An integrated approach for the assessment of the Aedes aegypti and Aedes albopictus global spatial distribution, and determination of the zones susceptible to the development of Zika virus

Spatial heterogeneity in the potential distribution of Aedes mosquitoes in India under current and future climatic scenarios

Aedes is the most globally distributed mosquito genus in the 21st century and transmits various arboviral diseases. The rapid expansion of Ae. Aegypti and Ae. albopictus breeding habitats is a significant threat to global public health, driven by temperature and precipitation changes. In this study, bioclimatic variables were employed to predict the spatial distribution of Ae. aegypti and Ae. albopictus in India. The reference coordinate points of (n = 583) Aedes occurrences at a scale of ∼1 km and nineteen bioclimatic factors were retrieved to train SDM (Species Distribution Models) for both species. Maximum entropy modelling was used to predict the species' fundamental climatic niche distributions. Future projections were made using global climate models for 2021-2040 and 2081-2100 separately. The models performed reasonably well (AUC > 0.77). Both species thrived in reduced diurnal temperature and higher annual mean temperatures, with suitability increasing alongside precipitation. Ae. aegypti's projected present and future distribution was broader than that of Ae. Albopictus. The expansion of Aedes suitability varied under different future climatic scenarios. Suitability for Ae. aegypti could expand from between 17.6 and 41.1 % in 2100 under SSP (shared socioeconomic pathways) scenarios 1 and 3, respectively, whereas for Ae. albopictus suitability increased from between 10.2 and 25 % under SSP scenarios 1 and 3 respectively. Preparing for future epidemics and outbreaks requires robust vector distribution models to identify high-risk areas, allocate resources for surveillance and control, and implement prevention strategies.

Geographic expansion of the introduced Aedes albopictus and other native Aedes species in the Democratic Republic of the Congo

BACKGROUND

Aedes albopictus has been reported in several Central African countries, including the Democratic Republic of the Congo (DRC). The establishment of this mosquito species poses a serious threat as a vector of various infectious diseases. Although Ae. albopictus has been reported in the western region of the DRC, information about its distribution is still scarce in the country. The aim of this study was to investigate the current nationwide distribution of the invasive Ae. albopictus, as well as other native Aedes mosquitoes, in the DRC and to identify suitable areas for its future expansion.

METHODS

Two entomological surveys were conducted in 2017-2019 and 2022. Based on the occurrence sites of Ae. albopictus, important environmental variables were identified. Then, geographical areas suitable for Ae. albopictus establishment were determined using the maximum entropy model. The distribution and abundance of Ae. albopictus were also compared with those of the major native Aedes species.

RESULTS

Aedes albopictus was found in the western, northern, central, and eastern regions of the DRC, but it was not found in the southeastern region. The maximum entropy model predicted that most parts of the DRC are suitable for the establishment of this mosquito. The unsuitable areas encompassed the eastern highlands, known for their low temperatures, and the southeastern highlands, which experience both low temperatures and a long dry season. The native Aedes species found were Aedes aegypti, Aedes simpsoni, Aedes africanus, and Aedes vittatus. Aedes albopictus dominated in the western and northern regions, while Ae. aegypti was more prevalent in other regions.

CONCLUSIONS

Aedes albopictus has been well established in the western and northern regions of the DRC. This mosquito is expanding its distribution while replacing the native Aedes species. Most of the country is suitable for the establishment of this mosquito species, except the highlands of the eastern and the southeastern regions.

Trends in mosquito species distribution modeling: insights for vector surveillance and disease control

Species distribution modeling (SDM) has become an increasingly common approach to explore questions about ecology, geography, outbreak risk, and global change as they relate to infectious disease vectors. Here, we conducted a systematic review of the scientific literature, screening 563 abstracts and identifying 204 studies that used SDMs to produce distribution estimates for mosquito species. While the number of studies employing SDM methods has increased markedly over the past decade, the overwhelming majority used a single method (maximum entropy modeling; MaxEnt) and focused on human infectious disease vectors or their close relatives. The majority of regional models were developed for areas in Africa and Asia, while more localized modeling efforts were most common for North America and Europe. Findings from this study highlight gaps in taxonomic, geographic, and methodological foci of current SDM literature for mosquitoes that can guide future efforts to study the geography of mosquito-borne disease risk.

Disease burden of congenital Zika virus syndrome in Brazil and its association with socioeconomic data

Zika virus (ZIKV) infection became a global public health concern, causing an epidemic in Latin America from 2015 to 2016, when a sudden increase in cases of microcephaly and other congenital anomalies was observed. In 2016, the Centers for Disease Control and Prevention and the World Health Organization defined congenital Zika-associated syndrome (CZS) as a set of congenital anomalies seen in children born to mothers with a history of gestational Zika fever, who have microcephaly as the most prevalent clinical sign. In order to describe the magnitude of CZS in Brazil, this study estimated the burden of disease due to CZS in Brazil using the disability-adjusted life years (DALY) indicator and other frequency measures, such as incidence and mortality rate, during the years 2015-2020. The association of these indicators with socioeconomic variables was also evaluated using Spearman's correlation coefficient. Choropleth maps were used to evaluate the spatial distribution of the indicators evaluated and the spatial autocorrelation was verified by the Bivariate Moran Local Index. From 2015 to 2020, 3,591 cases of CZS were confirmed in Brazil, with an incidence of 44.03 cases per 1000 live births, and a specific mortality of 12.35 deaths per 1000 live births. A global loss of 30,027.44 DALYs was estimated from 2015 to 2020. The Northeast region had the highest values for all health indicators assessed. Spatial correlation and autocorrelation analyses showed significant associations between health and socioeconomic indicators, such as per capita income, Gini index, illiteracy rate and basic sanitation. The study allowed us to have access to all reported cases of CZS, showing us the possible situation of the disease in Brazil; therefore, we believe that our results can help in the understanding of future studies.

