Global disease monitoring and forecasting with Wikipedia

Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with $r^2$ up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein and adjust novelty claims accordingly; revise title; various revisions for clarit

Estimating the reproductive number, total outbreak size, and reporting rates for Zika epidemics in South and Central America

As South and Central American countries prepare for increased birth defects from Zika virus outbreaks and plan for mitigation strategies to minimize ongoing and future outbreaks, understanding important characteristics of Zika outbreaks and how they vary across regions is a challenging and important problem. We developed a mathematical model for the 2015 Zika virus outbreak dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly available data provided by the Pan American Health Organization, using Approximate Bayesian Computation to estimate parameter distributions and provide uncertainty quantification. An important model input is the at-risk susceptible population, which can vary with a number of factors including climate, elevation, population density, and socio-economic status. We informed this initial condition using the highest historically reported dengue incidence modified by the probable dengue reporting rates in the chosen countries. The model indicated that a country-level analysis was not appropriate for Colombia. We then estimated the basic reproduction number, or the expected number of new human infections arising from a single infected human, to range between 4 and 6 for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We estimated the reporting rate to be around 16% in El Salvador and 18% in Suriname with estimated total outbreak sizes of 73,395 and 21,647 people, respectively. The uncertainty in parameter estimates highlights a need for research and data collection that will better constrain parameter ranges.Comment: 35 pages, 16 figure

Mathematical Modeling of the Effectiveness of Facemasks in Reducing the Spread of Novel Influenza A (H1N1)

On June 11, 2009, the World Health Organization declared the outbreak of novel influenza A (H1N1) a pandemic. With limited supplies of antivirals and vaccines, countries and individuals are looking at other ways to reduce the spread of pandemic (H1N1) 2009, particularly options that are cost effective and relatively easy to implement. Recent experiences with the 2003 SARS and 2009 H1N1 epidemics have shown that people are willing to wear facemasks to protect themselves against infection; however, little research has been done to quantify the impact of using facemasks in reducing the spread of disease. We construct and analyze a mathematical model for a population in which some people wear facemasks during the pandemic and quantify impact of these masks on the spread of influenza. To estimate the parameter values used for the effectiveness of facemasks, we used available data from studies on N95 respirators and surgical facemasks. The results show that if N95 respirators are only 20% effective in reducing susceptibility and infectivity, only 10% of the population would have to wear them to reduce the number of influenza A (H1N1) cases by 20%. We can conclude from our model that, if worn properly, facemasks are an effective intervention strategy in reducing the spread of pandemic (H1N1) 2009

Forecasting the 2013--2014 Influenza Season using Wikipedia

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects between 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013--2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method projected the actual outcome with a high probability. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has past.Comment: Second version. In previous version 2 figure references were compiling wrong due to error in latex sourc

Impacto de la auditoría clínica en la mejoría de la práctica de la ventilación mecánica en pacientes ingresados en reanimación.

Objective: describe the adherence to lung protective mechanical ventilation recommendations, before and after implementing educational interventions in our postoperative intensive care unit, by conducting three cycles of clinical audit. Material and methods: longitudinal and descriptive study carried out in a single centre.  Data collection took place in three different periods, the first audit was carried out in 2017 and results were obtained from arterial blood samples and mechanical ventilation registry. Ventilation was classified into three categories: unnecessary hyperventilation, acceptable ventilation and optimal ventilation. After the first cycle, several educational interventions were implemented and a lung protective ventilation protocol was created.  After the application of these measures, a second audit was carried out in 2018 and another in 2019. Results: following the implementation of the previous measures, the rate of unnecessary hyperventilation decreased from 15% to 1,9% and the rate of optimal ventilation increased from 2% to 22.9%. There was a significant shift from the initial broad use of pressure-controlled ventilation (66% of registrations in 2017) to a later predominant use of volume-controlled ventilation (89% in 2019). Conclusions: clinical audit is a useful tool to improve our clinical practice. We have demonstrated an improvement in mechanical ventilation parameters in patients admitted to our postoperative care unit, after implementing some educational and feedback measures.Objetivos: describir la tasa de cumplimiento de las recomendaciones clínicas respecto a la ventilación mecánica protectora en pacientes críticos, obtenida de forma basal y tras la aplicación de diversas medidas educativas mediante la realización de tres ciclos de auditoría clínica. Material y Métodos: estudio longitudinal y descriptivo realizado en un único centro. La recogida de datos tuvo lugar en tres periodos diferentes, la primera auditoría se realizó en el año 2017 y se tomaron resultados de la gasometría arterial y de todos los parámetros del ventilador. A partir de esos datos, se clasificó el tipo de ventilación en tres grupos: hiperventilación innecesaria, ventilación aceptable y ventilación óptima. Tras esta primera fase se implementaron varias medidas educativas, como la presentación de sesiones al personal y la elaboración de un protocolo de ventilación mecánica. Tras la aplicación de esas medidas se realizó una segunda auditoría en el año 2018 y otra en 2019. Resultados: con la aplicación de las medidas descritas, se consiguió disminuir la tasa de hiperventilación innecesaria del 15% al 1,9% y aumentar la tasa de ventilación óptima del 2% al 22.9%. Se observó un cambio significativo en el uso inicial mayoritario de la ventilación controlada por presión (66% de registros en 2017) que se modificó hacia la ventilación controlada por volumen (89% en 2019). Conclusiones: la auditoría clínica es una herramienta útil para mejorar nuestra práctica clínica. En este caso se demuestra una clara mejoría de la forma de ventilación mecánica en pacientes ingresados en la Unidad de Reanimación

Open-Source Bioinformatic Pipeline to Improve PMS2 Genetic Testing Using Short-Read NGS Data

The molecular diagnosis of mismatch repair- de fi cient cancer syndromes is hampered by difficulties fi culties in sequencing the PMS2 gene, mainly owing to the PMS2CL pseudogene. Next-generation sequencing short reads cannot be mapped unambiguously by standard pipelines, compromising variant calling accuracy. This study aimed to provide a refined fi ned bioinformatic pipeline for PMS2 mutational analysis and explore PMS2 germline pathogenic variant prevalence in an unselected hereditary cancer (HC) cohort. PMS2 mutational analysis was optimized using two cohorts: 192 unselected HC patients for assessing the allelic ratio of paralogous sequence variants, and 13 samples enriched with PMS2 (likely) pathogenic variants screened previously by long-range genomic DNA PCR amplification. fi cation. Reads were forced to align with the PMS2 reference sequence, except those corresponding to exon 11, where only those intersecting gene-specific fi c invariant positions were considered. Afterward, the refined fi ned pipeline's accuracy was validated in a cohort of 40 patients and used to screen 5619 HC patients. Compared with our routine diagnostic pipeline, the PMS2_vaR pipeline showed increased technical sensitivity (0.853 to 0.956, respectively) in the validation cohort, identifying all previously PMS2 pathogenic variants found by long-range genomic DNA PCR amplification. fi cation. Fifteen HC cohort samples carried a pathogenic PMS2 variant (15 of 5619; 0.285%), doubling the estimated prevalence in the general population. The refined fi ned open-source approach improved PMS2 mutational analysis accuracy, allowing its inclusion in the routine next-generation sequencing pipeline streamlining PMS2 screening. (J Mol Diagn 2024, 26: 727-738; https://doi.org/10.1016/j.jmoldx.2024.05.005