Conspectus The spread of infectious diseases due to travel and trade can be seen throughout history, whether from early settlers or traveling businessmen. Increased globalization has allowed infectious diseases to quickly spread to different parts of the world and cause widespread infection. Posthoc analysis of more recent outbreaksSARS, MERS, swine flu, and COVID-19has demonstrated that the causative viruses were circulating through populations for days or weeks before they were first detected, allowing disease to spread before quarantines, contact tracing, and travel restrictions could be implemented. Earlier detection of future novel pathogens could decrease the time before countermeasures are enacted. In this Account, we examined a variety of novel technologies from the past 10 years that may allow for earlier detection of infectious diseases. We have arranged these technologies chronologically from prehuman predictive technologies to population-level screening tools. The earliest detection methods utilize artificial intelligence to analyze factors such as climate variation and zoonotic spillover as well as specific species and geographies to identify where the infection risk is high. Artificial intelligence can also be used to monitor health records, social media, and various publicly available data to identify disease outbreaks faster than traditional epidemiology. Secondary to predictive measures is monitoring infection in specific sentinel animal species, where domestic animals or wildlife are indicators of potential disease hotspots. These hotspots inform public health officials about geographic areas where infection risk in humans is high. Further along the timeline, once the disease has begun to infect humans, wastewater epidemiology can be used for unbiased sampling of large populations. This method has already been shown to precede spikes in COVID-19 diagnoses by 1 to 2 weeks. As total infections increase in humans, bioaerosol sampling in high-traffic areas can be used for disease monitoring, such as within an airport. Finally, as disease spreads more quickly between humans, rapid diagnostic technologies such as lateral flow assays and nucleic acid amplification become very important. Minimally invasive point-of-care methods can allow for quick adoption and use within a population. These individual diagnostic methods then transfer to higher-throughput methods for more intensive population screening as an infection spreads. There are many promising early warning technologies being developed. However, no single technology listed herein will prevent every future outbreak. A combination of technologies from across our infection timeline would offer the most benefit in preventing future widespread disease outbreaks and pandemics.
Some medical diagnostic tools, such as urinalysis dipsticks, rely on reading colors accurately for making clinical decisions. Reading color visually can be subject to 1) perceptual differences (inter-observer), 2) environmental factors such as illumination, and 3) target coloring (metamerism), especially among users with limited training and experience. Mobile phone cameras and compact camera modules offer potential low-cost platforms for automated, objective color readouts. However, image colors are a function of camera sensor and illumination characteristics. To restore color fidelity, color correction techniques must be applied to account for systematic deviation. This work aims to provide a quantitative assessment of color correction techniques and reduce variability in color interpretation for urinalysis dipstick results using a low-cost imager. Three color correction methods -linear, polynomial, and root-polynomial regression -were compared for performance in color difference reduction. A standard color checker card was used as reference to compute color correction matrices. A custom imaging system with a low-cost camera module was developed to capture images under controlled illumination. Reference values of the color checker card were obtained with a CM-26d handheld spectrophotometer. The CIE2000 ∆E was used to quantify the color difference between the camera image and the spectrometer to evaluate 3 color correction algorithms. The derived color correction matrices were applied to urinalysis dipstick images and compared to the spectrometer readings. Results indicated that polynomial fitting showed the lowest ∆E during calibration but failed to properly correct urine dipstick colors. Root polynomial offered the best performance in reducing color differences to be below 3 to 4 ∆E. Utilizing L*a*b values for classifying a given dipstick result according to reference concentration levels, it was found that quadratic discriminant analysis (QDA) and k Nearest Neighbor (kNN) classifiers achieved an 82.9% and 97.1% accuracy, respectively.
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