3.1. Modeling of Infectious Leptospirosis Diseases

Studies on deterministic models have been common in the biological field, like modeling both symptomatic and asymptomatic diseases such as leptospirosis. Mathematical modeling helps researchers understand the transmission dynamics of leptospirosis disease and their control strategies to curb the spread of the disease. Few studies have been done to model the implications of misdiagnosis in leptospirosis dynamics. Therefore, there is an evidenced need for research-based information which can create awareness to specialists, health practitioners, data analysts, and researchers. Thus, this study bridges the gap by developing a deterministic model on the implications of misdiagnosis in leptospirosis dynamics. Mathematical modeling is a technique of expressing real-world problems in mathematical words in order to gain a better understanding of the significance and attributes of the problem. This provides understanding on how infectious diseases spread from one host to another and also helps us discover factors governing disease transmission dynamics, identify the most significant and sensitive parameters that give the most reliable prediction, and provide useful prevention and control interventions through the use of analysis such as sensitivity, qualitative, quantitative, and numerical analyses. In our research, a deterministic model was used to investigate the spread of leptospirosis disease with delimitation on misdiagnoses of the disease in the hospital. Due to the nature of the disease and its contemporary diagnostic technology, diagnosis of leptospirosis poses a significant challenge. The symptoms of leptospirosis disease are similar to those of dengue, malaria, and other viral infections such as influenza. This leads to a high rate of misdiagnosis emphasizing the urgent need to have precise and rapid diagnostic approaches in our hospitals. Consequently, the compartmental model formed was used to determine the effects of misdiagnosis in the community [15–18].

3.2. SEIR Model on Impact of Flooding on Leptospirosis Transmission

In the Philippines, an SEIR model was used to determine the impact of Typhoon Ketsana with reference to the latency period of the disease. This revealed that floods significantly elevated the exposure risk, and infections were observed at their highest rate at 2–3 weeks after a disaster. This determined the time of administering the preventive antibiotics [20–22]. In another instance, Martins et al. [23] applied the SEIR mathematical model to study leptospirosis outbreaks within urban settings by including environmental factors in the model. The result indicated that flooding and defective drainage systems increase the number of susceptible people exposed to the infection. The model prediction was that a locality prone to frequent flooding recorded higher transmission rates, which might be reduced with infrastructural improvement and awareness creation during the rainy season. Further, Holt et al. [24] and Daud et al. [25] used a modified SEIR model to test for the possibility of human-to-human transmission of leptospirosis in Southeast Asia, where it is endemic. The model showed that such a mode of transmission between humans is minimal since most of the cases are due to exposure to environmentally contaminated environments. It is due to this reason that the study advocated for the control of the environment, more especially in these cases where people are densely populated, as a means of preventing an outbreak [24].

3.3. SEIR Model Application on Leptospirosis Transmission

In the study by Castro et al. [26], a vaccination parameter was introduced in the SIR model to look at how a potential leptospirosis vaccination could affect outbreak dynamics. This model estimation showed that vaccination covering 70%–80% among at-risk populations would reverse much of the impact of the disease. It also highlighted the need to maintain high vaccine coverage in case of the reemergence of the disease [25].

Vinetz et al. [27] developed a cost-effectiveness model on the most effective and affordable intervention strategy on leptospirosis, such as vaccination, sanitary improvements, and rodent control. The findings were that in areas where the vaccine had a substantial impact in reducing the incurred leptospirosis cases, the best cost-effective method was in a fusion with vaccination and sanitary intervention. The study also pointed out the use of local economic circumstances as a factor in guiding the planning of intervention programs [26].

Kamath et al. [28] applied machine learning techniques using environmental, demographic, and clinical data in forecasting leptospirosis outbreaks. The model had a strong ability to predict the disease outbreaks and important factors, including rainfall patterns and the population density of settlements located near water sources. The report also revealed that machine learning could become a key part of early warning systems and targeted public health interventions.

According to Levett [29], in their study, leptospirosis is often misdiagnosed since its symptoms are similar to other febrile diseases including malaria, dengue fever, and typhoid. The study found that, in particular, sensitivity and specificity related to serological diagnostic tests are quite common and delay accurate diagnosis. The conclusion made was that advancement in diagnostic methods and increased awareness among healthcare professionals about leptospirosis are important in reducing misdiagnosis. Better diagnostic tests and improved differential diagnosis practices could help identify the disease more accurately and timely.

