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The pandemic of coronavirus disease (COVID)-19 is a global threat, causing high mortality, especially in the elderly. The main symptoms and the primary cause of death are related to interstitial pneumonia. Viral entry also into myocardial cells mainly via the angiotensin converting enzyme type 2 (ACE2) receptor and excessive production of pro-inflammatory cytokines, however, also make the heart susceptible to injury. In addition to the immediate damage caused by the acute inflammatory response, the heart may also suffer from long-term consequences of COVID-19, potentially causing a post-pandemic increase in cardiac complications. Although the main cause of cardiac damage in COVID-19 remains coagulopathy with micro- (and to a lesser extent macro-) vascular occlusion, open questions remain about other possible modalities of cardiac dysfunction, such as direct infection of myocardial cells, effects of cytokines storm, and mechanisms related to enhanced coagulopathy. In this opinion paper, we focus on these lesser appreciated possibilities and propose experimental approaches that could provide a more comprehensive understanding of the cellular and molecular bases of cardiac injury in COVID-19 patients. We first discuss approaches to characterize cardiac damage caused by possible direct viral infection of cardiac cells, followed by formulating hypotheses on how to reproduce and investigate the hyperinflammatory and pro-thrombotic conditions observed in the heart of COVID-19 patients using experimental in vitro systems. Finally, we elaborate on strategies to discover novel pathology biomarkers using omics platforms. [ABSTRACT FROM AUTHOR]

Background During the first epidemic wave, COVID-19 surveillance focused on quantifying the magnitude and the escalation of a growing global health crisis. The scientific community first assessed risk through basic indicators, such as the number of cases or rates of new cases and deaths, and later began using other direct impact indicators to conduct more detailed analyses. We aimed at synthesizing the scientific community's contribution to assessing the direct impact of the COVID-19 pandemic on population health through indicators reported in research papers. Methods We conducted a rapid scoping review to identify and describe health indicators included in articles published between January 2020 and June 2021, using one strategy to search PubMed, EMBASE and WHO COVID-19 databases. Sixteen experts from European public health institutions screened papers and retrieved indicator characteristics. We also asked in an online survey how the health indicators were added to and used in policy documents in Europe. Results After reviewing 3891 records, we selected a final sample of 67 articles and 233 indicators. We identified 52 (22.3%) morbidity indicators from 33 articles, 105 severity indicators (45.1%, 27 articles) and 68 mortality indicators (29.2%, 51). Respondents from 22 countries completed 31 questionnaires, and the majority reported morbidity indicators (29, 93.5%), followed by mortality indicators (26, 83.9%). Conclusions The indicators collated here might be useful to assess the impact of future pandemics. Therefore, their measurement should be standardized to allow for comparisons between settings, countries and different populations. [ABSTRACT FROM AUTHOR]

Background The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. Methods NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning–based model built to identify common topics/themes within the publications. Results and Conclusions We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning–based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 virus has, up to the time of this article, resulted in >770 million cases of COVID-19 illness worldwide, and approximately 7 million deaths, including >1.1 million in the United States. Although defined as a respiratory virus, early in the pandemic, it became apparent that considerable numbers of people recovering from COVID-19 illness experienced persistence or new onset of multi-system health problems, including neurologic and cognitive and behavioral health concerns. Persistent multi-system health problems are defined as Post-COVID-19 Condition (PCC), Post-Acute Sequelae of COVID-19, or Long COVID. A significant number of those with PCC report cognitive problems. This paper reviews the current state of scientific knowledge on persisting cognitive symptoms in adults following COVID-19 illness. A brief history is provided of the emergence of concerns about persisting cognitive problems following COVID-19 illness and the definition of PCC. Methodologic factors that complicate clear understanding of PCC are reviewed. The review then examines research on patterns of cognitive impairment that have been found, factors that may contribute to increased risk, behavioral health variables, and interventions being used to ameliorate persisting symptoms. Finally, recommendations are made about ways neuropsychologists can improve the quality of existing research. [ABSTRACT FROM AUTHOR]

