INTRODUCTION

Leptospirosis is a waterborne zoonotic disease classified as a neglected infectious disease and a re-emerging tropical illness with high morbidity and mortality [1]. Rodents harboring pathogenic Leptospira in their renal tubules serve as the primary reservoirs [2,3]. Humans typically become infected through contact with urine from infected animals or through exposure to contaminated environments via skin wounds or mucous membranes [4]. Globally, the disease affects approximately 14.8 individuals per 100 000 annually, amounting to roughly 1 million cases and resulting in 60 000 deaths from severe manifestations [5]. It is especially prevalent in tropical regions such as Southeast Asia, where environmental and climatic factors critically influence transmission. In Indonesia, leptospirosis remains a significant public health issue; in 2022, there were 1613 reported cases and 148 deaths (case fatality rate [CFR], 9.2%). Within the country, Central Java ranks third in disease burden (CFR, 14.0%), following Banten (CFR, 18.7%) and West Java (CFR, 16.0%) [4,6].

Disease transmission involves both biotic and abiotic factors. Abiotic determinants include climate, soil and water pH, and flooding, whereas biotic factors comprise rodent populations, vegetation, and Leptospira prevalence [7-12]. Evidence indicates that temperatures between 28°C and 30°C, relative humidity exceeding 31.4%, and heavy rainfall favor bacterial growth [9,13]. Conversely, ultraviolet (UV)-A radiation can inhibit bacterial proliferation by damaging cellular structures [14]. Near-neutral to slightly alkaline pH levels (7.2-8.4) are optimal for bacterial viability, whereas acidic conditions reduce it [10,11]. In flood-prone regions, stable pH conditions, optimal temperatures, and high moisture significantly elevate transmission risk [11].

Central Java’s climate—with temperatures ranging from 18-34°C, humidity between 73-94%, annual rainfall reaching 3990 mm, and approximately 195 rainy days annually—creates ideal conditions for leptospirosis transmission, further exacerbated by frequent flash floods dispersing contaminated water [15,16]. In Indonesia, surveillance primarily relies on case reporting and field verification, lacking effective integration of hydrometeorological data [17]. Furthermore, spatial studies correlating these variables with disease incidence remain limited, highlighting the necessity for further research to support effective prevention and control strategies.

METHODS

Study Design and Data Source

This research employs a quantitative approach using an ecological study design within a descriptive observational framework. In this design, entire populations or groups serve as analytical units, enabling the examination of correlations between hydrometeorological factors and leptospirosis through aggregated data. The study integrates geographic information systems (GIS) with statistical techniques to clearly demonstrate distinct disease spread patterns and spatial trends. To align with the incubation period of leptospirosis, which ranges from 3 days to 1-month, daily hydrometeorological data were aggregated into monthly values [18].

Monthly leptospirosis case data from 2018 to 2022 were sourced from the Ministry of Health of the Republic of Indonesia [6]. These monthly records originated from reports submitted by healthcare facilities where diagnoses were confirmed by physicians. However, this dataset is limited as it includes only cases recorded at these specific facilities, potentially leading to an underestimation of the true prevalence of leptospirosis. In rural areas of Central Java, restricted accessibility, economic constraints, or mild symptoms might deter individuals from seeking medical care, further contributing to underreporting. Climate data were obtained from 5 weather monitoring stations in Central Java—Ahmad Yani, Tanjung Emas Maritime, Tegal Maritime, and Tunggul Wulung Meteorological Stations—via the Indonesian Meteorology, Climatology, and Geophysics Agency data portal (BMKG; https://dataonline.bmkg.go.id/akses_data). BMKG employs uniform measurement techniques and standardized equipment across all stations, ensuring highly consistent data, particularly at these urban stations, which benefit from routine monitoring and maintenance [19]. Flood data were comprehensively gathered from 6 Water Resources Management Centers in Central Java (Pemali Comal, Bodri Kuto, Setang Lusi Juana, Bengawan Solo, Probolo, and Serayu Citanduy), accessible at https://pusdataru.jatengprov.go.id/portal_data/banjir, which similarly utilizes standardized technical protocols and equipment maintenance [20]. Additionally, a base map delineating the boundaries of the 35 regencies or cities in Central Java was obtained from the GADM Map and Data website (https://gadm.org/maps/IDN/jawatengah.html), providing the essential spatial framework for data visualization.

Data Analysis Procedure

Univariate analysis was conducted to illustrate the distribution of hydrometeorological factors and leptospirosis cases, with results visualized through line graphs. Prior to conducting correlation tests, a normality test was performed, which revealed that minimum temperature, flooding, and leptospirosis cases did not have normal distributions. To satisfy the assumptions of multiple linear regression analysis, the non-normally distributed variables were transformed using the log₁₀(x) function. Despite this transformation, flooding data remained irregular, necessitating their exclusion from subsequent multivariate analysis. Subsequently, bivariate analysis using Pearson’s correlation was employed to evaluate associations between hydrometeorological factors and leptospirosis cases. The Pearson correlation coefficient (r) was used to determine the significance, strength, and direction of these associations, categorized as very weak (0.00-0.25), moderate (0.26-0.50), strong (0.51-0.75), or very strong (0.76-1.00). The positive or negative sign of r indicates the direction of the relationship, where r=0 signifies no correlation, r=-1 indicates a perfect negative correlation, and r=1 denotes a perfect positive correlation [21].

In the multivariate analysis, variables significantly associated with leptospirosis cases in the bivariate analysis were selected. Initially, hydrometeorological factors with p-values below 0.25 were included in a multiple linear regression model, resulting in an R² of 0.435, which served as the basis for the final model. A stepwise elimination procedure was then conducted, sequentially removing variables with p-values greater than 0.05, ensuring that changes in R² and regression coefficients (B) did not exceed 10%. Maximum temperature, having the highest p-value, was removed first, followed by minimum temperature, rainfall, and solar radiation. However, this sequential removal reduced R² to 0.430 and caused unacceptable coefficient variations, thus prompting retention of the initial model. The final regression equation is: y=a+b₁X₁+b₂X₂+b₃X₃+b₄X₄+b₅X₅+e, where y represents leptospirosis cases and X₁ to X₅ correspond to minimum temperature, maximum temperature, humidity, rainfall, and weekly solar radiation. All analyses were conducted at the Sriwijaya University Computer Laboratory using SPSS version 27 (IBM Corp., Armonk, NY, USA).

We also conducted a spatial data visualization to identify clear spatial patterns linking leptospirosis cases and hydrometeorological variables. An interpolation method, which excluded flood data, generated overlay maps. This process involved creating a grid from the 5 weather stations, digitizing vector data for independent variables using single-band pseudocolor classification, and encoding the dependent variable data using severity-marking centroid symbols. These vector datasets were converted into raster plots and then overlaid using QGIS 3.38.3 (QGIS Development Team 2019), enabling comprehensive spatial-temporal analysis.

Ethics Statement

This study was approved by the Health Research Ethics Committee, Faculty of Public Health, Universitas Sriwijaya, No. 300/UN9.FKM/TU.KKE/2024.

RESULTS

Figure 1 illustrates that leptospirosis cases peaked at 110 in the 4th month and reached a minimum of just 4 cases in the 43rd month. Over the 2018-2022 period, monthly case numbers fluctuated, showing noticeable increases beginning in the fourth quarter and peaking in the first quarter of each year. During this period, temperatures ranged from 22.9°C to 33.9°C. Specifically, minimum temperatures varied between 22.9°C in the 57th month and 25.8°C in the 4th month, whereas maximum temperatures ranged from 30.4°C in the 15th month to 33.9°C in the 45th month. Average temperatures spanned from 26.8°C in the 13th month to 29.4°C in the 47th month. Humidity decreased from 86.5% initially to 69.0% by the 60th month. Rainfall varied between 0 mm and 23.6 mm, peaking notably at 19.6 mm in the 51st month. Solar radiation ranged between 3.1 hr/day and 9.3 hr/day, while flood frequency reached its highest point at 80 events during the 59th month.

