Spatio-temporal characterization of phenotypic resistance in malaria vector species Article Swipe
Eric Ali Ibrahim
,
Mark Wamalwa
,
John Odindi
,
Henri E. Z. Tonnang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12915-024-01915-z
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s12915-024-01915-z
Background Malaria, a deadly disease caused by Plasmodium protozoa parasite and transmitted through bites of infected female Anopheles mosquitoes, remains a significant public health challenge in sub-Saharan Africa. Efforts to eliminate malaria have increasingly focused on vector control using insecticides. However, the emergence of insecticide resistance (IR) in malaria vectors pose a formidable obstacle, and the current IR mapping models remain static, relying on fixed coefficients. This study introduces a dynamic spatio-temporal approach to characterize phenotypic resistance in Anopheles gambiae complex and Anopheles arabiensis . We developed a cellular automata (CA) model and applied it to data collected from Ethiopia, Nigeria, Cameroon, Chad, and Burkina Faso. The data encompasses georeferenced records detailing IR levels in mosquito vector populations across various classes of insecticides. In characterizing the dynamic patterns of confirmed resistance, we identified key driving factors through correlation analysis, chi-square tests, and extensive literature review. Results The CA model demonstrated robustness in capturing the spatio-temporal dynamics of confirmed IR states in the vector populations. In our model, the key driving factors included insecticide usage, agricultural activities, human population density, Land Use and Land Cover (LULC) characteristics, and environmental variables. Conclusions The CA model developed offers a robust tool for countries that have limited data on confirmed IR in malaria vectors. The embrace of a dynamical modeling approach and accounting for evolving conditions and influences, contribute to deeper understanding of IR dynamics, and can inform effective strategies for malaria vector control, and prevention in regions facing this critical health challenge.
Related Topics
Concepts
Malaria
Biology
Anopheles gambiae
Vector (molecular biology)
Anopheles
Robustness (evolution)
Vector control
Mosquito control
Population
Resistance (ecology)
Indoor residual spraying
Entomology
Ecology
Environmental health
Plasmodium falciparum
Genetics
Engineering
Immunology
Electrical engineering
Induction motor
Voltage
Recombinant DNA
Artemisinin
Medicine
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s12915-024-01915-z
- https://bmcbiol.biomedcentral.com/counter/pdf/10.1186/s12915-024-01915-z
- OA Status
- gold
- Cited By
- 3
- References
- 41
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4397293926
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4397293926Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s12915-024-01915-zDigital Object Identifier
- Title
-
Spatio-temporal characterization of phenotypic resistance in malaria vector speciesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-20Full publication date if available
- Authors
-
Eric Ali Ibrahim, Mark Wamalwa, John Odindi, Henri E. Z. TonnangList of authors in order
- Landing page
-
https://doi.org/10.1186/s12915-024-01915-zPublisher landing page
- PDF URL
-
https://bmcbiol.biomedcentral.com/counter/pdf/10.1186/s12915-024-01915-zDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://bmcbiol.biomedcentral.com/counter/pdf/10.1186/s12915-024-01915-zDirect OA link when available
- Concepts
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Malaria, Biology, Anopheles gambiae, Vector (molecular biology), Anopheles, Robustness (evolution), Vector control, Mosquito control, Population, Resistance (ecology), Indoor residual spraying, Entomology, Ecology, Environmental health, Plasmodium falciparum, Genetics, Engineering, Immunology, Electrical engineering, Induction motor, Voltage, Recombinant DNA, Artemisinin, Medicine, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
-
41Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.levels | 114 |
| abstract_inverted_index.model, | 167 |
| abstract_inverted_index.models | 60 |
| abstract_inverted_index.offers | 195 |
| abstract_inverted_index.public | 23 |
| abstract_inverted_index.remain | 61 |
| abstract_inverted_index.robust | 197 |
| abstract_inverted_index.states | 160 |
| abstract_inverted_index.tests, | 141 |
| abstract_inverted_index.usage, | 174 |
| abstract_inverted_index.vector | 37, 117, 163, 239 |
| abstract_inverted_index.Africa. | 28 |
| abstract_inverted_index.Burkina | 105 |
| abstract_inverted_index.Efforts | 29 |
| abstract_inverted_index.Results | 146 |
| abstract_inverted_index.applied | 94 |