Summary: Adjuvants, as the name indicates, are adjoined material aimed to assist in functioning as when added to vaccines they are meant to boost the effect and strongly stimulate the immune system. The response of the immune system can be unpredictable, and the autoimmune/inflammatory syndrome induced by adjuvants (ASIA) was developed to address possible adverse reactions of an autoimmune and inflammatory type that may be caused by adjuvants. While ASIA, as a syndrome, was coined and defined in 2011; reports describing patients with vague and nonspecific clinical symptoms following vaccinations appeared much earlier. In other words, ASIA came to define, arrange, and unite the variety of symptoms, related to autoimmunity, caused not by the vaccine itself, rather by the adjuvant part of the vaccine such as aluminum, among others. Accordingly, the introduction of ASIA enabled better understanding, proper diagnosis, and early treatment of the disorder. Furthermore, ASIA was shown to be associated with almost all body systems and various rheumatic and autoimmune diseases such as systemic lupus erythematosus, antiphospholipid syndrome, and systemic sclerosis. In addition, the correlation between COVID-19 and ASIA was noticed during the pandemic. In this review, we summarized the reported effects of adjuvants and medical literature before and after ASIA was defined, the several ways ASIA can manifest and impact different systems of the body, and the incidences of ASIA during the COVID-19 pandemic. It is important to clarify, that vaccines are among, if not the, most effective means of fighting infectious diseases however, we believe that vaccines manufacturing is not above criticism, particularly when it comes to added substances possessing a risk of side effects. Adjuvants such as aluminum, mineral oils, and silicone are added to vaccines in order to trigger a stronger immune response. Autoimmune/inflammatory syndrome induced by adjuvants (ASIA) was introduced in 2011 to address nonspecific symptoms and disorders appearing following vaccinations, which in fact were reported by many papers before the introduction of the syndrome. During the pandemic of COVID-19, a correlation between ASIA, COVID-19, and COVID-19 vaccines has been reported. [ABSTRACT FROM AUTHOR]

In response to the rapidly evolving coronavirus disease 2019 (COVID-19) pandemic, the All of Us Research Program longitudinal cohort study developed the COVID-19 Participant Experience (COPE) survey to better understand the pandemic experiences and health impacts of COVID-19 on diverse populations within the United States. Six survey versions were deployed between May 2020 and March 2021, covering mental health, loneliness, activity, substance use, and discrimination, as well as COVID-19 symptoms, testing, treatment, and vaccination. A total of 104,910 All of Us Research Program participants, of whom over 73% were from communities traditionally underrepresented in biomedical research, completed 275,201 surveys; 9,693 completed all 6 surveys. Response rates varied widely among demographic groups and were lower among participants from certain racial and ethnic minority populations, participants with low income or educational attainment, and participants with a Spanish language preference. Survey modifications improved participant response rates between the first and last surveys (13.9% to 16.1%, P  < 0.001). This paper describes a data set with longitudinal COVID-19 survey data in a large, diverse population that will enable researchers to address important questions related to the pandemic, a data set that is of additional scientific value when combined with the program's other data sources. [ABSTRACT FROM AUTHOR]

Background Early reports on the pandemic nature of coronavirus disease 2019 (COVID-19) directed the nephrology community to develop infection prevention and control (IPC) guidance. We aimed to make an inventory of strategies that dialysis centres followed to prevent infection with COVID-19 in the first pandemic wave. Methods We analyzed IPC measures taken by hemodialysis centres treating patients presenting with COVID-19 between 1 March 2020 and 31 July 2020 and that completed the European Renal Association COVID-19 Database centre questionnaire. Additionally, we made an inventory of guidelines published in European countries to prevent spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in dialysis centres. Results Data from 73 dialysis units located in and bordering Europe were analyzed. All participating centres implemented IPC measures to mitigate the impact of SARS-CoV-2 during the first pandemic wave. Measures mentioned most often included triage with questions before entering the dialysis ward, measuring body temperature, hand disinfection, masking for all patients and staff, and personal protective equipment for staff members. These measures were also recommended in most of the 14 guidelines that were identified in the inventory of national guidelines and were also scored as being among the most important measures by the authors of this paper. Heterogeneity existed between centres and national guidelines regarding the minimal distance between dialysis chairs and recommendations regarding isolation and cohorting. Conclusions Although variation existed, measures to prevent transmission of SARS-CoV-2 were relatively similar across centres and national guidelines. Further research is needed to assess causal relationships between measures taken and spread of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The pandemic situation of COVID-19 blocks many traditional information exchange paths, intensifies the information asymmetry in the implementation of the energy efficiency retrofitting project of existing building and seriously affects the decision-making effect of the energy performance contracting (EPC) subject. Therefore, based on the information-sharing platform, this paper constructs the synergistic incentive framework; designs the computational experimental models from the aspects of system information structure, system organization structure, synergistic communication strategy and subject knowledge structure; and analyzes the influence mechanism and evolution law of information on synergistic incentive performance from the two dimensions of the influence of the synergy cycle on the incentive performance and the influence of the incentive cycle on the synergy degree. The results show that increasing the information redundancy length and the information redundancy width can improve the synergy degree and incentive validity. The concentration of subject number or information dimension in a certain information area can quickly improve the synergy degree and incentive utility in the early stage of the synergistic incentive cycle, but the above situation is opposite in the later stage. Increasing the information communication path is beneficial to the effective information dissemination among subjects and can improve the efficiency of the decision-making of the subjects. Lastly, considering the background of COVID-19 epidemic situation, this paper puts forward the remedial methods and coping strategies make up for the lack of synergistic incentive information between EPC subjects. Research results can provide strong support for the design of synergistic incentive mechanism of EPC subjects. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 pandemic has alerted people of the threat caused by viruses. Vaccine is the most effective way to prevent the disease from spreading. The interaction between antibodies and antigens will clear the infectious organisms from the host. Identifying B-cell epitopes is critical in vaccine design, development of disease diagnostics and antibody production. However, traditional experimental methods to determine epitopes are time-consuming and expensive, and the predictive performance using the existing in silico methods is not satisfactory. This paper develops a general framework to predict variable-length linear B-cell epitopes specific for human-adapted viruses with machine learning approaches based on Protvec representation of peptides and physicochemical properties of amino acids. QR decomposition is incorporated during the embedding process that enables our models to handle variable-length sequences. Experimental results on large immune epitope datasets validate that our proposed model's performance is superior to the state-of-the-art methods in terms of AUROC (0.827) and AUPR (0.831) on the testing set. Moreover, sequence analysis also provides the results of the viral category for the corresponding predicted epitopes with high precision. Therefore, this framework is shown to reliably identify linear B-cell epitopes of human-adapted viruses given protein sequences and could provide assistance for potential future pandemics and epidemics. [ABSTRACT FROM AUTHOR]

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers. [ABSTRACT FROM AUTHOR]

Objective Although telehealth has become a central component of medical care in response to the coronavirus disease 2019 (COVID-19) pandemic, comprehensive pediatric neuropsychological assessment over virtual platforms lacks empirical efficacy. This paper presents: a) the results of a quality improvement project examining the feasibility of in-person evaluation in the context of safety measures that alter test standardization, b) the impact such changes had upon neuropsychological test scores, and c) how using a hybrid model of clinical service delivery affected access to care. Method We compared demographic and outcome variables between patients seen during the pandemic (N  = 87) to a group of patients seen in our service immediately prior to COVID-19 (N  = 87). A subset of those patients were case-matched for age and diagnosis (N  = 39 per group). Children seen for neuropsychological re-evaluation during the pandemic (N  = 10) were examined using pairwise comparison. Results Groups did not differ on age, sex, or FSIQ. Despite changes to standardized administration, no group differences were found for any selected neuropsychological test variables in the larger sample or subsamples. In fact, all variables were moderately to highly correlated in the re-evaluation subgroup. The hybrid model expedited feedback sessions and increased face-to-face (telehealth) feedbacks. Conclusions A hybrid model incorporating modified in-person testing and intake and feedback encounters via telehealth may be a feasible and effective way to provide pediatric neuropsychological services. These preliminary findings suggest such novel aspects of neuropsychological evaluation could represent an improvement over pre-COVID models, especially in rural settings. [ABSTRACT FROM AUTHOR]

