LSTM-Powered COVID-19 prediction in central Thailand incorporating meteorological and particulate matter data with a multi-feature selection approach.

The COVID-19 pandemic has significantly impacted public health and necessitated urgent actions to mitigate its spread. Monitoring and predicting the outbreak's progression have become vital to devise effective strategies and allocate resources efficiently. This study presents a novel approach utilizing Multivariate Long Short-Term Memory (LSTM) to analyze and predict COVID-19 trends in Central Thailand, particularly emphasizing the multi-feature selection process. To consider a comprehensive view of the pandemic's dynamics, our research dataset encompasses epidemiological, meteorological, and particulate matter features, which were gathered from reliable sources. We propose a multi-feature selection technique to identify the most relevant and influential features that significantly impact the spread of COVID-19 in the region to enhance the model's performance. Our results highlight that relative humidity is the key factor driving COVID-19 transmission in Central Thailand. The proposed multi-feature selection technique significantly improves the model's accuracy, ensuring that only the most informative variables contribute to the predictions, avoiding the potential noise or redundancy from less relevant features. The proposed LSTM model demonstrates its capability to forecast COVID-19 cases, facilitating informed decision-making for public health authorities and policymakers.

Exploring indoor and outdoor dust as a potential tool for detection and monitoring of COVID-19 transmission.

This study investigated the potential of using SARS-CoV-2 viral concentrations in dust as an additional surveillance tool for early detection and monitoring of COVID-19 transmission. Dust samples were collected from 8 public locations in 16 districts of Bangkok, Thailand, from June to August 2021. SARS-CoV-2 RNA concentrations in dust were quantified, and their correlation with community case incidence was assessed. Our findings revealed a positive correlation between viral concentrations detected in dust and the relative risk of COVID-19. The highest risk was observed with no delay (0-day lag), and this risk gradually decreased as the lag time increased. We observed an overall decline in viral concentrations in public places during lockdown, closely associated with reduced human mobility. The effective reproduction number for COVID-19 transmission remained above one throughout the study period, suggesting that transmission may persist in locations beyond public areas even after the lockdown measures were in place.