The Impact of Climatic Factors on Temporal Mosquito Distribution and Population Dynamics in an Area Targeted for Sterile Insect Technique Pilot Trials

Bibliographic Data

ID15498707
AuthorsTheresa Taona Mazarire (0000-0003-2194-3641, National Health Laboratory Service, corresponding author), Leanne N Lobb (0000-0002-3082-0806, National Health Laboratory Service), Leanne Lobb (Centre for Emerging Zoonotic and Parasitic Diseases, National Institute for Communicable Diseases, National Health Laboratory Service, Johannesburg 2131, South Africa), Solomon W Newete (0000-0001-5245-8732, University of the Witwatersrand), Givemore Munhenga (0000-0002-8431-6947, National Health Laboratory Service)
Year2024
Volume21
Issue5
Pages558-558
Publication date2024-04-28
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph21050558
PMID38791773
OpenAlexW4396547655
LanguageEN
Citations received1
References cited68

It is widely accepted that climate affects the mosquito life history traits; however, its precise role in determining mosquito distribution and population dynamics is not fully understood. This study aimed to investigate the influence of various climatic factors on the temporal distribution of Anopheles arabiensis populations in Mamfene, South Africa between 2014 and 2019. Time series analysis, wavelet analysis, cross-correlation analysis, and regression model combined with the autoregressive integrated moving average (ARIMA) model were utilized to assess the relationship between climatic factors and An. arabiensis population density. In total 3826 adult An. arabiensis collected was used for the analysis. ARIMA (0, 1, 2) (0, 0, 1) 12 models closely described the trends observed in An. arabiensis population density and distribution. The wavelet coherence and time-lagged correlation analysis showed positive correlations between An. arabiensis population density and temperature (r = 0.537 ), humidity (r = 0.495) and rainfall (r = 0.298) whilst wind showed negative correlations (r = -0.466). The regression model showed that temperature ( p = 0.00119), rainfall ( p = 0.0436), and humidity ( p = 0.0441) as significant predictors for forecasting An. arabiensis abundance. The extended ARIMA model (AIC = 102.08) was a better fit for predicting An. arabiensis abundance compared to the basic model. Anopheles arabiensis still remains the predominant malaria vector in the study area and climate variables were found to have varying effects on the distribution and abundance of An. arabiensis . This necessitates other complementary vector control strategies such as the Sterile Insect Technique (SIT) which involves releasing sterile males into the environment to reduce mosquito populations. This requires timely mosquito and climate information to precisely target releases and enhance the effectiveness of the program, consequently reducing the malaria risk

Abundance (ecology · Autoregressive integrated moving average · Biology · Pearson product-moment correlation coefficient · Population · Statistics · Time series · Demography · Insect Pest Control Strategies · Malaria Research and Control · Mathematics · Mosquito-borne diseases and control · Ecology

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