One-base-mismatch CRISPR-based transistors for single nucleotide resolution assay.

An effective strategy for accurately detecting single nucleotide variants (SNVs) is of great significance for genetic research and diagnostics. However, strict amplification conditions, complex experimental instruments, and specialized personnel are required to obtain a satisfactory tradeoff between sensitivity and selectivity for SNV discrimination. In this study, we present a CRISPR-based transistor biosensor for the rapid and highly selective detection of SNVs in viral RNA. By introducing a synthetic mismatch in the crRNA, the CRISPR-Cas13a protein can be engineered to capture the target SNV RNA directly on the surface of the graphene channel. This process induces a fast electrical signal response in the transistor, obviating the need for amplification or reporter molecules. The biosensor exhibits a detection limit for target RNA as low as 5 copies in 100 µL, which is comparable to that of real-time quantitative polymerase chain reaction (PCR). Its operational range spans from 10 to 5 × 105 copy mL-1 in artificial saliva solution. This capability enables the biosensor to discriminate between wild-type and SNV RNA within 15 min. By introducing 10 µL of swab samples during clinical testing, the biosensor provides specific detection of respiratory viruses in 19 oropharyngeal specimens, including influenza A, influenza B, and variants of SARS-CoV-2. This study emphasizes the CRISPR-transistor technique as a highly accurate and sensitive approach for field-deployable nucleic acid screening or diagnostics.

Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study.

BACKGROUND: Previous work suggests that Google searches could be useful in identifying conjunctivitis epidemics. Content-based assessment of social media content may provide additional value in serving as early indicators of conjunctivitis and other systemic infectious diseases. OBJECTIVE: We investigated whether large language models, specifically GPT-3.5 and GPT-4 (OpenAI), can provide probabilistic assessments of whether social media posts about conjunctivitis could indicate a regional outbreak. METHODS: A total of 12,194 conjunctivitis-related tweets were obtained using a targeted Boolean search in multiple languages from India, Guam (United States), Martinique (France), the Philippines, American Samoa (United States), Fiji, Costa Rica, Haiti, and the Bahamas, covering the time frame from January 1, 2012, to March 13, 2023. By providing these tweets via prompts to GPT-3.5 and GPT-4, we obtained probabilistic assessments that were validated by 2 human raters. We then calculated Pearson correlations of these time series with tweet volume and the occurrence of known outbreaks in these 9 locations, with time series bootstrap used to compute CIs. RESULTS: Probabilistic assessments derived from GPT-3.5 showed correlations of 0.60 (95% CI 0.47-0.70) and 0.53 (95% CI 0.40-0.65) with the 2 human raters, with higher results for GPT-4. The weekly averages of GPT-3.5 probabilities showed substantial correlations with weekly tweet volume for 44% (4/9) of the countries, with correlations ranging from 0.10 (95% CI 0.0-0.29) to 0.53 (95% CI 0.39-0.89), with larger correlations for GPT-4. More modest correlations were found for correlation with known epidemics, with substantial correlation only in American Samoa (0.40, 95% CI 0.16-0.81). CONCLUSIONS: These findings suggest that GPT prompting can efficiently assess the content of social media posts and indicate possible disease outbreaks to a degree of accuracy comparable to that of humans. Furthermore, we found that automated content analysis of tweets is related to tweet volume for conjunctivitis-related posts in some locations and to the occurrence of actual epidemics. Future work may improve the sensitivity and specificity of these methods for disease outbreak detection.

ATQ: alert time quality, an evaluation metric for assessing timely epidemic detection models within a school absenteeism-based surveillance system.

BACKGROUND: Wellington-Dufferin-Guelph Public Health (WDGPH) has conducted an absenteeism-based influenza surveillance program in the WDG region of Ontario, Canada since 2008, using a 10% absenteeism threshold to raise an alert for the implementation of mitigating measures. A recent study indicated that model-based alternatives, such as distributed lag seasonal logistic regression models, provided improved alerts for detecting an upcoming epidemic. However model evaluation and selection was primarily based on alert accuracy, measured by the false alert rate (FAR), and failed to optimize timeliness. Here, a new metric that simultaneously evaluates epidemic alert accuracy and timeliness is proposed. The alert time quality (ATQ) metric is investigated as a model selection criterion on both a simulated and real data set. METHODS: The ATQ assessed alerts on a gradient, where alerts raised incrementally before or after an optimal day were considered informative, but were penalized for lack of timeliness. Summary statistics of ATQ, average alert time quality (AATQ) and first alert time quality (FATQ), were used for model evaluation and selection. Alerts raised by ATQ and FAR selected models were compared. Daily elementary school absenteeism and laboratory-confirmed influenza case data collected by WDGPH were used for demonstration and evaluation of the proposed metric. A simulation study that mimicked the WDG population and influenza demographics was conducted for further evaluation of the proposed metric. RESULTS: The FATQ-selected model raised acceptable first alerts most frequently, while the AATQ-selected model raised first alerts within the ideal range most frequently. CONCLUSIONS: Models selected by either FATQ or AATQ would more effectively predict community influenza activity with the local community than those selected by FAR.

Exploring the threshold for the start of respiratory syncytial virus infection epidemic season using sentinel surveillance data in Japan.

Introduction: An unusual seasonality of respiratory syncytial virus (RSV) infection in Japan is observed in recent years after 2017, becoming challenging to prepare for: a seasonal shift from autumn-winter to summer-autumn in 2017-2019, no major epidemic in 2020, and an unusually high number of cases reported in 2021. Methods: To early detect the start-timing of epidemic season, we explored the reference threshold for the start-timing of the epidemic period based on the number of cases per sentinel (CPS, a widely used indicator in Japanese surveillance system), using a relative operating characteristic curve analysis (with the epidemic period defined by effective reproduction number). Results: The reference values of Tokyo, Kanagawa, Osaka, and Aichi Prefectures were 0.41, 0.39, 0.42, and 0.24, respectively. Discussion: The reference CPS value could be a valuable indicator for detecting the RSV epidemic and may contribute to the planned introduction of monoclonal antibody against RSV to prevent severe outcomes.

Methods for detecting seasonal influenza epidemics using a school absenteeism surveillance system.

BACKGROUND: School absenteeism data have been collected daily by the public health unit in Wellington-Dufferin-Guelph, Ontario since 2008. To date, a threshold-based approach has been implemented to raise alerts for community-wide and within-school illness outbreaks. We investigate several statistical modelling approaches to using school absenteeism for influenza surveillance at the regional level, and compare their performances using two metrics. METHODS: Daily absenteeism percentages from elementary and secondary schools, and report dates for influenza cases, were obtained from Wellington-Dufferin-Guelph Public Health. Several absenteeism data aggregations were explored, including using the average across all schools or only using schools of one type. A 10% absence threshold, exponentially weighted moving average model, logistic regression with and without seasonality terms, day of week indicators, and random intercepts for school year, and generalized estimating equations were used as epidemic detection methods for seasonal influenza. In the regression models, absenteeism data with various lags were used as predictor variables, and missing values in the datasets used for parameter estimation were handled either by deletion or linear interpolation. The epidemic detection methods were compared using a false alarm rate (FAR) as well as a metric for alarm timeliness. RESULTS: All model-based epidemic detection methods were found to decrease the FAR when compared to the 10% absence threshold. Regression models outperformed the exponentially weighted moving average model and including seasonality terms and a random intercept for school year generally resulted in fewer false alarms. The best-performing model, a seasonal logistic regression model with random intercept for school year and a day of week indicator where parameters were estimated using absenteeism data that had missing values linearly interpolated, produced a FAR of 0.299, compared to the pre-existing threshold method which at best gave a FAR of 0.827. CONCLUSIONS: School absenteeism can be a useful tool for alerting public health to upcoming influenza epidemics in Wellington-Dufferin-Guelph. Logistic regression with seasonality terms and a random intercept for school year was effective at maximizing true alarms while minimizing false alarms on historical data from this region.

A warning threshold for monitoring tuberculosis surveillance data: an alternative to hidden Markov model.

OBJECTIVES: Although hidden Markov model (HMM) is known as a powerful tool for the detection of epidemics based on the historical data, the frequent use of such a model poses some limitation especially when decision-making is required for new observations. This study was aimed to address a warning threshold for monitoring the weekly incidences of tuberculosis as an alternative to HMM. METHODS: We extracted the weekly counts of newly diagnosed patients with sputum smear-positive pulmonary TB from 2005 to 2011 nationwide. To detect unexpected incidences of the disease, two approaches: Serfling and HMM, were applied in presence/absence of linear, seasonal and autoregressive components. Models were subsequently evaluated in terms of goodness of fit, and their results were compared in detection of the disease phases. Then, multiple hypothetical thresholds were constructed based on the estimate of models and the optimal one was revealed through ROC curve analysis. RESULTS: Findings from both adjusted R-square (R~2) and Bayesian information criterion (BIC) presented a higher goodness of fit for periodic autoregressive HMM (BIC = -1323.6; R~2=0.74) than other models. According to ROC analysis, better values for both Youden's index and area under curve (0. 96 and 0. 98 respectively) were obtained by the threshold based on the estimate of periodic autoregressive model. CONCLUSIONS: As the optimal threshold presented in this study is simple in concept and has no limitation in practice, especially for monitoring new observations, we would recommend such a threshold to be used for monitoring of TB incidence data in the surveillance system.

