Color images have long been used as an important supplementary information to guide the super-resolution of depth maps. However, how to quantitatively measure the guiding effect of color images on depth maps has always been a neglected issue. To solve this problem, inspired by the recent excellent results achieved in color image super-resolution by generative adversarial networks, we propose a depth map super-resolution framework with generative adversarial networks using multiscale attention fusion. Fusion of the color features and depth features at the same scale under the hierarchical fusion attention module effectively measure the guiding effect of the color image on the depth map. The fusion of joint color–depth features at different scales balances the impact of different scale features on the super-resolution of the depth map. The loss function of a generator composed of content loss, adversarial loss, and edge loss helps restore clearer edges of the depth map. Experimental results on different types of benchmark depth map datasets show that the proposed multiscale attention fusion based depth map super-resolution framework has significant subjective and objective improvements over the latest algorithms, verifying the validity and generalization ability of the model. With the increasing emphasis on security, trustworthy artificial intelligence is on the rise. In trustworthy AI, various 3D applications play a crucial role in scene construction, understanding the relationship between entities and the scene, and reasoning about invisible factors outside the scene. In the research of stereo image technology, the quality of depth maps is very significant because the depth value reflects the spatial position of the object in the scene. However, the resolution of depth maps has been very low due to the limited capture capability of depth sensors. Therefore, depth map super-resolution (SR) has become an urgent problem to be solved.Due to the limited information contained in a single depth map, the corresponding high-resolution (HR) color image is generally used to guide the super-resolution of depth maps. Conventional methods use filters or Markov Random Fields (MRF) to implement depth map super-resolution with the guidance of the color image. Leveraging the HR color image and the given low-resolution (LR) depth map, Kopf et al.  proposedIn recent years, due to the rapid development of convolutional neural networks, color-guided depth map super-resolution methods based on convolutional neural networks have achieved more remarkable results. Hui et al.  proposedHowever, most color-guided depth map super-resolution methods use color images directly. How to quantitatively measure the guiding effect of color images on depth map super-resolution lacks the attention of researchers. In this paper, we propose a depth map super-resolution framework that uses hierarchical attention fusion modules to measure the guidance of color features on depth features. Inspired from the recent emergence of excellent color image super-resolution generative adversarial networks such as SRGAN  and ESRGOur main contributions are as follows: (1) We propose a depth map super-resolution framework with multiscale attention fusion based generative adversarial networks to quantitatively measure the effectiveness of color images as a guide to depth map super-resolution. (2) The hierarchical color–depth attention fusion module measures the guidance of the color image on the depth map super-resolution and generates fused features of various scales. (3) The multiscale fused feature balance module evaluates the correlation between scales and fused features, and integrates fused color–depth features of various scales proportionally. (4) A loss function consisting of content loss, adversarial loss, and edge loss helps our method produce clearer edges of the depth map.We organize the remainder of this paper as follows. After a brief review of related literature in In this section, we introduce color-guided depth map super-resolution and color image super-resolution generative adversarial networks methods.Conventional color-guided depth map super-resolution methods can be divided into three categories: filter based methods, MRF based methods, and sparse representation based methods.Filter-based methods ,11,12,13Optimization-based single depth map SR methods can be generally divided into two classes: Markov Random Fields (MRF) ,17,18,19Many sparse representation-based depth map SR methods ,23,24,25Single depth map SR methods can achieve a promising performance in preserving depth contour while alleviating the noise of the depth map. However, they can produce texture copy artifacts and blurring edge artifacts derived from the depth discontinuities that are not consistent with color discontinuities at the corresponding position.Depth map super-resolution methods based on neural networks have achieved promising success ,4,26,27.Images at different scales contain different feature information . HoweverSuper-resolution methods for color images based on generative adversarial networks ,29,30,31Denton et al.  introducSome excellent methods for color image super-resolution generative adversarial networks have emerged. However, they produce more artifacts and textures. Obviously, these networks are not suitable for depth map super-resolution. Therefore, considering the sharp edges and smooth interior of the depth map, we propose a multiscale attention fusion based super-resolution generative adversarial network for depth maps. In particular, building a generator loss function that includes content loss, adversarial loss, and edge loss facilitates the generation of sharper edges.In this section, we propose a multiscale attention fusion for depth map super-resolution generative adversarial networks.n residual dense blocks (RDBs), where n is the scale of feature extraction. The settings of RDBs are consistent with those in [ith RDB are represented as ith scale through the attention module. In this way, we obtain color–depth fused features …, n scales. Images at different scales contain different geometric structures. The contribution of fused features at different scales to depth map super-resolution is not equal. We input The framework of our proposed method is demonstrated in those in . The depUPNet as  as the sith scale to form the merged feature ith scale as in Equation Ci=fEquation .(2)Ffi=n attention modules, we concatenate these features and denote them as After obtaining the color–depth fused features Equation .(5)Ff=FIn the standard GAN, the discriminator outputs the probability that the input image is real to determine whether the input is real or fake. The type of GAN can be defined with the discriminator. In general, the discriminator loss of a standard GAN with the assumption of cross-entropy loss can be expressed as follows:Equation .(9)Dx=sIn the discriminator, In this paper, we adopt the relativistic standard GAN (RGAN)  structurEquation .(12)LG=The discriminator extracts features using the RDBs, and then performs a discriminant classification using the sigmoid function to determine whether the input depth map is fake or real. Compared to the standard GAN, the relativistic GAN can generate high-resolution depth maps from relatively small samples. Furthermore, the training time to achieve optimal performance is significantly reduced.Due to the fact that depth maps are a kind of piece-wise smooth images, they are characterized by sharp edges and smooth interiors. Conventional GAN-based color image super-resolution methods that only use mean squared error (MSE) as content loss are not suitable for depth map super-resolution. In order to improve the edge sharpness of the generated high-resolution depth maps, we propose a loss function consisting of content loss, adversarial loss, and edge loss, which is expressed by Equation .(13)L˜GWe train our network with 80 color and depth pairs. In the training dataset, 52 color–depth pairs are from the Middlebury dataset and others are from the MPI Sintel depth dataset. The color images are downsampled to the images of corresponding scale factor by interval interpolation. The patch size is 128 × 128 and the batch size is 16. To enrich data diversity, we flip the patches horizontally and vertically, and rotate them by 90Training DatasetsIn the training phase, we use two datasets, the Middlebury datasets and the MPI Sintel depth dataset. The Middlebury datasets  are a stThe MPI Sintel depth dataset  is a synTesting DatasetsAmong the Middlebury Stereo Datasets , we use The Multi-view depth (MVD) test sequences consist of multi-view video sequences and corresponding pixel-by-pixel depth information to support flexible synthesis of virtual views during rendering. It is widely used in studies on 3D applications such as free-view video, binocular stereoscopic video, and naked eye 3D stereo video, making it the most promising form of 3D video data representation today.ToFMark dataset containes three real-scene depth maps captured by ToF sensors. The low-resolution depth maps in it were acquired using the PMD Nano ToF camera with a resolution of 120 × 160, and the high-resolution color images were acquired using the CMOS camera with a resolution of 810 × 610.For reconstructed and enhanced images, many studies have proposed many objective evaluation criteria ,39. In tRMSE stands for root mean squared error, as in Equation .(15)RMSEquation .(16)MADEquation , which iIn this subsection, we explore the correlation between the quantity of scales in the multiscale fusion attention module on the performance of the depth map super-resolution. We tested the experimental results on the Middlebury datasets for four quantities of RDBs: 10, 16, 20, and 22. The selection type of GAN is RGAN and the loss of the generator is set to content loss + edge loss. The experimental results are shown in In this subsection, we compare the depth map SR results with different kinds of GANs. In this subsection, we compare the experimental results with generator losses of MSE loss and MSE loss + edge loss, besides adversarial loss, to verify the necessity of edge loss. The general color image super-resolution generative adversarial networks are reconstructed based on MSE loss, which can obtain closer objective experimental results to the ground truth. However, the visual performance of the image generated in this way is not perceptually the closest to ground truth. Therefore, we propose a generator loss function that includes an edge loss for the characteristics of the clear edges and the smooth interior of the depth maps. As shown in Our baseline state-of-the-art methods are joint bilateral upsampling (JBU) , noise-aIn Since depth maps are acquired by depth sensors in real scenes, we not only compare experimental results on the Middlebury datasets, but also conduct experiments and comparisons on real scene depth map datasets. In this article, we selected the ToFMark dataset captured by the ToF sensor and the multi-view depth (MVD) test sequences  as the tIn this section, we briefly discuss our proposed method and the directions we can focus on in the future. In the edge region of the depth map, the introduced color image corresponds to a smooth region, resulting in the generated high-resolution depth map with occasional edge blurring. In the future, we will focus on introducing color image edges aligned with the edges of the depth map into the framework to achieve more accurate depth map super-resolution as well as to generate sharper edges.In this paper, we propose a multiscale attention fusion based depth super-resolution generative adversarial networks for 3D reconstruction in trustworthy AI. Specifically, a hierarchical color–depth attention fusion module measures the guidance of the color image on the depth map super-resolution and generates fused features of various scales. The multiscale fused feature balance module evaluates the correlation between scales and fused features, and integrates fused color–depth features at different scales in a proportional manner. By constructing a loss function model consisting of content loss, adversarial loss, and edge loss, our proposed generative adversarial networks produce high-resolution depth maps with sharper edges. The robustness and generalization of the model is demonstrated by extensive experiments that show satisfactory subjective and objective results of our proposed method on several types of depth map datasets.


Background: Understanding symptomatology and accuracy of clinical case definitions for community COVID-19 cases is important for Test, Trace and Isolate (TTI) and future targeting of early antiviral treatment.  Methods: Community cohort participants prospectively recorded daily symptoms and swab results (mainly undertaken through the UK TTI system).  We compared symptom frequency, severity, timing, and duration in test positive and negative illnesses.  We compared the test performance of the current UK TTI case definition  with a wider definition adding muscle aches, chills, headache, or loss of appetite.    Results: Among 9706 swabbed illnesses, including 973 SARS-CoV-2 positives, symptoms were more common, severe and longer lasting in swab positive than negative illnesses.  Cough, headache, fatigue, and muscle aches were the most common symptoms in positive illnesses but also common in negative illnesses. Conversely, high temperature, loss or altered sense of smell or taste and loss of appetite were less frequent in positive illnesses, but comparatively even less frequent in negative illnesses.  The current UK definition had 81% sensitivity and 47% specificity versus 93% and 27% respectively for the broader definition. 