Global Distribution of Aedes aegypti and Aedes albopictus in a Climate Change Scenario of Regional Rivalry

Arboviral mosquito vectors are key targets for the surveillance and control of vector-borne diseases worldwide. In recent years, changes to the global distributions of these species have been a major research focus, aimed at predicting outbreaks of arboviral diseases. In this study, we analyzed a global scenario of climate change under regional rivalry to predict changes to these species' distributions over the next century. Using occurrence data from VectorMap and environmental variables (temperature and precipitation) from WorldClim v. 2.1, we first built fundamental niche models for both species with the boosted regression tree modelling approach. A scenario of climate change on their fundamental niche was then analyzed. The shared socioeconomic pathway scenario 3 (regional rivalry) and the global climate model Geophysical Fluid Dynamics Laboratory Earth System Model v. 4.1 (GFDL-ESM4.1; gfdl.noaa.gov) were utilized for all analyses, in the following time periods: 2021-2040, 2041-2060, 2061-2080, and 2081-2100. Outcomes from these analyses showed that future climate change will affect Ae. aegypti and Ae. albopictus distributions in different ways across the globe. The Northern Hemisphere will have extended Ae. aegypti and Ae. albopictus distributions in future climate change scenarios, whereas the Southern Hemisphere will have the opposite outcomes. Europe will become more suitable for both species and their related vector-borne diseases. Loss of suitability in the Brazilian Amazon region further indicated that this tropical rainforest biome will have lower levels of precipitation to support these species in the future. Our models provide possible future scenarios to help identify locations for resource allocation and surveillance efforts before a significant threat to human health emerges.

Congenital anomalies during the 2015-2018 Zika virus epidemic: a population-based cross-sectional study

BACKGROUND

Congenital anomalies are associated with several clinical and epidemiological complications. Following the Zika epidemic onset in Latin America, the incidence of congenital anomalies increased in Brazil. This study aimed to determine the frequency of congenital anomalies in one Brazilian state and assess potential factors associated with them.

METHODS

This cross-sectional descriptive study was based on data concerning congenital anomalies recorded in the Brazilian Live-Born Information System during the Zika epidemic in Mato Grosso do Sul state from 2015 to 2018. Congenital anomalies were stratified according to year of birth and classified using ICD-10 categories.

RESULTS

In total, 1,473 (0.85%) anomalies were registered. Within the number of cases recorded, microcephaly showed the greatest frequency and variations, with a 420% increase observed in the number of cases from 2015 to 2016. We identified an increase in the incidence of central nervous system anomalies, with the highest peak observed in 2016 followed by a subsequent decrease. Musculoskeletal, nervous, and cardiovascular system anomalies, and eye, ear, face, and neck anomalies represented 73.9% of all recorded anomalies. There was an increased chance of congenital anomalies in uneducated (odds ratio [OR] 5.56, 95% confidence interval [CI] 2.61-11.84) and Indigenous (OR 1.32, 95% CI 1.03-1.69) women, as well as among premature births (OR 2.74, 95% CI 2.39-3.13).

CONCLUSIONS

We estimated the incidence of congenital anomalies during the Zika epidemic. Our findings could help to support future research and intervention strategies in health facilities to better identify and assist children born with congenital anomalies.

Mapping the spatial distribution of the dengue vector Aedes aegypti and predicting its abundance in northeastern Thailand using machine-learning approach

BACKGROUND

Mapping the spatial distribution of the dengue vector Aedes (Ae.) aegypti and accurately predicting its abundance are crucial for designing effective vector control strategies and early warning tools for dengue epidemic prevention. Socio-ecological and landscape factors influence Ae. aegypti abundance. Therefore, we aimed to map the spatial distribution of female adult Ae. aegypti and predict its abundance in northeastern Thailand based on socioeconomic, climate change, and dengue knowledge, attitude and practices (KAP) and/or landscape factors using machine learning (ML)-based system.

METHOD

A total of 1066 females adult Ae. aegypti were collected from four villages in northeastern Thailand during January-December 2019. Information on household socioeconomics, KAP regarding climate change and dengue, and satellite-based landscape data were also acquired. Geographic information systems (GIS) were used to map the household-based spatial distribution of female adult Ae. aegypti abundance (high/low). Five popular supervised learning models, logistic regression (LR), support vector machine (SVM), k-nearest neighbor (kNN), artificial neural network (ANN), and random forest (RF), were used to predict females adult Ae. aegypti abundance (high/low). The predictive accuracy of each modeling technique was calculated and evaluated. Important variables for predicting female adult Ae. aegypti abundance were also identified using the best-fitted model.

