While the world is still attempting to recover from the damage caused by the broad spread of COVID-19, the monkeypox virus poses a new threat of becoming a global pandemic. Although the Monkeypox virus itself is not deadly and contagious as COVID-19, still every day, new patients case has been reported from many nations. Therefore, it will be no surprise if the world ever faces another global pandemic due to the lack of proper precautious steps. Recently, Machine learning (ML) has demonstrated huge potential in image-based diagnoses such as cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application can be adopted to diagnose the Monkeypox related disease as it infected the human skin, which image can be acquired and further used in diagnosing the disease. However, there is no publicly available Monkeypox dataset that can be used for training and experimenting the ML model development. Consequently, there is an immediate need to develop a dataset containing Monkeypox infected patients' images. Considering this opportunity, in this work, we introduce a newly developed "Monkeypox2022" dataset that is publicly available to use and can be obtained from our shared GitHub repository. The dataset is created by collecting images from multiple open-source and online portals that do not impose any restrictions on use, even for commercial purposes, hence giving a safer path to use and disseminate such data when constructing and deploying any type of ML models. Further, we propose and evaluate a modified VGG16 model, which includes two distinct studies: Study One and Two. Our exploratory computational results indicate that our suggested model can identify Monkeypox patients with an accuracy of 97 ± 1.8% (AUC = 97.2) and 88 ± 0.8% ( AUC = 0.867) for Study One and Two, respectively. Additionally, we explain our model's prediction and feature extraction utilizing Local

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Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during Hurricane Laura compounded by the COVID-19 pandemic. Hurricane Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana, U.S. Using an application programming interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Hurricane Laura. Online social networks were based on Twitter’s user influence feature (i.e., mentions or tags) that allows notification of other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into 21 components of various size, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those which heavily involved and closely interacted with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters.