Effects of rainfall on Culex mosquito population dynamics

Broadscale spatial synchrony in a West Nile virus mosquito vector across multiple timescales

Insects often exhibit irruptive population dynamics determined by environmental conditions. We examine if populations of the Culex tarsalis mosquito, a West Nile virus (WNV) vector, fluctuate synchronously over broad spatial extents and multiple timescales and whether climate drives synchrony in Cx. tarsalis, especially at annual timescales, due to the synchronous influence of temperature, precipitation, and/or humidity. We leveraged mosquito collections across 9 National Ecological Observatory Network (NEON) sites distributed in the interior West and Great Plains region USA over a 45-month period, and associated gridMET climate data. We utilized wavelet phasor mean fields and wavelet linear models to quantify spatial synchrony for mosquitoes and climate and to calculate the importance of climate in explaining Cx. tarsalis synchrony. We also tested whether the strength of spatial synchrony may vary directionally across years. We found significant annual synchrony in Cx. tarsalis, and short-term synchrony during a single period in 2018. Mean minimum temperature was a significant predictor of annual Cx. tarsalis spatial synchrony, and we found a marginally significant decrease in annual Cx. tarsalis synchrony. Significant Cx. tarsalis synchrony during 2018 coincided with an anomalous increase in precipitation. This work provides a valuable step toward understanding broadscale synchrony in a WNV vector.

The Role of Temperature, Wind Speed, and Precipitation on the Abundance of Culex Species and West Nile Virus Infection Rate in Rural West-Central Illinois

While most research on West Nile virus (WNV) and its main vector, the Culex mosquito, has been conducted in laboratory or urban settings, studies with field-caught mosquitoes in rural areas, such as west-central Illinois, are lacking. The objective of this research was to investigate key abiotic factors using macroclimate data, including temperature, precipitation, and wind speed, to determine their influence on field-caught mosquito abundance in 4 rural counties in Illinois from 2014 to 2016. Additionally, the relationship between minimum infection rate (MIR) and thermal time was examined. Using gravid traps at 15 sites, Culex mosquitoes were collected twice a week. A total of 5,255 adult female Culex mosquitoes (Cx. pipiens, Cx. quinquefasciatus, and Cx. restuans) were collected in 2014; 9,138 in 2015; and 5,702 in 2016. Regression models were developed based on outcomes of relationships between field-caught mosquitoes and abiotic factors. Precipitation and thermal time had the most significant relationship with mosquito abundance (r2 = 0.993 and r2 = 0.993, respectively), while wind speed was less (r2 = 0.714). The greatest number of Culex and the highest annual MIR were observed in 2015, which was also the driest of the 3 sampling seasons. Mosquito abundance was observed to increase with warmer degree days and MIR was found to increase with abundance in mosquitoes. These models can be used for other mosquito surveillance and monitoring studies in various climate types and environments.

Exploring the transmission modalities of Bunyamwera virus

Bunyamwera virus (BUNV) (Bunyamwera orthobunyavirus) has been found in Sub-Saharan Africa and demonstrated recently as cocirculating with Rift Valley Fever Virus (RVFV). Little is known regarding the breadth of transmission modalities of Bunyamwera. Given its co-occurence with RVFV, we hypothesized the transmission system of BUNV shared similarities to the RVFV system including transmission by Ae. aegypti mosquitoes and environmentally mediated transmission through fomites and environmental contamination. We exposed Ae. aegypti mosquitoes to BUNV and evaluated their ability to transmit both vertically and horizontally. Further, we investigated the potential for a novel transmission modality via environmental contamination. We found that the LSU colony of Ae. aegypti was not competent for the virus for either horizontal or vertical transmission; but, 20% of larva exposed to virus via contaminated aquatic habitat were positive. However, transstadial clearance of the virus was absolute. Finally, under simulated temperature conditions that matched peak transmission in Rwanda, we found that BUNV was stable in both whole blood and serum for up to 28 days at higher total volume in tubes at moderate quantities (103-5 genome copies/mL). In addition, infectiousness of these samples was demonstrated in 80% of the replicates. At lower volume samples (in plates), infectiousness was retained out to 6-8 days with a maximum infectious titer of 104 PFU/mL. Thus, the potential for contamination of the environment and/or transmission via contaminated fomites exists. Our findings have implications for biosafety and infection control, especially in the context of food animal production.

Spatial model of Dengue Hemorrhagic Fever (DHF) risk: scoping review

BACKGROUND

Creating a spatial model of dengue fever risk is challenging duet to many interrelated factors that could affect dengue. Therefore, it is crucial to understand how these critical factors interact and to create reliable predictive models that can be used to mitigate and control the spread of dengue.

METHODS

This scoping review aims to provide a comprehensive overview of the important predictors, and spatial modelling tools capable of producing Dengue Haemorrhagic Fever (DHF) risk maps. We conducted a methodical exploration utilizing diverse sources, i.e., PubMed, Scopus, Science Direct, and Google Scholar. The following data were extracted from articles published between January 2011 to August 2022: country, region, administrative level, type of scale, spatial model, dengue data use, and categories of predictors. Applying the eligibility criteria, 45 out of 1,349 articles were selected.

RESULTS

A variety of models and techniques were used to identify DHF risk areas with an arrangement of various multiple-criteria decision-making, statistical, and machine learning technique. We found that there was no pattern of predictor use associated with particular approaches. Instead, a wide range of predictors was used to create the DHF risk maps. These predictors may include climatology factors (e.g., temperature, rainfall, humidity), epidemiological factors (population, demographics, socio-economic, previous DHF cases), environmental factors (land-use, elevation), and relevant factors.

CONCLUSIONS

DHF risk spatial models are useful tools for detecting high-risk locations and driving proactive public health initiatives. Relying on geographical and environmental elements, these models ignored the impact of human behaviour and social dynamics. To improve the prediction accuracy, there is a need for a more comprehensive approach to understand DHF transmission dynamics.

Effective population size of Culex quinquefasciatus under insecticide-based vector management and following Hurricane Harvey in Harris County, Texas

Introduction: Culex quinquefasciatus is a mosquito species of significant public health importance due to its ability to transmit multiple pathogens that can cause mosquito-borne diseases, such as West Nile fever and St. Louis encephalitis. In Harris County, Texas, Cx. quinquefasciatus is a common vector species and is subjected to insecticide-based management by the Harris County Public Health Department. However, insecticide resistance in mosquitoes has increased rapidly worldwide and raises concerns about maintaining the effectiveness of vector control approaches. This concern is highly relevant in Texas, with its humid subtropical climate along the Gulf Coast that provides suitable habitat for Cx. quinquefasciatus and other mosquito species that are known disease vectors. Therefore, there is an urgent and ongoing need to monitor the effectiveness of current vector control programs. Methods: In this study, we evaluated the impact of vector control approaches by estimating the effective population size of Cx. quinquefasciatus in Harris County. We applied Approximate Bayesian Computation to microsatellite data to estimate effective population size. We collected Cx. quinquefasciatus samples from two mosquito control operation areas; 415 and 802, during routine vector monitoring in 2016 and 2017. No county mosquito control operations were applied at area 415 in 2016 and 2017, whereas extensive adulticide spraying operations were in effect at area 802 during the summer of 2016. We collected data for eighteen microsatellite markers for 713 and 723 mosquitoes at eight timepoints from 2016 to 2017 in areas 415 and 802, respectively. We also investigated the impact of Hurricane Harvey's landfall in the Houston area in August of 2017 on Cx. quinquefasciatus population fluctuation. Results: We found that the bottleneck scenario was the most probable historical scenario describing the impact of the winter season at area 415 and area 802, with the highest posterior probability of 0.9167 and 0.4966, respectively. We also detected an expansion event following Hurricane Harvey at area 802, showing a 3.03-fold increase in 2017. Discussion: Although we did not detect significant effects of vector control interventions, we found considerable influences of the winter season and a major hurricane on the effective population size of Cx. quinquefasciatus. The fluctuations in effective population size in both areas showed a significant seasonal pattern. Additionally, the significant population expansion following Hurricane Harvey in 2017 supports the necessity for post-hurricane vector-control interventions.

Effects of climatic and environmental factors on mosquito population inferred from West Nile virus surveillance in Greece

Mosquito-borne diseases' impact on human health is among the most prominent of all communicable diseases. With limited pool of tools to contrast these diseases, public health focus remains preventing mosquito-human contacts. Applying a hierarchical spatio-temporal Bayesian model on West Nile virus (WNV) surveillance data from Greece, we aimed to investigate the impact of climatic and environmental factors on Culex mosquitoes' population. Our spatio-temporal analysis confirmed climatic factors as major drivers of WNV-transmitting-Culex mosquitoes population dynamics, with temperature and long periods of moderate-to-warm climate having the strongest positive effect on mosquito abundance. Conversely, rainfall, high humidity, and wind showed a negative impact. The results suggest the presence of statistically significant differences in the effect of regional and seasonal characteristics, highlighting the complex interplay between climatic, geographical and environmental factors in the dynamics of mosquito populations. This study may represent a relevant tool to inform public health policymakers in planning preventive measures.

The spatial-temporal pattern of Japanese encephalitis and its influencing factors in Guangxi, China

Japanese encephalitis (JE) is a major global public health threat. Using Japanese encephalitis incidence data from 2004 to 2010 in Guangxi Province, China, this study comprehensively explored the driving forces and the interactive effects between environmental and social factors of Japanese encephalitis using the Geo-detector method. The results indicated that the incidence of Japanese encephalitis showed a fluctuating downward trend from 2004 to 2010. The onset of JE was seasonal, mainly concentrated in June-July, and highly aggregated in northwestern Guangxi. Among the factors associated with Japanese encephalitis, days with temperatures >30 °C, accumulated temperatures >25 °C, slope, the normalized difference vegetation index, the gross domestic product of tertiary industries, the gross domestic product of primary industries and the number of pigs slaughtered showed higher contributions to Japanese encephalitis incidence. An enhanced interactive effect was found between environmental and social factors, and the interaction between days with humidity levels >80% and the gross domestic product of tertiary industries had the greatest combined effect on JE. These findings enhanced the understanding of the combined effect of social and environmental factors on the incidence of Japanese encephalitis and could help improve Japanese encephalitis transmission control and prevention strategies.

Predicting Culex pipiens/restuans Population Dynamics Using a Weather-Driven Dynamic Compartmental Population Model

Mosquitoes of the genus Culex are important vectors of a variety of arthropod-borne viral infections. In most of the northern parts of the USA, Cx. pipiens/restuans is the predominant representative of this genus. As vectors, they play a key role in the spreading of arboviruses and thus, knowledge of the population dynamic of mosquitoes is important to understand the disease ecology of these viruses. As poikilotherm animals, the vital rates of mosquitoes are highly dependent on ambient temperature, and also on precipitation. We present a compartmental model for the population dynamics of Cx. pipiens/restuans. The model is driven by temperature, precipitation, and daytime length (which can be calculated from the geographic latitude). For model evaluation, we used long-term mosquito capture data, which were averaged from multiple sites in Cook County, Illinois. The model fitted the observation data and was able to reproduce between-year differences in the abundance of the Cx. pipiens/restuans mosquitoes, as well as the different seasonal trends. Using this model, we evaluated the effectiveness of targeting different vital rates for mosquito control strategies. The final model is able to reproduce the weekly mean Cx. pipiens/restuans abundance for Cook County with a high accuracy, and over a long time period of 20 years.

Detection of Wuchereria bancrofti in the city of São Luís, state of Maranhão, Brazil: New incursion or persisting problem?

