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Post-COVID-19 global health strategies: the need for an interdisciplinary approach.

ABSTRACT: For survivors of severe COVID-19 disease, having defeated the virus is just the beginning of an uncharted recovery path. What follows after the acute phase of SARS-CoV-2 infection depends on the extension and severity of viral attacks in different cell types and organs. Despite the ridiculously large number of papers that have flooded scientific journals and preprint-hosting websites, a clear clinical picture of COVID-19 aftermath is vague at best. Without larger prospective observational studies that are only now being started, clinicians can retrieve information just from case reports and or small studies. This is the time to understand how COVID-19 goes forward and what consequences survivors may expect to experience. To this aim, a multidisciplinary post-acute care service involving several specialists has been established at the Fondazione Policlinico Universitario A. Gemelli IRCSS (Rome, Italy). Although COVID-19 is an infectious disease primarily affecting the lung, its multi-organ involvement requires an interdisciplinary approach encompassing virtually all branches of internal medicine and geriatrics. In particular, during the post-acute phase, the geriatrician may serve as the case manager of a multidisciplinary team. The aim of this article is to describe the importance of the interdisciplinary approach--coordinated by geriatrician--to cope the potential post-acute care needs of recovered COVID-19 patients.

SUBMITTER: Gemelli Against COVID-19 Post-Acute Care Study Group

PROVIDER: S-EPMC7287410 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Post-COVID-19 global health strategies: the need for an interdisciplinary approach.

Aging clinical and experimental research 20200611 8

For survivors of severe COVID-19 disease, having defeated the virus is just the beginning of an uncharted recovery path. What follows after the acute phase of SARS-CoV-2 infection depends on the extension and severity of viral attacks in different cell types and organs. Despite the ridiculously large number of papers that have flooded scientific journals and preprint-hosting websites, a clear clinical picture of COVID-19 aftermath is vague at best. Without larger prospective observational studie ...[more]

PMID: 32529595

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Project description:BackgroundUnprecedented public health measures have been used during this coronavirus 2019 (COVID-19) pandemic to control the spread of SARS-CoV-2 virus. It is a challenge to implement timely and appropriate public health interventions.Methods and findingsPopulation and COVID-19 epidemiological data between 21st January 2020 to 15th November 2020 from 216 countries and territories were included with the implemented public health interventions. We used deep reinforcement learning, and the algorithm was trained to enable agents to try to find optimal public health strategies that maximized total reward on controlling the spread of COVID-19. The results suggested by the algorithm were analyzed against the actual timing and intensity of lockdown and travel restrictions. Early implementations of the actual lockdown and travel restriction policies, usually at the time of local index case were associated with less burden of COVID-19. In contrast, our agent suggested to initiate at least minimal intensity of lockdown or travel restriction even before or on the day of the index case in each country and territory. In addition, the agent mostly recommended a combination of lockdown and travel restrictions and higher intensity policies than the policies implemented by governments, but did not always encourage rapid full lockdown and full border closures. The limitation of this study was that it was done with incomplete data due to the emerging COVID-19 epidemic, inconsistent testing and reporting. In addition, our research focuses only on population health benefits by controlling the spread of COVID-19 without balancing the negative impacts of economic and social consequences.InterpretationCompared to actual government implementation, our algorithm mostly recommended earlier intensity of lockdown and travel restrictions. Reinforcement learning may be used as a decision support tool for implementation of public health interventions during COVID-19 and future pandemics.

