Spatio-Temporal Analysis of Leptospirosis Hotspot Areas and Its Association With Hydroclimatic Factors in Selangor, Malaysia: Protocol for an Ecological Cross-sectional Study (Preprint) Article Swipe
Muhammad Akram Ab Kadir
,
Rosliza Abdul Manaf
,
Siti Aisah Mokhtar
,
Luthffi Idzhar Ismail
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.43712
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.2196/preprints.43712
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 PRR1-10.2196/43712
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.2196/preprints.43712
- OA Status
- gold
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4317898402
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https://openalex.org/W4317898402Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2196/preprints.43712Digital Object Identifier
- Title
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Spatio-Temporal Analysis of Leptospirosis Hotspot Areas and Its Association With Hydroclimatic Factors in Selangor, Malaysia: Protocol for an Ecological Cross-sectional Study (Preprint)Work title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-12-05Full publication date if available
- Authors
-
Muhammad Akram Ab Kadir, Rosliza Abdul Manaf, Siti Aisah Mokhtar, Luthffi Idzhar IsmailList of authors in order
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https://doi.org/10.2196/preprints.43712Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.2196/preprints.43712Direct OA link when available
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Geography, Leptospirosis, Hotspot (geology), Georeference, Geographic information system, Longitude, Cartography, Mekong river, Latitude, Environmental health, Socioeconomics, Physical geography, Veterinary medicine, Medicine, Biology, Geophysics, Paleontology, Sociology, Geology, Structural basin, GeodesyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.study | 64, 90, 139, 352, 390 |
| abstract_inverted_index.their | 71, 191 |
| abstract_inverted_index.tool. | 238 |
| abstract_inverted_index.using | 114, 152, 234 |
| abstract_inverted_index.would | 77, 184 |
| abstract_inverted_index.</sec> | 86, 130, 327, 386, 432, 438 |
| abstract_inverted_index.ArcGIS | 219, 301 |
| abstract_inverted_index.Health | 172 |
| abstract_inverted_index.Phyton | 314 |
| abstract_inverted_index.Survey | 215 |
| abstract_inverted_index.change | 61 |
| abstract_inverted_index.design | 423 |
| abstract_inverted_index.funded | 333 |
| abstract_inverted_index.health | 84, 427 |
| abstract_inverted_index.images | 241 |
| abstract_inverted_index.layers | 297 |
| abstract_inverted_index.level. | 385 |
| abstract_inverted_index.levels | 280 |
| abstract_inverted_index.mapped | 233 |
| abstract_inverted_index.model. | 129, 326, 413 |
| abstract_inverted_index.neural | 308 |
| abstract_inverted_index.obtain | 264, 321 |
| abstract_inverted_index.public | 83, 426 |
| abstract_inverted_index.remote | 120, 145 |
| abstract_inverted_index.rivers | 205 |
| abstract_inverted_index.source | 181 |
| abstract_inverted_index.system | 118, 143 |
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| abstract_inverted_index.100,000 | 46 |
| abstract_inverted_index.January | 160 |
| abstract_inverted_index.average | 266, 276 |
| abstract_inverted_index.between | 48, 96 |
| abstract_inverted_index.climate | 60 |
| abstract_inverted_index.degrees | 272 |
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| abstract_inverted_index.disease | 8, 15, 67, 80, 430 |
| abstract_inverted_index.endemic | 14 |
| abstract_inverted_index.enhance | 79, 425 |
| abstract_inverted_index.examine | 93 |
| abstract_inverted_index.explore | 392 |
| abstract_inverted_index.factors | 76, 111, 375, 402 |
| abstract_inverted_index.further | 78, 304 |
| abstract_inverted_index.hotspot | 68, 102, 127, 324, 397 |
| abstract_inverted_index.mapping | 147 |
| abstract_inverted_index.monthly | 267, 277 |
| abstract_inverted_index.network | 309 |
| abstract_inverted_index.regions | 11 |
| abstract_inverted_index.results | 349 |
| abstract_inverted_index.sensing | 121, 146 |
| abstract_inverted_index.surface | 246, 379 |
| abstract_inverted_index.website | 260 |
| abstract_inverted_index.America, | 24 |
| abstract_inverted_index.Celsius. | 273 |
| abstract_inverted_index.December | 163, 345 |
| abstract_inverted_index.Drainage | 288 |
| abstract_inverted_index.Giovanni | 253 |
| abstract_inverted_index.Malaysia | 39 |
| abstract_inverted_index.Mapping. | 217 |
| abstract_inverted_index.National | 254 |
| abstract_inverted_index.November | 336 |
| abstract_inverted_index.Selangor | 113, 158, 170, 207, 404 |
| abstract_inverted_index.Southern | 25 |
| abstract_inverted_index.acquired | 250 |
| abstract_inverted_index.analysis | 149, 310, 342 |
| abstract_inverted_index.climates | 20 |
| abstract_inverted_index.clusters | 367 |
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| abstract_inverted_index.frequent | 59 |
| abstract_inverted_index.increase | 34 |
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| abstract_inverted_index.obtained | 167, 210, 283 |
| abstract_inverted_index.possible | 180 |
| abstract_inverted_index.rainfall | 243 |
| abstract_inverted_index.research | 331 |
| abstract_inverted_index.software | 221, 302, 315 |
| abstract_inverted_index.thematic | 296 |
| abstract_inverted_index.tropical | 19 |
| abstract_inverted_index.zoonotic | 7 |
| abstract_inverted_index.EarthData | 259 |
| abstract_inverted_index.Getis-Ord | 236 |
| abstract_inverted_index.Malaysia, | 57 |
| abstract_inverted_index.Selangor, | 56 |
| abstract_inverted_index.Southeast | 28 |
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| abstract_inverted_index.analysis. | 305 |
| abstract_inverted_index.commenced | 343 |
| abstract_inverted_index.countries | 17 |
| abstract_inverted_index.dynamics, | 62 |
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| abstract_inverted_index.infection | 183 |
| abstract_inverted_index.longitude | 192 |
| abstract_inverted_index.neglected | 6 |
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| abstract_inverted_index.rainfall, | 377 |
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| abstract_inverted_index.temperate | 10 |
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| abstract_inverted_index.boundaries | 200 |
| abstract_inverted_index.clustering | 228 |
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| abstract_inverted_index.considered | 4 |
| abstract_inverted_index.consisting | 197 |
| abstract_inverted_index.ecological | 137 |
| abstract_inverted_index.identified | 186 |
| abstract_inverted_index.increasing | 53 |
| abstract_inverted_index.latitudes. | 194 |
| abstract_inverted_index.population | 47 |
| abstract_inverted_index.predictive | 128, 325, 412, 416 |
| abstract_inverted_index.techniques | 122 |
| abstract_inverted_index.Aeronautics | 255 |
| abstract_inverted_index.Department. | 173 |
| abstract_inverted_index.Irrigation. | 290 |
| abstract_inverted_index.Topographic | 195 |
| abstract_inverted_index.association | 72, 95 |
| abstract_inverted_index.collection, | 339 |
| abstract_inverted_index.constructed | 415 |
| abstract_inverted_index.development | 408 |
| abstract_inverted_index.hydrometric | 279, 384 |
| abstract_inverted_index.information | 117, 142 |
| abstract_inverted_index.initiatives | 428 |
| abstract_inverted_index.millimeters | 270 |
| abstract_inverted_index.potentially | 419 |
| abstract_inverted_index.prevention. | 431 |
| abstract_inverted_index.processing, | 340 |
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| abstract_inverted_index.temperature | 247 |
| abstract_inverted_index.associations | 394 |
| abstract_inverted_index.distribution | 99, 203, 365 |
| abstract_inverted_index.geographical | 116, 141 |
| abstract_inverted_index.intelligence | 313 |
| abstract_inverted_index.subsequently | 406 |
| abstract_inverted_index.surveillance | 81 |
| abstract_inverted_index.temperature, | 380 |
| abstract_inverted_index.Leptospirosis | 2, 155 |
| abstract_inverted_index.georeferenced | 188 |
| abstract_inverted_index.hydroclimatic | 75, 110, 374, 401 |
| abstract_inverted_index.leptospirosis | 36, 101, 126, 151, 323, 364, 396, 411 |
| abstract_inverted_index.significantly | 370 |
| abstract_inverted_index.Administration | 258 |
| abstract_inverted_index.REPORT</title> | 436 |
| abstract_inverted_index.interventions. | 85 |
| abstract_inverted_index.cross-sectional | 138 |
| abstract_inverted_index.spatio-temporal | 98 |
| abstract_inverted_index.PRR1-10.2196/43712 | 437 |
| abstract_inverted_index.<title>INTERNATIONAL | 434 |
| abstract_inverted_index.Laboratory-confirmed | 174 |
| abstract_inverted_index.<title>METHODS</title> | 132 |
| abstract_inverted_index.<title>RESULTS</title> | 329 |
| abstract_inverted_index.<title>OBJECTIVE</title> | 88 |
| abstract_inverted_index.<title>BACKGROUND</title> | 1 |
| abstract_inverted_index.<title>CONCLUSIONS</title> | 388 |
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| institutions_distinct_count | 4 |
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| citation_normalized_percentile.is_in_top_10_percent | False |