BACKGROUND

The elimination of lymphatic filariasis (LF) from Brazil by 2020 was not accomplished; however, this goal can be achieved in the upcoming years with the assistance of specific strategies. The surveillance of LF can be performed using molecular xenomonitoring (MX), a noninvasive method used to infer the presence of the parasite in the human population. Herein, São Luís (state of Maranhão) was the first city to be investigated to identify whether LF transmission in Brazil has been interrupted and if there were any new incursions.

METHODOLOGY/PRINCIPAL FINDINGS

Mosquitoes were collected by aspiration at 901 points distributed among 11 neighborhoods in São Luís with records of patients with microfilaremia. Pools of engorged or gravid Culex quinquefasciatus females were evaluated by WbCx duplex PCR with endogenous control for mosquitoes and target for W. bancrofti for determining the vector infection rate. Among the 10,428 collected mosquitoes, the most abundant species were C. quinquefasciatus (85%) and Aedes aegypti (12%). Significantly larger numbers of mosquitoes were collected from the neighborhoods of Areinha and Coreia (p<0.05). MX performed using PCR validated 705 pools of engorged or gravid females, fifteen of which were positive for Wuchereria bancrofti in two neighborhoods.

CONCLUSIONS

The high density of engorged C. quinquefasciatus females per home, inadequate sanitation, and detection of W. bancrofti-infected mosquitoes in the city of São Luís represent a warning of the possible upsurge of LF, a disease that is still neglected; this underscores the need for the ostensive monitoring of LF in Brazil.

Interpretable machine learning approach to analyze the effects of landscape and meteorological factors on mosquito occurrences in Seoul, South Korea

Mosquitoes are the underlying cause of various public health and economic problems. In this study, patterns of mosquito occurrence were analyzed based on landscape and meteorological factors in the metropolitan city of Seoul. We evaluated the influence of environmental factors on mosquito occurrence through the interpretation of prediction models with a machine learning algorithm. Through hierarchical cluster analysis, the study areas were classified into waterside and non-waterside areas, according to the landscape patterns. The mosquito occurrence was higher in the waterside area, and mosquito abundance was negatively affected by rainfall at the waterside. The mosquito occurrence was predicted in each cluster area based on the landscape and cumulative meteorological variables using a random forest algorithm. Both models exhibited good performance (both accuracy and AUROC > 0.8) in predicting the level of mosquito occurrence. The embedded relationship between the mosquito occurrence and the environmental factors in the models was explained using the Shapley additive explanation method. According to the variable importance and the partial dependence plots for each model, the waterside area was more influenced by the meteorological and land cover variables than the non-waterside area. Therefore, mosquito control strategies should consider the effects of landscape and meteorological conditions, including the temperature, rainfall, and the landscape heterogeneity. The present findings can contribute to the development of mosquito forecasting systems in metropolitan cities for the promotion of public health.

A Process-based Model with Temperature, Water, and Lab-derived Data Improves Predictions of Daily Culex pipiens/restuans Mosquito Density

While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration, and validation is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a discrete, semi-stochastic, mechanistic process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens (Diptera, Culicidae) and Culex restuans (Diptera, Culicidae) mosquito populations. This model combines known models for development and survival into a fully connected age-structured model that can reproduce mosquito population dynamics. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. This approach accounts for the nonlinear interaction of temperature and aquatic habitat variability on the mosquito life stages. We demonstrate that the full model predicts the yearly variations in mosquito populations better than a statistical model using the same data sources. This improvement in modeling mosquito abundance can help guide interventions for reducing mosquito abundance in mitigating mosquito-borne diseases like West Nile virus.

Joint spatiotemporal modelling reveals seasonally dynamic patterns of Japanese encephalitis vector abundance across India