Project description:BackgroundThe COVID-19 outbreak has affected the lives of millions of people by causing a dramatic impact on many health care systems and the global economy. This devastating pandemic has brought together communities across the globe to work on this issue in an unprecedented manner.ObjectiveThis case study describes the steps and methods employed in the conduction of a remote online health hackathon centered on challenges posed by the COVID-19 pandemic. It aims to deliver a clear implementation road map for other organizations to follow.MethodsThis 4-day hackathon was conducted in April 2020, based on six COVID-19-related challenges defined by frontline clinicians and researchers from various disciplines. An online survey was structured to assess: (1) individual experience satisfaction, (2) level of interprofessional skills exchange, (3) maturity of the projects realized, and (4) overall quality of the event. At the end of the event, participants were invited to take part in an online survey with 17 (+5 optional) items, including multiple-choice and open-ended questions that assessed their experience regarding the remote nature of the event and their individual project, interprofessional skills exchange, and their confidence in working on a digital health project before and after the hackathon. Mentors, who guided the participants through the event, also provided feedback to the organizers through an online survey.ResultsA total of 48 participants and 52 mentors based in 8 different countries participated and developed 14 projects. A total of 75 mentorship video sessions were held. Participants reported increased confidence in starting a digital health venture or a research project after successfully participating in the hackathon, and stated that they were likely to continue working on their projects. Of the participants who provided feedback, 60% (n=18) would not have started their project without this particular hackathon and indicated that the hackathon encouraged and enabled them to progress faster, for example, by building interdisciplinary teams, gaining new insights and feedback provided by their mentors, and creating a functional prototype.ConclusionsThis study provides insights into how online hackathons can contribute to solving the challenges and effects of a pandemic in several regions of the world. The online format fosters team diversity, increases cross-regional collaboration, and can be executed much faster and at lower costs compared to in-person events. Results on preparation, organization, and evaluation of this online hackathon are useful for other institutions and initiatives that are willing to introduce similar event formats in the fight against COVID-19.

Project description:BackgroundStudying COVID-19 misinformation on Twitter presents methodological challenges. A computational approach can analyze large data sets, but it is limited when interpreting context. A qualitative approach allows for a deeper analysis of content, but it is labor-intensive and feasible only for smaller data sets.ObjectiveWe aimed to identify and characterize tweets containing COVID-19 misinformation.MethodsTweets geolocated to the Philippines (January 1 to March 21, 2020) containing the words coronavirus, covid, and ncov were mined using the GetOldTweets3 Python library. This primary corpus (N=12,631) was subjected to biterm topic modeling. Key informant interviews were conducted to elicit examples of COVID-19 misinformation and determine keywords. Using NVivo (QSR International) and a combination of word frequency and text search using key informant interview keywords, subcorpus A (n=5881) was constituted and manually coded to identify misinformation. Constant comparative, iterative, and consensual analyses were used to further characterize these tweets. Tweets containing key informant interview keywords were extracted from the primary corpus and processed to constitute subcorpus B (n=4634), of which 506 tweets were manually labeled as misinformation. This training set was subjected to natural language processing to identify tweets with misinformation in the primary corpus. These tweets were further manually coded to confirm labeling.ResultsBiterm topic modeling of the primary corpus revealed the following topics: uncertainty, lawmaker's response, safety measures, testing, loved ones, health standards, panic buying, tragedies other than COVID-19, economy, COVID-19 statistics, precautions, health measures, international issues, adherence to guidelines, and frontliners. These were categorized into 4 major topics: nature of COVID-19, contexts and consequences, people and agents of COVID-19, and COVID-19 prevention and management. Manual coding of subcorpus A identified 398 tweets with misinformation in the following formats: misleading content (n=179), satire and/or parody (n=77), false connection (n=53), conspiracy (n=47), and false context (n=42). The discursive strategies identified were humor (n=109), fear mongering (n=67), anger and disgust (n=59), political commentary (n=59), performing credibility (n=45), overpositivity (n=32), and marketing (n=27). Natural language processing identified 165 tweets with misinformation. However, a manual review showed that 69.7% (115/165) of tweets did not contain misinformation.ConclusionsAn interdisciplinary approach was used to identify tweets with COVID-19 misinformation. Natural language processing mislabeled tweets, likely due to tweets written in Filipino or a combination of the Filipino and English languages. Identifying the formats and discursive strategies of tweets with misinformation required iterative, manual, and emergent coding by human coders with experiential and cultural knowledge of Twitter. An interdisciplinary team composed of experts in health, health informatics, social science, and computer science combined computational and qualitative methods to gain a better understanding of COVID-19 misinformation on Twitter.

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Post-COVID-19 global health strategies: the need for an interdisciplinary approach.

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Post-COVID-19 global health strategies: the need for an interdisciplinary approach.

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