Background the COVID-19 pandemic disproportionately affected care home residents' and staffs' access to health care and advice. Health and social care professionals adapted rapidly to using video consultation (videoconferencing) technology without guidance. We sought to identify enablers and barriers to their use in supporting care home residents and staff. Methods a scoping review of the evidence on remote consultations between healthcare services and care homes. Interviews with English health and social care professionals about their experiences during the pandemic. Findings were synthesised using the non-adoption, abandonment, scale-up, spread, sustainability framework. Results 18 papers were included in the review. Twelve interviews were completed. Documented enablers and barriers affecting the uptake and use of technology (e.g. reliable internet; reduced travelling) resonated with participants. Interviews demonstrated rapid, widespread technology adoption overcame barriers anticipated from the literature, often strengthening working relationships with care homes. Novel implementation issues included using multiple platforms and how resident data were managed. Healthcare professionals had access to more bespoke digital platforms than their social care counterparts. Participants alternated between platforms depending on individual context or what their organisation supported. All participants supported ongoing use of technologies to supplement in-person consultations. Conclusions the evidence on what needs to be in place for video consultations to work with care homes was partly confirmed. The pandemic context demolished many documented barriers to engagement and provided reassurance that residents' assessments were possible. It exposed the need to study further differing resident requirements and investment in digital infrastructure for adequate information management between organisations. [ABSTRACT FROM AUTHOR]

The haemagglutination test (HAT)-field protocol described here is an optimization of the recently published HAT, for the detection of antibodies directed against the receptor binding domain (RBD) of the SARS-Cov-2 virus. HAT and HAT-field are both based on haemagglutination triggered by a single reagent, the IH4-RBD recombinant protein. A sample of IH4-RBD sufficient for several thousand tests or a plasmid encoding IH4-RBD can be obtained from the authors of our first paper. Using titration of IH4-RBD, HAT-field now allows a quantitative assessment of antibody levels in a single step, using a few microliters of whole blood, such as can be obtained by finger prick, and requires only very simple disposable equipment. Because it is based on a single soluble reagent, the test can be adapted very simply and rapidly to detect antibodies against variants of the SARS-CoV-2, or conceivably against different pathogens. HAT-field appears well suited to provide quantitative assessments of the serological protection of populations as well as individuals, and given its very low cost, the stability of the IH4-RBD reagent in the adapted buffer and the simplicity of the procedure, could be deployed pretty much anywhere, including in the poorest countries and the most remote corners of the globe. [ABSTRACT FROM AUTHOR]

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Background The COVID-19 outbreak is increasing and spreading rapidly globally, with over 20 million cases and 800, 000 thousand deaths reported in 216 countries as of 28th August 2020. Since the report of the index case in Nigeria in February 2020 by the Nigeria Center for Disease Control (NCDC), daily records of confirmed cases have been reported in all states in the country. On 3rd April 2020, an outbreak of Coronavirus disease-2019 (COVID-19) was confirmed in Ondo State, Southwest Nigeria. Field investigations were conducted by the State Ministry of Health (MoH) to identify and confirm additional cases. This paper provides the outcome of the epidemiological investigation of the outbreak to further guide outbreak response activities. Methods Outbreak settings Ondo State is in the South-West Zone of Nigeria with her capital at Akure. The State is situated between longitudes 40 151E and 60 001E of the Greenwich median and latitudes 50 451N and 70 451 N, which are to the North of the equator in the Southwestern geopolitical zones of the country. Field Investigation The investigation was conducted as part of outbreak control and response measures hence permission to conduct the study was obtained from the Ondo State Ministry of Health (OSMoH). Following an alert from clinicians at a government-owned Hospital, Akure, Ondo State on 30 March 2020, the index case of COVID-19, a 34-year-old male with recent travel history to India was investigated. He arrived at Ondo State on 21st March 2020 and presented at the hospital with cough, sore throat and running nose with the onset of symptoms on 23rd March 2020. The State public health emergency Rapid Response Team (RRT) comprising of