Costa et al. [1] in their research indicated that in tropical and subtropical regions where leptospirosis is highly prevalent, patients are frequently misdiagnosed with other infectious diseases because their symptoms are similar. This misdiagnosis results in incorrect treatments and poor prognosis of patient outcomes. The study noted that leptospirosis is often overlooked in differential diagnoses, despite being prevalent in these regions. The study recommended including leptospirosis in the differential diagnosis for patients with febrile illnesses in endemic regions and suggested enhancing diagnostic capabilities and training for healthcare professionals to reduce misdiagnosis rates.

According to the World Health Organization (WHO) [30], their report highlighted that misdiagnosis of malaria, especially in regions with a high prevalence of the disease, can result in incorrect treatments, increased illness, and higher mortality rates. It pointed out that many cases are mistakenly identified as other illnesses, placing a considerable strain on healthcare systems. The WHO report concluded by advocating for better diagnostic infrastructure, more comprehensive training for healthcare providers, and greater access to diagnostic tools to reduce misdiagnosis and improve treatment outcomes.

SEIR model has been used in this study to estimate the reproductive number for leptospirosis, which is an important parameter showing the average number of secondary cases due to a typical infective case. This was evident in Sri Lanka where researchers calculated a reproductive number of 1.8–2.3 in the course of the peak outbreak periods. The reproductive number estimates were useful in the identification of areas with high transmission rates, which were targeted for control measures.

SEIR models were applied in order to predict the effectiveness of interventions like vaccination, control of rodents, and increased public awareness. This kind of intervention was seen in Malaysia where an SEIR model assessed the effect of mass vaccination in flood-affected regions. The model used proposed that the number of infections could be cut by 50% by a combination of vaccination and control of rodents.

3.4. Application of Agent-Based Models in Leptospirosis Transmission Dynamics

3.5. Environmental Reservoir Interactions

The models incorporate the population of rodents and the environmental reservoirs of the bacterium as major determinants in leptospirosis. Illustratively, the investigation in Brazil conducted an ABM to model rodent–human interactions in slum settings with contaminated environments. They showed rodent control combined with public health education and improving sanitation led to a marked decrease in human infections [33–35].

3.6. Disaster Preparedness and Response

ABMs were important in disaster-prone regions for predicting the effects that natural disasters have on outbreaks of leptospirosis. Other applications have included the use of an ABM immediately after Hurricane Maria in Puerto Rico to predict how human displacement and flood contamination would affect disease spread. The results obtained have informed decisions on resource allocation, including deploying mobile health clinics in high-risk areas [36].

3.7. Incorporation of Diagnostic Accuracy Into Models

Embedding diagnostic accuracy in the models of leptospirosis, all in all, will offset the accuracy of model prediction and estimation of burden. False positives and false negatives are the common errors of diagnostics that largely skew model outputs and public health policy decisions [37, 38]. Recent modeling work has been on approaches that include diagnostic uncertainty through probability and sensitivity analysis, given the existing deficiencies in diagnostics. It would allow the researcher to develop more realistic strategies to manage and control the disease by accounting for diagnostic accuracy within the simulation [38, 39]. Mathematical modeling has achieved greater importance in understanding the dynamics of how infectious diseases are transmitted. These can help predict outbreaks, assess interventions, and estimate disease burden. In leptospirosis, several models have considered the environmental conditions, host species, and human behavior.

3.8. Impact of Misdiagnosis on Disease Modeling

Misdiagnosis can be a major problem undermining the precision and validity of disease models. Instances of infectious disease may either be under- or misrepresented if a case of misidentification of cases happens. This can spur skewed predictions; therefore, public health efforts may not be effective. Such misclassifications can lead to incorrect estimation of the prevalence, transmission rates, and effectiveness of the control efforts regarding the success of the control efforts and resource distribution. Disease models depend extensively on the precision and accuracy of the diagnostic data; errors in diagnosis may, therefore, severely distort model outcomes, leading to the derivation of erroneous conclusions and hence dangerous action [40–43].

