PREreview del Molecular detection and genetic characterisation of a large flood-borne outbreak of human leptospirosis in Jakarta, Indonesia: a retrospective analysis of surveillance data

Summary

This study investigated a large leptospirosis outbreak in Jakarta, Indonesia. A total of 282 cases were reported, primarily affecting adult males in West Jakarta. 241 cases tested, 164 (68.0%) had a positive IgM-based rapid diagnostic test (RDT). Of 118 cases tested with TagMan RT-PCR lipL32, 32 (27.1%) were positive, and RT-PCR detected an additional 5 cases who were RDT-negative (case detection increased by 4.2%, all of whom had fever <7 days. The leptospira species identified were L. interrogans and L. borrgpetersenii.

Strength

• Novelty: first city-wide molecular characterization of flood-related leptospirosis in an urban setting in Indonesia, addressing a critical gap in low-resource regions where such surveillance data are limited.

• Integrated Approach: combines molecular species identification with epidemiological analysis, demonstrating the feasibility and value of molecular surveillance tools in resource-limited contexts.

• Despite the limitation, the comparison of RDT and RT-PCR provides valuable real-world evidence about diagnostic test performance at different stages of illness, contributing to the understanding of appropriate test utilization in outbreak settings

Major Revisions

Appropriateness of Comparing RDT and RT-PCR Results

RT-PCR is most effective in the first week of illness, while rapid antibody tests work better and are optimally used in later stages, after the body has begun producing an immune response. Thus, a direct comparison of RT-PCR and antibody-based rapid tests may not be methodologically appropriate since they are designed to detect infection at different points in the disease course

Recommendation: 

The author could make the findings clearer by showing the diagnostic performance of each test based on how long symptoms have been present, helping readers see how these tests complement each other at different stages of illness. It would also be valuable to explain more about the absence of a gold standard and to discuss how this limitation impacts the way results are interpreted. This would strengthen both the analysis and the overall transparency of the study.

Incidence Rate Calculation Methodology

The manuscript calculates incidence rates by dividing the number of cases over a 3-month period by the population and expresses the result per 100,000 person-years. This method may lead to an overestimation of incidence, as the calculation does not fully account for the shorter observation window compared to annual reporting standards.

Recommendation:

The author should report the incidence as “per 100,000 people per 3 months” to reflect the study’s actual observation period. This approach makes the findings easier to understand, allows readers to compare the numbers fairly with other studies, and shows transparency in the methods used.

Minor Revision

Limited Sample Size for Molecular Characterization

With only 5 typed samples out of 282 cases (1.8%), the molecular data are quite limited, and therefore cannot reliably identify the predominant Leptospira species in this outbreak. These results should be interpreted with caution

Recommendation: 

Instead of "predominant," the author could say “species detected in this limited sample,” making it clear that the results are based only on the small number of cases analyzed and should not be generalized to the whole outbreak population.

Study Flow Diagram (Figure 3) and Case Classification Sequencing

Since RDT and PCR are part of the case classification process for probable and confirmed cases, the order shown in Figure 3 (study flow diagram) could be misleading. The testing must logically occur before or during case classification, not afterward. Presenting RDT as a downstream step after initial grouping may misrepresent the actual diagnostic sequence and could confuse readers. The same concern applies to Table 1, where the number of RDT and PCR tests reported within each case category raises similar confusion.

Recommendation: 

Revise the flow diagram to accurately reflect the temporal sequence of diagnostic testing and case classification. Clarify the timing and sequencing in the methods section.

Epidemic Curve x-axis Labeling (Figure 1)

Figure 1, which presents the epidemic curve alongside daily precipitation data, provides a valuable visual connection between case peaks and rainfall events. However, the date labels on the x-axis are overly crowded, making it difficult for readers to interpret temporal trends.

Recommendation: 

Reduce the frequency of x-axis date labels to improve readability

Overall Assessment

This manuscript presents a valuable contribution to the understanding of flood-related leptospirosis outbreaks in urban settings in Indonesia. The epidemiological analysis is thorough and well-presented, and the integration of molecular data, despite its limitations, provides important proof of concept for molecular surveillance approaches in resource-limited contexts. 

Competing interests

The authors declare that they have no competing interests.

Use of Artificial Intelligence (AI)

The authors declare that they used generative AI to come up with new ideas for their review.