Commissioner for Health, State Epidemiologist, State and LGA surveillance officers, and health development partners in the state visited the hospital to investigate and implement public health response. Nasopharygeal and oropharyngeal samples were collected and tested for COVID-19, and returned positive from the national reference laboratory on the 3rd of April 2020. Operational definitions During the onset of the outbreak, three categories of case definitions for COVID-19 were used to guide the outbreak investigations according to the Nigeria Center for Diseases Control (NCDC) guidelines. Suspect case: (1) This is a patient with acute respiratory illness (fever and at least one sign/ symptom of respiratory disease (e.g. cough, shortness of breath) and a history of travel to or residence in a country/area or territory reporting local transmission of COVID-19 disease during the 14 days prior to symptom onset; (2) or a patient/health care worker with any acute respiratory illness and has been in contact with a confirmed COVID-19 case in the last 14 days prior to the onset of symptoms; (3) or a patient with a severe acute respiratory infection (fever and at least one sign/symptom of respiratory disease (e.g. cough, shortness of breath) and requiring hospitalization and with no other aetiology that fully explains the clinical presentation; (4) or a case for whom testing for COVID-19 is inconclusive. Confirmed case: A person with laboratory confirmation of COVID-19 infection, irrespective of clinical signs and symptoms. Probable case: Any suspected case for whom testing for COVID-19 is indeterminate test result or for whom testing was positive on a pan-coronavirus assay Data analysis The State COVID-19 line-list and case investigation forms of all COVID-19 cases from 19th March to 9th August 2020 were retrieved from the state disease surveillance unit, information on key variables were extracted and exported into SPSS version 20 and analyzed. Descriptive statistics such as frequency table, mean (standard deviation) and charts were used to describe key variables including LGA, age, sex, occupation and education and clinical conditions of cases. The week of report of cases and outcomes were used to generate the epidemic curve. The Chi-square test was used to compare categorical variables including the socio-demographic characteristics, clinical condition and outcome of cases. Two-sided P-values <0.05 were considered statistically significant. Results Socio-demographic characteristics of cases A total of 4353 suspected cases were reported and tested for COVID-19, of which 1316 COVID-19 cases were confirmed, with a case fatality rate of 2.2% recorded in 7 of the 17 Local Government Areas that reported at least a confirmed case (Figure 1). Most of the confirmed cases (1169; 88.8%), resides in urban areas (LGAs) (Table 1). Majority [1110 (84.3%)] were within the age group 20 to 59 years, with a mean age of 37.8 ±14.8 years. Males (713; 54.2%) were more affected compare to females (603; 45.8). More than three quarters (1009; 76.7%) of the cases had a tertiary level of education. Health care workers (404; 30.7%) were most affected compared to other professionals as shown in Table 1. 1170 Figure 1: Open in new tab Download slide Distribution of suspected and confirmed COVID-19 cases in Ondo state, March to August 2020 1170 Figure 1: Open in new tab Download slide Distribution of suspected and confirmed COVID-19 cases in Ondo state, March to August 2020 1170 Table 1: Socio-demographic characteristics of respondents N = 1316 Variable.  Frequency.  Percentage.  Age in years       <5  18  1.4  5-19  84  6.4  20-39  666  50.6  40-59  444  33.7  60-79  96  7.3  ≥80  8  0.6  Gender       Male  713  54.2  Female  603  45.8  Location of cases       Rural  147  11.2  Urban  1169  88.8  Type of Education       None  15  1.1  Nursery  8  0.6  Primary  80  6.1  Secondary  204  15.5  Tertiary  1009  76.7  Occupation       Health workers  404  30.7  Businessman and woman  225  17.1  Civil servant  168  12.8  Others  182  13.8  Pupil/Student  129  9.8  Political office Holders  77  5.9  Engineer  48  3.6  Religious/traditional leader  24  1.8  Banker  24  1.8  Security officer  35  2.7  Variable.  Frequency.  Percentage.  Age in years       <5  18  1.4  5-19  84  6.4  20-39  666  50.6  40-59  444  33.7  60-79  96  7.3  ≥80  8  0.6  Gender       Male  713  54.2  Female  603  45.8  Location of cases       Rural  147  11.2  Urban  1169  88.8  Type of Education       None  15  1.1  Nursery  8  0.6  Primary  80  6.1  Secondary  204  15.5  Tertiary  1009  76.7  Occupation       Health workers  404  30.7  Businessman and woman  225  17.1  Civil servant  168  12.8  Others  182  13.8  Pupil/Student  129  9.8  Political office Holders  77  5.9  Engineer  48  3.6  Religious/traditional leader  24  1.8  Banker  24  1.8  Security officer  35  2.7  Open in new tab 1170 Table 1: Socio-demographic characteristics of respondents N = 1316 Variable.  Frequency.  Percentage.  