfall-related indicators as exposure variables
Studies reporting human leptospirosis incidence, including reported cases, incidence rates, or notified cases over time Studies not reporting human leptospirosis incidence or case occurrence
Observational studies using ecological time-series or spatiotemporal designs Ecological cross-sectional studies without a temporal component and other study designs not evaluating temporal associations
Original research articles published between 2005 and 2026 Reviews, systematic reviews, meta-analyses, editorials, commentaries, conference abstracts, protocols, case reports, and case series
Articles published in English or Indonesian Articles published outside the specified time frame or in languages other than English or Indonesian
Information sources and search strategy

A systematic literature search was conducted in PubMed, Scopus, ScienceDirect, and Google Scholar to identify studies evaluating the relationship between rainfall and human leptospirosis incidence. The initial search was conducted between September 23 and September 30, 2025. To ensure the inclusion of the most recent evidence, updated searches were subsequently performed until June 2026 using the same search strategy.

The search strategy was developed using a combination of controlled vocabulary and free-text keywords. In PubMed, Medical Subject Headings (MeSH) terms and title/abstract keywords were used to maximize retrieval of relevant studies. For scopus, ScienceDirect, and Google Scholar, equivalent free-text search terms were applied according to the search functionalities of each database. The search strategy was designed to capture studies examining rainfall-related exposures and their association with human leptospirosis incidence.

Search terms were organized around three main concepts: leptospirosis, rainfall-related exposures, and meteorological factors. Keywords related to leptospirosis included “leptospirosis,” “Leptospira,” and “Leptospira infection.” Rainfall-related terms included “rainfall,” “rain,” “precipitation,” “heavy rainfall,” “extreme rainfall,” “rainfall variability,” “rainfall intensity,” “flood,” and “flooding.” Additional meteorological terms included “weather,” “climate variability,” and “meteorological factors.” Boolean operators (AND, OR) were used to combine search concepts and construct database-specific search strategies.

Database-specific search syntaxes were adapted to the indexing systems and search functionalities of each database while maintaining the same core search concepts. Searches were limited to articles published between January 2005 and June 2026. Detailed search strategies for all databases are provided in the Extended Data repository.

 Study selection process

The study selection process was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. All records identified through database searching were imported into Rayyan software for reference management and study screening. After duplicate records were removed, titles and abstracts were independently screened by two reviewers (AFA and MKA) according to the predefined eligibility criteria.

Studies considered potentially eligible were retrieved in full text and independently assessed by the same reviewers. Disagreements arising during both the title/abstract screening and full-text assessment stages were resolved through discussion and consensus. If consensus could not be reached, the final decision was made through consultation with AAS and FP.

Reasons for exclusion at the full-text assessment stage were documented and recorded. The complete study selection process, including the number of records identified, screened, excluded, and included, is presented in the PRISMA 2020 flow diagram ( Figure 1).

PRISMA 2020 flow diagram of study selection. Data extraction

Data were independently extracted by three reviewers (MKA, RKG, and JM) using a standardized data extraction form developed for this review. Extracted information included the first author, publication year, study location, study period, ecological study design, rainfall-related exposure measures, leptospirosis outcome measures, statistical analysis methods, reported lag periods or temporal exposure windows, seasonal patterns, rainfall thresholds or anomalies, and key epidemiological findings. Information on effect estimates was also extracted, including the type of effect measure reported (e.g., relative risks (RR), incidence rate ratios (IRR), adjusted incidence rate ratios (aIRR), regression coefficients (β), correlation coefficients (r or ρ), cross-correlation coefficients, posterior effect estimates, and other reported measures of association), together with their corresponding effect estimates, confidence intervals, credible intervals, or p-values when available. The direction of association between rainfall and leptospirosis occurrence was also recorded. Any discrepancies in data extraction were resolved through discussion among the three reviewers. If consensus could not be reached, consultation with SY and MN was undertaken to reach a final decision. Extracted data were subsequently summarized in evidence tables and used to characterize rainfall metrics, temporal lag structures, seasonal patterns, and epidemiological associations reported across the included studies.

 Risk of bias assessment

Risk of bias was assessed using the Navigation Guide Systematic Review Methodology for environmental health studies. The assessment evaluated eight domains: selection bias, exposure assessment, outcome assessment, confounding, incomplete outcome data, selective reporting, conflict of interest, and other sources of bias. Risk of bias assessments were independently conducted by two reviewers (MKA and SSA) using predefined assessment criteria. Any disagreements were resolved through discussion between the reviewers. If consensus could not be reached, a third reviewer (LNR) was consulted to make the final decision.

Each domain was rated as low risk, probably low risk, probably high risk, or high risk of bias according to the Navigation Guide recommendations. An overall risk of bias judgment was subsequently assigned to each included study based on the combined assessment across all domains. Detailed study-level risk of bias assessments and justifications are available in the Extended Data repository.