1.7-fold more illnesses met the broader case definition than the current definition. Conclusions: Symptoms alone cannot reliably distinguishCOVID-19 from other respiratory illnesses. Adding additional symptoms to case definitions could identify more infections, but with a large increase in the number needing testing and the number of unwell individuals and contacts self-isolating whilst awaiting results. Understanding the natural history of symptomatic COVID-19 in the community is critical to the control of infection because it informs decisions about who should seek and be given access to testing, which symptoms should prompt self-isolation, and whether those in contact with symptomatic people should self-isolate. Understanding the normal course of symptoms is also potentially helpful to patients and clinicians assessing whether care needs to be escalated due to unexpectedly severe or prolonged symptoms. In future, symptom profiles may also trigger early use of antivirals to prevent deterioration and potentially minimise transmission. Finally, understanding symptom profiles is important to inform syndromic surveillance. The natural history of severe acute respiratory syndrome virus-2 (SARS-CoV-2) infection can range from asymptomatic infection in around 25% of cases. The European Centre for Disease Control and Prevention (ECDC) defines a possible case based on at least one of cough, fever, shortness of breath or sudden loss of sudden onset of anosmia, ageusia or dysgeusia, and US Centers for Disease Control and Prevention (CDC) use the following clinical criteria in the absence of a more likely diagnosis: at least two of fever (measured or subjective), chills, rigors, myalgia, headache, sore throat, nausea or vomiting, diarrhoea, fatigue, congestion or runny nose OR any one of cough, shortness of breath, difficulty breathing, new olfactory disorder, new taste disorder OR severe respiratory illness with at least one of the following: clinical or radiographic evidence of pneumonia, acute respiratory distress syndrome.A wide range of clinical case definitions for COVID-19 are available utilising different combinations of symptoms to alert individuals to the need for testing, isolation and contact tracing. For example, the World Health Organization (WHO) include the following symptoms in the clinical case definition of a suspected case: acute onset of fever AND cough; OR acute onset of ANY THREE OR MORE of the following signs or symptoms: fever, cough, general weakness/fatigue, headache, myalgia, sore throat, coryza, dyspnoea, anorexia/nausea/vomiting, diarrhoea, altered mental status.In the UK, the Test Trace and Isolate community testing programme (TTI) currently asks individuals to seek testing if they have any of the following symptoms: a new continuous cough, a high temperature, loss of or altered sense of smell or taste.Although it is clear that addition of further symptoms to case definitions could increase sensitivity , this is likely to be at the cost of reduced specificity  and increasing numbers of people who require testing, isolation and contact tracing. Altering clinical case definitions will have different implications at varying levels of prevalence, since positive and negative predictive values of tests depend upon disease prevalence as well as test sensitivity and specificity. The timing within the course of an illness at which people infected with SARS-CoV-2 meet the case definition is important since earlier isolation of cases and contacts could reduce transmission, particularly as viral loads peaks early in the course of illness.For example, recent analysis of data from the REACT community survey suggests that adding in loss of appetite, chills, headache or muscle aches could increase the proportion of cases identified from 53% to 75% but at the cost of a 2.7-fold increase in required testing capacityVirus Watch). We compare the test characteristics  for the current UK definition and a wider definition proposed following analysis of the REACT study, which adds headache or chills or loss of appetite or muscle aches to the existing UK definition. We explore how symptom profiles and case definition performance vary by age and stage of the pandemic.Prospective community studies where participants record symptoms in near-real time are needed to accurately measure COVID-19 symptom profiles with minimal recall bias. Here we describe prospectively recorded symptom profiles  of illnesses that tested positive for COVID-19 and illnesses that tested negative within a large community cohort study  tested for SARS-CoV-2 by real-time reverse transcriptase PCR (RT-PCR) from the end of December 2020 onwards. In brief, participating households completed a baseline survey at enrolment into the study, followed by prospective, detailed daily symptom diaries recording the presence and severity of any symptoms of acute respiratory and gastrointestinal infections during any episodes of illness occurring during follow-up. At the end of each week of follow-up, households were emailed a survey link to report any symptoms from the preceding week as well as results of any SARS-CoV-2 swab tests conducted, including testing through TTI and routine asymptomatic screening, and including both polymerase chain reaction (PCR) and lateral flow device testing. Within the main cohort there was a nested sub-cohort of 10,766 participants who additionally provided study-specific swab specimens following predetermined symptom triggers . The start date of an illness episode was defined as the first day any symptoms were reported, and the end date was the final day of reported symptoms. A 7-day washout period where no symptoms were reported was used to define the end of one illness episode and the start of a new illness episode. The data presented in this analysis includes illnesses which began between 22 June 2020 (start of the study) through to 20 June 2021. Within illness episodes, we investigated a wide range of individual symptoms  and the following symptom groupings:UK Case definition – one or more of the following: cough, measured fever (≥37.8C) or feeling feverish, or loss of, or altered, sense of smell or taste.Broader case definition - one or more of the following: cough, measured fever (≥37.8C) or feeling feverish, loss of, or altered, sense of smell or taste, headache, muscle aches, loss of appetite or chills.Symptom data gathered through the weekly survey were grouped into illness episodes and matched to swab results . We present simple descriptive analyses of the frequency, severity and duration of symptoms in SARS-CoV-2 test positive and test negative illnesses. We calculated sensitivity and specificity for individual symptoms and, for the two case definitions, we also calculated the positive predictive value (PPV), negative predictive value (NPV) number of people meeting the definition within the cohort and the numbers needed to test to identify one positive case (NNT) . This could have resulted in omission of some test results, particularly if there was a lag between swabbing and receipt of results. We also excluded illnesses beginning in the final week of data collection (week commencing 26 April 2021) as there may have been insufficient time to receive and report swab results. The analysis was conducted using the statistical software STATA MP 16 . We calculated the proportion of illnesses with SARS-CoV-2 test results and how this varied by UK case definition status and over time. We also calculated the ratio of the proportion of illnesses getting tested that met the UK case definition to those that did not meet the UK case definition. For both of these estimates we excluded illnesses beginning before 28 September 2020 as prior to this, our weekly surveys did not ask participants to report test results in weeks where there were no reported symptoms . Written informed consent for the collection and use of data was obtained from participants.Extended data: Table S1) reported in the cohort between 22 June 2020 and 20 June 2021. 