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Ranking the Next Pandemic - Eyes on Disease X

Ranking the Next Pandemic - Eyes on Disease X | healthcare technology | Scoop.it

The past several decades have seen an alarming spike in communicable disease outbreaks worldwide. Given a confluence of host, virologic, environmental, and human factors, experts agree that the next pandemic could already be on the horizon.

 

 

In a globalized world, changes in how people use land and interact with their ecosystems—such as rapid deforestation and agricultural expansion—have resulted in humans and animals coming into more frequent and intense contact with one another, increasing opportunities for what is known as "zoonotic disease spillover."

 

 

In the past few years alone, numerous disease outbreaks have had suspected or confirmed zoonotic origin, including mpox (formerly known as monkeypox), Ebola virus disease, dengue fever, and COVID-19.

 

Experts also recognize the need to prepare for another possible Disease X, a term used to describe a currently unknown pathogen with pandemic potential.

 

To direct resources toward the most high-consequence pathogens, it is paramount that leaders have an accurate concept of pandemic risk—for individual viruses as well as viral families. Several institutions are developing disease rankings at national and global levels, including the Priority Zoonotic Diseases Lists facilitated by the U.S. Centers for Disease Control and Prevention and the Research and Development (R&D) Blueprint created by the World Health Organization. 

 

The original SpillOver risk ranking framework (SpillOver 1.0), an open-source webtool launched by researchers at the University of California, Davis One Health Institute, estimated the relative spillover potential of wildlife-origin viruses to humans based on a series of host, viral, and environmental risk factors determined via expert opinion and scientific evidence. 

 

Its next iteration, SpillOvers 2.0, has rebranded to better describe the diversity and frequency of virus spillovers to people. The new platform uses a One Health approach, which recognizes the interdependence of human, animal, and environmental health. It will expand to include domestic animal and vector-borne viruses and assess pandemic risk rather than just spillover risk for wildlife viruses.

 

 

 

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Corona SEIR Workbench

Corona SEIR Workbench | healthcare technology | Scoop.it

Pandemic SEIR and SEIRV modelling software and infrastructure for the Corona SARS-COV-2 COVID-19 disease with data from Johns-Hopkins-University CSSE, Robert Koch-Institute and vaccination data from Our World In Data.

 

The SARS-COV-2 pandemic has been affecting our lives for months. The effectiveness of measures against the pandemic can be tested and predicted by using epidemiological models. The Corona SEIR Workbench uses a SEIR model and combines a graphical output of the results with a simple parameter input for the model. Modelled data can be compared country by country with the SARS-COV-2 infection data of the Johns Hopkins University. Additionally, the R₀ values of the Robert Koch Institute can be displayed for Germany. Vaccination data is used from Our World In Data.
 
 
 
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Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events

Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events | healthcare technology | Scoop.it

Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.


Objective: This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.


Methods: Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.


Results: Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.


Conclusions: Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems.

 

Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether.

 

The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus.

 

Such systems may aid future efforts to prevent and contain the spread of infectious diseases.

 

read the study at https://publichealth.jmir.org/2021/3/e26719

 

nrip's insight:

Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats. Using algorithms and/or learning models to extract travel related information from EHR's is not a novel concept but it has come into the spotlight(like most of digital health) in the past 18 months.

 

We should be adding short travel related questionnaires in patient intake forms going forward. The symptoms which trigger this sort of an intake form for a particular patient can change with time, month to month preferably, and be governed by a multi regional , multi national approach. What do you think?

 

 

 

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Capturing COVID-19–Like Symptoms at Scale Using Banner Ads on an Online News Platform

Capturing COVID-19–Like Symptoms at Scale Using Banner Ads on an Online News Platform | healthcare technology | Scoop.it

Identifying new COVID-19 cases is challenging. Not every suspected case undergoes testing, because testing kits and other equipment are limited in many parts of the world. Yet populations increasingly use the internet to manage both home and work life during the pandemic, giving researchers mediated connections to millions of people sheltering in place.


Objective: The goal of this study was to assess the feasibility of using an online news platform to recruit volunteers willing to report COVID-19–like symptoms and behaviors.

 


Methods: An online epidemiologic survey captured COVID-19–related symptoms and behaviors from individuals recruited through banner ads offered through Microsoft News. Respondents indicated whether they were experiencing symptoms, whether they received COVID-19 testing, and whether they traveled outside of their local area.


Results: A total of 87,322 respondents completed the survey across a 3-week span at the end of April 2020, with 54.3% of the responses from the United States and 32.0% from Japan. Of the total respondents, 19,631 (22.3%) reported at least one symptom associated with COVID-19. Nearly two-fifths of these respondents (39.1%) reported more than one COVID-19–like symptom. Individuals who reported being tested for COVID-19 were significantly more likely to report symptoms (47.7% vs 21.5%; P<.001). Symptom reporting rates positively correlated with per capita COVID-19 testing rates (R2=0.26; P<.001). Respondents were geographically diverse, with all states and most ZIP Codes represented. More than half of the respondents from both countries were older than 50 years of age.


Conclusions: News platforms can be used to quickly recruit study participants, enabling collection of infectious disease symptoms at scale and with populations that are older than those found through social media platforms. Such platforms could enable epidemiologists and researchers to quickly assess trends in emerging infections potentially before at-risk populations present to clinics and hospitals for testing and/or treatment.

 

source: Credit to Regenstrief Institute

 

read the entire study here : https://www.jmir.org/2021/5/e24742

 

nrip's insight:

Wow! Online news tools can be a useful strategy to reach a broad and diverse population during emerging outbreaks. This provides a quick and easy way to capture data on what is happening in the community at large rather than people hospitalized with the disease.

 

The beauty of this approach is that it offers access to a wide audience, many of whom might not be captured in other data gathering methods. Make no mistake, this is not useful when used in a silo. Its amazing if this is used as a step one tool to bring in participation to more involved mHealth tools for surveying.

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