RESULTS

Urban areas had higher abundance of female adult Ae. aegypti compared to rural areas. Overall, study respondents in both urban and rural areas had inadequate KAP regarding climate change and dengue. The average landscape factors per household in urban areas were rice crop (47.4%), natural tree cover (17.8%), built-up area (13.2%), permanent wetlands (21.2%), and rubber plantation (0%), and the corresponding figures for rural areas were 12.1, 2.0, 38.7, 40.1 and 0.1% respectively. Among all assessed models, RF showed the best prediction performance (socioeconomics: area under curve, AUC = 0.93, classification accuracy, CA = 0.86, F1 score = 0.85; KAP: AUC = 0.95, CA = 0.92, F1 = 0.90; landscape: AUC = 0.96, CA = 0.89, F1 = 0.87) for female adult Ae. aegypti abundance. The combined influences of all factors further improved the predictive accuracy in RF model (socioeconomics + KAP + landscape: AUC = 0.99, CA = 0.96 and F1 = 0.95). Dengue prevention practices were shown to be the most important predictor in the RF model for female adult Ae. aegypti abundance in northeastern Thailand.

CONCLUSION

The RF model is more suitable for the prediction of Ae. aegypti abundance in northeastern Thailand. Our study exemplifies that the application of GIS and machine learning systems has significant potential for understanding the spatial distribution of dengue vectors and predicting its abundance. The study findings might help optimize vector control strategies, future mosquito suppression, prediction and control strategies of epidemic arboviral diseases (dengue, chikungunya, and Zika). Such strategies can be incorporated into One Health approaches applying transdisciplinary approaches considering human-vector and agro-environmental interrelationships.

Zika virus infection and microcephaly: spatial analysis and socio-environmental determinants in a region of high Aedes aegypti infestation in the Central-West Region of Brazil

Background

More than 5 years after the Zika virus (ZIKV) epidemic, Zika infection remains a major concern in regions with high Aedes infestation. The objectives of this study were (i) to identify clusters of ZIKV infection and microcephaly, and/or central nervous system (CNS) alterations associated with congenital infection during the epidemic peak in 2016 and subsequently, in 2017 and 2018; (ii) to measure the non-spatial correlation between ZIKV infection and microcephaly and/or CNS alterations associated with congenital infection; and (iii) to analyse the sociodemographic/economic, health, and environmental determinants associated with the incidence of ZIKV in a region of high infestation by Aedes aegypti in the Central-West Region of Brazil.

Methods

This ecological study analysed 246 municipalities in the state of Goiás (6.9 million inhabitants). The data were obtained from the Information System for Notifiable Diseases (ZIKV cases) and the Public Health Event Registry (microcephaly and/or CNS alterations associated with congenital infection). Incidence rates and prevalence of ZIKA infection were smoothed by an empirical Bayesian estimator (LEbayes), producing the local empirical Bayesian rate (LEBR). In the spatial analysis, ZIKV infection and microcephaly cases were georeferenced by the municipality of residence for 2016 and grouped for 2017 and 2018. Global Moran's I and the Hot Spot Analysis tool (Getis-Ord Gi* statistics) were used to analyse the spatial autocorrelation and clusters of ZIKV infection and microcephaly, respectively. A generalised linear model from the Poisson family was used to assess the association between ecological determinants and the smoothing incidence rate of ZIKV infection.

Results

A total of 9892 cases of acute ZIKV infection and 121 cases of microcephaly were confirmed. The mean LEBR of the ZIKV infection in the 246 municipalities was 22.3 cases/100,000 inhabitants in 2016, and 10.3 cases/100,000 inhabitants in 2017 and 2018. The LEBR of the prevalence rate of microcephaly and/or CNS alterations associated with congenital infection was 7 cases/10,000 live births in 2016 and 2 cases/10,000 live births during 2017–2018. Hotspots of ZIKV infection and microcephaly cases were identified in the capital and neighbouring municipalities in 2016, with new clusters in the following years. In a multiple regression Poisson analysis, ZIKV infection was associated with higher population density, the incidence of dengue, Aedes larvae infestation index, and average rainfall. The important determinant of ZIKV infection incidence reduction was the increase in households attended by endemic disease control agents.

Conclusions

Our analyses were able to capture, in a more granular way, aspects that make it possible to inform public managers of the sentinel areas identified in the post-epidemic hotspots.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06805-1.

Wide and increasing suitability for Aedes albopictus in Europe is congruent across distribution models

The Asian tiger mosquito (Aedes albopictus), a vector of dengue, Zika and other diseases, was introduced in Europe in the 1970s, where it is still widening its range. Spurred by public health concerns, several studies have delivered predictions of the current and future distribution of the species for this region, often with differing results. We provide the first joint analysis of these predictions, to identify consensus hotspots of high and low suitability, as well as areas with high uncertainty. The analysis focused on current and future climate conditions and was carried out for the whole of Europe and for 65 major urban areas. High consensus on current suitability was found for the northwest of the Iberian Peninsula, southern France, Italy and the coastline between the western Balkans and Greece. Most models also agree on a substantial future expansion of suitable areas into northern and eastern Europe. About 83% of urban areas are expected to become suitable in the future, in contrast with ~ 49% nowadays. Our findings show that previous research is congruent in identifying wide suitable areas for Aedes albopictus across Europe and in the need to effectively account for climate change in managing and preventing its future spread.