Correlation analysis conducted in Central Java revealed significant, moderately positive correlations of minimum temperature and rainfall with leptospirosis cases, while humidity showed a strong positive correlation. Conversely, maximum temperature and solar radiation demonstrated significant, moderate negative correlations with leptospirosis case numbers (Table 1).

Annual humidity patterns (Figure 2) indicated that in 2018, 2019, and 2022, high humidity was prevalent predominantly in southern, southwestern, and southeastern regions. Moderate humidity was common in the northeastern and northwestern areas, with low humidity restricted to specific northern regions. In 2020, high humidity was observed across northern, eastern, and northwestern regions, whereas in 2021, high humidity was primarily confined to the southwest. Moderate humidity occurred in northern, eastern, and southern regions, with low humidity concentrated in a small northwestern area. Regions characterized by high or moderate humidity generally reported higher leptospirosis cases, particularly Semarang, which consistently recorded elevated numbers.

Spatial visualization of flooding (Figure 3) indicated a consistent annual pattern. Flooding events from 2018-2019 were predominantly concentrated in northern and central regions. However, from 2020 to 2022, flooding spread more uniformly across almost all regions. Overlay maps of flooding and leptospirosis cases (2018-2022) revealed that areas experiencing moderate to severe flooding generally reported higher case numbers. Notably, elevated leptospirosis cases were observed in Klaten, Semarang City, Pati, Banyumas, and Kebumen. Some regions without flooding in 2019 also reported substantial case numbers.

Table 2 indicates an R² of 0.435, meaning that the regression model explains 43.5% of the variance in leptospirosis cases. Humidity was identified as the only variable significantly associated with leptospirosis cases, with each 1.0% increase in humidity corresponding statistically to an additional 0.07 cases, controlling for other variables. Additionally, although statistically insignificant, an inverse relationship between maximum temperature and rainfall with leptospirosis cases was identified. Meanwhile, minimum temperature and solar radiation showed positive but statistically insignificant correlations with leptospirosis cases.

DISCUSSION

The analysis demonstrated that as maximum temperature decreased, leptospirosis cases tended to increase. Conversely, a rise in minimum temperature was associated with increased cases. Interestingly, average temperature showed no significant relationship with leptospirosis incidence. These findings align with a Korean study, which reported that a 1°C rise in minimum temperature correlated with a 13.1% increase in leptospirosis cases within the same week (lag 0), peaking at a 22.7% increase after an 11-week lag [22]. Additionally, research from Lower Saxony, Germany, indicated that each 5°C increase in maximum temperature led to a reduction in leptospirosis infection rates among muskrats by a factor of 0.97 [23]. These outcomes can be attributed to the optimal temperature range (28-30°C) promoting the growth of Leptospira bacteria, even though they survive across a broader range (4-40°C) with varying survival durations [24,25]. In Central Java, average temperatures vary from 22.1°C to 35.5°C, implying periods when conditions fall outside the optimal range for Leptospira survival. Deviation from this optimal temperature range can diminish bacterial survival, whereas a narrowing of temperature variations—rising minimum temperatures and decreasing maximum temperatures toward optimal conditions—can enhance bacterial survival, thereby elevating transmission risk. However, after adjusting for multiple variables in the multivariate analysis, neither maximum nor minimum temperature maintained a significant independent relationship with leptospirosis cases (explained below).

Regarding rainfall, the analysis indicated that higher rainfall was associated with increased leptospirosis incidence in the region. Numerous studies from different countries support this finding, showing that intense rainfall often correlates with higher leptospirosis incidence, particularly in the same month or within 2 months following heavy rain events [24,26-29]. Moreover, the La Niña phenomenon has been linked with increases in leptospirosis cases by up to 25% [30]. Despite the clear bivariate relationship, the multivariate analysis revealed a directional shift in the association for rainfall, which did not demonstrate a statistically significant correlation with leptospirosis cases. This suggests rainfall’s influence might be indirectly connected to other environmental or social factors [31].