Predicting vector abundance and seasonality, key components of mosquito-borne disease (MBD) hazard, is essential to determine hotspots of MBD risk and target interventions effectively. Japanese encephalitis (JE), an important MBD, is a leading cause of viral encephalopathy in Asia with 100,000 cases estimated annually, but data on the principal vector Culex tritaeniorhynchus is lacking. We developed a Bayesian joint-likelihood model that combined information from available vector occurrence and abundance data to predict seasonal vector abundance for C. tritaeniorhynchus (a constituent of JE hazard) across India, as well as examining the environmental drivers of these patterns. Using data collated from 57 locations from 24 studies, we find distinct seasonal and spatial patterns of JE vector abundance influenced by climatic and land use factors. Lagged precipitation, temperature and land use intensity metrics for rice crop cultivation were the main drivers of vector abundance, independent of seasonal, or spatial variation. The inclusion of environmental factors and a seasonal term improved model prediction accuracy (mean absolute error [MAE] for random cross validation = 0.48) compared to a baseline model representative of static hazard predictions (MAE = 0.95), signalling the importance of seasonal environmental conditions in predicting JE vector abundance. Vector abundance varied widely across India with high abundance predicted in northern, north-eastern, eastern, and southern regions, although this ranged from seasonal (e.g., Uttar Pradesh, West Bengal) to perennial (e.g., Assam, Tamil Nadu). One-month lagged predicted vector abundance was a significant predictor of JE outbreaks (odds ratio 2.45, 95% confidence interval: 1.52–4.08), highlighting the possible development of vector abundance as a proxy for JE hazard. We demonstrate a novel approach that leverages information from sparse vector surveillance data to predict seasonal vector abundance–a key component of JE hazard–over large spatial scales, providing decision-makers with better guidance for targeting vector surveillance and control efforts.

Japanese encephalitis (JE) is the leading cause of viral encephalopathy in Asia with an estimated 100,000 annual cases and 25,000 deaths. However, insufficient data on the predominant mosquito vector Culex tritaeniorhynchus–a key component of JE hazard–precludes hazard estimation required to target public health interventions. Previous studies have provided limited estimates of JE hazard, often predicting geographic distributions of potential vector occurrence without accounting for vector abundance, seasonality, or uncertainty in predictions. This study details a novel approach to predict spatiotemporal patterns in JE vector abundance using a joint-likelihood modelling technique that leverages information from sparse vector surveillance data. We showed that patterns in JE vector abundance were driven by seasonality and environmental factors and so demonstrated the limitations of previously available static vector distribution maps in estimating the vector population component of JE hazard. One-month lagged vector abundance predictions showed a positive relationship with JE outbreaks, signalling the potential use of vector abundance as a proxy for JE hazard. While vector surveillance data are limited, joint-likelihood models offer a useful approach to inform vector abundance predictions. This study provides decision-makers with a more complete picture of the distribution of JE vector abundance and can be used to target vector surveillance and control efforts and enhance the allocation of resources.

The abundance of Culex mosquito vectors for West Nile Virus and other flaviviruses: A time-series analysis of rainfall and temperature dependence in Singapore

Culex mosquitoes are important vectors of West Nile Virus (WNV), St. Louis Encephalitis Virus (SLEV) and Japanese Encephalitis Virus (JEV). Climate change is expected to alter their ability to spread diseases in human populations. Studies examining the influence of climate variability on Culex mosquitoes in South East Asia are scarce. We examined the influence of climate variability on reported Culex mosquito larval habitats from 2009 to 2018 in Singapore. We analysed the non-linear immediate and lagged weather dependence of Culex habitats over 5 weeks in negative binomial regression models using nationally representative data. We adjusted for the effects of long-term trend, seasonality, public holidays and autocorrelation. There were 41,170 reported Culex larval habitats over the study period. Non-residential premises were associated with more reports of habitats compared to residential premises [Rate Ratio (RR): 113.9, 95% CI: 110.9, 116.9]. Larvae in more than 90% of these habitats were entomologically identified as Culex quinquefasciatus. In residences, every 10 mm increase in rainfall above a 90 mm threshold was associated with a 10.1% [Incidence Rate Ratio (IRR): 0.899, 95% CI: 0.836, 0.968] cumulative decline in larval habitats. Public holidays were not significantly included in the model analysing larval habitats in residences. In non-residences, a 1 °C increase in the ambient air temperature with respect to the mean was associated with a 36.0% (IRR: 1.360, 95% CI: 1.057, 1.749) cumulative increase in Culex larval habitats. Public holidays were associated with a decline in Culex larval habitats in the same week. Our study provides evidence of how ambient air temperature and rainfall variability influences the abundance of Culex mosquito larval habitats. Our findings support the utility of using weather data in predictive models to inform the timing of vector control measures aimed at reducing the risk of WNV and other Culex-borne flavivirus transmission in urban areas.

