Mathematical modelling and estimation of seasonal variation of mosquito population: a real case study

Dhar, Joydip; Chaudhary, Monica; Baghel, Randhir Singh; Pandey, Abhed

doi:10.5269/bspm.v33i2.23330

Cited by 1 publication

(1 citation statement)

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“…Further contributions to the field include the development of a deterministic mathematical model focusing on mosquito population dynamics with chemical control interventions [ 17 ], and the integration of mathematical models with trap data to estimate the growth, prevalence, and management strategies for Aedes aegypti [ 18 ]. Other mathematical modeling efforts have aimed at understanding mosquito population dynamics [ 19 ], dispersal patterns in varied environments [ 20 ], and the effects of seasonal changes on mosquito population sizes [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal characterization of phenotypic resistance in malaria vector species

Ibrahim,

Wamalwa,

Odindi

et al. 2024

BMC Biol

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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.

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Section: Introductionmentioning

confidence: 99%