| abstract_inverted_index.classes | 121 |
| abstract_inverted_index.complex | 81 |
| abstract_inverted_index.control | 38 |
| abstract_inverted_index.current | 57 |
| abstract_inverted_index.disease | 5 |
| abstract_inverted_index.driving | 135, 170 |
| abstract_inverted_index.dynamic | 71, 127 |
| abstract_inverted_index.embrace | 212 |
| abstract_inverted_index.factors | 136, 171 |
| abstract_inverted_index.focused | 35 |
| abstract_inverted_index.gambiae | 80 |
| abstract_inverted_index.limited | 203 |
| abstract_inverted_index.malaria | 32, 49, 209, 238 |
| abstract_inverted_index.mapping | 59 |
| abstract_inverted_index.records | 111 |
| abstract_inverted_index.regions | 244 |
| abstract_inverted_index.relying | 63 |
| abstract_inverted_index.remains | 20 |
| abstract_inverted_index.review. | 145 |
| abstract_inverted_index.static, | 62 |
| abstract_inverted_index.through | 13, 137 |
| abstract_inverted_index.various | 120 |
| abstract_inverted_index.vectors | 50 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 41 |
| abstract_inverted_index.Malaria, | 2 |
| abstract_inverted_index.Nigeria, | 101 |
| abstract_inverted_index.approach | 73, 217 |
| abstract_inverted_index.automata | 90 |
| abstract_inverted_index.cellular | 89 |
| abstract_inverted_index.control, | 240 |
| abstract_inverted_index.critical | 247 |
| abstract_inverted_index.density, | 179 |
| abstract_inverted_index.dynamics | 156 |
| abstract_inverted_index.evolving | 221 |
| abstract_inverted_index.included | 172 |
| abstract_inverted_index.infected | 16 |
| abstract_inverted_index.modeling | 216 |
| abstract_inverted_index.mosquito | 116 |
| abstract_inverted_index.parasite | 10 |
| abstract_inverted_index.patterns | 128 |
| abstract_inverted_index.protozoa | 9 |
| abstract_inverted_index.vectors. | 210 |
| abstract_inverted_index.Anopheles | 18, 79, 83 |
| abstract_inverted_index.Cameroon, | 102 |
| abstract_inverted_index.Ethiopia, | 100 |
| abstract_inverted_index.analysis, | 139 |
| abstract_inverted_index.capturing | 153 |
| abstract_inverted_index.challenge | 25 |
| abstract_inverted_index.collected | 98 |
| abstract_inverted_index.confirmed | 130, 158, 206 |
| abstract_inverted_index.countries | 200 |
| abstract_inverted_index.detailing | 112 |
| abstract_inverted_index.developed | 87, 194 |
| abstract_inverted_index.dynamical | 215 |
| abstract_inverted_index.dynamics, | 231 |
| abstract_inverted_index.effective | 235 |
| abstract_inverted_index.eliminate | 31 |
| abstract_inverted_index.emergence | 43 |
| abstract_inverted_index.extensive | 143 |
| abstract_inverted_index.obstacle, | 54 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.Plasmodium | 8 |
| abstract_inverted_index.accounting | 219 |
| abstract_inverted_index.arabiensis | 84 |
| abstract_inverted_index.challenge. | 249 |
| abstract_inverted_index.chi-square | 140 |
| abstract_inverted_index.conditions | 222 |
| abstract_inverted_index.contribute | 225 |
| abstract_inverted_index.formidable | 53 |
| abstract_inverted_index.identified | 133 |
| abstract_inverted_index.introduces | 69 |
| abstract_inverted_index.literature | 144 |
| abstract_inverted_index.phenotypic | 76 |
| abstract_inverted_index.population | 178 |
| abstract_inverted_index.prevention | 242 |
| abstract_inverted_index.resistance | 46, 77 |
| abstract_inverted_index.robustness | 151 |
| abstract_inverted_index.strategies | 236 |
| abstract_inverted_index.variables. | 189 |
| abstract_inverted_index.Conclusions | 190 |
| abstract_inverted_index.activities, | 176 |
| abstract_inverted_index.correlation | 138 |
| abstract_inverted_index.encompasses | 109 |
| abstract_inverted_index.influences, | 224 |
| abstract_inverted_index.insecticide | 45, 173 |
| abstract_inverted_index.mosquitoes, | 19 |
| abstract_inverted_index.populations | 118 |
| abstract_inverted_index.resistance, | 131 |
| abstract_inverted_index.significant | 22 |
| abstract_inverted_index.sub-Saharan | 27 |
| abstract_inverted_index.transmitted | 12 |
| abstract_inverted_index.agricultural | 175 |
| abstract_inverted_index.characterize | 75 |
| abstract_inverted_index.demonstrated | 150 |
| abstract_inverted_index.increasingly | 34 |
| abstract_inverted_index.populations. | 164 |
| abstract_inverted_index.coefficients. | 66 |
| abstract_inverted_index.environmental | 188 |
| abstract_inverted_index.georeferenced | 110 |
| abstract_inverted_index.insecticides. | 40, 123 |
| abstract_inverted_index.understanding | 228 |
| abstract_inverted_index.characterizing | 125 |
| abstract_inverted_index.spatio-temporal | 72, 155 |
| abstract_inverted_index.characteristics, | 186 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.86535207 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |