controlling disease outbreaks
Web-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality because of swift public health action.
Apps accessible through the internet and on mobile devices create an opportunity to enhance our traditional indicator-based surveillance systems, which have high specificity but issues with timeliness.
Objective: The aim of this study is to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community with regard to their design, implementation, and evaluation.
Results: Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised either complex algorithmic or statistical outbreak detection mechanisms or both.
We identified a need for these apps to have more features to support secure information exchange and outbreak response actions, with a focus on outbreak verification processes and staff and resources to support app operations.
Conclusions: Public health officials designing new or improving existing disease outbreak web-based apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and staff and resources are available to support the operations of the app and conduct rigorous and holistic evaluations.
read the study at https://publichealth.jmir.org/2021/4/e24330
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Since the start of the pandemic, new technologies have been developed to help reduce the spread of the infection.
Some of the most common safety measures today include measuring a person’s temperature, covering your nose and mouth with a mask, contact tracing, disinfection, and social distancing. Many businesses have adopted various technologies, including those with artificial intelligence (AI) underneath, helping to adhere to the COVID-19 safety measures.
As an example, numerous airlines, hotels, subways, shopping malls, and other institutions are already using thermal cameras to measure an individual’s temperature before people are allowed entry. In its turn, public transport in France relies on AI-based surveillance cameras to monitor whether or not people are social-distancing or wearing masks. Another example is requiring the download of contact-tracing apps delivered by governments across the globe.
However, there are a number of issues.
While many of these solutions help to ensure that COVID-19 prevention practices are observed, many of them have flaws or limits. In this article, we will cover some of the issues creating obstacles for fighting the pandemic.
Issue #1. Manual temperature scanning is tricky
Issue #2. Monitoring crowds is even more complex
Issue #3. Contact tracing leads to privacy concerns
Issue #4. UV rays harm eyes and skin
Issue #5. UVC robots are extremely expensive
Issue #6. No integration, no compliance, no transparency
Regardless of the safety measures in place and existing issues, innovations are already playing a vital role in the fight against COVID-19. By improving on existing technology, we can make everyone safer as we all adjust to the new normal.
read the details at https://www.altoros.com/blog/whats-wrong-with-ai-tools-and-devices-preventing-covid-19/
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The COVID-19 pandemic is the greatest public health crisis of the last 100 years. Countries have responded with various levels of lockdown to save lives and stop health systems from being overwhelmed. At the same time, lockdowns entail large socioeconomic costs.
One exit strategy under consideration is a mobile phone app that traces the close contacts of those infected with COVID-19.
Recent research has demonstrated the theoretical effectiveness of this solution in different disease settings. However, concerns have been raised about such apps because of the potential privacy implications. This could limit the acceptability of app-based contact tracing in the general population. As the effectiveness of this approach increases strongly with app uptake, it is crucial to understand public support for this intervention.
Objective: The objective of this study is to investigate the user
acceptability of a contact-tracing app in five countries hit by the pandemic.
Methods: We conducted a largescale, multicountry study (N=5995) to measure public support for the digital contact tracing of COVID-19 infections.
We ran anonymous online surveys in France, Germany, Italy, the United Kingdom, and the United States and measured intentions to use a contact-tracing app across different installation regimes (voluntary installation vs automatic installation by mobile phone providers) and studied how these intentions vary across individuals and countries.
Results: We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates.We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption.
Conclusions:
Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. Our findings show that the willingness to install the app is very high. The available evidence suggests that app-based contact tracing may be a viable approach to control the diffusion of COVID-19.
read the study at https://mhealth.jmir.org/2020/8/e19857
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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
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