In terms of solar radiation, the analysis showed that higher levels of solar radiation could reduce leptospirosis incidence. Studies from Sri Lanka and Reunion Island support this conclusion, indicating that increased solar radiation impedes bacterial proliferation and consequently lowers disease transmission. Although Leptospira bacteria exhibit some tolerance to UV light, exposure beyond 2 hours typically becomes lethal [32,33]. Conversely, a Korean study found that a 1 mJ/m² increase in solar radiation corresponded to a 13.7% increase in leptospirosis cases, peaking after 2 weeks [22]. This apparent discrepancy underscores the complexity of solar radiation as a predictor—it appears that local climatic conditions, ecological contexts, and interactions with other meteorological factors significantly affect outcomes. In tropical regions like Central Java, Sri Lanka, and Reunion Island, high solar radiation typically decreases humidity and interacts with other conditions to inhibit bacterial growth. Conversely, in Korea, increased solar radiation may encourage agricultural activities and thus human exposure [22,32,33]. Nonetheless, after controlling for multiple variables in the multivariate analysis, solar radiation reversed the association direction but did not show a statistically significant independent relationship with leptospirosis cases (explained below).

The study found no statistically significant association between flooding and leptospirosis cases. However, the weak yet positive relationship suggests a tendency for increased cases during flooding. This finding contrasts with several earlier studies identifying flooding as a significant risk factor, where flooded regions experienced a 2.19 times to 8.99 times higher infection risk compared to non-flooded areas [34,35]. However, some literature indicates that the relationship between flooding and leptospirosis may not always be significant. For instance, research from India compared leptospirosis data in non-flood years (2017), severe flood years (2018), and moderate flood years (2019), concluding only the severe floods in 2018 significantly increased cases, with flood duration rather than extent or intensity being the main determinant [36].

From a spatial perspective, this study examined the relationship between flooding and leptospirosis in Central Java more closely. Areas with moderate to severe flooding, such as Klaten, Semarang City, Pati, Banyumas, and Kebumen, generally experienced higher leptospirosis case numbers compared to unaffected regions. Although most cases occurred in flood-affected areas, certain non-flooded areas, such as Demak, also recorded elevated infection rates. This aligns with findings from a study in Kerala, India, where flood-prone areas, notably Alappuzha district, reported substantial leptospirosis cases. Additionally, that study highlighted the possibility of secondary spread due to population movements from post-flood displacements or relocations [33]. However, the robustness of these findings is limited by the spatial data visualization approach used in this study, as it does not include a confirmed statistical measure of association.

The relationship between flooding and leptospirosis arises from the interactions among humans, animals, and environmental factors [37]. Leptospira bacteria are transmitted through direct contact with urine or reproductive fluids from infected animals, such as rats, or through exposure to contaminated water or soil, as well as by consuming contaminated food and water [4,17,24]. These transmission mechanisms are exacerbated by flooding, particularly in areas with poor sanitation and inadequate sewage systems, which spread the contaminating bacteria over broader regions [17,37]. Community behavior further influences leptospirosis incidence, particularly among individuals frequently engaged in outdoor activities or occupations involving exposure to contaminated water [34]. Specific occupational groups, such as those employed in agriculture, forestry, and fisheries, representing the majority in Central Java (24.78%) [38], face heightened risks, especially when not using adequate personal protective equipment [17,34]. This occupational pattern aligns with findings from Korea, where agricultural and fisheries workers accounted for the majority of leptospirosis cases (58.16%) [22].