Environmental drivers, climate change and emergent diseases transmitted by mosquitoes and their vectors in southern Europe: A systematic review

Mosquito borne diseases are a group of infections that affect humans. Emerging or reemerging diseases are those that (re)occur in regions, groups or hosts that were previously free from these diseases: dengue virus; chikungunya virus; Zika virus; West Nile fever and malaria. In Europe, these infections are mostly imported; however, due to the presence of competent mosquitoes and the number of trips both to and from endemic areas, these pathogens are potentially emergent or re-emergent. Present and future climatic conditions, as well as meteorological, environmental and demographic aspects are risk factors for the distribution of different vectors and/or diseases. This review aimed to identify and analyze the existing literature on the transmission of mosquito borne diseases and those factors potentially affecting their transmission risk of them in six southern European countries with similar environmental conditions: Croatia, France, Greece, Italy, Portugal and Spain. In addition, we would identify those factors potentially affecting the (re)introduction or spread of mosquito vectors. This task has been undertaken with a focus on the environmental and climatic factors, including the effects of climate change. We undertook a systematic review of the vectors, diseases and their associations with climactic and environmental factors in European countries of the Mediterranean region. We followed the PRISMA guidelines and used explicit and systematic methods to identify, select and critically evaluate the studies which were relevant to the topic. We identified 1302 articles in the first search of the databases. Of those, 160 were selected for full-text review. The final data set included 61 articles published between 2000 and 2017.39.3% of the papers were related with dengue, chikungunya and Zika virus or their vectors. Temperature, precipitation and population density were key factors among others. 32.8% studied West Nile virus and its vectors, being temperature, precipitation and NDVI the most frequently used variables. Malaria have been studied in 23% of the articles, with temperature, precipitation and presence of water indexes as the most used variables. The number of publications focused on mosquito borne diseases is increasing in recent years, reflecting the increased interest in that diseases in southern European countries. Climatic and environmental variables are key factors on mosquitoes' distribution and to show the risk of emergence and/or spread of emergent diseases and to study the spatial changes in that distributions.

Robustness of the reproductive number estimates in vector-borne disease systems

BACKGROUND

The required efforts, feasibility and predicted success of an intervention strategy against an infectious disease are partially determined by its basic reproduction number, R0. In its simplest form R0 can be understood as the product of the infectious period, the number of infectious contacts and the per-contact transmission probability, which in the case of vector-transmitted diseases necessarily extend to the vector stages. As vectors do not usually recover from infection, they remain infectious for life, which places high significance on the vector's life expectancy. Current methods for estimating the R0 for a vector-borne disease are mostly derived from compartmental modelling frameworks assuming constant vector mortality rates. We hypothesised that some of the assumptions underlying these models can lead to unrealistic high vector life expectancies with important repercussions for R0 estimates.

METHODOLOGY AND PRINCIPAL FINDINGS

Here we used a stochastic, individual-based model which allowed us to directly measure the number of secondary infections arising from one index case under different assumptions about vector mortality. Our results confirm that formulas based on age-independent mortality rates can overestimate R0 by nearly 100% compared to our own estimate derived from first principles. We further provide a correction factor that can be used with a standard R0 formula and adjusts for the discrepancies due to erroneous vector age distributions.

CONCLUSION

Vector mortality rates play a crucial role for the success and general epidemiology of vector-transmitted diseases. Many modelling efforts intrinsically assume these to be age-independent, which, as clearly demonstrated here, can lead to severe over-estimation of the disease's reproduction number. Our results thus re-emphasise the importance of obtaining field-relevant and species-dependent vector mortality rates, which in turn would facilitate more realistic intervention impact predictions.