9706 (30.4%) illnesses were swabbed and reported a corresponding result and of these 973 (10.0%) tested positive for SARS-CoV-2; 465 of these swabs were conducted as part of the study, including 23 positives. The percentage of swabbed illnesses testing positive were highest in young adults aged 16-24 years and lowest in children aged 0-15 years; highest in London and lowest in the South East and South West regions; and peaked in December 2020 (Extended data: Table S2).Overall, there were 31,962 illnesses . Illnesses meeting the case definition were more likely to be swabbed than illnesses not meeting the case definition, although the differences in swabbing behaviour of these two groups changed over time . The ratio of the proportion of illnesses reporting a test result among those meeting the case definition to those not meeting the case definition was highest in September 2020, when those meeting the case definition were 4.4 times more likely to report a test result. This ratio steadily declined over time to its lowest point in May 2021 when illnesses meeting the case definition were only 1.4 more likely to report a test result compared to illnesses not meeting the case definition. When excluding illnesses without full opportunity to report swab results, the proportion of illnesses reporting swab results steadily increases from September 2020 to January 2021, temporarily drops in February and increases again through May 2021 and begins to dip again in June 2021 (Extended data) shows the sensitivity and specificity of each symptom and the mean and median day of illness on which they are first reported among all swabbed illnesses with a reported result. Among all illnesses reported (including non-swabbed and SARS-CoV-2 negative illnesses), most symptoms arise early in the course of illness  although loss or change to sense of smell or taste and the two chest pain symptoms appear slightly later (mean onset late in day 2 to middle of day 3 respectively) during the course of illness. It can be seen that although cough and some constitutional symptoms such as headache, fatigue and muscle aches are fairly common among COVID-19 illnesses , they are also a common feature of non-COVID-19 illnesses and thus do not have a high specificity. In contrast the loss or change to sense of smell or taste is slightly less common among COVID-19 illnesses but comparatively much less common among non-COVID-19 illnesses, leading to a lower sensitivity but higher specificity.Table S2 (Extended data) show the maximum reported severity for a range of key symptoms in test positive and test negative illnesses. When symptoms do occur, they are more likely to be severe in COVID-19 illnesses than in non-COVID-19 illnesses.Extended data) shows how illness duration varies by age and sex in COVID-19 and non-COVID-19 illnesses and Figure S4 (Extended data) shows the distribution of duration of illnesses among COVID-19 illnesses by age group. Illnesses tend to be of longer duration in older individuals.. The PPV  was 15% for the current UK case definition and 12% for the broader definition. PPV estimates were substantially lower (between 5-6%) in the youngest age group and during June through September 2020 and in February through June 2021, corresponding with the age group and time periods with the lowest disease rates. NPV  was 96% for the current UK case definition and 97% for the broader case definition. The number of illnesses meeting the broader case definition was 1.7-fold higher than those meeting the current definition.Sensitivity of the current UK case definition was 81% (95% CI 79-84%) compared to 93% (95% CI 91-95%) for the broader case definition. Specificity was 47% (95% CI 46-48%) for the current case definition and 27% (95% CI 26-28%) for the broader case definition. Sensitivity and specificity of both case definitions was lower in children aged 0–15 years than in older age groups, particularly for the current case definition. Sensitivity of the current case definitions remained stable (81-85%) between June 2020 through June 2021 apart from a decrease in February to April 2021 . In contrast the sensitivity of the proposed case definition remained stable over the entire period (87-95%). Specificity of both case definitions generally increased over time although the current case definition dipped in May-June 2021. The periods of lowest specificity coincide with periods when other acute respiratory viruses were more common in the population, such as during a large national outbreak of Rhinovirus in children in late summer and autumn 2020We characterised the symptom profiles and estimated the accuracy of clinical case definitions for COVID-19 among community cases arising in a large, prospective, population-based cohort study based in the UK. All symptoms asked about were more frequently reported, and when present were generally more severe and longer-lasting, in COVID-19 illnesses compared to non-COVID-19 illnesses. Individually, cough and some constitutional symptoms including headache, muscle ache and fatigue presented early in the course of illness and had moderate sensitivity and specificity as they were common in both test positive and test negative illnesses. In contrast, fever and loss of or altered sense of smell or taste had a lower sensitivity but higher specificity as they were not as common in COVID-19 illnesses but were even less common in non-COVID-19 illnesses and loss of or altered sense of smell or taste also presented slightly later in the course of illness. The combination of symptoms in the current UK case definition had a higher sensitivity (81%) than any individual symptom, with a specificity of 47%. Adding additional symptoms to the case definition could lead to earlier case identification and higher sensitivity, but at the cost of specificity and consequently, a substantial increase in the number of non-COVID-19 illnesses eligible for testing. For example, when we compare the broader case definition to the current UK case definition, cases on average met the case definition 0.3 days earlier and there was a moderate increase in sensitivity to 93% but at a much lower specificity of 27%, with 1.7 times more illnesses eligible for testing. . Whilst we collected information on a very wide range of symptoms, testing primarily relied on that conducted through the national TTI programme meaning that those meeting the current case definition are more likely to be tested. This is likely to lead to an overestimation of the sensitivity of the current UK case definition. This bias towards testing illnesses meeting the case definition however was reduced over time. This change in testing behaviour and the drop in sensitivity seen between February-April 2021 may be attributed, at least in part, to the roll-out and widespread availability and use of at-home lateral flow testing to the wider population from March 2021. The availability and ease of at-home lateral flow devices may have lowered individuals’ threshold for testing, leading to more mild illnesses being tested. This phenomenon would not explain the subsequent apparent rise in sensitivity from May-June 2021, although the confidence intervals for the sensitivity estimates between the February-April and May-June strata overlap (55%-76% and 65%-94% respectively) so the increased sensitivity could be due to chance. Due to limited data during the relevant time period, we are unable to assess whether any of the apparent changes in the sensitivity or specificity in May-June 2021 may be due to differing symptom profiles between the Alpha Variant of Concern (which was the dominant UK strain in winter and early spring 2021) and the Delta Variant of Concern (which became the dominant variant during May 2021). Strengths of this work include the prospective daily recording