After multiple linear regression analysis, humidity emerged as the only hydrometeorological factor significantly associated with leptospirosis cases. Specifically, each 1% increase in humidity was associated with an additional leptospirosis case per month. This finding concurs with research from multiple countries, including Brazil, Sri Lanka, and Colombia, demonstrating that elevated relative humidity significantly contributes to increased leptospirosis incidence, particularly within the same month or within the subsequent 2 months [26,27,32]. A study from the Philippines further supports these findings, confirming that high humidity prolongs the environmental survival of Leptospira [39], with additional support from related environmental factors such as high rainfall, low temperature, optimal water pH, and salinity [39,40]. High humidity slows the drying of contaminated surfaces, such as soil and standing water, thereby extending the survival period of Leptospira and facilitating human transmission, especially in areas characterized by inadequate sanitation and waste management [26,40]. Although temperature, rainfall, and solar radiation showed significant correlations in bivariate analysis, their fluctuating interactions with other environmental factors led to inconsistent results when controlled simultaneously. This suggests these variables likely have overlapping or weaker effects compared to the direct and substantial impact of humidity on Leptospira survival.

Spatial analysis further clarified the relationship between humidity and leptospirosis cases in Central Java. Areas exhibiting high or moderate humidity, such as Banyumas, Klaten, and Demak, generally recorded higher leptospirosis case numbers compared to drier regions. However, some areas, including Semarang City, also reported substantial case numbers despite variations in humidity. These findings are reinforced by Warnasekara et al. [32] in Sri Lanka, who documented correlations between increased relative humidity and leptospirosis outbreaks in the Wet Zone. Outbreaks in these regions typically coincided with humidity peaks [32], possibly also influenced by agricultural activities, notably rice cultivation, a significant occupational risk factor [29].

Despite these insights, the current study has several limitations. Notably, the analysis did not include lag-effect evaluation, which would clarify how hydrometeorological factors influence leptospirosis incidence over specific periods. Additionally, the spatial data visualization used lacks confirmed statistical measures, and available data were insufficient for analyzing detailed variations such as flood duration or the geographic extent of flooding.

To improve future studies, it is recommended that researchers incorporate lag-effect analyses, employ spatial lag models to identify spatial autocorrelation, and develop more sophisticated predictive models. These advanced models should integrate additional variables, including sanitation conditions, population distribution, community behaviour, and a more comprehensive set of hydrometeorological factors. This integrated approach would likely enhance predictive accuracy, particularly in urban areas with limited infrastructure.

The findings from this study offer valuable insights for government policymakers, supporting the development of a comprehensive, scientifically informed leptospirosis management strategy. One recommended strategy is to establish a spatial monitoring system targeting high-risk areas, especially regions characterized by elevated humidity and frequent flooding. Furthermore, integrating hydrometeorological factors into predictive models would enable accurate disease forecasting. For optimal impact, such predictive models should connect with early warning systems, facilitating proactive disease management. Additionally, mitigation efforts should focus on enhancing drainage and flood management, improving rodent control measures, and regularly monitoring water quality. These measures are critical for reducing leptospirosis impact and enhancing preparedness for hydrometeorological health risks.

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

None.

Author Contributions

Conceptualization: Saputra YA. Data curation: Saputra YA, Armawan LVA, Lisa M, Muharammah DH, Pratiwi LD. Formal analysis: Lisa M, Saputra YA. Funding acquisition: None. Methodology: Saputra YA. Project administration: Lisa M. Visualization: Lisa M, Saputra YA. Writing – original draft: Saputra YA. Writing – review & editing: Saputra YA, Armawan LVA, Lisa M, Muharammah DH, Pratiwi LD.

Figure. 1.

Leptospirosis and hydrometeorological factors distribution (y axis) per month (x axis) which includes: (A) leptospirosis cases, (B) temperature (°C), (C) humidity (%), (D) rainfall (mm), (E) solar radiation (hours), and (F) flood frequency.

Figure. 2.

Spatial pattern of humidity and leptospirosis cases.

Figure. 3.

Spatial pattern of floods and leptospirosis cases.

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