ABSTRACT
The COVID-19 pandemic underscored the crucial importance of enhanced indoor air quality control measures to mitigate the spread of respiratory pathogens. Far-UVC is a type of germicidal ultraviolet technology, with wavelengths between 200 and 235 nm, that has emerged as a highly promising approach for indoor air disinfection. Due to its enhanced safety compared to conventional 254 nm upper-room germicidal systems, far-UVC allows for whole-room direct exposure of occupied spaces, potentially offering greater efficacy, since the total room air is constantly treated. While current evidence supports using far-UVC systems within existing guidelines, understanding the upper safety limit is critical to maximizing its effectiveness, particularly for the acute phase of a pandemic or epidemic when greater protection may be needed. This review article summarizes the substantial present knowledge on far-UVC safety regarding skin and eye exposure and highlights research priorities to discern the maximum exposure levels that avoid adverse effects. We advocate for comprehensive safety studies that explore potential mechanisms of harm, generate action spectra for crucial biological effects and conduct high-dose, long-term exposure trials. Such rigorous scientific investigation will be key to determining safe and effective levels for far-UVC deployment in indoor environments, contributing significantly to future pandemic preparedness and response.
ABSTRACT
Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated with encountered ticks and by providing researchers and public health agencies with additional data on tick activity and geographic range. Here we outline the requirements for such a system, present a model that meets those requirements, and discuss remaining challenges and frontiers in automated tick identification. We compiled a user-generated dataset of more than 12,000 images of the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. We used image augmentation to further increase the size of our dataset to more than 90,000 images. Here we report the development and validation of a convolutional neural network which we call "TickIDNet," that scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network's ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet's performance can be increased by using confidence thresholds to introduce an "unsure" class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.