 Data synthesis

Given the substantial heterogeneity in study designs, analytical approaches, rainfall exposure definitions, temporal lag structures, outcome measures, and reported effect estimates, a quantitative meta-analysis was not considered appropriate. Studies differed considerably in their rainfall metrics (e.g., cumulative rainfall, rainfall anomalies, rainfall intensity, extreme rainfall events, and flooding-related indicators), temporal units of analysis (daily, weekly, monthly, and annual), statistical methods, and effect measures. Therefore, findings were synthesized narratively. Data synthesis focused on effect estimates and the direction of association between rainfall and leptospirosis occurrence, temporal lag structures reported between rainfall exposure and leptospirosis outcomes, and seasonal patterns associated with disease occurrence.

Reported effect estimates were extracted and summarized descriptively. Because studies reported diverse effect measures, including relative risks (RR), incidence rate ratios (IRR), adjusted incidence rate ratios (aIRR), regression coefficients (β), correlation coefficients (r or ρ), posterior effect estimates, and other measures of association, quantitative pooling was not undertaken. When multiple effect estimates were reported within a study, the estimate derived from the final model, best-fitting model, or primary rainfall-related analysis identified by the study authors was extracted. Corresponding confidence intervals, credible intervals, or p-values were also recorded when available.

Temporal lag structures were synthesized by extracting lag periods reported as statistically significant, epidemiologically relevant, or identified by the original study authors as the principal lag associated with leptospirosis occurrence. When studies reported multiple statistically significant lag periods, all relevant lag structures were recorded and included in the synthesis. Lag periods were subsequently summarized according to their reported temporal windows (e.g., weeks or months) to identify recurring patterns across studies.

Seasonal patterns were synthesized descriptively by extracting information on recurring rainfall-associated peaks, wet-season trends, monsoon-related patterns, flooding periods, and other temporal rainfall anomalies associated with leptospirosis occurrence. Seasonal findings were summarized according to the temporal patterns reported by the original study authors rather than being reclassified using a standardized seasonal framework. The direction of association was classified as positive, negative, mixed, or no significant association based on the findings reported in each study. Risk-of-bias assessments were considered during the interpretation of findings, with greater emphasis placed on evidence derived from studies judged to have lower risk of bias.

 Results Overview of included studies

A total of 22 studies met the inclusion criteria and were included in the final synthesis. 15 – 36 The included studies were conducted across diverse geographical regions, including Asia, South America, Oceania, Europe, and the Indian Ocean region. Most studies were undertaken in tropical and subtropical areas where leptospirosis is endemic, whereas only a limited number of studies originated from temperate regions. Of the 22 included studies, 17 employed an ecological time-series design, while five used ecological spatiotemporal time-series approaches. The included studies demonstrated considerable heterogeneity in the definition of rainfall exposure and the measurement of leptospirosis outcomes. Rainfall was evaluated using daily, weekly, monthly, and annual measurements, as well as more specific indicators such as cumulative rainfall, rainfall anomalies, number of rainy days, extreme rainfall indices, and the Standardized Precipitation–Evapotranspiration Index (SPEI). Outcomes included laboratory-confirmed leptospirosis cases, incidence rates at different administrative levels, and leptospirosis-related hospitalizations. A variety of analytical approaches were used to evaluate the association between rainfall and leptospirosis occurrence, including Poisson and negative binomial regression models, generalized additive models (GAMs), distributed lag non-linear models (DLNMs), ARIMA/ARIMAX models, wavelet analyses, machine-learning approaches, and Bayesian spatiotemporal modelling. Differences in exposure definitions, temporal scales, outcome measures, and statistical methods indicate substantial methodological heterogeneity across studies. Detailed characteristics of the included studies are presented in Table 2. 15 – 36