Failure to diagnose can affect vaccine development and the public health. Misdiagnosis results to wrong estimations of the disease prevalence, the effectiveness of vaccines, and safety, hence causing negative impacts to vaccination campaigns. Disease surveillance is important in ascertaining the disease incidence and its distribution in the population. Misdiagnosis can change the epidemiological statistics and develop faulty public health measures. For example, in measles outbreaks, clinical misdiagnosis has been found to decrease healthcare workers’ confidence in measles vaccine and thus the importance of laboratory confirmation in diagnosis and maintaining confidence in vaccination programs [44]. Inaccurate diagnoses can result in wrong assessments of the effectiveness of vaccines. If a disease is misdiagnosed, the efficacy of a vaccine may be over- or underestimated in vaccine implementation decisions and among the masses. Moreover, misdiagnosis leads to wrong labeling of negative outcomes to vaccines, which affect public perception and acceptance. For instance, in connection with the COVID-19 virus, doubts about reinfection were sometimes related to mistakes made in the laboratory, which is why accurate diagnosis is essential to evaluate the effectiveness of the vaccine [30]. Failure to diagnose can lead to the creation of wrong information which may be a cause of people not taking vaccines. Misinformation about the vaccines can quickly circulate to the public resulting in lower vaccination rates and subsequent disease outbreaks. The WHO states that people should have correct information about vaccines and their role in preventing disease, countering myths about vaccination [45]. For instance, during the COVID-19 outbreak, some patients presented with symptoms similar to COVID-19 and were diagnosed with leptospirosis, hence confusing the disease surveillance and control [46]. The vaccines that should be produced must be effective against common Leptospira serovars in a specific region. Misdiagnosis complicates the identification of circulating strains, hence why vaccines may not protect adequately. The many serovars of Leptospira are another challenge, because protection from vaccines depends on the serovar. When diagnosis is imprecise, the formulation of vaccines that provide cross-protection is challenging [47].

On resource distribution, when the disease is diagnosed as another illness, it will be treated as such, meaning that it will be treated with the wrong drugs and other medical necessities, hence a misuse of commodities. For instance, managing suspected dengue or COVID-19 cases with antibiotics for leptospirosis does not only not treat the bacterial infection but also wastes resources that could be used for actual viral cases. This misallocation can put great pressure on healthcare facilities, particularly during times of febrile disease outbreaks. Leptospirosis has a significant economic burden. In Brazil, the study estimated that the total economic losses due to leptospirosis were $4.33 million in terms of lost wages and approximately $157,000 for hospitalization in 2010. For Manila, Philippines, the total economic value of avoiding leptospirosis was put at $124.97 million annually or 1.13% of Metro Manila’s gross domestic product. These statistics show that leptospirosis is a costly disease to healthcare facilities and that proper diagnosis can greatly prevent such expenses. In resource-limited settings, due to the unavailability of sophisticated diagnostic tools, the issue of misdiagnosis is compounded. Studying clinical features only may result in misdiagnosis due to vagueness of symptoms and, therefore, incorrect treatment. It has been recommended that diagnostic scoring models should be developed from clinical features and routine laboratory investigations in order to enhance the diagnostic capabilities in such environments [48].

3.9. Implication of Misdiagnosis on National Public Health Policy

On national health policy, there was an underestimation of leptospirosis disease burden because misdiagnosis leads to underreporting of leptospirosis cases, hence an underestimation of the disease burden. Such underestimation makes health authorities underestimate the disease in the national health priorities, hence providing inadequate funding and control measures for leptospirosis. According to the WHO, there is a need to come up with the correct burden estimates to help in the formulation of the public health policy on the control and prevention of leptospirosis [49]. Inadequate reporting of leptospirosis is usually attributed to clinical misdiagnosis. The disease presents clinical signs that are indistinguishable from other diseases, hence misclassification and underreporting. This means that much of the data used is not accurate, and this affects the ability to design and implement good surveillance systems that are crucial when it comes to disease trends and the need to take action [50].

The misdiagnosis factor in leptospirosis disease distorts the ethical principle of human rights, including the misallocation of resources, for example, the wasting of resources such as medication and hospital beds towards treating conditions patients do not have. This often burdens healthcare systems, particularly in outbreaks. Disproportionately, low-income regions face limited healthcare infrastructures due to this resource misallocation, an ethical concern pertaining to issues of justice and equity of access to care.

In a desire to control leptospirosis disease burden, the governments and health institutions have a moral obligation through investment in diagnostic tools and training programs to reduce misdiagnosis. Besides, ethical public health policy requires transparency in case reporting, including uncertainty in diagnosis and community education about leptospirosis and symptoms of the disease that may enable individuals to seek timely care; this also addresses ethical and practical challenges of disease control.

Sensitivity analysis cannot be overlooked for efforts in understanding how the models of diseases are influenced by diagnostic errors. By changing the levels of diagnostic accuracy to be used in model simulations, researchers analyze the stability of model outcomes and compare the effects of different types of diagnostic errors on model outputs. For instance, recent research has used sensitivity analyses to study the impacts of false-positive and false-negative diagnoses on model results for diseases like leptospirosis and dengue fever [51, 52]. In these analyses, we can now expect the magnitude of the discrepancy introduced by the inaccuracy in the diagnosis that could eventually improve both model development and public health strategies.

4.1. Underestimation of Disease Burden in Brazil

Brazil has one of the highest burdens of leptospirosis in the world, especially due to urban flooding and the fact that the symptoms of leptospirosis overlap with those of dengue fever, malaria, and influenza that are prevalent diseases in the region; hence, misdiagnosis usually occurs.

It has been shown that during outbreaks, over 40% of the cases of leptospirosis were misdiagnosed as dengue, thus delaying treatments and misallocating resources. Definitive diagnostic tools, like the MAT, were available only in a few specialized centers, thus leaving many cases undiagnosed, and in resource-poor settings; diagnosis based on clinical presentation without confirmation through laboratory tests further contributed to underreporting. Other frequently faced issues included rapid urbanization along with poor sanitation conditions in the urban centers; these were factors that truly favored the incubation of favorable environments for leptospirosis transmission. In periods of the rainy seasons, flooding heightened the disease transmissions, but most of those cases were unrecorded simply because the then-existing surveillance system was very weak.

This study demonstrated that the actual number of leptospirosis cases in urban centers was three to five times higher than officially reported. For instance, in São Paulo and Rio de Janeiro, misdiagnosis as dengue accounted for part of the underreporting of leptospirosis, and high-risk population groups, such as residents of informal settlements, were disproportionately affected. In assessing the economic impact, the study estimated that misdiagnosed and untreated leptospirosis cases contributed to a higher economic burden, including lost productivity and increased healthcare costs. Also, hospitalization costs alone for severe cases were reported to exceed $157,000 annually in regions with recurrent outbreaks.

These studies validated the data by performing retrospective reviews of hospital and public health records for cases of leptospirosis that may have been misdiagnosed as dengue or other febrile illnesses. We did some cross-referencing of clinical symptoms with laboratory-confirmed test results to help validate the suspected misdiagnoses. Researchers further developed models using environmental data such as rainfall, flooding patterns, and rodent population densities to estimate the prevalence of leptospirosis more accurately. These models were then validated by predicting cases against the usually laboratory-confirmed cases from the sentinel surveillance systems.

In conclusion, mathematical models have been created in Brazil that can forecast the outbreaks of leptospirosis based on rainfall, flooding patterns, and rodent populations. These models have assisted in better resource allocation of healthcare resources during the rainy season. There is a need for accuracy in diagnostic tools. Improvement in access to diagnostic tools and integration of point-of-care testing into primary healthcare systems will go a long way in reducing underreporting. Even providing enhanced training for healthcare workers to recognize the clinical features of leptospirosis could also minimize diagnostic errors. Lastly, on policy implications, the findings call for increased investment in leptospirosis control programs, including vaccination campaigns in high-risk areas. At the same time, national health policies must acknowledge leptospirosis as a priority public health issue, alongside other endemic diseases like dengue and malaria to minimize the spread and transmission of leptospirosis disease [53–56].

4.2. Philippines: Postdisaster Public Health Response

The Philippines is a country often exposed to typhoons and flooding, which gives very good conditions for outbreaks of leptospirosis. In 2009, following Typhoon Ketsana, more than 3000 cases of leptospirosis were reported, many of them initially misdiagnosed as influenza.

These include resource-constrained health facilities that exacerbated the problem of misdiagnosis and delayed the national response; clinical misdiagnosis, which led to delays in appropriate treatment, contributing to increased morbidity and mortality; and diagnostic limitations, where a number of healthcare facilities lacked point-of-care diagnostic tools, especially in rural and disaster-affected areas. Other limitations include confirmation testing, mostly through MATs, which were unavailable in most settings; thus, only clinical diagnosis could be relied upon. The true magnitude of the outbreaks of leptospirosis was not captured by the existing disease surveillance systems because of underreporting and misclassification, and this collection of data in disasters was also inconsistent and not standardized, hence increasing the spread and transmission of leptospirosis disease. A very similar outbreak ensued after Typhoon Haiyan in 2013 and following other devastating flood cases. Contaminated floodwater with rodent urine was determined to be the major vehicle for Leptospira bacteria.

Poor drainage systems in urban areas further aggravated the problem. They also realized that the urban slum dwellers living in flood-prone areas and having poor access to healthcare were disproportionately affected, and the use of prophylactic administration of doxycycline during disasters significantly reduced the incidence of severe leptospirosis in high-risk populations. Researchers validated their data through epidemiological investigations conducted by the DOH and international partners, such as the WHO. These studies estimated the true burden of leptospirosis using hospital records, community surveys, and mortality reports. The country has since adopted GIS-based models mapping high-risk areas to monitor the disease for more targeted interventions, including prophylactic antibiotic distribution during disasters. Lastly, there is a dire need for expansion in access to rapid diagnostic tools, especially in disaster-prone and rural areas. Point-of-care tests should be deployed to improve diagnostic accuracy and reduce dependence on clinical suspicion alone. Lastly, public education campaigns about the transmission of leptospirosis, symptoms, and preventive measures should be prioritized with response efforts, focusing on avoiding floodwaters, wearing protective gear, and seeking medical care early [20–22, 57–65].

4.3. Rural and Urban Disparities in India

In India, leptospirosis especially occurs in the rural population involved in agricultural activities. The rural leptospirosis details of Tamil Nadu, Kerala, and Maharashtra show that agricultural workers are the main group affected by rural leptospirosis. The study done in Tamil Nadu in 2020 revealed that 70% of the cases of leptospirosis were due to farming. Urban cases have also been observed in places such as Mumbai during monsoon seasons. Leptospirosis incidence is higher in urban areas, especially in slum areas due to poor handling of sewage. Targeted interventions such as providing doxycycline during flooding events in Surat showed that the incidence of leptospirosis could be cut by 60%. This often happens mainly because there are few diagnostic facilities in the rural areas. Most are presented as malaria or typhoid, which hampers the right treatment and raises death rates. Therefore, India has started using community-based data collection to augment machine learning algorithms in outbreak prediction and resource allocation in both the urban and rural areas [66–69].

5.1. Recent Advances in Diagnostic Techniques

Recent advances in diagnostics have improved the accuracy, time of detection, and reliability of disease diagnostics. The paradigm shift in this approach was the adoption of massive parallel sequencing for comprehensive pathogen genome analysis. NGS has revolutionized the identification of genetic variation and the discovery of novel pathogens with unprecedented specificity and sensitivity [70]. Enhanced by technology, infectious disease diagnosis is now able to provide in-depth genetic profiles that will be useful in identifying and understanding pathogen evolution [71].

It should also be mentioned that significant advancements were seen in molecular diagnostic technologies, particularly the PCR and newer technologies based on it. Real-time and digital PCRs improve precision and sensitivity of quantification [72]. The latter improvements are vital for pathogens that are present in low numbers and for close surveillance with disease progression. Moreover, loop-mediated isothermal amplification (LAMP) is considered a potent method, since it is fast to execute, provides rapid results, and is robust under diverse conditions [73].

Technological advancement has also mobilized immunoassays. Advances made with immunoassays, such as those by the lateral flow, as well as those multiplexed, have realized a much-improved capacity to detect many biomarkers from a single sample [74]. These certainly allow for a very rapid, precise disease diagnosis, a feat most suitable for point-of-care settings [75]. In addition, integration of nanotechnology with biosensors has enabled much higher sensitivity and specificity in immunoassays, thus offering the capacity to detect pathogens at very low concentrations [76].

The further incorporation of new diagnoses to be used in the epidemiological model is important for the further refinement and improvement of the prediction of the disease status and control approaches. With improved diagnostics, the model results and public health strategies for this zoonotic disease can be greatly enhanced.

