ArticlePloS one2020
Association of Anopheles sinensis average abundance and climate factors: Use of mosquito surveillance data in Goyang, Korea.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Developing machine learning models to forecast mosquito abundance for climate change impacts and early warning system.One health (Amsterdam, Netherlands) · 2026Article
- Nowcasting Vector Mosquito Abundance and Determining Its Association With Malaria Epidemics in South Korea.Transboundary and emerging diseases · 2025Article
- Versatility of reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) from diagnosis of early pathological infection to mutation detection in organisms.Molecular biology reports · 2024Review
- Identification of breeding habitats and kdr mutations in Anopheles spp. in South Korea.Malaria journal · 2023Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Malaria is a vector-borne disease transmitted by Anopheline mosquitoes. In Korea, Plasmodium vivax malaria is an endemic disease and the main vector is Anopheles sinensis. Plasmodium vivax malaria is common in the northwestern part of South Korea, including in the city of Goyang in regions near the demilitarized zone. This study aimed to identify the best time-series model for predicting mosquito average abundance in Goyang, Korea. Mosquito data were obtained from the Mosquito Surveillance Program of the Goyang Ilsanseogu Public Health Center for the period 2008-2012. Black light traps were set up periodically in a park, a senior community center, and a village community center, public health center, drainage pumping station, cactus research center, restaurant near forest, in which many activities occur at night. In total, 9,512 female mosquitoes were collected at 12 permanent trapping sites during the mosquito season in the study period. Weekly An. sinensis average abundance was positively correlated with minimum grass temperature (r = 0.694, p < 0.001), precipitation (r = 0.326, p = 0.001). The results showed that seasonal autoregressive integrated moving average (SARIMA) (1,0,0)(0,0,1)21 with minimum grass temperature variable at time lag0 weeks and the precipitation variable at time lag1 weeks provided that best model of mosquito average abundance. The multivariate model accounted for about 54.1% of the mosquito average abundance variation. Time-series analysis of mosquito average abundance and climate factors provided basic information for predicting the occurrence of malaria mosquitoes.
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