Project description:People with COVID-19 might have sustained postinfection sequelae. Known by a variety of names, including long COVID or long-haul COVID, and listed in the ICD-10 classification as post-COVID-19 condition since September, 2020, this occurrence is variable in its expression and its impact. The absence of a globally standardised and agreed-upon definition hampers progress in characterisation of its epidemiology and the development of candidate treatments. In a WHO-led Delphi process, we engaged with an international panel of 265 patients, clinicians, researchers, and WHO staff to develop a consensus definition for this condition. 14 domains and 45 items were evaluated in two rounds of the Delphi process to create a final consensus definition for adults: post-COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset, with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include, but are not limited to, fatigue, shortness of breath, and cognitive dysfunction, and generally have an impact on everyday functioning. Symptoms might be new onset following initial recovery from an acute COVID-19 episode or persist from the initial illness. Symptoms might also fluctuate or relapse over time. A separate definition might be applicable for children. Although the consensus definition is likely to change as knowledge increases, this common framework provides a foundation for ongoing and future studies of epidemiology, risk factors, clinical characteristics, and therapy.
Project description:The global COVID-19 pandemic is unprecedented in its scope and impact. While a great deal of research has been directed towards the response in high-income countries, relatively little is known about the way in which decision-makers in low-income and crisis-affected countries have contended with the epidemic. Through use of an a priori decision framework, we aimed to evaluate the process of policy and operational decision-making in relation to the COVID-19 response in Somalia, a chronically fragile country, focusing particularly on the use of information and the role of transparency. We undertook a desk review, observed a number of key decision-making fora and conducted a series of key informant and focus group discussions with a range of decision-makers including state authority, civil society, humanitarian and development actors. We found that nearly all actors struggled to make sense of the scale of the epidemic and form an appropriate response. Decisions made during the early months had a large impact on the course of the epidemic response. Decision-makers relied heavily on international norms and were constrained by a number of factors within the political environment including resource limitations, political contestation and low population adherence to response measures. Important aspects of the response suffered from a transparency deficit and would have benefitted from more inclusive decision-making. Development of decision support tools appropriate for crisis-affected settings that explicitly deal with individual and environmental decision factors could lead to more effective and timely epidemic response.
Project description:We investigated the association between endogenous vitamin D and the severity of COVID-19 as well as the mechanisms of action of vitamin D supplementation. Vitamin D deficiency and insufficiency were associated with increased severity and unfavourable outcome after 28 days. Vitamin D levels were negatively associated with biomarkers of COVID-19 severity. Vitamin D supplementation after challenge of mice with COVID-19 plasma led to reduced levels of TNFα, IL-6, IFNγ and MPO in the lung, as well as down-regulation of pro-inflammatory pathways as derived from RNA-seq experiments. Thus, vitamin D demonstrates a protective effect against severity and unfavorable outcome in COVID-19, possibly through attenuation of tissue-specific hyperinflammation.
Project description:Introduction We examined the contribution of community health workers as frontline responders for the community-based surveillance in Somalia during the first year of the COVID-19 pandemic for detection of COVID-19 cases and identification of contacts. Methods We retrieved COVID-19 surveillance data from 16 March 2020 to 31 March 2021 from the health ministry’s central database. These data were collected through community health workers, health facilities or at the points of entry. We compared the number of suspected COVID-19 cases detected by the three surveillance systems and the proportion that tested positive using the chi-squared test. We used logistic regression analysis to assess association between COVID-19 infection and selected variables. Results During the study period, 154,004 suspected cases of COVID-19 were detected and tested, of which 10,182 (6.6%) were positive. Of the notified cases, 32.7% were identified through the community-based surveillance system, 54.0% through the facility-based surveillance system, and 13.2% at points of entry. The positivity rate of cases detected by the community health workers was higher than that among those detected at health facilities (8.6% versus 6.4%; p < 0.001). The community health workers also identified more contacts than those identified through the facility-based surveillance (13,279 versus 1,937; p < 0.001). The odds of COVID-19 detection generally increased by age. Community-based surveillance and health facility-based surveillance had similar odds of detecting COVID-19 cases compared with the points-of-entry surveillance (aOR: 7.0 (95% CI: 6.4, 7.8) and aOR: 7.5 (95% CI: 6.8, 8.3), respectively). Conclusion The community health workers proved their value as first responders to COVID-19. They can be effective in countries with weak health systems for targeted community surveillance in rural and remote areas which are not covered by the facility-based surveillance system.