Assessing the suitability for Aedes albopictus and dengue transmission risk in China with a delay differential equation model

Dengue is considered non-endemic to mainland China. However, travellers frequently import the virus from overseas and local mosquito species can then spread the disease in the population. As a consequence, mainland China still experiences large dengue outbreaks. Temperature plays a key role in these outbreaks: it affects the development and survival of the vector and the replication rate of the virus. To better understand its implication in the transmission risk of dengue, we developed a delay differential equation model that explicitly simulates temperature-dependent development periods and tested it with collected field data for the Asian tiger mosquito, Aedes albopictus. The model predicts mosquito occurrence locations with a high accuracy (Cohen's κ of 0.78) and realistically replicates mosquito population dynamics. Analysing the infection dynamics during the 2014 dengue outbreak that occurred in Guangzhou showed that the outbreak could have lasted for another four weeks if mosquito control interventions had not been undertaken. Finally, we analyse the dengue transmission risk in mainland China. We find that southern China, including Guangzhou, can have more than seven months of dengue transmission per year while even Beijing, in the temperate north, can have dengue transmission during hot summer months. The results demonstrate the importance of using detailed vector and infection ecology, especially when vector-borne disease transmission risk is modelled over a broad range of climatic zones.

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.

An Investigation of Human-Mosquito Contact Using Surveys and Its Application in Assessing Dengue Viral Transmission Risk

Aedes-borne viral diseases such as dengue fever are surging in incidence in recent years. To investigate viral transmission risks, the availability of local transmission parameters is essential. One of the most important factors directly determining infection risk is human-mosquito contact. Yet the contact rate is not often characterized, compared with other risk metrics such as vector density, because of the limited research tool options. In this study, human-mosquito contact was assessed in two study sites in the Southern United States using self-administered standardized survey instruments. The fraction of mosquito bites attributed to important vector species was estimated by human landing sampling. The survey participants reported a significantly higher outdoor mosquito bite exposure than indoor. The reported bite number was positively correlated with outdoor time during at-risk periods. There was also a significant effect of the study site on outdoor bite exposure, possibly due to the differing vector density. Thus, the levels of human-mosquito contact in this study were influenced both by the mosquito density and human behaviors. A dengue virus transmission model demonstrated that the observed difference in the contact rates results in differential virus transmission risks. Our findings highlight the practicality of using surveys to investigate human-mosquito contact in a setting where bite exposure levels differ substantially, and serve as a basis for further evaluations. This study underscores a new avenue that can be used in combination with other field methods to understand how changes in human behavior may influence mosquito bite exposure which drives mosquito-borne virus transmission.

Development and calibration of a model for the potential establishment and impact of Aedes albopictus in Europe

The Asian tiger mosquito (Aedes albopictus) is one of the most invasive disease vectors worldwide. The species is a competent vector of dengue, chikungunya, Zika viruses and other severe parasites and pathogens threatening human health. The capacity of this mosquito to colonize and establish in new areas (including temperate regions) is enhanced by its ability of producing diapausing eggs that survive relatively cold winters. The main drivers of population dynamics for this mosquito are water and air temperature and photoperiod. In this paper, we present a mechanistic model that predicts the potential distribution, abundance and activity of Asian tiger mosquito in Europe. The model includes a comprehensive description of: i) the individual life-history strategies, including diapause, ii) the influence of weather-driven individual physiological responses on population dynamics and iii) the density-dependent regulation of larval mortality rate. The model is calibrated using field data from several locations along an altitudinal gradient in the Italian Alps, which enabled accurate prediction of cold temperature effects on population abundance, including identification of conditions that prevent overwintering of the species. Model predictions are consistent with the most updated information on species' presence and absence. Predicted population abundance shows a clear south-north decreasing gradient. A similar yet less evident pattern in the activity of the species is also predicted. The model represents a valuable tool for the development of strategies aimed at the management of Ae. albopictus and for the implementation of effective control measures against vector-borne diseases in Europe.

Geographic shifts in the bioclimatic suitability for Aedes aegypti under climate change scenarios in Colombia

The Dengue, Chikungunya and Zika viruses are arboviruses predominantly transmitted to humans through the bite of the female mosquito Aedes aegypti. Currently, the vector represents a potential epidemiological risk in several Latin American and Pacific countries. However, little is known about the geographical distribution and bioclimatic suitability of this mosquito in the projected climate change scenarios in Colombia. Using a species distribution model of maximum entropy (MaxEnt) based on presence-only records obtained from Global Biodiversity Information Facility (GBIF), land elevation obtained from Shuttle Radar Topography Mission (SRTM) and bioclimatic variables (WorldClim), we produced environmental suitability maps of this mosquito vector for present and future geographic distribution. The future distribution were constructed based on the Community Climate System Model (CCSM4) for the years 2050 and 2070, projected according to the Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 described by the Intergovernmental Panel on Climate Change (IPCC). For the current conditions, Colombia has ~140,612.8 square km of areas with the possible presence of the vector; however, for the future, this will be reduced by more than 30%. For the future conditions, the suitable areas for A. aegypti decreased compared to the present, mainly for the year 2070 under RCP scenarios 4.5 and 8.5, however, the probability of mosquito occurrence increases in some departments of Colombia. Areas susceptible to the presence of A. aegypti are affected by climate change. The Caribbean and Andean regions have a high probability of mosquito distribution; therefore, control and epidemiological surveillance are required in these areas. The results can serve as an input to define preventive and control measures, especially in areas with a higher risk of contracting the virus.

