Spatio-Temporal Analysis of Leptospirosis Hotspot Areas and Its Association With Hydroclimatic Factors in Selangor, Malaysia: Protocol for an Ecological Cross-sectional Study

Background Leptospirosis is considered a neglected zoonotic disease in temperate regions but an endemic disease in countries with tropical climates such as South America, Southern Asia, and Southeast Asia. There has been an increase in leptospirosis incidence in Malaysia from 1.45 to 25.94 cases per 100,000 population between 2005 and 2014. With increasing incidence in Selangor, Malaysia, and frequent climate change dynamics, a study on the disease hotspot areas and their association with the hydroclimatic factors would further enhance disease surveillance and public health interventions. Objective This study aims to examine the association between the spatio-temporal distribution of leptospirosis hotspot areas from 2011 to 2019 with the hydroclimatic factors in Selangor using the geographical information system and remote sensing techniques to develop a leptospirosis hotspot predictive model. Methods This will be an ecological cross-sectional study with geographical information system and remote sensing mapping and analysis concerning leptospirosis using secondary data. Leptospirosis cases in Selangor from January 2011 to December 2019 shall be obtained from the Selangor State Health Department. Laboratory-confirmed cases with data on the possible source of infection would be identified and georeferenced according to their longitude and latitudes. Topographic data consisting of subdistrict boundaries and the distribution of rivers in Selangor will be obtained from the Department of Survey and Mapping. The ArcGIS Pro software will be used to evaluate the clustering of the cases and mapped using the Getis-Ord Gi* tool. The satellite images for rainfall and land surface temperature will be acquired from the Giovanni National Aeronautics and Space Administration EarthData website and processed to obtain the average monthly values in millimeters and degrees Celsius. Meanwhile, the average monthly river hydrometric levels will be obtained from the Department of Drainage and Irrigation. Data are then inputted as thematic layers and in the ArcGIS software for further analysis. The artificial neural network analysis in artificial intelligence Phyton software will then be used to obtain the leptospirosis hotspot predictive model. Results This research was funded as of November 2022. Data collection, processing, and analysis commenced in December 2022, and the results of the study are expected to be published by the end of 2024. The leptospirosis distribution and clusters may be significantly associated with the hydroclimatic factors of rainfall, land surface temperature, and the river hydrometric level. Conclusions This study will explore the associations of leptospirosis hotspot areas with the hydroclimatic factors in Selangor and subsequently the development of a leptospirosis predictive model. The constructed predictive model could potentially be used to design and enhance public health initiatives for disease prevention. International Registered Report Identifier (IRRID) PRR1-10.2196/43712


Justification of 'selected disease link to climate' need to be strengen
The rationale for leptospirosis linked with the environmental factors studied in this research (the hydroclimatic factors) was explained in the paragraphs in the introduction and literature review chapters.
The following paragraphs were added to the Grant Application Form: 1.3 Problem statements, 1.4 Significance of study 1.5 Leptospirosis disease 1.6 The transmission cycle 1.7 Leptospirosis hotspot 9, 10, and 11 2 The factors that contributes to Leptospirosis are many e.g. student central, sanitation level, food hygiene, waste diagnosedwhich seem to be more important to study The reason for the factors mentioned studied in the research was explained in the problem statement section.
Based on the epidemiological triad, this research focuses on environmental factors. Besides, this study utilises tools that analyses climate factors that include rainfall and temperature from satellite data and river hydrometric levels data from the Department of Irrigation and Drainage (DID).
Other factors that contributes to leptospirosis incidence were not included in this research. This limitation would be an opportunity for future researchers to explore.

and 8 3
The analysis seems to be very complex and not actually taught in any of our Dept's courses.
Analysis of data obtained will be processed with the supervision of supervisor and co-supervisors from the Department of Community Health (JKK) and the Department of Electrical & Electronic Engineering.
Statistical analysis of Moran's I, Getis-Ord Gi and GLMM using ArcGIS and SPSS Softwares, will be further studied from external lectures with the supervision of supervisors from JKK.
The analysis using the Python Software running the ANN analysis and modelling to develop the leptospirosis predictive model, will also be further studied from external lectures with the supervision of a supervisor from the Department of Electrical & Electronic Engineering.
Since the methodology and analysis using remote sensing and ANN analysis is relatively new in the public health field, it will be a pleasure to be shared with the department in the JKK's CME session and other occasions.

'similarity test' with method previous DrPH student research
To the best of the researcher's knowledge, this topic and methodology has never been used by DrPH candidate from the JKK Department, UPM.
Using remote sensing data in public health research would be a new field to explore in public health. Future researchers could also apply artificial intelligence analysis to predict events that may be of public health concern and importance.

To address the weakness mentioned
The weaknesses mentioned were addressed and discussed with the research team. Each weakness was addressed with the required actions and corrections. -6

Students need to show he understands the analysis (method & results) by explaining step by step of how they are done
Knowledge of the theories and application of the analysis will be gained from the supervisory committee and external sources, such as attending required courses. The research's general objective is to examine the spatio-temporal distribution of leptospirosis hotspot areas using GIS and remote sensing techniques. Thus the title begins with "Spatio-temporal analysis ". In addition, the research team has agreed on the research title. The research also obtained ethical approval from the NMRR on August 25, 2022.