Three lessons for the COVID-19 response from pandemic HIV.
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ABSTRACT:
SUBMITTER: Hargreaves J
PROVIDER: S-EPMC7195084 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has been characterized by unprecedented rates of spatio-temporal spread. Here, we summarize the main events in the pandemic's timeline and evaluate what has been learnt by the public health community. We also discuss the implications for future public health policy and, specifically, the practice of epidemic control. We critically analyze this ongoing pandemic's timeline and contrast it with the 2002-2003 SARS outbreak. We identify specific areas (e.g., pathogen identification and initial reporting) wherein the international community learnt valuable lessons from the SARS outbreak. However, we also identify the key areas where international public health policy failed leading to the exponential spread of the pandemic. We outline a clear agenda for improved pandemic control in the future.
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Project description:Countries around the world have had to respond to the COVID-19 outbreak with limited information and confronting many uncertainties. Their ability to be agile and adaptive has been stressed, particularly in regard to the timing of policy measures, the level of decision centralization, the autonomy of decisions and the balance between change and stability. In this contribution we use our observations of responses to COVID-19 to reflect on agility and adaptive governance and provide tools to evaluate it after the dust has settled. Whereas agility relates mainly to the speed of response within given structures, adaptivity implies system-level changes throughout government. Existing institutional structures and tools can enable adaptivity and agility, which can be complimentary approaches. However, agility sometimes conflicts with adaptability. Our analysis points to the paradoxical nature of adaptive governance. Indeed, successful adaptive governance calls for both decision speed and sound analysis, for both centralized and decentralized decision-making, for both innovation and bureaucracy, and both science and politics.
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Project description:The first cluster of COVID-19 cases was reported in Wuhan, China on December 29th, 2019. Since then, China has experienced a pandemic of COVID-19.ObjectiveThis study aims to present the context in which the pandemic has evolved, the government's response and the pandemic's impact on public health and national economy.MethodsA review was conducted to collect relevant data from press releases and government reports.ResultsCOVID-19 poses a major public health threat on China with a cumulative number of cases over 89,000 (data cut-off date: August 9th, 2020). Between January and February 2020, China implemented a series of escalating policies (including a stringent nation-wide lockdown) to combat the pandemic. Therefore, it has been to a large extent limited to the Wuhan region. Social media such as WeChat and SinaWeibo played a crucial role in disseminating government information and public campaigns during the pandemic. Technologies were adopted to enable contact tracing and population travel patterns. The Chinese central government mobilized healthcare resources including healthcare personnel and medical materials to Wuhan in a highly effective way. Both central and regional governments launched financial policies to stimulate the economy, including special loans, tax extension, reduction or waiver. Nevertheless, the economy in China was significantly impacted especially during the lockdown period.ConclusionsChina has responded to the COVID-19 epidemic in a highly centralized and effective way. Balancing the needs to prevent a future pandemic and to boost economic recovery remains a challenge.
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Project description:Purpose of this reviewWe discuss the role of observational studies and cardiac registries during the COVID-19 pandemic. We focus on published cardiac registries and highlight contributions to the field that have had clinical implications.Recent findingsWe included observational studies of COVID-19 patients published in peer-reviewed medical journals with defined inclusion and exclusion criteria, defined study design, and primary outcomes. A PubMed and MEDLINE literature review results in 437 articles, of which 52 include patients with COVID-19 with cardiac endpoints. From July 2020 to December 2021, the average time from last data collected to publication was 8.9 ± 4.1 months, with an increasing trend over time (R = 0.9444, p < 0.0001). Of the 52 articles that met our inclusion criteria, we summarize main findings of 4 manuscripts on stroke, 14 on acute coronary syndrome, 4 on cardiac arrest, 7 on heart failure, 7 on venous thromboembolism, 5 on dysrhythmia, and 11 on different populations at risk for cardiovascular. Registries are cost effective, not disruptive to essential health services, and can be rapidly disseminated with short intervals between last data point collected and publication. In less than 2 years, cardiac registries have filled important gaps in knowledge and informed the care of COVID-19 patients with cardiovascular conditions.
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Project description:Racial and ethnic minorities have borne a particularly acute burden of the COVID-19 pandemic in the United States. There is a growing awareness from both researchers and public health leaders of the critical need to ensure fairness in forecast results. Without careful and deliberate bias mitigation, inequities embedded in data can be transferred to model predictions, perpetuating disparities, and exacerbating the disproportionate harms of the COVID-19 pandemic. These biases in data and forecasts can be viewed through both statistical and sociological lenses, and the challenges of both building hierarchical models with limited data availability and drawing on data that reflects structural inequities must be confronted. We present an outline of key modeling domains in which unfairness may be introduced and draw on our experience building and testing the Google-Harvard COVID-19 Public Forecasting model to illustrate these challenges and offer strategies to address them. While targeted toward pandemic forecasting, these domains of potentially biased modeling and concurrent approaches to pursuing fairness present important considerations for equitable machine-learning innovation.
| S-EPMC9090910 | biostudies-literature
Project description:To understand and analyse the global impact of COVID-19 on outpatient services, inpatient care, elective surgery, and perioperative colorectal cancer care, a DElayed COloRectal cancer surgery (DECOR-19) survey was conducted in collaboration with numerous international colorectal societies with the objective of obtaining several learning points from the impact of the COVID-19 outbreak on our colorectal cancer patients which will assist us in the ongoing management of our colorectal cancer patients and to provide us safe oncological pathways for future outbreaks.
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Project description:BackgroundRegarding the COVID-19 pandemic, concerted efforts have been invested in research to investigate and communicate the importance of complying with protective behaviors, such as handwashing and mask wearing. Protective measures vary in how effective they are in protecting the individual against infection, how much experience people have with them, whether they provide individual or societal protection, and how they are perceived on these dimensions.MethodsThis study assessed the willingness to follow recommended measures, depending on these features, among participants from Germany (n = 333), Hong Kong (n = 367), and the U.S. (n = 495). From April 24th to May 1st, 2020, individuals completed an online survey that assessed the antecedents of interest.ResultsIt was shown that assumed effectiveness, previous experience, and intended self- and other-protection positively predicted willingness to comply across countries. When measures were mainly perceived as protecting others (vs. the self), individuals were less prone to adopt them. When a measure's effectiveness to protect the individual was perceived as lower, willingness to adopt the measure increased with higher levels of prior experience and collectivism. Moreover, protecting others was more strongly related to adoption when individuals had higher levels of collectivism and lower levels of individualism.ConclusionsEmphasizing the benefit for others could be a means to lower the potential detrimental effects of low assumed effectiveness for individual protection.
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