Mosquito Ovitraps IoT Sensing System (MOISS): Internet of Things-based System for Continuous, Real-Time and Autonomous Environment Monitoring

The monitoring of environmental parameters is indispensable for controlling mosquito populations. The abundance of mosquitoes mainly depends on climate conditions, weather and water (i.e., physicochemical parameters). Traditional techniques for immature mosquito surveillance are based on remote sensing and weather stations as primary data sources for environmental variables, as well as water samples which are collected in the field by environmental health agents to characterize water quality impacts. Such tools may lead to misidentifications, especially when comprehensive surveillance is required. Innovative methods for timely and continuous monitoring are crucial for improving the mosquito surveillance system, thus, increasing the efficiency of mosquitoes' abundance models and providing real-time prediction of high-risk areas for mosquito infestation and breeding. Here, we illustrate the design, implementation, and deployment of a novel IoT -based environment monitoring system using a combination of weather and water sensors with a real-time connection to the cloud for data transmission in Madeira Island, Portugal. The study provides an approach to monitoring some environmental parameters, such as weather and water, that are related to mosquito infestation at a fine spatiotemporal scale. Our study demonstrates how a combination of sensor networks and clouds can be used to create a smart and fully autonomous system to support mosquito surveillance and enhance the decision-making of local environmental agents

Classifying Mosquito Presence and Genera Using Median and Interquartile Values From 26-Filter Wingbeat Acoustic Properties

Mosquitoes are known to be one of the deadliest creatures in the world. There have been several studies that aim to identify mosquito presence and species using various techniques. The most common ones involve automatic identification of mosquito species from the sounds produced by flapping its wings. The development of these important concepts and technologies can help reduce the spread of mosquito-borne diseases. This paper presents a simple model based on mean and interquartile values that aim to solve the mosquito classification. Despite its simplicity, the proposed model significantly outperforms a Convolutional Neural Network (CNN) model in identifying the mosquito genus from the classes of Aedes, Anopheles and Culex, with an additional fourth class of No-Mosquito. A dataset of sound recordings from the Humbug Zooniverse, collected by researchers from Oxford University, and augmented with locally collected audio recordings of mosquitoes in the Philippines were used in this study. The proposed technique uses the numerical data from a series of 26 different pass-band filter values generated from spectrograms of audio recordings, specifically computing the statistical measures of median and interquartile values for each filter from instances of the same class. To predict the class of an instance, the sum of squares of differences was computed between the actual values of the instance against the expected values of each class on each of these three statistical measures. The average classification accuracy of our proposed model was 92.8%, and this was higher than the 86.6% classification accuracy yielded by the CNN model. Moreover, the proposed model required much less time for both training and classification than the CNN model. As the proposed model outperformed the CNN model in accuracy and efficiency, the results offer a promising technique that may also simplify the process of solving other sound-based classification problems

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

Semi-supervised text classification framework : an overview of Dengue landscape factors and satellite earth observation

In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context

Modeling Urban Areas Epidemiological Risk Exposure Using Multispectral Spaceborne Data

