A dengue outbreak in a rural community in Northern Coastal Ecuador: An analysis using unmanned aerial vehicle mapping

Development of a trash classification system to map potential Aedes aegypti breeding grounds using unmanned aerial vehicle imaging

Aedes aegypti mosquitos are the primary vector for dengue, chikungunya, and Zika viruses and tend to breed in small containers of water, with a propensity to breed in small piles of trash and abandoned tires. This study piloted the use of aerial imaging to map and classify potential Ae. aegypti breeding sites with a specific focus on trash, including discarded tires. Aerial images of coastal and inland sites in Kenya were obtained using an unmanned aerial vehicle. Aerial images were reviewed for identification of trash and suspected trash mimics, followed by extensive community walk-throughs to identify trash types and mimics by description and ground photography. An expert panel reviewed aerial images and ground photos to develop a classification scheme and evaluate the advantages and disadvantages of aerial imaging versus walk-through trash mapping. A trash classification scheme was created based on trash density, surface area, potential for frequent disturbance, and overall likelihood of being a productive Ae. aegypti breeding site. Aerial imaging offers a novel strategy to characterize, map, and quantify trash at risk of promoting Ae. aegypti proliferation, generating opportunities for further research on trash associations with disease and trash interventions.

Automatic mapping of high-risk urban areas for Aedes aegypti infestation based on building facade image analysis

BACKGROUND

Dengue, Zika, and chikungunya, whose viruses are transmitted mainly by Aedes aegypti, significantly impact human health worldwide. Despite the recent development of promising vaccines against the dengue virus, controlling these arbovirus diseases still depends on mosquito surveillance and control. Nonetheless, several studies have shown that these measures are not sufficiently effective or ineffective. Identifying higher-risk areas in a municipality and directing control efforts towards them could improve it. One tool for this is the premise condition index (PCI); however, its measure requires visiting all buildings. We propose a novel approach capable of predicting the PCI based on facade street-level images, which we call PCINet.

METHODOLOGY

Our study was conducted in Campinas, a one million-inhabitant city in São Paulo, Brazil. We surveyed 200 blocks, visited their buildings, and measured the three traditional PCI components (building and backyard conditions and shading), the facade conditions (taking pictures of them), and other characteristics. We trained a deep neural network with the pictures taken, creating a computational model that can predict buildings' conditions based on the view of their facades. We evaluated PCINet in a scenario emulating a real large-scale situation, where the model could be deployed to automatically monitor four regions of Campinas to identify risk areas.

PRINCIPAL FINDINGS

PCINet produced reasonable results in differentiating the facade condition into three levels, and it is a scalable strategy to triage large areas. The entire process can be automated through data collection from facade data sources and inferences through PCINet. The facade conditions correlated highly with the building and backyard conditions and reasonably well with shading and backyard conditions. The use of street-level images and PCINet could help to optimize Ae. aegypti surveillance and control, reducing the number of in-person visits necessary to identify buildings, blocks, and neighborhoods at higher risk from mosquito and arbovirus diseases.

Whole Genome Sequencing of DENV-2 isolated from Aedes aegypti mosquitoes in Esmeraldas, Ecuador. Genomic epidemiology of genotype III Southern Asian-American in the country

Ecuador is a tropical country reporting Dengue virus (DENV) outbreaks with areas of hyperendemic viral transmission. Entomo-virological surveillance and monitoring effort conducted in the Northwestern border province of Esmeraldas in April 2022, five pools of female Aedes aegypti mosquitoes from a rural community tested positive for DENV serotype 2 by RT-qPCR. One pool was sequenced by Illumina MiSeq, and it corresponded to genotype III Southern Asian-American. Comparison with other genomes revealed genetic similarity to a human DENV genome sequenced in 2021, also from Esmeraldas. Potential introduction events to the country could have originated from Colombia, considering the vicinity of the collection sites to the neighboring country and high human movement. The inclusion of genomic information complements entomo-virological surveillance, providing valuable insights into genetic variants. This contribution enhances our understanding of Dengue virus (DENV) epidemiology in rural areas and guides evidence-based decisions for surveillance and interventions.

Increasing transmission of dengue virus across ecologically diverse regions of Ecuador and associated risk factors

The distribution and intensity of viral diseases transmitted by Aedes aegypti mosquitoes, including dengue, have rapidly increased over the last century. Here, we study dengue virus (DENV) transmission across the ecologically and demographically distinct regions or Ecuador. We analyzed province-level age-stratified dengue incidence data from 2000-2019 using catalytic models to estimate the force of infection of DENV over eight decades. We found that provinces established endemic DENV transmission at different time periods. Coastal provinces with the largest and most connected cities had the earliest and highest increase in DENV transmission, starting around 1980 and continuing to the present. In contrast, remote and rural areas with reduced access, like the northern coast and the Amazon regions, experienced a rise in DENV transmission and endemicity only in the last 10 to 20 years. The newly introduced chikungunya and Zika viruses have age-specific distributions of hospital-seeking cases consistent with recent emergence across all provinces. To evaluate factors associated with geographic differences in DENV transmission potential, we modeled DENV vector risk using 11,693 Aedes aegypti presence points to the resolution of 1 hectare. In total, 56% of the population of Ecuador, including in provinces identified as having increasing DENV transmission in our models, live in areas with high risk of Aedes aegypti, with population size, trash collection, elevation, and access to water as important determinants. Our investigation serves as a case study of the changes driving the expansion of DENV and other arboviruses globally and suggest that control efforts should be expanded to semi-urban and rural areas and to historically isolated regions to counteract increasing dengue outbreaks.