Patterns of spatial genetic structures in Aedes albopictus (Diptera: Culicidae) populations in China

Background

The Asian tiger mosquito, Aedes albopictus, is one of the 100 worst invasive species in the world and the vector for several arboviruses including dengue, Zika and chikungunya viruses. Understanding the population spatial genetic structure, migration, and gene flow of vector species is critical to effectively preventing and controlling vector-borne diseases. Little is known about the population structure and genetic differentiation of native Ae. albopictus in China. The aim of this study was to examine the patterns of the spatial genetic structures of native Ae. albopictus populations, and their relationship to dengue incidence, on a large geographical scale.

Methods

During 2016–2018, adult female Ae. albopictus mosquitoes were collected by human landing catch (HLC) or human-bait sweep-net collections in 34 localities across China. Thirteen microsatellite markers were used to examine the patterns of genetic diversity, population structure, and gene flow among native Ae. albopictus populations. The correlation between population genetic indices and dengue incidence was also examined.

Results

A total of 153 distinct alleles were identified at the 13 microsatellite loci in the tested populations. All loci were polymorphic, with the number of distinct alleles ranging from eight to sixteen. Genetic parameters such as PIC, heterozygosity, allelic richness and fixation index (F_ST) revealed highly polymorphic markers, high genetic diversity, and low population genetic differentiation. In addition, Bayesian analysis of population structure showed two distinct genetic groups in southern-western and eastern-central-northern China. The Mantel test indicated a positive correlation between genetic distance and geographical distance (R² = 0.245, P = 0.01). STRUCTURE analysis, PCoA and GLS interpolation analysis indicated that Ae. albopictus populations in China were regionally clustered. Gene flow and relatedness estimates were generally high between populations. We observed no correlation between population genetic indices of microsatellite loci in Ae. albopictus populations and dengue incidence.

Conclusion

Strong gene flow probably assisted by human activities inhibited population differentiation and promoted genetic diversity among populations of Ae. albopictus. This may represent a potential risk of rapid spread of mosquito-borne diseases. The spatial genetic structure, coupled with the association between genetic indices and dengue incidence, may have important implications for understanding the epidemiology, prevention, and control of vector-borne diseases.

The Role of Temperature in Transmission of Zoonotic Arboviruses

We reviewed the literature on the role of temperature in transmission of zoonotic arboviruses. Vector competence is affected by both direct and indirect effects of temperature, and generally increases with increasing temperature, but results may vary by vector species, population, and viral strain. Temperature additionally has a significant influence on life history traits of vectors at both immature and adult life stages, and for important behaviors such as blood-feeding and mating. Similar to vector competence, temperature effects on life history traits can vary by species and population. Vector, host, and viral distributions are all affected by temperature, and are generally expected to change with increased temperatures predicted under climate change. Arboviruses are generally expected to shift poleward and to higher elevations under climate change, yet significant variability on fine geographic scales is likely. Temperature effects are generally unimodal, with increases in abundance up to an optimum, and then decreases at high temperatures. Improved vector distribution information could facilitate future distribution modeling. A wide variety of approaches have been used to model viral distributions, although most research has focused on the West Nile virus. Direct temperature effects are frequently observed, as are indirect effects, such as through droughts, where temperature interacts with rainfall. Thermal biology approaches hold much promise for syntheses across viruses, vectors, and hosts, yet future studies must consider the specificity of interactions and the dynamic nature of evolving biological systems.

A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern

INTRODUCTION

Epidemic forecasting and prediction tools have the potential to provide actionable information in the midst of emerging epidemics. While numerous predictive studies were published during the 2016-2017 Zika Virus (ZIKV) pandemic, it remains unknown how timely, reproducible, and actionable the information produced by these studies was.

METHODS

To improve the functional use of mathematical modeling in support of future infectious disease outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE, and grey literature review, we identified studies that forecasted, predicted, or simulated ecological or epidemiological phenomena related to the Zika pandemic that were published as of March 01, 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, reproducibility, accessibility, and clarity by independent reviewers.

RESULTS

2034 studies were identified, of which n = 73 met the eligibility criteria. Spatial spread, R0 (basic reproductive number), and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-Barré Syndrome burden (4%), sexual transmission risk (4%), and intervention impact (4%). Most studies specifically examined populations in the Americas (52%), with few African-specific studies (4%). Case count (67%), vector (41%), and demographic data (37%) were the most common data sources. Real-time internet data and pathogen genomic information were used in 7% and 0% of studies, respectively, and social science and behavioral data were typically absent in modeling efforts. Deterministic models were favored over stochastic approaches. Forty percent of studies made model data entirely available, 29% provided all relevant model code, 43% presented uncertainty in all predictions, and 54% provided sufficient methodological detail to allow complete reproducibility. Fifty-one percent of predictions were published after the epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV predictions by a median of 119 days sooner than journal publication dates, they were used in only 30% of studies.