In recent decades, the world has been fast urbanizing. More than half of the world’s human population now live in urban areas. Such high density of urban population is resulting in air and water pollution, land degradation, and infectious diseases spread risks prominence. However, the increasing quality (in terms of finer spatial and temporal resolution)and quantity of Earth Observation (EO) satellite data provide new perspectives for analysing these phenomena. Within the specific domain of epidemiological risks dynamics in urban areas which is the focus of this work, the use of multispectral optical EO sensor data has created new opportunities. These data through their visible, near, mid, far and thermal infrared bands provide planetary-­‐scale access to environmental variables such as temperature, humidity, and vegetation types, location and conditions. Since these environmental variables affect the development of vectors causing infectious disease (e.g., mosquitoes), there is the possibility to use EO data to estimate them, and obtain disease risk models. The Ae. aegypti mosquito species transmits Zika, Dengue, and Chikungunya, diseases widespread in more than 100 world countries, and is concentrated in urban areas. The development of this vector depends significantly on local environmental temperature, humidity, precipitation and vegetation. In this regard, multispectral EO data can provide globally consistent and scalable sources to obtain the required environmental variable inputs, and extract significant and consistent monitoring and forecasting models for vector population. The work reported in this thesis about this topic has led to the following results: 1) A method to map vegetation types in urban areas at high spatial resolution using Sentinel2 multispectral EO data. The results show an improvement in the quality of the resulting vegetation maps with respect to what is available by means of state-­of-­the-­art techniques. 2) A method that combines EO-­based spectral indices, temperature layers, and precipitation measurement to model the temporal evolution of the local mean Ae. aegypti population. The approach leverages the random forest (RF) machine learning (ML) technique and its embedded nonlinear features importance ranking (mean decrease impurity, MDI) to rank the effects of environmental variables and explain the resulting model. 3) A weighted generalized linear modeling (GLM) technique to predict Ae. aegypti population using multispectral EO data covariate inputs. GLMs are generally simple to implement and explain, but do not provide the same level of prediction quality as ML methods. The proposed weighted GLM compares well with ML techniques in quality, and provides capability for more explicitly interpretation of the results. 4) A recurrent neural network (RNN) technique for spatio­‐temporal modeling of Ae. Aegypti population at the urban block level using multispectral EO data as inputs. This study is needed because spatial models obscure seasonality effects while temporal model are blind to spatial changes in micro-­climates. The proposed technique shows great promise with respect to the use of free multispectral EO data for spatio-­temporal epidemiological modeling. All the proposed techniques have been applied in the Latin American region where the risk of Ae. aegypti vector transmitted diseases are the highest in the world. They were validated thanks to the long term partnership with the University of Alagoas in Maceio (Brazil) and the Brazilian company: ECOVEC

Applications and advances in acoustic monitoring for infectious disease epidemiology

Emerging infectious diseases continue to pose a significant burden on global public health, and there is a critical need to better understand transmission dynamics arising at the interface of human activity and wildlife habitats. Passive acoustic monitoring (PAM), more typically applied to questions of biodiversity and conservation, provides an opportunity to collect and analyse audio data in relative real time and at low cost. Acoustic methods are increasingly accessible, with the expansion of cloud-based computing, low-cost hardware, and machine learning approaches. Paired with purposeful experimental design, acoustic data can complement existing surveillance methods and provide a novel toolkit to investigate the key biological parameters and ecological interactions that underpin infectious disease epidemiology

Sustainable control of infestations using image processing and modelling

A sustainable pest control system integrates automated pest detection and recognition to evaluate the pest density using image samples taken from habitats. Novel predator/prey modelling algorithms assess control requirements for the UAV system, which is designed to deliver measured quantities of naturally beneficial predators to combat pest infestations within economically acceptable timeframes. The integrated system will reduce the damaging effect of pests in an infested habitat to an economically acceptable level without the use of chemical pesticides. Plant pest recognition and detection is vital for food security, quality of life and a stable agricultural economy. The research utilises a combination of the k-means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detection is achieved by partitioning the data space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The detection is established by extracting the variant and distinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identify the plant pests to obtain correlation peak values for the different datasets. The correspondence filter can achieve rotationally invariant recognition of pests for a full 360 degrees, which proves the effectiveness of the algorithm and provides a count of the number of pests in the image. A series of models has been produced that will permit an assessment of common pest infestation problems and estimate the number of predators that are required to control the problem within a time schedule. A UAV predator deployment system has been designed. The system is offered as a replacement for chemical pesticides to improve peoples’ health opportunities and the quality of food products

Inteligencia artificial al servicio de la salud pública: caso de estudio detección temprana de focos larvarios de mosquitos.