Characteristics of included studies and methodological approaches.
No Study Location Study design Unit of analysis Rainfall/climate variable Analytical method
1 Gutierrez et al. (2021) Colombia Ecological Time-Series Study Weekly human incidence Weekly cumulative rainfall Multilevel negative binomial regression
2 Phosri et al. (2022) Thailand Ecological Time-Series Study Monthly human incidence Monthly cumulative rainfall Quasi-Poisson regression with DLNM
3 Syakbanah et al. (2021) Indonesia Ecological Time-Series Study Monthly human incidence Monthly cumulative rainfall Pearson correlation with lag analysis
4 Obels et al. (2025) Netherlands Ecological Time-Series Study Weekly and annual human incidence Weekly and annual precipitation Negative binomial regression
5 Chadsuthi et al. (2012) Thailand Ecological Time-Series Study Monthly human incidence Monthly rainfall Seasonal ARIMA and ARIMAX
6 Rees et al. (2023) Fiji Ecological Spatiotemporal Time-Series Study Weekly/monthly human incidence Six-week cumulative rainfall Bayesian hierarchical mixed-effects model
7 Cunha et al. (2022) Brazil Ecological Time-Series Study Weekly confirmed human incidence Weekly rainfall anomalies Dynamic generalized linear model (Bayesian INLA)
8 Warnasekara et al. (2022) Sri Lanka Ecological Time-Series Study Monthly human incidence Monthly rainfall and rainy days SARIMA and ARDL
9 Ghizzo Filho et al. (2018) Brazil Ecological Time-Series Study Monthly human incidence Mean monthly rainfall Correlation and regression analysis
10 Tana et al. (2025) New Zealand Ecological Time-Series Study Monthly human incidence Total monthly rainfall Poisson and negative binomial regression
11 Ehelepola et al. (2019) Sri Lanka Ecological Time-Series Study Weekly human incidence Rainfall and wet days Time-series wavelet analysis
12 Safera et al. (2023) Indonesia Ecological Spatiotemporal Time-Series Study Monthly human incidence Monthly rainfall and flooding Generalized Additive Model (GAM) and spatial analysis
13 Jayaramu et al. (2023) Malaysia Ecological Time-Series Study Weekly human incidence Weekly hydrometeorological indices (average and extreme) Cross-correlation and random forest
14 Matsushita et al. (2018) Philippines Ecological Time-Series Study Weekly hospitalized human cases Weekly cumulative rainfall DLNM with quasi-Poisson regression
15 Dhewantara et al. (2019) China Ecological Spatiotemporal Time-Series Study Monthly human incidence Monthly rainfall Negative binomial regression
16 Sumi et al. (2017) Philippines Ecological Time-Series Study Weekly confirmed human incidence Total weekly rainfall Time-series spectral analysis
17 Fanelli et al. (2026) Europe Ecological Spatiotemporal Time-Series Study Monthly human incidence SPEI-3 Bayesian spatiotemporal negative binomial model
18 Gunathilaka et al. (2026) Sri Lanka Ecological Spatiotemporal Time-Series Study Monthly district-level human incidence Total monthly rainfall ZAGA-GAMLSS with spatial autocorrelation
19 Wang et al. (2025) China Ecological Spatiotemporal Time-Series Study Provincial-level annual human incidence (1955–2022) Mean annual precipitation Moran’s I, space-time scan statistics, and Bayesian spatiotemporal INLA
20 Desvars et al. (2011) Réunion Island, France Ecological Time-Series Study Monthly human incidence Monthly cumulative rainfall Time-series analysis, cross-correlation analysis, and ARIMAX
21 Pawar et al. (2018) India Ecological Time-Series Study Monthly human incidence Total monthly rainfall CCF, ACF, PACF, and seasonal forecasting model
22 Coelho & Massad (2012) Brazil Ecological Time-Series Study Daily leptospirosis-related hospital admissions Daily precipitation Negative binomial model, stepwise regression, and Spearman correlation
Association between rainfall and leptospirosis occurrence

The synthesis of findings indicated that increased rainfall was generally associated with increased leptospirosis occurrence, although the magnitude of the reported effects varied according to the rainfall indicators, analytical scales, and statistical approaches used. Associations were reported using a range of effect measures, including incidence rate ratios (IRRs), relative risks (RRs), regression coefficients, correlation coefficients, and Bayesian effect estimates. Among studies reporting relative risk measures, effect sizes ranged from relatively small increases in risk for each incremental increase in rainfall to substantially higher risks under conditions of greater rainfall exposure. Positive effect estimates ranged from an adjusted incidence rate ratio (aIRR) of 1.001 (95% CI: 1.001–1.002) to a relative risk (RR) of 2.45 (95% CI: 1.80–3.33). In addition, long-term spatial analyses consistently identified positive associations between rainfall and leptospirosis occurrence across different analytical approaches and measurement scales. Similar positive associations were also observed in studies using correlation-based and time-series analyses. Reported association strengths ranged from moderate to very strong, with correlation coefficients varying between 0.34 and 0.92. These findings indicate a consistent temporal relationship between increased rainfall and increased leptospirosis occurrence across diverse geographical and epidemiological contexts. Although positive associations were commonly reported, heterogeneity in findings was observed across studies. Some studies found no statistically significant association between precipitation and leptospirosis occurrence (IRR 1.00; 95% CI: 0.99–1.01), whereas others reported differing directions of association across locations, with positive associations in one setting and negative associations in another. These findings suggest that the relationship between rainfall and leptospirosis occurrence may vary across geographical contexts. A summary of the reported effect estimates and directions of association for each included study is presented in Table 3.

Effect estimates and directions of association between rainfall and leptospirosis occurrence.
No Study Effect estimate metric Estimate value (95% CI/CrI) Main extracted lag window Direction of association
1 Gutierrez et al., 2020 aIRR 1.001 (1.001–1.002) 1 week Positive
2 Phosri et al., 2022 RR 1.18 (1.05–1.34) 0 month Positive
3 Syakbanah et al., 2021 Correlation coefficient (r) 0.7451 [lag 1 month] 0.8561 [lag 3 months] 1 and 3 months Positive
4 Obels et al., 2025 IRR 1.00 (0.99–1.01) Weekly and annual analyses No significant association
5 Chadsuthi et al., 2012 ARIMAX coefficient (β) − 0.0401 (North) 0.0161 (Northeast) 8 months (North) 10 months (Northeast) Positive
6 Rees et al., 2023 Posterior coefficient (β) 0.24 (95% CrI: 0.15–0.33) 1 week Positive
7 Cunha et al., 2022 RR 1.006 (1.004–1.007) [lag 1] 1.004 (1.003–1.006) [lag 2] 1 and 2 weeks Positive
8 Warnasekara et al., 2022 Regression coefficient (β) 0.31 (p = 0.019) 3 months Positive
9 Ghizzo Filho et al., 2018 Pearson correlation coefficient (r) 0.68 (p = 0.023) No explicit lag analysis Positive
10 Tana et al., 2025 IRR 1.017 (1.007–1.026) [lag 1] 1.023 (1.013–1.032) [lag 2] 1.018 (1.009–1.028) [lag 3] 1, 2, and 3 months Positive
11 Ehelepola et al., 2019 Wavelet time-series association Temporal synchronization between rainfall and incidence peaks 1 week (1–2 weeks) Positive
12 Safera et al., 2023 Correlation coefficient (r) 0.47 (p < 0.001) 2 months Positive
13 Jayaramu et al., 2023 Cross-correlation coefficient (r) 0.34 3 weeks Positive
14 Matsushita et al., 2018 RR 2.45 (1.80–3.33) 2 weeks Positive
15 Dhewantara et al., 2019 IRR Mengla: 0.989 (0.985–0.993) [lag 6 months] Yilong: 1.013 (1.003–1.023) [lag 1 month] 6 months (Mengla) 1 month (Yilong) Mixed association
16 Sumi et al., 2017 Spearman’s ρ 0.92 (p < 0.01) 1 month Positive
17 Fanelli et al., 2026 RR 1.62 (1.20–2.10) 1 month Positive
18 Gunathilaka et al., 2026 Risk estimate Lag 1: 1.0004 (1.0002–1.0007) Lag 3: 1.0003 (1.0000–1.0005) 1 and 3 months Positive
19 Wang et al., 2025 Posterior effect estimate 3.68 (95% CrI: 2.25–5.12) No explicit lag analysis Positive
20 Desvars et al., 2011 ARIMAX coefficient (β) 0.145 2 months Positive
21 Pawar et al., 2018 Correlation coefficient (r) 0.78 0–1 month Positive
22 Coelho & Massad, 2012 Relative Risk (RR) 1.1–2.8 14–18 days Positive
Temporal patterns of rainfall-associated leptospirosis

The synthesis of temporal patterns indicated that the effects of rainfall on leptospirosis occurrence may emerge across a broad range of time intervals, from immediate effects to several months after exposure. Reported lag patterns could be broadly categorized into short lags (0–3 weeks), intermediate lags (1–3 months), and long lags (>3 months). Lag periods within the 1–3 month range were reported across diverse geographical settings and analytical approaches, suggesting that increases in leptospirosis occurrence frequently follow a period of delay after increased rainfall. In addition to lag effects, relatively consistent seasonal patterns were observed across multiple study settings. Increased leptospirosis occurrence generally coincided with rainy seasons, monsoon periods, or annual rainfall cycles. Temporal synchronization between peak rainfall and peak leptospirosis occurrence was also reported in various geographical contexts, with increases in cases occurring after periods of elevated rainfall. However, seasonal patterns were not observed uniformly across all study settings, reflecting temporal variation among geographical and climatic contexts. Overall, the reported temporal patterns indicate that increases in leptospirosis occurrence may occur at different time intervals following increased rainfall and often correspond to local seasonal rainfall cycles. A summary of the lag structures and seasonal patterns reported by the included studies is presented in Table 4.

Temporal patterns of rainfall associated with leptospirosis occurrence.
No Study Temporal lag structure Seasonal pattern Rainfall metric/threshold/anomaly Key epidemiology finding
1 Gutierrez et al., 2020 1 week No clear seasonal pattern Rainfall increase per 1 mm Increased rainfall was followed by increased leptospirosis incidence within a short time window
2 Phosri et al., 2022 Lag 0 (immediate effect) Rainy-season peak Extreme rainfall (≥90th percentile; 34.5 cm/month) Extreme rainfall periods were associated with increased leptospirosis occurrence
3 Syakbanah et al., 2021 Significant lags at 1 and 3 months Post-cyclone outbreak period Extreme rainfall (175–250 mm/day) following Cyclone Cempaka Heavy rainfall and flooding after the cyclone preceded outbreaks several months later
4 Obels et al., 2025 Short lag periods evaluated No clear seasonal pattern No significant rainfall threshold identified Rainfall showed limited influence compared with other environmental factors in a temperate setting
5 Chadsuthi et al., 2012 Lags at 8 and 10 months Rainy-season peak (August–October) Monthly average rainfall Rainfall effects appeared substantially delayed relative to leptospirosis occurrence
6 Rees et al., 2023 1 week Rainfall and flooding periods Six-week cumulative rainfall Sustained wet conditions increased leptospirosis transmission risk
7 Cunha et al., 2022 Significant lags at 1 and 2 weeks Year-round transmission Positive rainfall anomaly (+20 mm/week above expected seasonal level) Even moderate rainfall anomalies increased disease risk outside the principal rainy season
8 Warnasekara et al., 2022 Significant lag at 3 months Monsoon-associated peak Total monthly rainfall Sustained rainfall contributed to increased leptospirosis transmission in dry-zone regions of Sri Lanka
No Study Temporal lag structure Seasonal pattern Rainfall metric/threshold/anomaly Main epidemiological interpretation
9 Ghizzo Filho et al., 2018 Seasonal pattern without explicit lag analysis Distinct peak during October–March Mean monthly rainfall 158.68 mm; October–March average 176.41 mm/month; April–September average 140.95 mm/month Higher rainfall months coincided with substantially higher leptospirosis incidence, indicating strong seasonal coupling
10 Tana et al., 2025 Significant lags at 1, 2, and 3 months (strongest at 2 months) Annual seasonal cycle Monthly rainfall increase per 1 cm Rainfall demonstrated cumulative delayed effects, with increased leptospirosis incidence persisting for up to three months following higher rainfall levels
11 Ehelepola et al., 2019 Approximately 1–3 weeks Wet-season peak Extreme rainfall (>100 mm/week) Weekly extreme rainfall events consistently preceded increases in leptospirosis incidence
12 Safera et al., 2023 Significant lags at 1–3 months (strongest at 2 months) Flood-associated peak Rainfall-related flooding and monthly rainfall variation Rainfall and flood occurrence showed delayed positive associations with leptospirosis incidence
13 Jayaramu et al., 2023 3 weeks Monsoon-associated peak Extreme rainfall index d50mm (>50 mm/day) and percentile-based rainfall indices Days with extreme rainfall were among the strongest predictors in forecasting models
14 Matsushita et al., 2018 Significant effects at 1–3 weeks (peak at 2 weeks) Rainy season and flooding periods Heavy rainfall (≥16 cm/week), intense rainfall (≥32 cm/week), torrential rainfall (≥63 cm/week) Risk increased progressively with increasing rainfall intensity and flooding related conditions.
15 Dhewantara et al., 2019 Lag 1 month (Yilong) and lag 6 months (Mengla) Unimodal (Yilong) and bimodal (Mengla) seasonal patterns Rainfall exposure measured continuously Rainfall effects varied across ecological settings, with differing temporal responses between regions
16 Sumi et al., 2017 Seasonal rainfall peak preceded leptospirosis peak by approximately 1 month Monsoon synchronization Seasonal rainfall cycle Seasonal peaks in rainfall consistently preceded peaks in leptospirosis incidence, suggesting temporal synchronization between climatic conditions and disease occurrence
No Study Temporal lag structure Seasonal pattern Rainfall metric/threshold/anomaly Main epidemiological interpretation
17 Fanelli et al., 2026 1-month lag Late spring and summer peak (highest risk in August) SPEI-3 (1-month lag; wetter-than-normal conditions, maximum value 2.79) Wetter-than-normal climatic conditions were associated with increased leptospirosis risk across Europe, highlighting the role of hydrometeorological anomalies in disease transmission
18 Gunathilaka et al., 2026 Significant lags at 1 and 3 months Monsoon-associated peak Monthly total rainfall Increased rainfall was associated with higher leptospirosis incidence after one and three months, indicating delayed climate-sensitive transmission dynamics
19 Wang et al., 2025 No explicit lag analysis July–October peak (91% of reported cases) Annual average precipitation Higher annual average precipitation was associated with increased long-term leptospirosis occurrence, particularly in southern China
20 Pawar et al., 2018 Significant lag at 0–1 month Monsoon-associated peak (July–September) Heavy rainfall and high relative humidity Rainfall increases were followed by rises in leptospirosis cases within approximately one month
21 Coelho & Massad, 2012 Significant lags at 14–18 days Summer peak (February) Daily rainfall increments of 20–140 mm Increasing rainfall intensity was associated with progressively greater hospital admission burden
22 Desvars et al., 2011 Significant lags at 0 and 2 months (strongest at 2 months) Rainy-season peak (February–May) Monthly cumulative rainfall Rainfall recorded several weeks to months earlier contributed to seasonal leptospirosis occurrence
Rainfall characteristics associated with increased leptospirosis risk