Project description:BackgroundExpanding and providing access to early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through testing community-based strategies among socially vulnerable communities (SVC) are critical to reducing health disparities. The Epidemiological Intelligence Community Network (EpI-Net) community-based intervention sought to increase coronavirus 2019 (COVID-19) testing uptake and prevention practices among SVC in Puerto Rico (PR). We evaluated EpI-Net's community leaders' capacity-building component by assessing pre-post COVID-19 public health workshops' tests' score changes and satisfaction among trained community leaders.MethodsA total of 24 community leaders from SVC in PR have completed four community workshops. Pre- and post-assessments were completed as part of the health promotors training program to evaluate participants' tests score changes and satisfaction outcomes.ResultsPreliminary results showed: (1) high intervention retention levels of community leaders (85.7% acceptance rate); (2) change in post-test scores for community engagement strategies (p = 0.012); (3) change in post-test educational scores in COVID-19 prevention practices (p = 0.014); and (4) a change in scores in public health emergency management strategies (p < 0.001).ConclusionsThe overall workshop satisfaction was 99.6%. Community leaders have shown the importance of community capacity building as a key component for intervention feasibility and impact.Trial registrationOur study was retrospectively registered under the ClinicalTrial.gov ID NCT04910542.
Project description:The ongoing COVID-19 pandemic is among the worst in recent history, resulting in excess of 520,000,000 cases and 6,200,000 deaths worldwide. The United States (U.S.) has recently surpassed 1,000,000 deaths. Individuals who are elderly and/or immunocompromised are the most susceptible to serious sequelae. Rising sentiment often implicates younger, less-vulnerable populations as primary introducers of COVID-19 to communities, particularly around colleges and universities. Adjusting for more than 32 key socio-demographic, economic, and epidemiologic variables, we (1) implemented regressions to determine the overall community-level, age-adjusted COVID-19 case and mortality rate within each American county, and (2) performed a subgroup analysis among a sample of U.S. colleges and universities to identify any significant preliminary mitigation measures implemented during the fall 2020 semester. From January 1, 2020 through March 31, 2021, a total of 22,385,335 cases and 374,130 deaths were reported to the CDC. Overall, counties with increasing numbers of university enrollment showed significantly lower case rates and marginal decreases in mortality rates. County-level population demographics, and not university level mitigation measures, were the most significant predictor of adjusted COVID-19 case rates. Contrary to common sentiment, our findings demonstrate that counties with high university enrollments may be more adherent to public safety measures and vaccinations, likely contributing to safer communities.
Project description:We collated contact tracing data from COVID-19 clusters in Singapore and Tianjin, China and estimated the extent of pre-symptomatic transmission by estimating incubation periods and serial intervals. The mean incubation periods accounting for intermediate cases were 4.91 days (95%CI 4.35, 5.69) and 7.54 (95%CI 6.76, 8.56) days for Singapore and Tianjin, respectively. The mean serial interval was 4.17 (95%CI 2.44, 5.89) and 4.31 (95%CI 2.91, 5.72) days (Singapore, Tianjin). The serial intervals are shorter than incubation periods, suggesting that pre-symptomatic transmission may occur in a large proportion of transmission events (0.4-0.5 in Singapore and 0.6-0.8 in Tianjin, in our analysis with intermediate cases, and more without intermediates). Given the evidence for pre-symptomatic transmission, it is vital that even individuals who appear healthy abide by public health measures to control COVID-19.
Project description:Many organizations, including the US Centers for Disease Control and Prevention, have developed risk indexes to help determine community transmission levels for the ongoing COVID-19 pandemic. These risk indexes are largely based on newly reported cases and percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests, which are well-established as biased estimates of COVID-19 transmission. However, transmission risk indexes should accurately and precisely communicate community risks to decision-makers and the public. Therefore, transmission risk indexes would ideally quantify actual, and not just reported, levels of disease prevalence or incidence. Here, we develop a robust data-driven framework for determining and communicating community transmission risk levels using reported cases and test positivity. We use this framework to evaluate the previous CDC community risk level metrics that were proposed as guidelines for determining COVID-19 transmission risk at community level in the US. Using two recently developed data-driven models for COVID-19 transmission in the US to compute community-level prevalence, we show that there is substantial overlap of prevalence between the different community risk levels from the previous CDC guidelines. Using our proposed framework, we redefined the risk levels and their threshold values. We show that these threshold values would have substantially reduced the overlaps of underlying community prevalence between counties/states in different community risk levels between 3/19/2020-9/9/2021. Our study demonstrates how the previous CDC community risk level indexes could have been calibrated to infection prevalence to improve their power to accurately determine levels of COVID-19 transmission in local communities across the US. This method can be used to inform the design of future COVID-19 transmission risk indexes.
Project description:On 11 March 2020 the SARS-CoV-2 virus was officially declared a pandemic and measures were set up in various countries to avoid its spread among the population. This paper aims to analyse the perception of risk of COVID-19 infection in the Spanish population. A cross-sectional, descriptive observational study was conducted with a total of 16,372 Spanish participants. An online survey was used to gather data for 5 consecutive days over the compulsory lockdown period which was established after the state of emergency was declared. There is an association between socio-demographic variables and risk perception, and a very strong relationship between this perception and contact and direct experience with the virus in a family, social or professional setting. We also found that compared to working from home, working outside the home increased the perception of risk of infection and the perception of worsening health. Understanding the public perception of the risk of COVID-19 infection is fundamental for establishing effective prevention measures.