CONCLUSIONS

Many ZIKV predictions were published during the 2016-2017 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in these published predictions varied and indicates there is substantial room for improvement. To enhance the utility of analytical tools for outbreak response it is essential to improve the sharing of model data, code, and preprints for future outbreaks, epidemics, and pandemics.

Estimating Past, Present, and Future Trends in the Global Distribution and Abundance of the Arbovirus Vector Aedes aegypti Under Climate Change Scenarios

Background:Aedes aegypti is the principal vector for several important arbovirus diseases, including dengue, chikungunya, yellow fever, and Zika. While recent empirical research has attempted to identify the current global distribution of the vector, the seasonal, and longer-term dynamics of the mosquito in response to trends in climate, population, and economic development over the twentieth and the twenty-first century remains to be elucidated.

Methods: In this study, we use a process-based mathematical model to estimate global vector distribution and abundance. The model is based on the lifecycle of the vector and its dependence on climate, and the model sensitivity to socio-economic development is tested. Model parameters were generally empirically based, and the model was calibrated to global databases and time series of occurrence and abundance records. Climate data on temperature and rainfall were taken from CRU TS3.25 (1901–2015) and five global circulation models (CMIP5; 2006–2099) forced by a high-end (RCP8.5) and a low-end (RCP2.6) emission scenario. Socio-economic data on global GDP and human population density were from ISIMIP (1950–2099).

Findings: The change in the potential of global abundance in A. aegypti over the last century up to today is estimated to be an increase of 9.5% globally and a further increase of 20 or 30% by the end of this century under a low compared to a high carbon emission future, respectively. The largest increase has occurred in the last two decades, indicating a tipping point in climate-driven global abundance which will be stabilized at the earliest in the mid-twenty-first century. The realized abundance is estimated to be sensitive to socioeconomic development.

Interpretation: Our data indicate that climate change mitigation, i.e., following the Paris Agreement, could considerably help in suppressing risks of increased abundance and emergence of A. aegypti globally in the second half of the twenty-first century.

Application of convolutional neural networks for classification of adult mosquitoes in the field

Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of adequately trained professionals. We implemented a computational model based on a convolutional neural network (CNN) to extract features from mosquito images to identify adult mosquitoes from the species Aedes aegypti, Aedes albopictus and Culex quinquefasciatus. To train the CNN to perform automatic morphological classification of mosquitoes, we used a dataset that included 4,056 mosquito images. Three neural networks, including LeNet, AlexNet and GoogleNet, were used. During the validation phase, the accuracy of the mosquito classification was 57.5% using LeNet, 74.7% using AlexNet and 83.9% using GoogleNet. During the testing phase, the best result (76.2%) was obtained using GoogleNet; results of 52.4% and 51.2% were obtained using LeNet and AlexNet, respectively. Significantly, accuracies of 100% and 90% were achieved for the classification of Aedes and Culex, respectively. A classification accuracy of 82% was achieved for Aedes females. Our results provide information that is fundamental for the automatic morphological classification of adult mosquito species in field. The use of CNN's is an important method for autonomous identification and is a valuable and accessible resource for health workers and taxonomists for the identification of some insects that can transmit infectious agents to humans.

Mapping the transmission risk of Zika virus using machine learning models

Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public health problems with its rapid transnational expansion fueled by increased global travel and trade. Suitability mapping of the transmission risk of Zika virus is essential for drafting public health plans and disease control strategies, which are especially important in areas where medical resources are relatively scarce. Predicting the risk of Zika virus outbreak has been studied in recent years, but the published literature rarely includes multiple model comparisons or predictive uncertainty analysis. Here, three relatively popular machine learning models including backward propagation neural network (BPNN), gradient boosting machine (GBM) and random forest (RF) were adopted to map the probability of Zika epidemic outbreak at the global level, pairing high-dimensional multidisciplinary covariate layers with comprehensive location data on recorded Zika virus infection in humans. The results show that the predicted high-risk areas for Zika transmission are concentrated in four regions: Southeastern North America, Eastern South America, Central Africa and Eastern Asia. To evaluate the performance of machine learning models, the 50 modeling processes were conducted based on a training dataset. The BPNN model obtained the highest predictive accuracy with a 10-fold cross-validation area under the curve (AUC) of 0.966 [95% confidence interval (CI) 0.965-0.967], followed by the GBM model (10-fold cross-validation AUC = 0.964[0.963-0.965]) and the RF model (10-fold cross-validation AUC = 0.963[0.962-0.964]). Based on training samples, compared with the BPNN-based model, we find that significant differences (p = 0.0258* and p = 0.0001***, respectively) are observed for prediction accuracies achieved by the GBM and RF models. Importantly, the prediction uncertainty introduced by the selection of absence data was quantified and could provide more accurate fundamental and scientific information for further study on disease transmission prediction and risk assessment.

