The relative importance of key meteorological factors affecting numbers of mosquito vectors of dengue fever

Abstract

Although single factors such as rainfall are known to affect the population dynamics of Aedes albopictus, the main vector of dengue fever in Eurasia, the synergistic effects of different meteorological factors are not fully understood. To address this topic, we used meteorological data and mosquito-vector association data including Breteau and ovitrap indices in key areas of dengue outbreaks in Guangdong Province, China, to formulate a five-stage mathematical model for Aedes albopictus population dynamics by integrating multiple meteorological factors. Unknown parameters were estimated using a genetic algorithm, and the results were analyzed by k-Shape clustering, random forest and grey correlation analysis. In addition, the population density of mosquitoes in 2022 was predicted and used for evaluating the effectiveness of the model. We found that there is spatiotemporal heterogeneity in the effects of temperature and rainfall and their distribution characteristics on the diapause period, the numbers of peaks in mosquito densities in summer and the annual total numbers of adult mosquitoes. Moreover, we identified the key meteorological indicators of the mosquito quantity at each stage and that rainfall (seasonal rainfall and annual total rainfall) was more important than the temperature distribution (seasonal average temperature and temperature index) and the uniformity of rainfall annual distribution (coefficient of variation) for most of the areas studied. The peak rainfall during the summer is the best indicator of mosquito population development. The results provide important theoretical support for the future design of mosquito vector control strategies and early warnings of mosquito-borne diseases.

Fig 1. Meteorology data.

Average monthly rainfall (blue) and average monthly temperature (red) of 12 cities in Guangdong Province from 2016 to 2021.

Fig 2. Model fitting results for Yangjiang, Guangzhou, Shenzhen and Huizhou.

(a)-(d) Fitting results for adult mosquitoes (MOI); (e)-(h) Fitting results for larvae (BI). Red circles represent the actual MOI and BI values. The blue asterisks represent the compressed fitting result. In order to clearly show the consistency of the changing trend in the simulation results and monitoring indicators in the figure, we compressed the simulation results.

Fig 3. Results of sensitivity analysis in Shenzhen from 2016 to 2021.

(a)-(f) Variation of adult mosquito quantity with increasing winter temperature in Shenzhen. The red dashed line from left to right represents the start and end times of diapause, and the black dashed line from left to right represents the ends of winter, spring, summer and autumn. (g)-(l) Effects of seasonal climate change on annual total adult mosquito quantity in Shenzhen. The three sets of data for each season in the subfigure show the results of increasing seasonal rainfall, increasing both seasonal rainfall and seasonal temperature, and increasing seasonal temperature from left to right. The "winter" in figure (g)-(l) represents the winter of the previous year. For example, "winter" in figure (g) represents the winter of 2015, including December 2015 and January and February 2016.

Fig 4. The k-Shape classification results of rainfall and temperature in 84 years.

The horizontal coordinate represents the month, and the vertical coordinate represents the monthly mean temperature in (a)-(f) and the monthly total rainfall after normalization in (g)-(l).

Fig 5. The change of annual total adult mosquito numbers under different rainfall and temperature distribution combinations.

The color in the figure represents the annual total adult mosquito numbers. The yellower the color, the higher the annual total adult mosquito numbers. (a)-(d) Variation of annual total adult mosquito quantity with annual total rainfall under the same combination of rainfall and temperature distribution. The y-axis label "R, T" represents the combinations of rainfall distributions (R1, R2, R3, R4, R5, R6) and temperature distributions (T1, T2, T3, T4, T5, T6), such as R1 and T1, R2 and T1, R3 and T5, and so on. There are 36 of them, so the y-axis ranges from 1 to 36. (e)-(h) Variation of annual total adult mosquito quantity when the combination of rainfall and temperature distribution is different under the same annual total rainfall (3500mm). In other words, (e)-(h) are representations of the third column in (a)-(d), respectively.

Fig 6. Division and importance rank of meteorological indicators.

(a) Taking Shenzhen in 2021 as an example, the figure shows the division of meteorological indicators within a year, calculated using the circlize package in R [35]. The red dotted line in the figure represents 1 January, the "residue" includes January, February and December in a year, and a sector represents a season. From the outside to the inside, the first three rings together show the changes of daily mean temperature on 365 days in a year clockwise, among which the data values are in the range of 25°C-35°C, 15°C-25°C, and less than 15°C, respectively. The fourth ring shows the changes of daily total rainfall in a year clockwise. The mean temperature and total rainfall in the four sectors are denoted as residueMT, springMT, summerMT, autumnMT, residueR, springR, summerR and autumnR. In addition, the coefficient of variation (cvR) of rainfall data is used to represent the uniformity of the rainfall distribution, which is equal to the standard deviation of the annual rainfall data divided by the mean, and the annual total rainfall is denoted sumR. The difference between the number of days between 25°C-35°C and less than 15°C is defined as the temperature index (dayT), that is, the difference between the number of data points in the first ring band and the third ring band. (b) Importance ranking results of the above meteorological indicators by random forest and verification results of indicators importance ranking.

Fig 7. Predicted results for the MOIs for 12 cities in Guangdong Province in 2022.

Different colour bands represent different risk levels which are "meet the prevention and control requirements" (MOI<5), "low risk level" (520) from bottom to top, respectively. The left side of the dotted line is January-May in which the MOI is predicted by real weather data, and the right side is June-December in which the MOI is predicted by substituting the mean of the weather data of corresponding months in the previous seven years into the model. The white asterisk in the figure represents the MOI value published on the official website of Guangdong Provincial Health Commission in 2022.

Fig 8. Grey correlation analysis results for 12 cities from 2016 to 2022.

The numbers on top of the bars in the figure are the values of the grey correlation degree (GRA). The asterisks represent the policy implementation strength (PES) of the 12 cities from 2016 to 2021. According to the notice on the official website of each municipal government, there are two kinds of specific mosquito control policies: (1) the special mosquito eradication campaign, and (2) the patriotic health campaign which includes measures such as killing adult mosquitoes and removing standing water. According to the frequency of policy implementation, we divide its enforcement strength into five levels. If no policy has been implemented, the 0 is denoted; if the policy has been implemented for ≤ 3 months, the number is 1; if the policy has been implemented for ≤ 6 months or regional control mosquitoes is frequent, the number is 2; if mosquito control was carried out over 6 months, 3 is denoted; if the mosquitoes were controlled for more than 6 months but also at least twice a month, 4 is denoted.