Role of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in local dengue epidemics in Taiwan

The effect of attractive toxic sugar bait on the Asian tiger mosquito, Aedes albopictus (Diptera: Culicidae) in community farms in Northern Taiwan

Attractive toxic sugar baits (ATSBs) lure mosquitoes to feed on the baits and subsequently killed them. We investigated the effects of a boric acid-containing ATSB on the population of Aedes albopictus at 48 h exposure and assessed the field effectiveness on this ATSB on two types of community farms in New Taipei City, Taiwan, including isolated ATSB farms and nonisolated ATSB farms. The result showed that mosquitoes exposed to the ATSB solution for 48 h were killed within 7 d under laboratory conditions. Exposure of female and male mosquitoes to ATSB resulted in mean survival times ranging from 52 to 62 h and 30 to 48 h, respectively. For field efficacy test, on isolated ATSB farms, a significant reduction of ovitrap density index (ODI) up to 24 % was noted after the replacement frequency was increased to every 2 weeks. However, the intervention efficacy on nonisolated ATSB farms had mixed results. The ODI significantly reduced by 21.4 % and 6.9 % on the nonisolated ATSB Chongmin and Nanjing farms, respectively, when bait replacement was done every 2 weeks instead of every 3 weeks. By contrast, the ODI on the nonisolated ATSB Yongchang farms increased significantly, irrespectively of the bait replacement frequency. Nevertheless, the total number of eggs trapped on all ATSB farms exhibited a concave curve pattern; while the mosquito population on non-ATSB control farms continued to increase over time. In conclusion, deploying simple ATSB stations containing boric acid is a practical approach for integrated vector management programs.

Profiling Insecticide Susceptibility of Aedes Albopictus From Hot Springs in Selangor, Malaysia

The present study establishes insecticide susceptibility profiles of Aedes albopictus adult populations from 4 hot springs in Selangor, Malaysia, against 7 pyrethroids through an adult mosquito susceptibility bioassay. All Ae. albopictus populations were subjected to a 1-h exposure to each pyrethroid following the World Health Organization. The mortalities were recorded at 60 min of exposure to bifenthrin, 30 min for other pyrethroids, and 24 h posttreatment for all pyrethroids. Complete mortalities were observed upon exposures to the pyrethroids under 60 min and at 24 h posttreatment, excluding permethrin 0.25%, alpha-cypermethrin 0.05%, and bifenthrin 0.2%. These findings indicated that permethrin, deltamethrin, lambda-cyhalothrin, cyfluthrin, and etofenprox possess the recommended pyrethroid adulticide active ingredients that could be applied in vector control programs at these hot springs in the future. Nevertheless, the application of pyrethroids should be carefully monitored in rotation with other insecticide classes, including organophosphates and carbamates to avoid the development of insecticide resistance among mosquito vectors towards all insecticides. Although there were no reported cases of Aedes-borne pathogens at these hot springs to date, the current study results could still assist the Malaysian health authorities in determining approaches to control Aedes populations in these hot springs, if required in the future.

Weather-Based Prediction Models for the Prevalence of Dengue Vectors Aedes aegypti and Ae. albopictus

Dengue is an important vector-borne disease transmitted by the mosquitoes Aedes aegypti and Ae. albopictus. In the absence of an effective vaccine, vector control has become the key intervention tool in controlling the disease. Vector densities are significantly affected by the changing weather patterns of a region. The present study was conducted in three selected localities, i.e., urban Bandaranayakapura, semiurban Galgamuwa, and rural Buluwala in the Kurunegala district of Sri Lanka to assess spatial and temporal distribution of dengue vector mosquitoes and to predict vector prevalence with respect to changing weather parameters. Monthly ovitrap surveys and larval surveys were conducted from January to December 2019 and continued further in the urban area up to December 2021. Aedes aegypti was found moderately in the urban area and to a lesser extent in semiurban but not in the rural area. Aedes albopictus had the preference for rural over urban areas. Aedes aegypti preferred indoor breeding, while Ae. albopictus preferred both indoor and outdoor. For Ae. albopictus, ovitrap index (OVI), premise index (PI), container index (CI), and Breteau index (BI) correlated with both the rainfall (RF) and relative humidity (RH) of the urban site. Correlations were stronger between OVI and RH and also between BI and RF. Linear regression analysis was fitted, and a prediction model was developed using BI and RF with no lag period (R ² (sq) = 86.3%; F = 53.12; R ² (pred) = 63.12%; model: Log10 (BI) = 0.153 + 0.286 ^∗ Log10 (RF); RMSE = 1.49). Another prediction model was developed using OVI and RH with one month lag period (R ² (sq) = 70.21%; F = 57.23; model: OVI predicted = 15.1 + 0.528 ^∗ Lag 1 month RH; RMSE = 2.01). These two models can be used to monitor the population dynamics of Ae. albopictus in urban settings to predict possible dengue outbreaks.