Zika might not be acting alone: Using an ecological study approach to investigate potential co-acting risk factors for an unusual pattern of microcephaly in Brazil

Zika virus infections can cause a range of neurologic disorders including congenital microcephaly. However, while Zika infections have been notified across all regions in Brazil, there has been an unusual number of congenital microcephaly case notifications concentrated in the Northeast of the country. To address this observation, we investigated epidemiological data (2014–2016) on arbovirus co-distribution, environmental and socio-economic factors for each region in Brazil. Data on arbovirus reported cases and microcephaly were collected from several Brazilian Ministry of Health databases for each Federal unit. These were complemented by environmental management, social economic and Aedes aegypti infestation index data, extracted from multiple databases. Spatial time “ecological” analysis on the number of arboviruses transmitted by Aedes mosquitoes in Brazil show that the distribution of dengue and Zika was widespread in the whole country, with higher incidence in the West-Central region. However, reported chikungunya cases were higher in the Northeast, the region also with the highest number of microcephaly cases registered. Social economic factors (human development index and poverty index) and environmental management (water supply/storage and solid waste management) pointed the Northeast as the less wealthy region. The Northeast is also the region with the highest risk of Aedes aegypti house infestation due to the man-made larval habitats. In summary, the results of our ecological analysis support the hypothesis that the unusual distribution of microcephaly might not be due to Zika infection alone and could be accentuated by poverty and previous or co-infection with other pathogens. Our study reinforces the link between poverty and the risk of disease and the need to understand the effect on pathogenesis of sequential exposure to arboviruses and co-viral infections. Comprehensive large-scale cohort studies are required to corroborate our findings. We recommend that the list of infectious diseases screened, particularly during pregnancy, be regularly updated to include and effectively differentiate all viruses from ongoing outbreaks.

Insecticide Resistance Associated with kdr Mutations in Aedes albopictus: An Update on Worldwide Evidences

Insecticide resistance is an increasing problem worldwide that limits the efficacy of control methods against several pests of health interest. Among them, Aedes albopictus mosquitoes are efficient vectors of relevant pathogens causing animal and human diseases worldwide, including yellow fever, chikungunya, dengue, and Zika. Different mechanisms are associated in conferring resistance to chemical insecticides. One of the most widespread and analysed mechanisms is the knockdown resistance (kdr) causing resistance to DDT and pyrethroids. The mechanism is associated with mutations in the voltage sensitive sodium channel, which is involved in beginning and propagation of action potentials in nervous cells. The mechanism was originally discovered in the housefly and then it was found in a large number of arthropods. In 2011, a kdr associated mutation was evidenced for the first time in A. albopictus and afterward several evidences were reported in the different areas of the world, including China, USA, Brazil, India, and Mediterranean Countries. This review aims to update and summarize current evidences on kdr in A. albopictus, in order to stimulate further researches to analyse in depth A. albopictus resistance status across the world, especially in countries where the presence of this vector is still an emerging issue. Such information is currently needed given the well-known vector role of A. albopictus in the transmission of severe infectious diseases. Furthermore, the widespread use of chemical insecticides for control strategies against A. albopictus progressively lead to pressure selection inducing the rise of insecticide resistance-related mutations in the species. Such event is especially evident in some countries as China, often related to a history of uncontrolled use of chemical insecticides. Thus, a careful picture on the diffusion of kdr mutations worldwide represents a milestone for the implementation of control plans and the triggering of novel research on alternative strategies for mosquito-borne infections.

Ecological niche modeling of Aedes mosquito vectors of chikungunya virus in southeastern Senegal

Background

Chikungunya virus (CHIKV) originated in a sylvatic cycle of transmission between non-human animal hosts and vector mosquitoes in the forests of Africa. Subsequently the virus jumped out of this ancestral cycle into a human-endemic transmission cycle vectored by anthropophilic mosquitoes. Sylvatic CHIKV cycles persist in Africa and continue to spill over into humans, creating the potential for new CHIKV strains to enter human-endemic transmission. To mitigate such spillover, it is first necessary to delineate the distributions of the sylvatic mosquito vectors of CHIKV, to identify the environmental factors that shape these distributions, and to determine the association of mosquito presence with key drivers of virus spillover, including mosquito and CHIKV abundance. We therefore modeled the distribution of seven CHIKV mosquito vectors over two sequential rainy seasons in Kédougou, Senegal using Maxent.

Methods

Mosquito data were collected in fifty sites distributed in five land cover classes across the study area. Environmental data representing land cover, topographic, and climatic factors were included in the models. Models were compared and evaluated using area under the receiver operating characteristic curve (AUROC) statistics. The correlation of model outputs with abundance of individual mosquito species as well as CHIKV-positive mosquito pools was tested.

Results

Fourteen models were produced and evaluated; the environmental variables most strongly associated with mosquito distributions were distance to large patches of forest, landscape patch size, rainfall, and the normalized difference vegetation index (NDVI). Seven models were positively correlated with mosquito abundance and one (Aedes taylori) was consistently, positively correlated with CHIKV-positive mosquito pools. Eight models predicted high relative occurrence rates of mosquitoes near the villages of Tenkoto and Ngary, the areas with the highest frequency of CHIKV-positive mosquito pools.

Conclusions

Of the environmental factors considered here, landscape fragmentation and configuration had the strongest influence on mosquito distributions. Of the mosquito species modeled, the distribution of Ae. taylori correlated most strongly with abundance of CHIKV, suggesting that presence of this species will be a useful predictor of sylvatic CHIKV presence.