Canales endémicos y calidad de la información para su elaboración en municipios seleccionados / Endemic ranges and the quality of information in selected municipalities

La detección precoz del comportamiento anormal de enfermedades transmisibles depende de la calidad y oportunidad de la información disponible y de métodos eficientes. En Cuba generalmente se basa en anàlisis de series cronológicas y canales endémicos para vigilancia semanal o mensual desde el nivel primario de atención. En el país no se ha evaluado suficientemente la calidad de las series cronológicas ni los métodos empleados. En el año 2006 se realizó un estudio descriptivo en 18 unidades de anàlisis y tendencias municipales y en 12 àreas de salud de municipios seleccionados de Ciudad de La Habana y Pinar del Río, para evaluar la calidad de los datos utilizados en la confección de canales endémicos, identificar los principales métodos utilizados para su elaboración y personal que los analiza. Se encontró que se vigilan un número suficiente de Enfermedades de Declaración Obligatoria aunque no de manera uniforme, el soporte de conservación màs utilizado en àreas de salud fue el papel, se conservan un número elevado de años en casi todas las unidades, el método màs utilizado para el canal endémico es el Màximo maximorum y Mínimo minimorum y el personal que realiza el anàlisis està suficientemente capacitado. Las series existentes en esas unidades permiten realizar anàlisis apropiados y es conveniente extender la conservación digital como norma así como incrementar la preparación de profesionales dedicados a esta actividad(AU)

Early detection of the anomalous behavior of communicable diseases depends on the quality and promptness of available data and of efficient methods. In Cuba, this detection generally supports on the analysis of time series and endemic ranges for weekly or monthly surveillance at primary health care. There have not been thorough assessment of the quality of times series and methods. A descriptive study was conducted in 18 municipal health analysis and trends units and in 12 health areas located in selected municipalities of the City of Havana and Pinar del Río provinces in 2006, with the purpose of evaluating the quality of data in the preparation of endemic ranges; to identify the main methods for their preparation and the staff in charge of the analysis. It was found that an adequate number of compulsory notification diseases are under surveillance, but not in a consistent way; the most used preservation means is paperback in health areas; a high number of years in analysis are kept in almost all the units, the most common method for preparation of endemic ranges is Maximum Maximorum and Minimum Minimorum whereas the staff in charge of the analysis is properly trained. The existing time series allow making suitable analysis, so, it is desirable to increase the use of computerization to keep them as well as to raise the level of training for the professionals devoted to this task(AU)

Canales endémicos y calidad de la información para su elaboración en municipios seleccionados / Endemic ranges and the quality of information in selected municipalities

La detección precoz del comportamiento anormal de enfermedades transmisibles depende de la calidad y oportunidad de la información disponible y de métodos eficientes. En Cuba generalmente se basa en anàlisis de series cronológicas y canales endémicos para vigilancia semanal o mensual desde el nivel primario de atención. En el país no se ha evaluado suficientemente la calidad de las series cronológicas ni los métodos empleados. En el año 2006 se realizó un estudio descriptivo en 18 unidades de anàlisis y tendencias municipales y en 12 àreas de salud de municipios seleccionados de Ciudad de La Habana y Pinar del Río, para evaluar la calidad de los datos utilizados en la confección de canales endémicos, identificar los principales métodos utilizados para su elaboración y personal que los analiza. Se encontró que se vigilan un número suficiente de Enfermedades de Declaración Obligatoria aunque no de manera uniforme, el soporte de conservación màs utilizado en àreas de salud fue el papel, se conservan un número elevado de años en casi todas las unidades, el método màs utilizado para el canal endémico es el Màximo maximorum y Mínimo minimorum y el personal que realiza el anàlisis està suficientemente capacitado. Las series existentes en esas unidades permiten realizar anàlisis apropiados y es conveniente extender la conservación digital como norma así como incrementar la preparación de profesionales dedicados a esta actividad

Early detection of the anomalous behavior of communicable diseases depends on the quality and promptness of available data and of efficient methods. In Cuba, this detection generally supports on the analysis of time series and endemic ranges for weekly or monthly surveillance at primary health care. There have not been thorough assessment of the quality of times series and methods. A descriptive study was conducted in 18 municipal health analysis and trends units and in 12 health areas located in selected municipalities of the City of Havana and Pinar del Río provinces in 2006, with the purpose of evaluating the quality of data in the preparation of endemic ranges; to identify the main methods for their preparation and the staff in charge of the analysis. It was found that an adequate number of compulsory notification diseases are under surveillance, but not in a consistent way; the most used preservation means is paperback in health areas; a high number of years in analysis are kept in almost all the units, the most common method for preparation of endemic ranges is Maximum Maximorum and Minimum Minimorum whereas the staff in charge of the analysis is properly trained. The existing time series allow making suitable analysis, so, it is desirable to increase the use of computerization to keep them as well as to raise the level of training for the professionals devoted to this task