Species-specific climate Suitable Conditions Index and dengue transmission in Guangdong, China

Background

Optimal climatic conditions for dengue vector mosquito species may play a significant role in dengue transmission. We previously developed a species-specific Suitable Conditions Index (SCI) for Aedes aegypti and Aedes albopictus, respectively. These SCIs rank geographic locations based on their climatic suitability for each of these two dengue vector species and theoretically define parameters for transmission probability. The aim of the study presented here was to use these SCIs together with socio-environmental factors to predict dengue outbreaks in the real world.

Methods

A negative binomial regression model was used to assess the relationship between vector species-specific SCI and autochthonous dengue cases after accounting for potential confounders in Guangdong, China. The potential interactive effect between the SCI for Ae. albopictus and the SCI for Ae. aegypti on dengue transmission was assessed.

Results

The SCI for Ae. aegypti was found to be positively associated with autochthonous dengue transmission (incidence rate ratio: 1.06, 95% confidence interval: 1.03, 1.09). A significant interaction effect between the SCI of Ae. albopictus and the SCI of Ae. aegypti was found, with the SCI of Ae. albopictus significantly reducing the effect of the SCI of Ae. aegypti on autochthonous dengue cases. The difference in SCIs had a positive effect on autochthonous dengue cases.

Conclusions

Our results suggest that dengue fever is more transmittable in regions with warmer weather conditions (high SCI for Ae. aegypti). The SCI of Ae. aegypti would be a useful index to predict dengue transmission in Guangdong, China, even in dengue epidemic regions with Ae. albopictus present. The results also support the benefit of the SCI for evaluating dengue outbreak risk in terms of vector sympatry and interactions in the absence of entomology data in future research.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05453-x.

How Spatial Epidemiology Helps Understand Infectious Human Disease Transmission

Both directly and indirectly transmitted infectious diseases in humans are spatial-related. Spatial dimensions include: distances between susceptible humans and the environments shared by people, contaminated materials, and infectious animal species. Therefore, spatial concepts in managing and understanding emerging infectious diseases are crucial. Recently, due to the improvements in computing performance and statistical approaches, there are new possibilities regarding the visualization and analysis of disease spatial data. This review provides commonly used spatial or spatial-temporal approaches in managing infectious diseases. It covers four sections, namely: visualization, overall clustering, hot spot detection, and risk factor identification. The first three sections provide methods and epidemiological applications for both point data (i.e., individual data) and aggregate data (i.e., summaries of individual points). The last section focuses on the spatial regression methods adjusted for neighbour effects or spatial heterogeneity and their implementation. Understanding spatial-temporal variations in the spread of infectious diseases have three positive impacts on the management of diseases. These are: surveillance system improvements, the generation of hypotheses and approvals, and the establishment of prevention and control strategies. Notably, ethics and data quality have to be considered before applying spatial-temporal methods. Developing differential global positioning system methods and optimizing Bayesian estimations are future directions.

Modelling distributions of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competition

Florida faces the challenge of repeated introduction and autochthonous transmission of arboviruses transmitted by Aedes aegypti and Aedes albopictus. Empirically-based predictive models of the spatial distribution of these species would aid surveillance and vector control efforts. To predict the occurrence and abundance of these species, we fit a mixed-effects zero-inflated negative binomial regression to a mosquito surveillance dataset with records from more than 200,000 trap days, representative of 53% of the land area and ranging from 2004 to 2018 in Florida. We found an asymmetrical competitive interaction between adult populations of Aedes aegypti and Aedes albopictus for the sampled sites. Wind speed was negatively associated with the occurrence and abundance of both vectors. Our model predictions show high accuracy (72.9% to 94.5%) in validation tests leaving out a random 10% subset of sites and data since 2017, suggesting a potential for predicting the distribution of the two Aedes vectors.

Merging High-Resolution Satellite Surface Radiation Data with Meteorological Sunshine Duration Observations over China from 1983 to 2017