Validity of Self-Reported Mosquito Bites to Assess Household Mosquito Abundance in Six Communities of Esmeraldas Province, Ecuador

Mosquito-borne diseases are a global burden; however, current methods of evaluating human-mosquito contact rates are expensive and time consuming. Validated surveys of self-reported mosquito bites may be an inexpensive way to determine mosquito presence and bite exposure level in an area, but this remains untested. In this study, a survey of self-reported mosquito bites was validated against household mosquito abundance from six communities in Esmeraldas, Ecuador. From February 2021 to July 2022, households were interviewed monthly, and five questions were used to ask participants how often they were bitten by mosquitoes at different times during the day. At the same time, adult mosquitoes were collected using a Prokopack aspirator. Species were identified and counted. Survey responses were compared with the total number of mosquitoes found in the home using negative binomial regression. More frequent self-reported mosquito bites were significantly associated with higher numbers of collected adult mosquitoes. These associations were driven by the prevalence of the dominant genera, Culex. These results suggest that surveys of perceived mosquito bites relate to actual mosquito presence, making them a potentially useful tool for determining the impact of vector-control interventions on community perceptions of risk but less useful for assessing the risk of nondominant species such as Aedes aegypti. Further work is needed to examine the robustness of these results in other contexts.

Evolution and emergence of mosquito-borne viruses of medical importance: towards a routine metagenomic surveillance approach

During the last two decades, the world has witnessed the emergence and re-emergence of arthropod-borne viruses, better known as arboviruses. The close contact between sylvatic, rural and peri-urban vector species and humans has been mainly determined by the environment-modifying human activity. The resulting interactions have led to multiple dead-end host infections and have allowed sylvatic arboviruses to eventually adapt to new vectors and hosts, contributing to the establishment of urban transmission cycles of some viruses with enormous epidemiologic impact. The metagenomic next-generation sequencing (NGS) approach has allowed obtaining unbiased sequence information of millions of DNA and RNA molecules from clinical and environmental samples. Robust bioinformatics tools have enabled the assembly of individual sequence reads into contigs and scaffolds partially or completely representing the genomes of the microorganisms and viruses being present in biological samples of clinical relevance. In this review, we describe the different ecological scenarios for the emergence of viral diseases, the virus adaptation process required for the establishment of a new transmission cycle and the usefulness of NGS and computational methods for the discovery and routine genomic surveillance of mosquito-borne viruses in their ecosystems.

A Chikungunya Outbreak in a Dengue-endemic Region in Rural Northern Coastal Ecuador

Dengue virus (DENV) reemerged in the Americas in the 1980s and 1990s, whereas chikungunya virus (CHIKV) emerged in 2014. Although CHIKV produced large epidemics from 2014 to 2017, dengue fever has been the prominent arboviral disease identified through passive surveillance, bringing to question the degree to which cases are misdiagnosed. To address this concern, we conducted an active household-based surveillance of arboviral-like illnesses in six rural and remote communities in northern coastal Ecuador from May 2019 to February 2020. Although passive surveillance conducted by the Ecuadorian Ministry of Health reported only DENV cases in the region, more than 70% of the arbovirus-like illnesses detected by active surveillance in our study were positive for CHIKV. These findings underline the need for active surveillance of arboviral infections with laboratory confirmation, especially in rural communities where arboviral illnesses are more likely to be underreported.

A Privacy-Preserved Internet-of-Medical-Things Scheme for Eradication and Control of Dengue Using UAV

Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.

Improved Use of Drone Imagery for Malaria Vector Control through Technology-Assisted Digitizing (TAD)

Drones have the potential to revolutionize malaria vector control initiatives through rapid and accurate mapping of potential malarial mosquito larval habitats to help direct field Larval Source Management (LSM) efforts. However, there are no clear recommendations on how these habitats can be extracted from drone imagery in an operational context. This paper compares the results of two mapping approaches: supervised image classification using machine learning and Technology-Assisted Digitising (TAD) mapping that employs a new region growing tool suitable for non-experts. These approaches were applied concurrently to drone imagery acquired at seven sites in Zanzibar, United Republic of Tanzania. Whilst the two approaches were similar in processing time, the TAD approach significantly outperformed the supervised classification approach at all sites (t = 5.1, p < 0.01). Overall accuracy scores (mean overall accuracy 62%) suggest that a supervised classification approach is unsuitable for mapping potential malarial mosquito larval habitats in Zanzibar, whereas the TAD approach offers a simple and accurate (mean overall accuracy 96%) means of mapping these complex features. We recommend that this approach be used alongside targeted ground-based surveying (i.e., in areas inappropriate for drone surveying) for generating precise and accurate spatial intelligence to support operational LSM programmes.