The synthesis of findings indicated that increased leptospirosis risk was associated not only with the amount of rainfall but also with specific rainfall characteristics. These characteristics included extreme rainfall events, accumulated rainfall over defined periods, positive rainfall anomalies, wetter-than-normal conditions, and rainfall-related flooding events. Extreme rainfall was consistently associated with increased leptospirosis occurrence. Indicators used across studies included rainfall exceeding the 90th percentile (34.5 cm/month), daily rainfall of 175–250 mm, extreme rainfall exceeding 100 mm/week, and threshold-based extreme rainfall indices of more than 50 mm/day. Increased risk was also reported during periods of heavy rainfall (≥16 cm/week), intense rainfall (≥32 cm/week), and very heavy rainfall (≥63 cm/week). These findings suggest that elevated leptospirosis risk was observed across a range of extreme rainfall indicators. Leptospirosis risk was also associated with accumulated rainfall over longer periods, including six-week cumulative rainfall, monthly rainfall, and annual precipitation. Higher risk was further observed under conditions of positive rainfall anomalies of +20 mm/week above expected seasonal levels and wetter-than-normal conditions measured using the Standardized Precipitation–Evapotranspiration Index (SPEI-3), with values up to 2.79. Rainfall-related flooding was also reported as an important characteristic accompanying increased leptospirosis risk. Overall, these findings indicate that increased leptospirosis risk is influenced not only by the quantity of rainfall but also by its intensity, accumulation, deviation from normal conditions, and associated hydrological impacts.

 Risk of bias assessment

Risk of bias was assessed for all 22 included studies using the Navigation Guide Systematic Review Methodology. Overall, seven studies were judged to have a low risk of bias, five were classified as probably low risk of bias, nine were rated as probably high risk of bias, and one study was judged to have a high risk of bias. Detailed study-level risk of bias assessments are available in the Extended Data repository. Across the included studies, confounding was the domain most frequently associated with elevated risk of bias. Although many studies adjusted for selected meteorological variables, several were unable to account for other determinants of leptospirosis transmission, including flooding characteristics, sanitation conditions, land-use patterns, rodent reservoirs, occupational exposures, and socioeconomic factors. Additional concerns included limited geographical coverage and potential exposure misclassification. In contrast, exposure assessment and outcome assessment were generally rated as low or probably low risk of bias. Rainfall exposure data were predominantly obtained from official meteorological agencies, monitoring systems, or validated environmental datasets, whereas leptospirosis outcomes were primarily derived from surveillance databases, hospital records, or laboratory-confirmed cases. Selective reporting and conflict of interest were also generally assessed as low risk of bias. Overall, the risk of bias assessment suggests that the included studies provided reasonably reliable evidence regarding the association between rainfall and leptospirosis occurrence. Nevertheless, residual confounding remained an important methodological concern and should be considered when interpreting the findings of this review.

 Discussion

The main findings of this systematic review indicate that increased rainfall is generally associated with an increase in leptospirosis cases across various geographic and epidemiological contexts. Although there is variation in rainfall indicators, timescales, and analytical approaches used, the synthesis of evidence suggests that the positive association between rainfall and leptospirosis incidence is relatively consistent. This relationship is observed across various rainfall characteristics, ranging from general increases in rainfall to extreme rainfall conditions and hydrometeorological anomalies. These findings align with a systematic review by Mwachui et al. (2015), which identified heavy rainfall and flooding as key environmental determinants in leptospirosis transmission, particularly in Asian regions and island nations. However, compared with previous studies that emphasized the general relationship between environmental factors and leptospirosis, this review demonstrates that the relationship between rainfall and leptospirosis incidence is not only generally consistent but is also influenced by temporal variations and specific rainfall characteristics that play a role in increasing disease risk.

The available evidence regarding the relationship between rainfall and leptospirosis primarily comes from tropical and subtropical regions, which are areas with a high burden of leptospirosis cases. 3 , 13 This pattern aligns with the global distribution of leptospirosis, which is known to occur more frequently in regions with environmental conditions that support the survival and transmission of Leptospira. 3 , 37

The relationship between rainfall and leptospirosis identified in this review has a strong biological and ecological basis. Leptospirosis is an infectious disease whose transmission is heavily influenced by environmental conditions (environment-borne infection). 38 Leptospira bacteria can survive in water and moist soil for weeks to months after being excreted into the environment, so increased rainfall can prolong the pathogen’s survival. 5 In addition to providing conditions that support bacterial persistence, heavy rain can also increase the mobilization of Leptospira in the environment through the resuspension of contaminated soil particles into surface water bodies. 38 Surface runoff and subsequent flooding can further expand the distribution of contamination from areas exposed to reservoir animal urine into residential environments and areas of human activity. 39 This increases the likelihood of human contact with contaminated water or soil, particularly among groups with high environmental exposure, including communities in areas with inadequate sanitation and those engaged in agriculture and other outdoor occupations. 5 , 40 These mechanisms support the view that the influence of rainfall on leptospirosis incidence is determined not only by the amount of rainfall, but also by how the resulting environmental changes evolve over time.

