| Citation: |
Meili Li, Xian Zhang, Wei Ding, Junling Ma. ESTIMATING THE MOSQUITO DENSITY IN GUANGZHOU CITY, CHINA[J]. Journal of Applied Analysis & Computation, 2023, 13(1): 329-343. doi: 10.11948/20220129
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ESTIMATING THE MOSQUITO DENSITY IN GUANGZHOU CITY, CHINA
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Abstract
Mosquito is a vector of many diseases. Predicting the trend of mosquito density is important for early warning and control of mosquito diseases. In this paper, we fit a discrete time mosquito model developed by Gong et al. in 2011, which considers the immature and adult stages, and weather dependent model parameters, to the Breteau Index and Bite Index data for Aedes aegypti in Guangzhou city, China in 2014, as well as the weather data for average temperature, precipitation, evaporation and daylight for the same period. We estimated the model parameters using the Markov Chain Monte-Carlo (MCMC) method. We find that many parameters are not identifiable. We revise and simplify the model so that the parameters of our new model are identifiable. Our results indicate that the model predicted mosquito prevalence agrees well with data. We then use the fitted parameter values against the Breteau Index and Bite Index data for Guangzhou city in 2017 and 2018, and show that the estimated parameter values are applicable for other seasons.
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Keywords:
- Weather /
- mosquito density /
- parameters estimation /
- Markov chain monte-carlo
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References
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Meili Li, Xian Zhang, Wei Ding, Junling Ma. ESTIMATING THE MOSQUITO DENSITY IN GUANGZHOU CITY, CHINA[J]. Journal of Applied Analysis & Computation, 2023, 13(1): 329-343. doi: 10.11948/20220129
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Figure 1.
The posterior distributions of initial values of Model (2.1) for the 12 districts in Guangzhou city.
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Figure 2.
The posterior distributions of the parameters of Model (2.1).
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Figure 3.
The posterior distributions of capture probability ratios of Model (2.1) for the 12 districts in Guangzhou city.
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Figure 4.
The posterior distributions of initial values of Model (3.1) for the 12 districts in Guangzhou city.
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Figure 5.
The posterior distributions of parameters of Model (3.1).
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Figure 6.
The posterior distributions of capture probability ratios of Model (3.1) for the 12 districts in Guangzhou city.
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Figure 7.
The comparison of the predicted mosquito density (red dots) with the Breteau Index and Bite Index for 12 districts in Guangzhou city in 2014 (blue dots).
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Figure 8.
The comparison of the predicted mosquito density (red dots) with the Breteau Index and Bite Index for 11 districts in Guangzhou city in 2017 (blue dots).
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Figure 9.
The comparison of the predicted mosquito density (red dots) with the Breteau Index and Bite Index for 11 districts in Guangzhou city in 2018 (blue dots).