Las enfermedades transmitidas por mosquitos se consideran emergentes y en constante aumento debido al crecimiento de la población y cambio climático. Con el fin de monitorear la ocurrencia de brotes de larvas de mosquitos, una identificación de larvas. Se ha desarrollado un sistema basado en la detección y clasificación de objetos mediante visión artificial y aprendizaje automático desarrollado y evaluado. Para ello, se utilizaron 45 imágenes de muestra de un recipiente con agua que contenía Se han recolectado mosquitos en estado larval y pupal. El detector basado en la umbralización adaptativa y la detección de contornos pudo encontrar todos los objetos relevantes en las imágenes de muestra. Para identificar cada objeto encontrado por el detector como larva o no, un clasificador basado en HOG y SVM, y otro basado en CNN han sido entrenados y evaluados, obteniendo valores F1 de 0.951 y 0.991 respectivamente.CONACYT - Consejo Nacional de Ciencia y TecnologíaPROCIENCI

UP4DREAM CAPACITY BUILDING PROJECT: UAS BASED MAPPING IN DEVELOPING COUNTRIES

UP4DREAM (UAV Photogrammetry for Developing Resilience and Educational Activities in Malawi) is a cooperative project cofounded by ISPRS between the Polytechnic University of Turin and the United Nations Children Fund (UNICEF) Malawi, with the support of two local Universities (Lilongwe University of Agriculture and Natural Resources, and Mzuzu University), and Agisoft LLC (for the use of their photogrammetry and computer vision software suite). Malawi is a flood-prone landlocked country constantly facing natural and health challenges, which prevent the country's sustainable socio-economic development. Frequent naturals shocks leave vulnerable communities food insecure. Moreover, Malawi suffers from high rates of HIV, as well as it has endemic malaria. The UP4DREAM project focuses on one of the drone project's critical priorities in Malawi (Imagery). It aims to start a capacity-building initiative in line with other mapping missions in developing countries, focusing on the realization and management of large-scale cartography (using GIS - Geographic Information Systems) and on the generation of 3D products based on the UAV-acquired data. The principal aim of UP4DREAM is to ensure that local institutions, universities, researchers, service companies, and manufacturers operating in the humanitarian drone corridor, established by UNICEF in 2017, will have the proper knowledge and understanding of the photogrammetry and spatial information best practices, to perform large-scale aerial data acquisition, processing, share and manage in the most efficient, cost-effective and scientifically rigorous way

The Vezo communities and fisheries of the coral reef ecosystem in the Bay of Ranobe, Madagascar

Madagascar, a country whose extraordinary levels of endemism and biodiversity are celebrated globally by scientists and laymen alike, yet historically has received surprisingly little research attention, is the setting of the present dissertation. Here, I contribute to the need for applied research by: 1) focusing on the most intensely fished section of the Toliara Barrier Reef, the Bay of Ranobe; 2) characterizing the marine environment, the human population, and the fisheries; and 3) collecting the longest known time-series of data on fisheries of Madagascar, thereby providing a useful baseline for future analyses. In Chapter 1, the bathymetry of the Bay was characterized following a unique application of the boosted regression tree classifier to the RGB bands of IKONOS imagery. Derivation of water depths, based on DOS-corrected images, following a generic, log-transformed multiple linear regression approach produced a predictive accuracy of 1.28 m, whereas model fitting performed using the boosted regression tree classifier, allowing for interaction effects (tree complexity= 2), provided increased accuracy (RMSE= 1.01 m). Estimates of human population abundance, distribution, and dynamics were obtained following a dwelling-unit enumeration approach, using IKONOS Panchromatic and Google Earth images. Results indicated, in 2016, 31,850 people lived within 1 km of the shore, and 28,046 people lived within the 12 coastal villages of the Bay. Localized population growth rates within the villages, where birth rates and migration are combined, ranged from 2.96% - 6.83%, greatly exceeding official estimates of 2.78%. Annual pirogue counts demonstrated a shift in fishing effort from south to the north. Gear and boat (pirogue) profiles were developed, and the theoretical maximum number of fishermen predicted (n= 4,820), in 2013, from a regression model based on pirogue lengths (R2= 0.49). Spatial fishing effort distribution was mapped following a satellite-based enumeration of fishers-at-sea, resulting in a bay-wide estimate of intensity equaling 33.3 pirogue-meters km-2. Landings and CPUE were characterized, with respect to finfish, by family, species, gear, and village. Expansion of landings to bay-wide fisheries yields indicated 1,885.8 mt year-1 of mixed fisheries productivity, with an estimated wholesale value of 1.64 million USD per annum