Successful flattening of COVID-19 epidemiological curve in Jordan.
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ABSTRACT:
SUBMITTER: Al-Tamimi M
PROVIDER: S-EPMC7688063 | biostudies-literature | 2020 Dec
REPOSITORIES: biostudies-literature
Publications
Successful flattening of COVID-19 epidemiological curve in Jordan.
Al-Tamimi Mohammad M Qaisieh Rami R Tadros Ramzy Edward RE Shalabi Marwan M Alhasoun Anas A Kilani Muna M MM
Journal of global health 20201201 2
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Project description:IntroductionAcross four countries (Canada, USA, UK, and Italy), we explored the effects of persuasive messages on intended and actual preventive actions related to COVID-19, and the role of emotions as a potential mechanism for explaining these effects.MethodsOne thousand seventy-eight participants first reported their level of concern and emotions about COVID-19 and then received a positive persuasive text, negative persuasive text, or no text. After reading, participants reported their emotions about the pandemic and their willingness to take preventive action. One week following, the same participants reported the frequency with which they engaged in preventive action and behaviors that increased the risk of contracting COVID-19.ResultsResults revealed that the positive persuasive text significantly increased individuals' willingness to and actual engagement in preventive action and reduced risky behaviors 1 week following the intervention compared to the control condition. Moreover, significant differences were found between the positive persuasive text condition and negative persuasive text condition whereby individuals who read the positive text were more willing and actually engaged in more preventive action compared to those who read the negative text. No differences were found, however, at the 1-week follow-up for social distancing and isolation behaviors. Results also revealed that specific discrete emotions mediated relations between the effects of the texts and preventive action (both willing and actual).DiscussionThis research highlights the power of educational interventions to prompt behavioral change and has implications for pandemic-related interventions, government policy on health promotion messages, and future research.
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Project description:PurposeWe examined multidimensional, heterogeneous reactions to the COVID-19 pandemic and associated measures to provide further insights into the developmental processes of risk and adaptation.MethodWe used three-wave questionnaire data from 8156 individuals participating in the Norwegian County Public Health Survey assessed 1-5 months before and three (June 2020) and nine (December 2020) months after the outbreak. Latent profile and latent transition analyses were used to identify latent quality of life (QoL) classes and multiform changes, their probabilities, and predictors.ResultsWe identified five distinct QoL classes of varying proportions, namely Flourishing (i.e. 24-40%), Content (31-46%), Content-Symptomatic (8-10%), Languishing (14-20%), and Troubled (2-5%). Despite higher levels of negative affect and lower levels of life satisfaction and positive emotions, most individuals remained in their pre-pandemic QoL profiles. Yet, changes occurred for a meaningful proportion, with transition to a less favourable class more common than to a favourable. Between time 1 and 3, the flourishing and troubled groups decreased by 40% and 60%, while the content and languishing groups increased by 48% and 43%, respectively. Favourable pre-pandemic relational (marital status, support, interpersonal trust, and belonging), health, and economy-related status predicted significantly lower odds of belonging to the high-risk groups both pre-pandemic and during the pandemic.ConclusionsOverall, this study shows lower levels of QoL amid the COVID-19 pandemic, but substantial stability in the QoL distribution, and an overall levelling of the QoL distribution. Our findings also underscore the importance of financial, health-related, and social capital to QoL.
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Project description:BackgroundIn March 2020, an outbreak of coronavirus 19 (COVID-19) was detected in the North of Jordan. This retrospective study is the first from Jordan to report the epidemiologic, clinical, laboratory, and radiologic characteristics of COVID-19 infected patients.MethodsAll patients with laboratory-confirmed COVID-19 infection by RT-PCR in the North of Jordan admitted between March 15 and April 2, 2020 were included. The clinical features, radiological, and laboratory findings were reviewed.ResultsOf 81 patients affected, 79 (97.5%) shared a common exposure to four recent travelers from endemic areas. The mean age was 40 years. Although about half (44 [54.3%]) were females, symptomatic patients were mostly females (75%). The most common presenting symptoms were nasal congestion, sore throat and dry cough. Less than one-third (31%) had chronic diseases. Although 84% of patients reported receiving Bacille Calmette-Guérin (BCG) vaccination, more asymptomatic patients had BCG than symptomatic (p = 0.017). Almost all patients (97.5%) had an elevated D-dimer level. Erythrocyte sedimentation rate (ESR) and c-reactive protein were elevated in 50% and 42.7% of patients, respectively. High ESR found to be the predictor of abnormal chest radiograph observed in 13 (16%) patients with OR of 14.26 (95% CI 1.37-147.97, p = 0.026).ConclusionsAn outbreak of COVID-19 infection in northern Jordan affected more females and relatively young individuals and caused mainly mild illnesses. The strict outbreak response measures applied at early stages probably contributed to the lenient nature of this outbreak, but the contribution of other factors to such variability in COVID-19 presentation is yet to be explained.
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Project description:The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9-30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.
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