of a wide range of symptoms across a large community cohort and linkage of these to self-reported swab results ascertained on a weekly basis. This should maximise the accuracy of symptom data amongst swabbed participants. While not fully representative of the population of England and Wales, our sample includes participants in every local authority area; however, there is a moderate overrepresentation of those aged over 65 and an underrepresentation of those in more deprived areas. As such, systems to identify symptomatic cases, test them, and isolate cases and their contacts can only ever reduce, rather than prevent all transmission. These programmes, when part of a broader programme of non-pharmaceutical interventions, can however contribute to control of infection and may be particularly effective when introduced during periods of lower incidence. For example, countries that combined early and strict border controls, intensive testing, and rapid triggering of lockdowns have had substantially lower COVID-19 transmission and mortality than other countries such as the UK.COVID-19 is difficult to distinguish from other respiratory infections or common ailments on the basis of symptoms alone. Also, a high proportion of infections are asymptomatic or have a pre-symptomatic phase when transmission can occur. Engagement was lower in younger people and amongst those in financial hardship. Engagement with population level asymptomatic testing using lateral flow testing has also been shown to be low, particularly in socioeconomically disadvantaged areas.The success of testing and isolation programmes is dependent on public understanding and engagement. Data from behavioural surveys in England show only 51.5% of participants knew the symptoms that testing is recommended for, only 18.0% sought testing if they had the symptoms and only 42.5% fully adhered to self-isolation.Policy makers considering which symptoms might prompt testing, tracing and isolation need to balance the availability of testing capacity at different stages of the pandemic, the speed with which samples can be taken and results returned, the harms incurred by asking large numbers of people who do not have COVID-19 and their contacts to self-isolate whilst awaiting test results, the consistency and simplicity of public health messaging and the likely public engagement with the system. Alteration of symptom profiles triggering testing and isolation may have less impact than other approaches to increase uptake and engagement, and to ensure timely and effective tracing and isolation of household and non-household contacts, including through providing financial and non-financial support for people to isolateThe fact that COVID-19 may present as any of a very wide range of symptoms, or with no symptoms at all, is one of the key challenges in implementing successful TTI systems. Low levels of engagement also limit effectiveness. This emphasises the importance of not placing undue reliance on such systems as a mechanism to allow relaxation of other social distancing measures and the critical importance of protecting populations globally through immunisation. https://ucl-virus-watch.net/. We are sharing individual record level data on the Office of National Statistics Secure Research Service, and given the sensitive content in our dataset for this study, we cannot release the data at the individual level. In sharing the data we will work within the principles set out in the UKRI Guidance on best practice in the management of research data. Access to use of the data whilst research is being conducted will be managed by the Chief Investigators (ACH and RWA) in accordance with the principles set out in the UKRI guidance on best practice in the management of research data. Data access requests to data can also be made directly to the Virus Watch chief investigators (ACH or RWA) at the following email address:viruswatch@ucl.ac.uk.We aim to share aggregate data from this project on our website and via a "Findings so far" section on our website -https://doi.org/10.5281/zenodo.5888582.Zenodo: UCL-Public-Health-Data-Science/Symptom-profiles-and-accuracy-of-clinical-case-definitions-for-COVID-19-: first release,This project contains the following extended data:- Fragaszy_et_al_Symptom Profiles_Appendix_for_subm.docx ○ Figure S2. Percent of illnesses with a swab test result by month, overall and by UK COVID-19 Case Definition status○ Figure S3. Ratio of swabbing among illnesses meeting the UK COVID-19 case definition to those not meeting the case definition over time○ Figure S4. Distribution of illness duration* by age group among confirmed COVID-19 illnesses○ Table S1. Details of symptoms and symptom groupings○ Table S2. Speed of symptom onset, proportion of all community cases tested, and the test characteristics of each symptom○ Table S3. Severity of symptoms among swab-confirmed COVID-19 and non-COVID-19 (swab-negative) illnesses○ Table S4. Duration of illness among swab-confirmed COVID-19 illnesses and non-COVID-19 (swab-negative) illnesses, overall and stratified by age group and sex)- symp-stat01_Table_1_v10.do- symp-stat02_v5_Figure_1.doCreative Commons Attribution 4.0 International license (CC-BY 4.0).Data are available under the terms of the Fragaszy and colleagues describe symptomology of SARS-CoV-2 infections in a prospectively enrolled community cohort between June 2020 and June 2021. This research provides important information on the role of symptom-based surveillance and guidance. I have several minor comments. Discussion:).  It is likely worth discussing or at least mentioning the mixture of testing modalities and the bias that they could introduce . I'm thinking more generally between the test but also regarding the likelihood of detection given the timing of the test  were after the index symptom? Could you include a reference to the definition mild/moderate/severe as used in the supplemental tables? I imagine it was in the diary, but I could not find the definition . Fantastic work!Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:EpidemiologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Fragaszy and colleagues describe the symptom profiles of COVID-19 based on data from a large community cohort study. This is valuable work. I had mainly minor comments.Major comment:To what extent has clinical symptomatology changed over time, either with changes in the variants, or with immunity following vaccination doses and/or infections? There are many media reports of "symptom X" is more common with "variant Y". For example in April 2023 "red eye" is being mentioned as a symptom appearing with the latest Omicron subvariant . This issue of potentially changing symptom profiles should perhaps be mentioned in various places, starting in the Introduction.Minor comments:Abstract background - "...Test, Trace and Isolate (TTI)..." is TTI still being done? Wouldn't antiviral treatment largely be based on testing, either by LFT or PCR, given that testing is now widely available?Abstract results and main text Results, please clarify the variants involved, or at least the predominant variants during various periods, for example when was Alpha or Delta predominant?Do you have data on the subset of illnesses which led to medical attention, and how was the symptom profile in that subset of cases?Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:EpidemiologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Proper risk assessment is important for the primary prevention of atherosclerotic cardiovascular disease (ASCVD). However, no validated risk prediction tools are currently in use in Korea. This study sought to develop a 10-year risk prediction model for incident ASCVD.Using the National Sample Cohort of Korea, 325,934 subjects aged 20-80 years without previous ASCVD were enrolled. ASCVD was defined as a composite of cardiovascular death, myocardial infarction, and stroke. The Korean atherosclerotic cardiovas cular disease risk prediction (K-CVD) model was developed separately for men and women using the development dataset and validated in the validation dataset. Furthermore, the model performance was compared with the Framingham risk score (FRS) and pooled cohort equation (PCE).2=4.73, goodness-of-fit p=0.32). Compared with our model, both FRS and PCE showed worse calibration, overestimating ASCVD risk in the Korean population.Over 10 years of follow-up, 4,367 ASCVD events occurred in the overall population. The predictors of ASCVD included in the model were age, smoking status, diabetes, systolic blood pressure, lipid profiles, urine protein, and lipid-lowering and blood pressure-lowering treatment. The K-CVD model had good discrimination and strong calibration in the validation dataset (time-dependent area under the curve=0.846; 95% confidence interval, 0.828 to 0.864; calibration χThrough a nationwide cohort, we developed a model for 10-year ASCVD risk prediction in a contemporary Korean population. The K-CVD model showed excellent discrimination and calibration in Koreans. This population-based risk prediction tool would help to appropriately identify high-risk individuals and provide preventive interventions in the Korean population. Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of premature death worldwide and has become an important burden on the healthcare system in recent decades . RegardiCurrently, many guidelines on the prevention of ASCVD strongly recommend the use of risk assessment tools for risk stratification ,4. HowevKorea is among the countries with the lowest cardiovascular mortality rates and the fastest decline in age-adjusted cardiovascular mortality worldwide . In thisTo address these issues, we aimed to develop and validate a risk prediction model to estimate the 10-year ASCVD risk in individuals without previous ASCVD using a representative nationwide cohort of the Korean population.This study was based on the National Health Insurance Service–National Sample Cohort (NHIS-NSC), which is a populationbased cohort established by the National Health Insurance Service (NHIS) in Korea . The mosTo develop a 10-year ASCVD risk prediction model in a representative Korean population, we identified 363,270 subjects aged 20 years to 80 years who underwent a general health examination between January 2009 and December 2010 from the NHIS-NSC database. This time period potentially provided 10 years of median follow-up until the end of 2019. For the 3,678 subjects who underwent general health examinations in both years, the records of health checkups from 2009 were included and the records from 2010 were excluded. Subjects were further excluded if their claims records indicated that they had a history of ASCVD before the index health checkup. Accordingly, 33,658 subjects were sequentially excluded based on the following criteria: (1) previous history of angina or myocardial infarction ; (2) previous history of coronary revascularization ; and (3) previous history of stroke . Subjects diagnosed with atrial fibrillation or those taking antiplatelet drugs for reasons other than ASCVD were not excluded from the study population. Finally, 325,934 subjects were included in this study .All relevant variables associated with ASCVD risk were included in this study, as shown in An ASCVD event was defined as the first occurrence of the composite endpoint of cardiovascular death, MI, or stroke. Cardiovascular death was identified from death records linked to the National Statistical Office using diagnostic codes; the ICD-codes I00-I99 and R96 indicated cardiovascular deaths. MI and stroke were defined based on discharge diagnosis codes from the hospitalization claims data (ICD-10 codes I21-I22 for MI and I60-I69 for stroke) . In thisWe first summarized the subjects’ characteristics by calculating means and percentages for continuous and categorical variables. We randomly split the men and women subjects in the study population into development and validation datasets at a ratio of 4:1, respectively. The prediction model was developed in the development dataset and validated in the validation dataset. A gender-specific Cox proportional-hazards regression model was employed as the base model for the development of the Korean atherosclerotic cardiovas cular disease risk prediction (K-CVD) model. To address missing data, we followed a complete-case analysis. The tentative candidate variables for the ASCVD risk prediction model included all common cardiovascular predictors that were available in the study population based on the published literature and prediction models. We also used the bootstrapping method to select statistically important predictive variables. For each bootstrapped sample, we applied the multivariable Cox model with all candidate variables and the backward variable selection approach based on Akaike information criterion values. The variables included in the prediction model had at least a 50% relative frequency in 1,000 bootstrap samples to be included. During the model-building process, clinically less-meaningful variables were sequentially excluded. We tested the interaction effect of total cholesterol and systolic BP levels with corresponding medical treatment status and included the interaction effect in the final model if there were statistically significant and clinically plausible interactions between the variables and treatment status. In the final prediction models, we examined the linearity assumption for the continuous variables using a multiple fractional polynomial model. The 10-year baseline survival probability was obtained with the Breslow estimator , and theThis study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guideline for the development and validation of the prediction model . All staThe study protocol was approved by the Institutional Review Board of Ulsan University Hospital/Ulsan (IRB No. UUH 2018-07-008-004).The baseline characteristics and the ASCVD event rates of the study population are shown in The gender-specific K-CVD model was developed separately from the development dataset of each gender for predicting 10-year ASCVD events in the Korean population. The included variables, coefficients, and baseline survival function for the K-CVD model are shown in 2 statistic in the development and validation datasets   .We also evaluated the performance of the FRS and PCE for the prediction of 10-year ASCVD risk in the Korean population . The timThe observed and predicted mean 10-year risks of ASCVD in the development and validation datasets are shown in https://kcvd.shinyapps.io/shiny_app_up/). The formula for calculating the 10-year ASCVD risk in the K-CVD model and an example of the risk calculation process are shown in the To facilitate the clinical application of the K-CVD model, a webbased tool for 10-year ASCVD risk prediction was developed based on the coefficients and baseline survival function , which could also lead to bias. Second, clinical outcomes were identified from death records and hospitalization claims data. Although codes for clinical outcomes have been validated in previous studies ,30, therIn this nationwide study, we developed a model for predicting the 10-year ASCVD risk in the contemporary Korean population. The K-CVD model showed excellent discrimination and calibration in Korean adults. This new population-based risk prediction tool would help to appropriately identify high-risk individuals and provide preventive interventions in the Korean population. Further studies are warranted to validate the utility of the K-CVD model in the Korean population before the model is implemented by the Korean guidelines and widely applied at the population level.https://nhiss.nhis.or.kr/).The present study was based on the National Health Insurance Service–National Sample Cohort (NHIS-NSC) in Korea. Data are accessible to researchers after receiving permission from the National Health Insurance Sharing Service (NHISS) in Korea. Qualified, interested researchers may request access to these data from the NHISS (


As a result, this study was designed to assess the perceived impact of COVID-19 on routine care of chronic non-communicable disease patients in Ethiopia.st 2021 to May 30th 2021. Data were collected via interviewer administered questionnaires administered by pre-tested interviewers on socio-demographic characteristics, treatment and clinical features and routine care questionnaires that have been adapted and modified from different literatures. The study consisted of all adult outpatients with at least one chronic non-communicable disease who were followed up. Data were analyzed using the Statistical Package for Social Sciences Version 23.a cross-section survey was conducted among 404 participants from April 1of the 422 participants, 404 responded for a response rate of 95.7%. One out of two  participants was aged 40 to 50 years. Ninety-one out of hundred  participants continued to receive routine care face-to-face during COVID-19. One-third  of study participants had good management of the chronic non-communicable diseases care in the middle of pandemic. A total of 167(41.34%) participants thought they were moderately affected changes in healthcare services since the COVID-19 outbreak. Nearly one-third  of participants were sometimes affected by medication shortages since the start of COVID-19.this study highlights that most participants continued to receive routine care face-to-face during the COVID-19. About forty-one out of 100 participants perceived that they were moderately affected changes in healthcare services since the outbreak of COVID-19. One-third of participants sometimes perceived that they were affected by medication shortages since the start of COVID-19. The coronavirus COVID-19) pandemic began in Wuhan, China at the end of 2019 and spread to a dozen countries in early 2020. Since then, the number of cases has continued to rise exponentially throughout the world [9 pandemiEthiopia experienced its first case of COVID-19 in March 2020. The infection has spread across all regions of the country. In response, the government has taken a number of actions to prevent the spread of this disease. Locking down all schools, declaring social distancing and hand hygiene, restricting large gatherings, limiting travel, preparing health facilities for treatment and quarantining individuals who had known contact and travel history for 5 days were some of the actions the Ethiopian government took . This inThe COVID-19 pandemic has prompted the world to explore innovative ways of continuing outpatient care  includinStudy area and period: the study was conducted in public hospitals in Jimma zone, south-west Ethiopia from April 1st 2021 to May 30th 2021. Jimma zone is located 350 km from Addis Ababa. The zone is divided into 18 districts and a town administration. There are eight hospitals , 115 health centres and 520 health posts in Jimma zone. An estimated 15 million people from within the country as well as from border zones and South Sudan avail health services from eight public hospitals in Ethiopia, with Jimma Medical Center (JMC) being a major service provider. During the study period, there were a total of 5800 patients on follow-up in the four public hospitals in the South west of Ethiopia i.e. 2000 patients with chronic non-communicable diseases at JMC, 1300 at Agaro Hospital, 1000 Seka hospital and 1500 Shenen Gibe Hospital.Study design, source population and study population: facility based cross-sectional study was used. All patients on chronic follow-up at public hospitals in the Jimma zone, southwest Ethiopia were used as the source population.Eligibility criteria: all Adult outpatients with at least one chronic illness on chronic follow-up at public hospitals in Jimma zone who visited chronic follow-up during the study period were included and severely sick patients in need of immediate medical intervention were excluded.Sample size determination and sampling technique: sample size was determined by using a single population proportion formula by considering a 50% proportion as there is no previous study. The formula for calculating the sample size (n) is as follows:β/2= standard normal variance (1.96)2 is mostly 5%, that is with a 95% confidence level.Where, n= Minimum sample size, p= an estimate of the prevalence rate for the population, d= the margin of the sampling error and Z2x0.5(1-0.5)/(0.05)2= 384n = (1.96)By considering 10% non-response rate the final sample size is 422.Sampling procedures:Data collection tools: a pre-tested, structured interviewer-administered questionnaire was used for data collection which contained three parts including socio-demographic characteristics, clinical and treatment related characteristics and were used to assess the perceived impact of COVID-19 on routine care which was adapted and modified from different literature [terature -6. Perceterature  and eachData collection procedure: interviewer-administered structured questionnaire and card review were used to collect data. Two Bachelor of Science degree (BSC) holder nurses and one Masters of Science (MSC) nurse were recruited as data collectors and supervisor respectively.Data quality control: both data collectors and supervisor were trained for one day on the objectives of study and data collection techniques. A supervisor checked the completeness and consistency of the questionnaire. The principal investigator evaluated the data before analysis to verify the completeness of the collected data. The pre-test was performed with 5% of the sample size participants prior to actual data collection at the Limmu hospital chronic illness clinic follow up to assess the clarity and reliability of the data collection tool. Limmu hospital was chosen as the pilot study because it is far from the selected hospitals which in turn helps to prevent information dissemination/contamination and second it has similar sociocultural background participants with approximate patient flow and services in comparison with the study hospitals. Data from the pre-test were not included in the actual data. Scale reliability was assessed using the internal consistency of the