Environmental condition changes triggered by rainfall are reflected in the temporal pattern of the relationship between rainfall and leptospirosis. The findings of this review indicate that the effect of rainfall on the risk of leptospirosis cannot be understood as a response that occurs immediately after exposure, but rather as a process involving a series of environmental and epidemiological changes . 41 This pattern indicates that time is an important component in the relationship between rainfall and leptospirosis incidence, where environmental conditions formed after rainfall may influence disease risk within a certain period. 39 , 42 This interpretation is consistent with previous literature showing that the dynamics of leptospirosis incidence are strongly influenced by seasonal variations in rainfall and hydrometeorological conditions in various endemic regions. 13 , 39 Therefore, the relationship between rainfall and leptospirosis is determined not only by the magnitude of rainfall exposure, but also by the environmental dynamics that develop following rainfall events.

The observed lag pattern is likely the result of the interaction of various biological, environmental, and behavioral processes that occur following an increase in rainfall. One factor that may explain this pattern is the incubation period of leptospirosis, which generally ranges from 2 to 30 days, resulting in a time lag between exposure to a contaminated environment and the onset of clinical symptoms. 5 In addition, Leptospira is known to survive in moist environments for weeks to months, so the conditions formed after rainfall may continue to serve as a source of infection for a certain period of time. 38 , 41 Post-rainfall and post-flood conditions may also create environments that are increasingly favorable for bacterial persistence and growth, including the formation of standing water and waterlogged soil, which have been reported to support the growth of Leptospira bacteria . 43 Human exposure to contaminated environments also does not always occur during rainfall events, but may occur after standing water has formed or when communities resume activities in areas affected by rainfall or flooding. This risk may be further influenced by the presence of animal reservoirs that continuously contaminate the environment through the excretion of urine into soil and water sources. 6 Overall, the combination of the incubation period, environmental persistence, changes in patterns of human exposure, and animal reservoir dynamics indicates that the relationship between rainfall and leptospirosis is an ecological and epidemiological process that develops gradually following exposure.

In addition to the temporal variations identified, this synthesis also indicates that the increased risk of leptospirosis is associated not only with the magnitude of rainfall but also with specific rainfall characteristics that reflect the level of hydrological and environmental disturbance generated. These characteristics include high-intensity rainfall, rainfall accumulated over extended periods, and conditions that are wetter than normal. Extreme rainfall and flooding events are known to increase the risk of leptospirosis because they generate greater surface runoff, cause water bodies to overflow, and expand the spread of contaminants into residential areas and surrounding environments. 12 These conditions may increase human contact with sources of infection that were previously more spatially restricted. 13 , 44 In addition, disruptions to drainage and sanitation systems during flooding events may further expand contaminated areas and increase the likelihood of leptospirosis transmission. 12 , 44 On the other hand, rainfall accumulated over extended periods and conditions that are wetter than normal reflect sustained hydrological pressure and may increase both the frequency and extent of standing water and flooding events. 42 , 44 , 45 Therefore, these indicators not only represent the amount of rainfall that occurs, but also reflect the degree of environmental change that may influence the dynamics of leptospirosis transmission. Thus, rainfall characteristics that reflect intensity, duration, and deviations from normal hydrometeorological conditions may provide a more comprehensive representation of leptospirosis transmission risk than rainfall amount alone.

Although rainfall is frequently identified as a factor associated with leptospirosis occurrence, research findings have shown considerable variation across regions. 3 , 13 Such heterogeneity may even occur in areas with relatively similar geographical backgrounds. Dhewantara et al. (2019) reported that rainfall was positively associated with leptospirosis incidence in Yilong County but showed a negative association in Mengla County. The authors suggested that these differences were likely influenced by variations in socio-ecological conditions, including hydrological characteristics, land use patterns, and agricultural activities that differed between regions. Similar heterogeneity was also observed in the study by Obels et al. (2025) in the Netherlands, which found no significant association between rainfall and leptospirosis incidence. In that study, temperature was reported as a more dominant factor, while the increased disease risk was more closely related to recreational water activities during warm-weather periods than to rainfall or flooding events. This indicates that the impact of rainfall on leptospirosis is determined not only by the magnitude of rainfall exposure but also by the complex interactions among climatic factors, environmental conditions, animal reservoirs, land use, and human behavior. 3 , 6 , 37 Nevertheless, these findings suggest that the relationship between rainfall and leptospirosis should be understood within a broader geographical and epidemiological context. 3 The variation in associations observed across regions indicates that the influence of rainfall on the transmission dynamics of leptospirosis is strongly shaped by local conditions that determine disease risk. 3 , 13 , 37

The findings of this review have important implications for leptospirosis prevention and control efforts. The relationship between rainfall and increased disease risk suggests that meteorological information has the potential to be utilized as part of surveillance and early warning systems to identify periods with a higher risk of transmission. In addition, the lag pattern between increased rainfall and leptospirosis occurrence identified in this review provides an opportunity to implement preventive or anticipatory measures before an increase in cases occurs. The integration of rainfall data into public health monitoring systems may help support the planning of more timely interventions. However, given that the magnitude of the effect of rainfall may vary across regions, the use of such information should be adapted to local geographical, environmental, and epidemiological characteristics to ensure that the control strategies implemented are more effective.

Limitations

This review has several limitations that should be considered when interpreting the findings. First, the literature search was restricted to articles published in English and Indonesian and indexed in four databases, namely PubMed, Scopus, ScienceDirect, and Google Scholar. In addition, grey literature was not included in the search strategy; therefore, it is possible that some relevant studies were not identified. Second, the reviewed studies exhibited substantial heterogeneity in the definition and measurement of rainfall exposure, including differences in temporal scales, lag structures, and the use of various rainfall indicators and hydrometeorological indices. Variations in the analytical approaches employed across studies also limited direct comparisons of the reported effect sizes. This heterogeneity precluded the conduct of a quantitative synthesis through meta-analysis, and therefore the findings of this review are presented as a narrative synthesis. Furthermore, most of the included studies used ecological or time-series observational designs, which allow the identification of patterns of association at the population level but cannot be used to draw direct causal inferences at the individual level. Consequently, the findings should be interpreted with caution, as associations observed at the population level may not necessarily reflect relationships at the individual level, a limitation commonly referred to as the ecological fallacy. The observed associations may also have been influenced by other environmental factors that frequently interact with rainfall, such as temperature, humidity, flooding, land use, and animal reservoir dynamics, which were not consistently controlled for across studies. Nevertheless, this review successfully integrates evidence from diverse geographical and epidemiological contexts and provides a more comprehensive understanding of the relationship between rainfall and leptospirosis occurrence, including the temporal patterns and rainfall characteristics associated with increased disease risk.

 Conclusion

This systematic review aimed to evaluate the relationship between rainfall and leptospirosis occurrence and to identify the characteristics of this relationship across different geographical and epidemiological contexts. Based on the synthesis of studies that met the inclusion criteria, the findings indicate that increased rainfall is generally associated with an increased risk of leptospirosis occurrence. This relationship was observed not only for increases in rainfall in general but also for specific rainfall characteristics, such as extreme rainfall, rainfall accumulated over longer periods, and hydrometeorological conditions that are wetter than normal. In addition, the synthesis of evidence identified a lag pattern between increased rainfall and the occurrence of leptospirosis cases, indicating that the influence of rainfall on this disease develops through ecological and epidemiological processes that occur gradually over time.

This review demonstrates that the relationship between rainfall and leptospirosis is determined not only by the amount of rainfall but also by specific rainfall characteristics and the temporal dynamics through which their effects occur. Although the direction of the association is generally consistent, the magnitude of the effect of rainfall may vary across regions due to differences in local geographical, hydrological, environmental, and epidemiological characteristics. These findings suggest that the interpretation of the relationship between rainfall and leptospirosis cannot be separated from the local conditions that shape the risk of disease transmission. Overall, the findings of this review highlight the importance of considering rainfall information in leptospirosis surveillance, prevention, and control efforts. The use of relevant rainfall indicators, together with an understanding of the temporal patterns underlying the relationship between rainfall and leptospirosis, has the potential to support the development of more effective early warning systems. Future research should develop more standardized approaches to measuring rainfall exposure and further explore the interactions between rainfall and other environmental factors to improve understanding of the mechanisms underlying leptospirosis transmission and to support more targeted control strategies.

 Ethics statement

This systematic review synthesized findings exclusively from published studies retrieved from scientific databases. The review did not involve the recruitment of human participants, the collection of primary data, or access to identifiable personal information. Accordingly, ethical approval from an institutional review board or ethics committee was not required for this study. All studies included in this review had been previously published and were assumed to have obtained any necessary ethical approvals from their respective institutions where applicable. No personal or sensitive data were accessed, collected, or reanalyzed during the conduct of this review. This study was conducted in accordance with the principles of transparency, reproducibility, and responsible research practice. The review protocol was prospectively registered in PROSPERO (CRD420261330699). Data extraction forms, risk-of-bias assessments, search strategies, the PRISMA 2020 checklist, and other supplementary materials are openly available in Zenodo ( DOI: 10.5281/zenodo.20714676).

under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

 Data availability

The dataset supporting this systematic review is openly available in Zenodo with the title “Dataset of Systematic Review of Rainfall and Human Leptospirosis Incidence” (Version 1.0) through the following DOI:

https://doi.org/10.5281/zenodo.20714676 46

The repository includes the following files:

Supplementary Dataset 1: Data Extraction.

Supplementary Dataset 2: Risk of Bias Assessment.

Extended Data 1: Detailed Search Strategy.

Extended Data 2: PRISMA 2020 Checklist.

Figure 1: PRISMA 2020 Flow Diagram.

All data are available under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

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