Surface solar radiation (Rs) is essential to climate studies. Thanks to long-term records from the Advanced Very High-Resolution Radiometers (AVHRR), the recent release of International Satellite Cloud Climatology Project (ISCCP) HXG cloud products provide a promising opportunity for building long-term Rs data with high resolutions (3 h and 10 km). In this study, we compare three satellite Rs products based on AVHRR cloud products over China from 1983 to 2017 with direct observations of Rs and sunshine duration (SunDu)-derived Rs. The results show that SunDu-derived Rs have higher accuracy than the direct observed Rs at time scales of a month or longer by comparing with the satellite Rs products. SunDu-derived Rs is available from the 1960s at more than 2000 stations over China, which provides reliable decadal estimations of Rs. However, the three AVHRR-based satellite Rs products have significant biases in quantifying the trend of Rs from 1983 to 2016 (−4.28 W/m2/decade to 2.56 W/m2/decade) due to inhomogeneity in satellite cloud products and the lack of information on atmospheric aerosol optical depth. To adjust the inhomogeneity of the satellite Rs products, we propose a geographically weighted regression fusion method (HGWR) to merge ISCCP-HXG Rs with SunDu-derived Rs. The merged Rs product over China from 1983 to 2017 with a spatial resolution of 10 km produces nearly the same trend as that of the SunDu-derived Rs. This study makes a first attempt to adjust the inhomogeneity of satellite Rs products and provides the merged high-resolution Rs product from 1983 to 2017 over China, which can be downloaded freely.

Impacts of Low Temperatures on Wolbachia (Rickettsiales: Rickettsiaceae)-Infected Aedes aegypti (Diptera: Culicidae)

In recent decades, the occurrence and distribution of arboviral diseases transmitted by Aedes aegypti mosquitoes has increased. In a new control strategy, populations of mosquitoes infected with Wolbachia are being released to replace existing populations and suppress arboviral disease transmission. The success of this strategy can be affected by high temperature exposure, but the impact of low temperatures on Wolbachia-infected Ae. aegypti is unclear, even though low temperatures restrict the abundance and distribution of this species. In this study, we considered low temperature cycles relevant to the spring season that are close to the distribution limits of Ae. aegypti, and tested the effects of these temperature cycles on Ae. aegypti, Wolbachia strains wMel and wAlbB, and Wolbachia phage WO. Low temperatures influenced Ae. aegypti life-history traits, including pupation, adult eclosion, and fertility. The Wolbachia-infected mosquitoes, especially wAlbB, performed better than uninfected mosquitoes. Temperature shift experiments revealed that low temperature effects on life history and Wolbachia density depended on the life stage of exposure. Wolbachia density was suppressed at low temperatures but densities recovered with adult age. In wMel Wolbachia there were no low temperature effects specific to Wolbachia phage WO. The findings suggest that Wolbachia-infected Ae. aegypti are not adversely affected by low temperatures, indicating that the Wolbachia replacement strategy is suitable for areas experiencing cool temperatures seasonally.

Evaluation of geostatistical techniques and their hybrid in modelling of groundwater quality index in the Marand Plain in Iran

In many parts of the world, groundwater is considered as one of the main sources of urban and rural drinking water. Over the past three decades, the qualitative and quantitative characteristics of aquifers have been negatively affected by different factors such as excessive use of chemical fertilizers in agriculture, indiscreet, and over-exploitation use of groundwater. Therefore, finding the effective method for mapping the water quality index (WQI) is important for locating suitable and non-suitable areas for urban and rural drinking waters. In the present paper, the best method to estimate the spatial distribution of WQI was assessed using the inverse distance weighted, kriging, cokriging, geographically weighted regression (GWR), and hybrid models. Creating hybrid models can increase modeling capabilities. Hybrid methods make use of a combination of estimated model capabilities. In addition, to improve the results of cokriging, GWR, and hybrid methods, the auxiliary parameters of land slope, groundwater table, and groundwater transmissibility were used. In order to assess the proposed methodology, 11 qualitative parameters obtained from 63 observation wells in Marand Plain (Iran) were utilized. Four statistical measures, namely the root mean square error (RMSE), the mean absolute error (MAE), the Akaike coefficient (AIC), and the correlation coefficient (R²) along with the Taylor diagram, have been done. Classification of the WQI index showed that the quality of a number of 1, 27, 18, and 17 wells was, respectively, in excellent, good, moderate, and poor grades. The results of modeling the WQI index based on IDW, kriging, cokriging, GWR, and hybrid methods showed that the best estimate of WQI was obtained by using hybrid GWR-kriging method with three input parameters of land slope, groundwater table, and groundwater transmissibility. Therefore, hybrid kriging and GWR methods have been fairly well able to simulate the WQI index.

Risk prediction system for dengue transmission based on high resolution weather data

BACKGROUND

Dengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction.

METHODS AND FINDINGS

We make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM's Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach.

CONCLUSIONS

We demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised.