Age in years       <5  18  1.4  5-19  84  6.4  20-39  666  50.6  40-59  444  33.7  60-79  96  7.3  ≥80  8  0.6  Gender       Male  713  54.2  Female  603  45.8  Location of cases       Rural  147  11.2  Urban  1169  88.8  Type of Education       None  15  1.1  Nursery  8  0.6  Primary  80  6.1  Secondary  204  15.5  Tertiary  1009  76.7  Occupation       Health workers  404  30.7  Businessman and woman  225  17.1  Civil servant  168  12.8  Others  182  13.8  Pupil/Student  129  9.8  Political office Holders  77  5.9  Engineer  48  3.6  Religious/traditional leader  24  1.8  Banker  24  1.8  Security officer  35  2.7  Variable.  Frequency.  Percentage.  Age in years       <5  18  1.4  5-19  84  6.4  20-39  666  50.6  40-59  444  33.7  60-79  96  7.3  ≥80  8  0.6  Gender       Male  713  54.2  Female  603  45.8  Location of cases       Rural  147  11.2  Urban  1169  88.8  Type of Education       None  15  1.1  Nursery  8  0.6  Primary  80  6.1  Secondary  204  15.5  Tertiary  1009  76.7  Occupation       Health workers  404  30.7  Businessman and woman  225  17.1  Civil servant  168  12.8  Others  182  13.8  Pupil/Student  129  9.8  Political office Holders  77  5.9  Engineer  48  3.6  Religious/traditional leader  24  1.8  Banker  24  1.8  Security officer  35  2.7  Open in new tab Admission and clinical conditions of confirmed cases From Table 2 , 88 (6.7%) of the confirmed cases were admitted as inpatient during investigation, while 325 (24.7%) were symptomatic. The first symptom reported by cases were as follows; cough (98; 30.2%), fever (74; 22.8%), headache (30; 9.2%), runny nose (39; 12.0%), sore throat (24; 7.4%) and difficulty in breathing (15; 4.6%). 1170 Table 2: Admission and clinical conditions of confirmed cases.  Frequency.  Percentage.  Admitted as inpatient before lab testing   Yes  88  6.7  No  1228  93.3  Symptomatic       Yes  325  24.7  No  991  75.3  First symptom reported by respondents (n = 325)   Cough  98  30.2  Fever  74  22.8  Headache  30  9.2  Runny nose  39  12.0  Sore throat/pharyngitis  24  7.4  Difficulty breathing  15  4.6  New loss of taste  9  2.7  New loss of smell  8  2.5  Chest pain  7  2.2  General Body weakness  6  1.8  Others  15  4.6  Outcome       Alive  1287  97.8  Dead  29  2.2 .  Frequency.  Percentage.  Admitted as inpatient before lab testing   Yes  88  6.7  No  1228  93.3  Symptomatic       Yes  325  24.7  No  991  75.3  First symptom reported by respondents (n = 325)   Cough  98  30.2  Fever  74  22.8  Headache  30  9.2  Runny nose  39  12.0  Sore throat/pharyngitis  24  7.4  Difficulty breathing  15  4.6  New loss of taste  9  2.7  New loss of smell  8  2.5  Chest pain  7  2.2  General Body weakness  6  1.8  Others  15  4.6  Outcome       Alive  1287  97.8  Dead  29  2.2  Others= abdominal pain; chills/sweats; joint pain; inability to walk; Nausea Open in new tab 1170 Table 2: Admission and clinical conditions of confirmed cases.  Frequency.  Percentage.  Admitted as inpatient before lab testing   Yes  88  6.7  No  1228  93.3  Symptomatic       Yes  325  24.7  No  991  75.3  First symptom reported by respondents (n = 325)   Cough  98  30.2  Fever  74  22.8  Headache  30  9.2  Runny nose  39  12.0  Sore throat/pharyngitis  24  7.4  Difficulty breathing  15  4.6  New loss of taste  9  2.7  New loss of smell  8  2.5  Chest pain  7  2.2  General Body weakness  6  1.8  Others  15  4.6  Outcome       Alive  1287  97.8  Dead  29  2.2 .  Frequency.  Percentage.  Admitted as inpatient before lab testing   Yes  88  6.7  No  1228  93.3  Symptomatic       Yes  325  24.7  No  991  75.3  First symptom reported by respondents (n = 325)   Cough  98  30.2  Fever  74  22.8  Headache  30  9.2  Runny nose  39  12.0  Sore throat/pharyngitis  24  7.4  Difficulty breathing  15  4.6  New loss of taste  9  2.7  New loss of smell  8  2.5  Chest pain  7  2.2  General Body weakness  6  1.8  Others  15  4.6  Outcome       Alive  1287  97.8  Dead  29  2.2  Others= abdominal pain; chills/sweats; joint pain; inability to walk; Nausea Open in new tab Figure 2 described the epidemic curve of the outbreak from March to August 2020. The index case was confirmed on April 4, 2020. Thereafter, there was a surge in the number of confirmed COVID-19 cases with the outbreak reaching its peak on July 2, 2020. Afterwards, fluctuations in the number of cases were observed before a steady decline was recorded between August 3, 2020 and August 9, 2020. 1170 Figure 2 Open in new tab Download slide Epi-Curve of confirmed cases of COVID-19 in Ondo State, March to August 2020 1170 Figure 2 Open in new tab Download slide Epi-Curve of confirmed cases of COVID-19 in Ondo State, March to August 2020 Association between socio-demographic characteristics, clinical conditions and outcomes of cases In Table 3 , significant proportion of death occurred among cases within the age group 60 years and above (14; 13.5%) compared to other age groups (p < 0.001). Death occurred more among males (26; 3.6%) compared to the females (3; 0.5%) (p < 0.001). Furthermore, symptomatic cases had higher proportion (27; 8.3%) of deaths compared to asymptomatic cases (2; 0.2%) (P < 0.001). Among the symptomatic cases, a high proportion of death was found among those with difficulty in breathing (3; 20%), fever (11; 14.9%), new loss of taste (1; 11.1%), cough (9; 9.2%) and sore throat (2; 8.3%) (P < 0.001) 1170 Table 3: Association between socio-demographic characteristics and outcomes of cases Variable.  Outcome. .  Total.  P-value.  Dead.  Alive.  Age in years           <5  0 (0.0)  18 (100.0)  18  <0.001  5-19  0 (0.0)  84 (100.0)  84    20-39  2 (0.3)  664 (99.7)  666    40-59  13 (2.9)  431 (97.1)  444    ≥60  14 (13.5)  90 (86.5)  104    Gender           Male  26 (3.6)  687 (96.4)  713  <0.001  Female  3 (0.5)  600 (99.5)  603    Location of cases           Rural  5 (3.4)  142 (96.6)  147  0.294  Urban  24 (2.1)  1145 (97.9)  1169    Type of Education           None  1 (6.7)  14 (93.3)  15  0.630  Nursery  0 (0.0)  8 (100.0)  8    Primary  3 (3.8)  77 (96.2)  80    Secondary  4 (2.0)  200 (98.0)  204    Tertiary  21 (2.1)  988 (97.9)  1009    Occupation           Health workers  2 (0.5)  402 (99.5)  24    Businessman and woman  14 (6.2)  211 (93.8)  225    Civil servant  1 (0.6)  167 (99.4)  168  <0.001  Others  10 (5.5)  172 (94.5)  182    Pupil/Student  0 (0.0)  129 (100.0)  129    Politician  0 (0.0)  77 (100.0)  77    Engineer  0 (0.0)  48 (100.0)  48    Religious/traditional ruler  2 (8.3)  22 (91.7)  24    Banker  0 (0.0)  24 (100.0)  24    Security officer  0 (0.0)  35 (100.0)  35    Symptomatic           Yes  27 (8.3)  298 (91.7)  325  <0.001  No  2 (0.2)  989 (99.8)  991    First symptom       N = 325    Cough  9 (9.2)  89 (90.8)  98    Fever  11 (14.9)  63 (85.1)  74  <0.001  Headache  1 (3.3)  29 (96.7)  30    Running nose  0 (0.0)  39 (100.0)  39    Sore throat  2 (8.3)  22 (91.7)  24    New loss of taste  1 (11.1)  8 (88.9)  9    Difficulty in breathing  3 (20.0)  12 (80.0)  15    New loss of smell  0 (0.0)  5 (100.0)  5    Others  0 (0.0)  31 (100.0)  31    Variable.  Outcome. .  Total.  P-value.  Dead.  Alive.  Age in years           <5  0 (0.0)  18 (100.0)  18  <0.001  5-19  0 (0.0)  84 (100.0)  84    20-39  2 (0.3)  664 (99.7)  666    40-59  13 (2.9)  431 (97.1)  444    ≥60  14 (13.5)  90 (86.5)  104    Gender           Male  26 (3.6)  687 (96.4)  713  <0.001  Female  3 (0.5)  600 (99.5)  603    Location of cases           Rural  5 (3.4)  142 (96.6)  147  0.294  Urban  24 (2.1)  1145 (97.9)  1169    Type of Education           None  1 (6.7)  14 (93.3)  15  0.630  Nursery  0 (0.0)  8 (100.0)  8    Primary  3 (3.8)  77 (96.2)  80    Secondary  4 (2.0)  200 (98.0)  204    Tertiary  21 (2.1)  988 (97.9)  1009    Occupation           Health workers  2 (0.5)  402 (99.5)  24    Businessman and woman  14 (6.2)  211 (93.8)  225    Civil servant  1 (0.6)  167 (99.4)  168  <0.001  Others  10 (5.5)  172 (94.5)  182    Pupil/Student  0 (0.0)  129 (100.0)  129    Politician  0 (0.0)  77 (100.0)  77    Engineer  0 (0.0)  48 (100.0)  48    Religious/traditional ruler  2 (8.3)  22 (91.7)  24    Banker  0 (0.0)  24 (100.0)  24    Security officer  0 (0.0)  35 (100.0)  35    Symptomatic           Yes  27 (8.3)  298 (91.7)  325  <0.001  No  2 (0.2)  989 (99.8)  991    First symptom       N = 325    Cough  9 (9.2)  89 (90.8)  98    Fever  11 (14.9)  63 (85.1)  74  <0.001  Headache  1 (3.3)  29 (96.7)  30    Running nose  0 (0.0)  39 (100.0)  39    Sore throat  2 (8.3)  22 (91.7)  24    New loss of taste  1 (11.1)  8 (88.9)  9    Difficulty in breathing  3 (20.0)  12 (80.0)  15    New loss of smell  0 (0.0)  5 (100.0)  5    Others  0 (0.0)  31 (100.0)  31    Others = chest pain, diarrhea, fatigue, joint pain, malaise, nausea, vomiting Open in new tab 1170 Table 3: Association between socio-demographic characteristics and outcomes of cases Variable.  Outcome. .  Total.  P-value.  Dead.  Alive.  Age in years           <5  0 (0.0)  18 (100.0)  18  <0.001  5-19  0 (0.0)  84 (100.0)  84    20-39  2 (0.3)  664 (99.7)  666    40-59  13 (2.9)  431 (97.1)  444    ≥60  14 (13.5)  90 (86.5)  104    Gender           Male  26 (3.6)  687 (96.4)  713  <0.001  Female  3 (0.5)  600 (99.5)  603    Location of cases           Rural  5 (3.4)  142 (96.6)  147  0.294  Urban  24 (2.1)  1145 (97.9)  1169    Type of Education           None  1 (6.7)  14 (93.3)  15  0.630  Nursery  0 (0.0)  8 (100.0)  8    Primary  3 (3.8)  77 (96.2)  80    Secondary  4 (2.0)  200 (98.0)  204    Tertiary  21 (2.1)  988 (97.9)  1009    Occupation           Health workers  2 (0.5)  402 (99.5)  24    Businessman and woman  14 (6.2)  211 (93.8)  225    Civil servant  1 (0.6)  167 (99.4)  168  <0.001  Others  10 (5.5)  172 (94.5)  182    Pupil/Student  0 (0.0)  129 (100.0)  129    Politician  0 (0.0)  77 (100.0)  77    Engineer  0 (0.0)  48 (100.0)  48    Religious/traditional ruler  2 (8.3)  22 (91.7)  24    Banker  0 (0.0)  24 (100.0)  24    Security officer  0 (0.0)  35 (100.0)  35    Symptomatic           Yes  27 (8.3)  298 (91.7)  325  <0.001  No  2 (0.2)  989 (99.8)  991    First symptom       N = 325    Cough  9 (9.2)  89 (90.8)  98    Fever  11 (14.9)  63 (85.1)  74  <0.001  Headache  1 (3.3)  29 (96.7)  30    Running nose  0 (0.0)  39 (100.0)  39    Sore throat  2 (8.3)  22 (91.7)  24    New loss of taste  1 (11.1)  8 (88.9)  9    Difficulty in breathing  3 (20.0)  12 (80.0)  15    New loss of smell  0 (0.0)  5 (100.0)  5    Others  0 (0.0)  31 (100.0)  31    Variable.  Outcome. .  Total.  P-value.  Dead.  Alive.  