Electronic supplementary material

The online version of this article (10.1186/s13071-018-2832-6) contains supplementary material, which is available to authorized users.

Consensus and conflict among ecological forecasts of Zika virus outbreaks in the United States

Ecologists are increasingly involved in the pandemic prediction process. In the course of the Zika outbreak in the Americas, several ecological models were developed to forecast the potential global distribution of the disease. Conflicting results produced by alternative methods are unresolved, hindering the development of appropriate public health forecasts. We compare ecological niche models and experimentally-driven mechanistic forecasts for Zika transmission in the continental United States. We use generic and uninformed stochastic county-level simulations to demonstrate the downstream epidemiological consequences of conflict among ecological models, and show how assumptions and parameterization in the ecological and epidemiological models propagate uncertainty and produce downstream model conflict. We conclude by proposing a basic consensus method that could resolve conflicting models of potential outbreak geography and seasonality. Our results illustrate the usually-undocumented margin of uncertainty that could emerge from using any one of these predictions without reservation or qualification. In the short term, ecologists face the task of developing better post hoc consensus that accurately forecasts spatial patterns of Zika virus outbreaks. Ultimately, methods are needed that bridge the gap between ecological and epidemiological approaches to predicting transmission and realistically capture both outbreak size and geography.

Mapping the spatial distribution of Aedes aegypti and Aedes albopictus

Mosquito-borne infectious diseases, such as Rift Valley fever, Dengue, Chikungunya and Zika, have caused mass human death with the transnational expansion fueled by economic globalization. Simulating the distribution of the disease vectors is of great importance in formulating public health planning and disease control strategies. In the present study, we simulated the global distribution of Aedes aegypti and Aedes albopictus at a 5×5km spatial resolution with high-dimensional multidisciplinary datasets and machine learning methods Three relatively popular and robust machine learning models, including support vector machine (SVM), gradient boosting machine (GBM) and random forest (RF), were used. During the fine-tuning process based on training datasets of A. aegypti and A. albopictus, RF models achieved the highest performance with an area under the curve (AUC) of 0.973 and 0.974, respectively, followed by GBM (AUC of 0.971 and 0.972, respectively) and SVM (AUC of 0.963 and 0.964, respectively) models. The simulation difference between RF and GBM models was not statistically significant (p>0.05) based on the validation datasets, whereas statistically significant differences (p<0.05) were observed for RF and GBM simulations compared with SVM simulations. From the simulated maps derived from RF models, we observed that the distribution of A. albopictus was wider than that of A. aegypti along a latitudinal gradient. The discriminatory power of each factor in simulating the global distribution of the two species was also analyzed. Our results provided fundamental information for further study on disease transmission simulation and risk assessment.

Quantifying Zika: Advancing the Epidemiology of Zika With Quantitative Models

When Zika virus (ZIKV) emerged in the Americas, little was known about its biology, pathogenesis, and transmission potential, and the scope of the epidemic was largely hidden, owing to generally mild infections and no established surveillance systems. Surges in congenital defects and Guillain-Barré syndrome alerted the world to the danger of ZIKV. In the context of limited data, quantitative models were critical in reducing uncertainties and guiding the global ZIKV response. Here, we review some of the models used to assess the risk of ZIKV-associated severe outcomes, the potential speed and size of ZIKV epidemics, and the geographic distribution of ZIKV risk. These models provide important insights and highlight significant unresolved questions related to ZIKV and other emerging pathogens.

Zika Virus: An Emerging Worldwide Threat

ZIKA virus (ZIKV) poses a severe threat to the world. Recent outbreaks of ZIKV after 2007 along with its quick transmission have made this virus a matter of international concern. The virus shows symptoms that are similar to those caused in the wake of dengue virus (DENV) and other flaviviruses, which makes it difficult to discern the viral infection. Diagnosis is further complicated as the virus cross-reacts with antibodies of other viruses. Currently, molecular diagnosis of the virus is being performed by RT-PCR and IgM-captured enzyme-linked immunosorbent assay (MAC-ELISA). The real brunt of the virus is, however, borne by children and adults alike. Case studies of the ZIKV outbreaks in the French Polynesia and other places have suggested that there is a close link between the ZIKV and Gullian-Barre syndrome (GBS). The GBS has closely followed in areas facing ZIKV outbreaks. Although solid evidence is yet to emerge, clinical data integration has revealed a large number of ZIKV patients having GBS. Moreover, the amniotic fluids, blood cord, and miscarriage tissues of mothers have been detected with ZIKV, which indicates that the virus either gets transferred from mother to fetus or seeks direct entry in the fetus, causing microcephaly and other brain anomalies in the newborn babies. Studies on mice have confirmed the link between the ZIKV infection during pregnancy and microcephaly in babies. Reports have highlighted the sexual transmission of the ZIKV, as it has been detected in the semen and saliva of affected persons. The intensity with which the ZIKA is spreading can collapse the health sector of several countries, which are poor. A comprehensive strategy is a need of an hour to combat this virus so as to prevent its transmission and avert the looming threat. At the same time, more research on the cure of the ZIKV is imperative.