PMID- 37184897
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230601
IS  - 1929-0748 (Print)
IS  - 1929-0748 (Electronic)
IS  - 1929-0748 (Linking)
VI  - 12
DP  - 2023 May 15
TI  - Spatio-Temporal Analysis of Leptospirosis Hotspot Areas and Its Association With 
      Hydroclimatic Factors in Selangor, Malaysia: Protocol for an Ecological 
      Cross-sectional Study.
PG  - e43712
LID - 10.2196/43712 [doi]
LID - e43712
AB  - 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.
CI  - (c)Muhammad Akram Ab Kadir, Rosliza Abdul Manaf, Siti Aisah Mokhtar, Luthffi Idzhar 
      Ismail. Originally published in JMIR Research Protocols 
      (https://www.researchprotocols.org), 15.05.2023.
FAU - Ab Kadir, Muhammad Akram
AU  - Ab Kadir MA
AUID- ORCID: 0000-0002-8483-3469
AD  - Department of Community Health, Faculty of Medicine and Health Sciences, 
      Universiti Putra Malaysia, Selangor, Malaysia.
FAU - Abdul Manaf, Rosliza
AU  - Abdul Manaf R
AUID- ORCID: 0000-0003-1488-1235
AD  - Department of Community Health, Faculty of Medicine and Health Sciences, 
      Universiti Putra Malaysia, Selangor, Malaysia.
FAU - Mokhtar, Siti Aisah
AU  - Mokhtar SA
AUID- ORCID: 0000-0001-5639-6188
AD  - Department of Community Health, Faculty of Medicine and Health Sciences, 
      Universiti Putra Malaysia, Selangor, Malaysia.
FAU - Ismail, Luthffi Idzhar
AU  - Ismail LI
AUID- ORCID: 0000-0003-4362-0907
AD  - Department of Electrical & Electronic Engineering, Faculty of Engineering, 
      Universiti Putra Malaysia, Selangor, Malaysia.
LA  - eng
PT  - Journal Article
DEP - 20230515
PL  - Canada
TA  - JMIR Res Protoc
JT  - JMIR research protocols
JID - 101599504
PMC - PMC10227704
OTO - NOTNLM
OT  - GIS
OT  - Selangor
OT  - geographical information system
OT  - hotspot areas
OT  - hydroclimatic factors
OT  - leptospirosis
OT  - predictive model
COIS- Conflicts of Interest: None declared.
EDAT- 2023/05/15 13:06
MHDA- 2023/05/15 13:07
PMCR- 2023/05/15
CRDT- 2023/05/15 11:52
PHST- 2022/12/05 00:00 [received]
PHST- 2023/04/30 00:00 [accepted]
PHST- 2023/04/27 00:00 [revised]
PHST- 2023/05/15 13:07 [medline]
PHST- 2023/05/15 13:06 [pubmed]
PHST- 2023/05/15 11:52 [entrez]
PHST- 2023/05/15 00:00 [pmc-release]
AID - v12i1e43712 [pii]
AID - 10.2196/43712 [doi]
PST - epublish
SO  - JMIR Res Protoc. 2023 May 15;12:e43712. doi: 10.2196/43712.