More recently, models for leptospirosis have been based on new diagnostic techniques that more accurately reflect test performance, hence improving the models’ predictive capacity. To this end, high-throughput sequencing has also supported detailed characterization of Leptospira strains and their genetic diversity, which can be combined into models seeking to understand the impact of genetic differences with the bacteria on transmission patterns and disease severity [77]. The relevant genetic data includes enriching epidemiological models with regard to pathogen evolution and possible changes in virulence.

The application of advanced PCR techniques, such as the real-time PCR and digital PCR technologies in the leptospirosis models, will make it possible to measure pathogen load with accuracy and, hence, get better prevalence estimates [78]. Implementation of such techniques allows for fining up of occurrence measures and better estimation of intervention effectiveness. Moreover, the use of multiplexed immunoassays and biosensors in the modeling of leptospirosis further creates a solution to these coinfections and the ability to differentiate leptospirosis from other febrile diseases [79]. With the capacity to deliver findings for several biomarkers at the same time, it has highly improved the accuracy in diagnostics and minimized errors in diagnosis. Integration of these diagnostic approaches with mathematical models provides better predictions on the spread and impact of diseases, hence more effective control.

5.2. Recommendation

Disease models can include diagnostic uncertainty to improve dependability and robustness. Diagnostic uncertainty has a great impact on model forecasts and thus leads to a serious effect on public health strategies, particularly the effects caused by false positives and false negatives. To address the situation, more and more researchers have taken to probabilistic and sensitivity analysis methods. The underlying uncertainties from the diagnostic data are tackled through the use of probabilistic models, which involve the introduction of variations in diagnostic accuracy as one of the components of the model parameters. This method makes the potential outcome estimation better while strongly increasing the reliability of its predictions [80, 81].

Sensitivity analysis is another crucial method for testing the way the changes in diagnostic accuracy influence what is found in the outcome. Sensitivity analysis, whereby diagnostic parameters are changed progressively one at a time and then the result is investigated, can pinpoint which aspect of diagnostic uncertainty will impact the predictions and, thus, which area would require improvement [82, 83]. Model sensitivity is further assessed using Bayesian and Monte Carlo methods, and the approach is updated to integrate diagnostic uncertainty to make epidemiological predictions [84, 85].

The incorporation of diagnostic uncertainty in disease models has important policy implications. Popular disease models that represent diagnostic inaccuracies cater to better public health interventions and better resource distribution. For example, a fine-scale appreciation of how diagnostic uncertainty is expected to manifest would be useful in enabling policymakers to allocate resources in a more efficient way and plan for targeted interventions in the face of uncertain disease surveillance [19, 86]. Moreover, by integrating diagnostic uncertainty within the models, it will drive the development of better diagnostic tools and protocols in order to enhance overall disease surveillance and control [87, 88].

There was importance of clear communication on model uncertainties between the researcher and the policymakers. Explicit reporting, conveying the influence of diagnostic uncertainties on model predictions, provides background for making informed and rational choices in the decisions on public health responses. It will make sure that the decisions by the policymakers do not simply arise from a clear understanding of the uncertainties but also better prepare them; such that if there are any unexpected outcomes, they will not be as challenging as expected [89].

Personalized medicine approaches can bring up manifold promises in modeling leptospirosis with basis from this study, especially diagnostic misdiagnosis studies. Personalized medicine refers to the optimization of treatment that a patient can benefit from, making it special and individualistic, based on genetic, environmental, and lifestyle characteristics. Integration of personalized medicine into models for leptospirosis will further enhance the predictions upon consideration of individual patient data and variability [90, 91].

Personalized variables, such as genetic predisposition or personalized response to infections, may then inform the models to be more reflective of the diversity of impact that can occur within a diverse population with leptospirosis. This would allow more accurate predictions or estimations in terms of progression of the disease and treatment outcomes, respectively [92, 93]. Personalized approaches may also inform those at greatest risk of severe outcomes and/or misdiagnosis, allowing for targeted interventions and improved management strategies [94, 95].

Individualized treatment can further be extrapolated to create and provide tailor-made diagnostic and treatment protocols so as to address any given profile of an individual. Customization can have the characteristics of an improvement in the accuracy of diagnosis, less likelihood to misdiagnose, and efficacy of treatment [96, 97]. With diagnostic armamentarium and techniques for personalized medicine moving forward, we can probably expect their integration into leptospirosis models to lead to more accuracy, and public health strategies will be more effective based on respective interventions.