Age in years           <5  0 (0.0)  18 (100.0)  18  <0.001  5-19  0 (0.0)  84 (100.0)  84    20-39  2 (0.3)  664 (99.7)  666    40-59  13 (2.9)  431 (97.1)  444    ≥60  14 (13.5)  90 (86.5)  104    Gender           Male  26 (3.6)  687 (96.4)  713  <0.001  Female  3 (0.5)  600 (99.5)  603    Location of cases           Rural  5 (3.4)  142 (96.6)  147  0.294  Urban  24 (2.1)  1145 (97.9)  1169    Type of Education           None  1 (6.7)  14 (93.3)  15  0.630  Nursery  0 (0.0)  8 (100.0)  8    Primary  3 (3.8)  77 (96.2)  80    Secondary  4 (2.0)  200 (98.0)  204    Tertiary  21 (2.1)  988 (97.9)  1009    Occupation           Health workers  2 (0.5)  402 (99.5)  24    Businessman and woman  14 (6.2)  211 (93.8)  225    Civil servant  1 (0.6)  167 (99.4)  168  <0.001  Others  10 (5.5)  172 (94.5)  182    Pupil/Student  0 (0.0)  129 (100.0)  129    Politician  0 (0.0)  77 (100.0)  77    Engineer  0 (0.0)  48 (100.0)  48    Religious/traditional ruler  2 (8.3)  22 (91.7)  24    Banker  0 (0.0)  24 (100.0)  24    Security officer  0 (0.0)  35 (100.0)  35    Symptomatic           Yes  27 (8.3)  298 (91.7)  325  <0.001  No  2 (0.2)  989 (99.8)  991    First symptom       N = 325    Cough  9 (9.2)  89 (90.8)  98    Fever  11 (14.9)  63 (85.1)  74  <0.001  Headache  1 (3.3)  29 (96.7)  30    Running nose  0 (0.0)  39 (100.0)  39    Sore throat  2 (8.3)  22 (91.7)  24    New loss of taste  1 (11.1)  8 (88.9)  9    Difficulty in breathing  3 (20.0)  12 (80.0)  15    New loss of smell  0 (0.0)  5 (100.0)  5    Others  0 (0.0)  31 (100.0)  31    Others = chest pain, diarrhea, fatigue, joint pain, malaise, nausea, vomiting Open in new tab Conclusions The outcome of this investigation indicating high transmission among urban residence and health care workers are key public health concerns in the response to the COVID-19 outbreak in Ondo State, Nigeria. Furthermore, high case mortality among the older age groups requires public health intervention. Thus, we recommend intensified risk communication, enhanced surveillance activities, and use of community structures such as community and religious leaders, market and commercial vehicles associations, Ward Development Committee (WDC) and Village Development Committee (VDC) to ensure compliance with public health COVID-19 preventive measures particularly in the urban areas and among those facing a high risk of death. Furthermore, there is a need to prioritize public health interventions including training and vaccination among the vulnerable groups including health care workers who serve as front liners during case investigation, testing and case management. Key messages Enforcement of public health preventive measures particularly in urban settings, and supporting government to strengthen and monitor Infection Prevention and Control practices in hospital settings. [ABSTRACT FROM AUTHOR]

Background the negative consequences of COVID-19 distancing measures on older adults' mental health and ability to access services have been well documented. Online cognitive behavioural therapy and mindfulness interventions for older adults, carried out during the COVID-19 pandemic, have shown that these interventions are feasible and have potential mental health benefits. However, little research has been carried out on older adults' experiences of engaging with online psychological therapy, and specifically mindfulness therapy. Objectives to understand the experience of older adults engaging with online mindfulness therapy during the COVID-19 pandemic. Study design a qualitative analysis of four community-based focus groups. Participants thirty-six community dwelling older adults aged between 65 and 85 years were recruited via older adult organisations, charities and the local press. Nineteen percent had long-term physical health conditions, 25% had severe and enduring mental health difficulties and 19% had mild to moderate mental health difficulties. Results there was a strong sense of group cohesion and community from the participants. Three main themes were identified: reasons for applying, experience of the mindfulness therapy and connecting at home. Conclusions the majority of participants were positive about attending a mindfulness group online. This extended to the perceived psychological and social benefits as well as practical considerations. While some participants noted technological hurdles at the beginning of the course, the findings challenge previous studies that suggest older adults are reluctant to engage in online psychological therapies and has important implications for the future provision of psychological therapies to this population. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic and the associated policy responses to it were highly disruptive of supply chains throughout the economy in 2020 - disruptive on an unprecedented scale. Massive shocks on both the demand and supply sides of the market presented particular challenges in agricultural product markets. These were arguably most pronounced in the protein sector. This paper will examine these protein sector supply chain shocks, exploring the nature and causes of the market disruptions during the height of the pandemic, evaluating the effects of these shocks from an economic perspective, and identifying potential market and policy responses that could mitigate the adverse effects of similar events in the future. [ABSTRACT FROM AUTHOR]