Tracking mosquito-borne diseases via social media: a machine learning approach to topic modelling and sentiment analysis.

Mosquito-borne diseases (MBDs) are a major threat worldwide, and public consultation on these diseases is critical to disease control decision-making. However, traditional public surveys are time-consuming and labor-intensive and do not allow for timely decision-making. Recent studies have explored text analytic approaches to elicit public comments from social media for public health.

Vector-borne diseases on Borneo island: a scoping review.

Background: Borneo, the third largest island in the world, is facing a significant burden of emerging and re-emerging vector-borne diseases due to rapid changes in primary tropical rainforests and urban landscapes. These vector-borne diseases include the endemic epidemic cycles that occur in the more populated and urbanized areas, as well as the possible transmission through enzootic and sylvatic transmission cycles that occur mainly in the overlapping landscapes or among the indigenous population in the forest. The island will be changed significantly in the future due to the increase in human activities, especially mega events such as the relocation of the Indonesian capital to Nusantara in East Kalimantan Borneo, increasing urbanization, agriculture, hydropower projects, ecotourism activities in Sabah, North Borneo, and Sarawak, Central and South Borneo.

Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps.

Background: The use of computer vision and deep learning models to automatically classify insect species on sticky traps has proven to be a cost- and time-efficient approach to pest monitoring. As different species are attracted to different colours, the variety of sticky trap colours poses a challenge to the performance of the models. However, the effectiveness of deep learning in classifying pests on different coloured sticky traps has not yet been sufficiently explored.

Comparative analyses of the transcriptome among three development stages of Zeugodacus tau larvae (Diptera: Tephritidae).

Studying differences in transcriptomes across various development stages of insects is necessary to uncover the physiological and molecular mechanism underlying development and metamorphosis. We here present the first transcriptome data generated under Illumina Hiseq platform concerning Zeugodacus tau (Walker) larvae from Nanchang, China. In total, 11,702 genes were identified from 9 transcriptome libraries of three development stages of Z.

An annotated image dataset of pests on different coloured sticky traps acquired with different imaging devices.

The sticky trap is probably the most cost-effective tool for catching insect pests, but the identification and counting of insects on sticky traps is very labour-intensive. When investigating the automatic identification and counting of pests on sticky traps using computer vision and machine learning, two aspects can strongly influence the performance of the model - the colour of the sticky trap and the device used to capture the images of the pests on the sticky trap. As far as we know, there are no available image datasets to study these two aspects in computer vision and deep learning algorithms.

Public Awareness, Perception, and Knowledge of Bed Bug Infestation Prevalence in Iraq.

Background: Bed bugs are blood-feeding insects and are an important urban pest. Bed bugs are nocturnal insects and hide in cracks in walls and beds during the day. The study aims to: (1) determine the bed bugs species that infest Iraq, their infestation source, and their distribution; (2) determine the level of awareness and concern regarding bed bugs among the Iraqi community.

Morphometric dataset of Varanus salvator for non-invasive sex identification using machine learning.

Reliable sex identification in Varanus salvator traditionally relied on invasive methods like genetic analysis or dissection, as less invasive techniques such as hemipenes inversion are unreliable. Given the ecological importance of this species and skewed sex ratios in disturbed habitats, a dataset that allows ecologists or zoologists to study the sex determination of the lizard is crucial. We present a new dataset containing morphometric measurements of V.

Application of computer vision and deep learning models to automatically classify medically important mosquitoes in North Borneo, Malaysia.

Mosquito-borne diseases have emerged in North Borneo in Malaysia due to rapid changes in the forest landscape, and mosquito surveillance is key to understanding disease transmission. However, surveillance programmes involving sampling and taxonomic identification require well-trained personnel, are time-consuming and labour-intensive. In this study, we aim to use a deep leaning model (DL) to develop an application capable of automatically detecting mosquito vectors collected from urban and suburban areas in North Borneo, Malaysia.

A protocol and training guidelines for mosquito sampling in remote areas with limited power supply.

Mosquito-borne diseases pose a significant threat in many Southeast Asian countries, particularly through the sylvatic cycle, which has a wildlife reservoir in forests and rural areas. Studying the composition and diversity of vectors and pathogen transmission is especially challenging in forests and rural areas due to their remoteness, limited accessibility, lack of power, and underdeveloped infrastructure. This study is based on the WHO mosquito sampling protocol, modifies technical details to support mosquito collection in difficult-to-access and resource-limited areas.

Epidemiology, clinical characteristics and outcomes of multisystem inflammatory syndrome in children.

Background: We describe the epidemiology, clinical characteristics, and outcomes of multisystem inflammatory syndrome in children (MIS-C) among children from Negeri Sembilan, Malaysia.

Methods: A retrospective, multicentre, observational study was performed among children ≤15 years old who were hospitalized for MIS-C between January 18, 2021 and June 30, 2023. The incidence of MIS-C was estimated using reported SARS-CoV-2 cases and census population data.

Predicting dengue transmission rates by comparing different machine learning models with vector indices and meteorological data.

Machine learning algorithms (ML) are receiving a lot of attention in the development of predictive models for monitoring dengue transmission rates. Previous work has focused only on specific weather variables and algorithms, and there is still a need for a model that uses more variables and algorithms that have higher performance. In this study, we use vector indices and meteorological data as predictors to develop the ML models.

Digitization of natural history collections: A guideline and nationwide capacity building workshop in Malaysia.

Natural history museum collections are the most important sources of information on the present and past biodiversity of our planet. Most of the information is primarily stored in analogue form, and digitization of the collections can provide further open access to the images and specimen data to address the many global challenges. However, many museums do not digitize their collections because of constraints on budgets, human resources, and technologies.

Risk factors for disease severity among children with Covid-19: a clinical prediction model.

Background: Children account for a significant proportion of COVID-19 hospitalizations, but data on the predictors of disease severity in children are limited. We aimed to identify risk factors associated with moderate/severe COVID-19 and develop a nomogram for predicting children with moderate/severe COVID-19.

Methods: We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system.

A protocol for developing a classification system of mosquitoes using transfer learning.

Mosquito identification and classification are the most important steps in a surveillance program of mosquito-borne diseases. With conventional approach of data collection, the process of sorting and classification are laborious and time-consuming. The advancement of computer vision with transfer learning provides excellent alternative to the challenge.

Next generation insect taxonomic classification by comparing different deep learning algorithms.

Insect taxonomy lies at the heart of many aspects of ecology, and identification tasks are challenging due to the enormous inter- and intraspecies variation of insects. Conventional methods used to study insect taxonomy are often tedious, time-consuming, labor intensive, and expensive, and recently, computer vision with deep learning algorithms has offered an alternative way to identify and classify insect images into their taxonomic levels. We designed the classification task according to the taxonomic ranks of insects-order, family, and genus-and compared the generalization of four state-of-the-art deep convolutional neural network (DCNN) architectures.

An annotated image dataset of medically and forensically important flies for deep learning model training.

Conventional methods to study insect taxonomy especially forensic and medical dipterous flies are often tedious, time-consuming, labor-intensive, and expensive. An automated recognition system with image processing and computer vision provides an excellent solution to assist the process of insect identification. However, to the best of our knowledge, an image dataset that describes these dipterous flies is not available.

An annotated image dataset for training mosquito species recognition system on human skin.

This paper introduces a new mosquito images dataset that is suitable for training and evaluating a recognition system on mosquitoes in normal or smashed conditions. The images dataset served mainly for the development a machine learning model that can recognize the mosquito in the public community, which commonly found in the smashed/damaged form by human. Especially the images of mosquito in hashed condition, which to the best of our knowledge, a dataset that fulfilled such condition is not available.

Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia.

Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We aim to investigate the attitudes to the COVID-19 vaccination booster in Malaysia by using sentiment analysis.

Text mining in mosquito-borne disease: A systematic review.

Mosquito-borne diseases are emerging and re-emerging across the globe, especially after the COVID19 pandemic. The recent advances in text mining in infectious diseases hold the potential of providing timely access to explicit and implicit associations among information in the text. In the past few years, the availability of online text data in the form of unstructured or semi-structured text with rich content of information from this domain enables many studies to provide solutions in this area, e.

Development of a deep learning model from breeding substrate images: a novel method for estimating the abundance of house fly (Musca domestica L.) larvae.

Background: The application of computer vision and deep learning to pest monitoring has recently received much attention. Although several studies have demonstrated the application of object detection to the number of pests on a substrate, for house flies (Musca domestica L.), in which the larvae were aggregated and overlapped together, the object detection technique was difficult to implement.

Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time.

Classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment.

Implications of the COVID-19 Lockdown on Dengue Transmission in Malaysia.

We aim to investigate the effect of large-scale human movement restrictions during the COVID-19 lockdown on both the dengue transmission and vector occurrences. This study compared the weekly dengue incidences during the period of lockdown to the previous years (2015 to 2019) and a Seasonal Autoregressive Integrated Moving Average (SARIMA) model that expected no movement restrictions. We found that the trend of dengue incidence during the first two weeks (stage 1) of lockdown decreased significantly with the incidences lower than the lower confidence level (LCL) of SARIMA.

The Diet for Edible-Nest Swiftlets: Nutritional Composition and Cost of Life Stages of Loew (Diptera: Phoridae) Bred on 3 Commercial Breeding Materials.

(Loew) is one of the best-known diets for the swiftlet. Previous studies have addressed the problem of some mass rearing conditions for this insect; unfortunately, the details of the nutritional composition of the life stages and cost of the breeding materials were insufficiently reported, even though this information is crucial for farming the edible-nest swiftlet. We aimed to investigate the nutritional composition of the life stages of on a cost basis using 3 common commercial breeding materials: chicken pellets (CPs), fish pellets (FPs), and mouse pellets (MPs).

Substrate Moisture Affects the Development of Megaselia scalaris (Diptera: Phoridae): An Implication of the Growth Circumstances of the Fly in Forensic Entomology.

Megaselia scalaris (Loew) (Diptera: Phoridae) provides great evidential value in estimating the postmortem interval (PMI) compared with other dipterans due to its common occurrence on human corpses both indoors and in concealed environments. Studies have focused on the effect of temperature, larval diet, and photoperiod on the development of the species; however, knowledge of M. scalaris development at different moisture levels is insufficient.