Increased Adult Aedes aegypti and Culex quinquefasciatus (Diptera: Culicidae) Abundance in a Dengue Transmission Hotspot, Compared to a Coldspot, within Kaohsiung City, Taiwan

The assumption that vector abundance differences might drive spatial and temporal heterogeneities in vector-borne disease transmission is common, though data supporting it is scarce. Here, we present data from two common mosquito species Aedes aegypti (Linnaeus) and Culex quinquefasciatus Say, biweekly sampled as adults, from March 2016 through December 2017, with BG-sentinel traps in two neighboring districts of Kaohsiung City (KC), Taiwan. One district has historically been a dengue transmission hotspot (Sanmin), and the other a coldspot (Nanzih). We collected a total 41,027 mosquitoes, and we found that average mosquito abundance (mean ± S.D.) was higher in Sanmin (Ae. aegypti: 9.03 ± 1.46; Cx. quinquefasciatus: 142.57 ± 14.38) than Nanzih (Ae. aegypti: 6.21 ± 0.47; Cx. quinquefasciatus: 63.37 ± 8.71) during the study period. In both districts, Ae. aegypti and Cx. quinquefasciatus population dynamics were sensitive to changes in temperature, the most platykurtic environmental variable at KC during the study period, a pattern predicted by Schmalhausen’s law, which states that organisms are more sensitive to small changes in environmental variables whose average value is more uncertain than its extremes. Our results also suggest that differences in Ae. aegypti abundance might be responsible for spatial differences in dengue transmission at KC. Our comparative approach, where we also observed a significant increment in the abundance of Cx. quinquefasciatus in the dengue transmission hotspot, suggests this area might be more likely to experience outbreaks of other vector borne diseases and should become a primary focus for vector surveillance and control.

Critical low temperature for the survival of Aedes aegypti in Taiwan

Background

Taiwan is geographically located in a region that spans both tropical and subtropical climates (22–25°N and 120–122°E). The Taiwan Centers for Disease Control have found that the ecological habitat of Aedes aegypti appears only south of 23.5°N. Low temperatures may contribute to this particular habitat distribution of Ae. aegypti under the influence of the East Asian winter monsoon. However, the threshold condition related to critically low temperatures remains unclear because of the lack of large-scale spatial studies. This topic warrants further study, particularly through national entomological surveillance and satellite-derived land surface temperature (LST) data.

Methods

We hypothesized that the distribution of Ae. aegypti is highly correlated with the threshold nighttime LST and that a critical low LST limits the survival of Ae. aegypti. A mosquito dataset collected from the Taiwan Centers for Disease Control was utilized in conjunction with image data obtained from the moderate resolution imaging spectroradiometer (MODIS) during 2009–2011. Spatial interpolation and phi coefficient methods were used to analyze the correlation between the distributions of immature forms of Ae. aegypti and threshold LST, which was predicted from MODIS calculations for 348 townships in Taiwan.

Results

According to the evaluation of the correlation between estimated nighttime temperatures and the occurrence of Ae. aegypti, winter had the highest peak phi coefficient, and the corresponding estimated threshold temperatures ranged from 13.7 to 14 °C in the ordinary kriging model, which was the optimal interpolation model in terms of the root mean square error. The mean threshold temperature was determined to be 13.8 °C, which is a critical temperature to limit the occurrence of Ae. aegypti.

Conclusions

An LST of 13.8 °C was found to be the critical temperature for Ae. aegypti larvae, which results in the near disappearance of Ae. aegypti during winter in the subtropical regions of Taiwan under the influence of the prevailing East Asian winter monsoon.

Exploring Determinants of Spatial Variations in the Dengue Fever Epidemic Using Geographically Weighted Regression Model: A Case Study in the Joint Guangzhou-Foshan Area, China, 2014

Dengue fever (DF) is a common and rapidly spreading vector-borne viral disease in tropical and subtropical regions. In recent years, this imported disease has posed an increasing threat to public health in China, especially in many southern cities. Although the severity of DF outbreaks in these cities is generally associated with known risk factors at various administrative levels, spatial heterogeneities of these associations remain little understood on a finer scale. In this study, the neighboring Guangzhou and Foshan (GF) cities were considered as a joint area for characterizing the spatial variations in the 2014 DF epidemic at various grid levels from 1 × 1 km² to 6 × 6 km². On an appropriate scale, geographically weighted regression (GWR) models were employed to interpret the influences of socioeconomic and environmental factors on this epidemic across the GF area. DF transmissions in Guangzhou and Foshan cities presented synchronous temporal changes and spatial expansions during the main epidemic months. Across the GF area, this epidemic was obviously spatially featured at various grid levels, especially on the 2 × 2 km² scale. Its spatial variations were relatively sufficiently explained by population size, road density, and economic status integrated in the GWR model with the lowest Akaike Information Criterion (AICc = 5227.97) and highest adjusted R square (0.732) values. These results indicated that these three socioeconomic factors acted as geographical determinants of spatial variability of the 2014 DF epidemic across the joint GF area, although some other potential factors should be added to improve the explaining the spatial variations in the central zones. This work improves our understanding of the effects of socioeconomic conditions on the spatial variations in this epidemic and helps local hygienic authorities to make targeted joint interventions for preventing and controlling this epidemic across the GF area.