reliability test. To date, all constructs of routine health care, (RHC) questionnaire reported excellent reliability with an overall alpha value of 0.90.Data processing and analysis: the data were entered and coded in Epi-Data version 3.1. Data were exported to SPSS version 23 for analysis. Descriptive statistics were computed.Ethical consideration: prior to data collection, ethical approval was obtained from the Institutional Review Board (IRB005/2021) of Jimma University and administrative permission was also obtained from JMC. During data collection, written informed consent was obtained from the literate participants and oral consent from the illiterate participants. The study participants were briefed on the study objectives and right to withdraw at any point. Data were collected anonymously.Operational definitionChronic non-communicable diseases:  [tension) .Perceived impact of COVID-19: was measured by 5-items [ 5-items  and eachVariables: socio-demographic variables  and clinical and treatment related characteristics .Funding: Jimma University covered only the survey cost for this study and there is no funding organization. This funding organization had no role in the design of the study, collection, analysis, and interpretation of data, or in writing the manuscript.Availability of data and materials: due to the lack of consent from the study participants to disclose raw data, these data could not be made available to protect the participants' identities.Sample characteristics:Clinical and treatment related characteristic:Routine Health Care (RHC):COVID-19 is a global public health emergency unprecedented in modern history . It is wHowever, there is a paucity of information on perceived impact of COVID-19 on routine care for people with chronic conditions in the study area. Thus, to our knowledge, this is the first study in the area. Overall, most participants continued to receive routine care face-to face during COVID-19 which is higher than study conducted in United Kingdom . This diOver one-third of study participants had poor management of chronic non-communicable diseases care since the COVID-19 outbreak. This means that the pandemic diverted the attention of world to adapting to new ways of delivering care using telemedicine to reduce face-to-face contact. Adapting new ways of virtual healthcare and digital technologies is imperative to allow HCPs to continue routine appointments and the use of apps can support the self-management of chronic conditions. Nonetheless, most people with non-communicable diseases live in low-middle income countries, where these technologies may not be widely available or practical  includinHowever, majority participants thought they were moderately affected by changes in healthcare services since COVID-19 outbreak. People with chronic conditions are not only affected by the COVID-19 pandemic in a direct manner, but also in an indirect manner, that is, People with chronic conditions focus more on contracting infection than the existing disease. The COVID-19 pandemic has disrupted all societies, including routine health care systems . ResourcOne-third of the participants were sometimes affected due to medication shortages since the start of COVID-19. Because of its high transmissibility and capacity to disrupt international travel and business, the pandemic created travel and business restrictions and was responsible for  impact oMore than half of the participants reported that their health had worsened since the start of the COVID-19 pandemic. This means that the pandemic has put patients with chronic conditions in complications and difficulties in routine medical care due to delayed transportation, shortage of medications, and human resources are among the contributing factors for patients with chronic non-communicable diseases to suffer from worsened health conditions during infectious disease pandemic . The stuIn conclusion, people with chronic conditions in low income countries including Ethiopia are disproportionately affected by the pandemic as they continue to receive face-to-face routine care. Thus, healthcare providers (HCP) should provide meticulous care to patients who receive routine care face-to-face to curb the transmission of the pandemic and manage the existing chronic condition. On the top of this, HCP should offer all inclusive patient centered care for all patients not only to treat the pandemic but also to tackle chronic conditions related comorbidity, mortality and complications.Nowadays, COVID-19 is a global pandemic with unprecedented medical, economic and social consequences affecting nations across the world including Ethiopia;In developed countries many patients with chronic non-communicable diseases can communicate with their own health care professionals especially during this pandemic through internet-based programs and applications (telemedicine), unlike for developing countries like such as Ethiopia, Ethiopia;Routine care for chronic disease is an ongoing major challenge during pandemic.In our study, most participants continued to receive routine care face-to-face during COVID-19;One-third of the patients reported being inconvenienced due to shortage of medications;Care was unaffected for every one in three patients.


Untreated perinatal depression and anxiety can have detrimental consequences on family function. Logistical barriers prevent many perinatal women from accessing treatment, and these barriers are compounded for women residing in rural areas. This paper describes a Family-Based Collaborative Care Model (FBCCM) that is designed to bypass barriers to increase access to care for depressed and anxious perinatal women in rural regions of the United States. The FBCCM includes the following two components: (1) a 10-session video-delivered family therapy treatment for perinatal depression and anxiety and (2) a video-delivered infant care provider training on addressing the parenting needs of depressed and anxious mothers.This paper describes the feasibility of implementing the FBCCM with families and infant care providers. Findings are presented on the preliminary effectiveness of the video-delivered family therapy treatment in reducing maternal depressive and anxiety symptoms, and family conflict.This pilot study was carried out using an implementation-effectiveness hybrid trial design without a comparison group. Changes in maternal depressive symptoms, maternal anxiety symptoms, and family conflict were measured at posttreatment, 3 months, and 6 months later.P<.001) and anxiety symptoms (P<.001) from baseline to the 6-month follow-up. Mothers reported a significant reduction in conflict (P<.001), and their family members also reported a significant reduction in conflict (P=.007) from baseline to the 6-month follow-up.On average, mothers (n=24) attended 9.79 (SD 1.02) sessions. On average, their family members (n=24) attended 9.42 (SD 1.28) sessions. A total of 31 infant care providers attended the training on addressing the parenting needs of depressed and anxious mothers. Mothers reported a significant reduction in depressive symptoms (The findings from this study provide support for the feasibility and preliminary effectiveness of the FBCCM. The findings will be used to inform a larger study of the FBCCM. Perinatal depression and anxiety are increasing in the United States with prevalence rates ranging from 10% to 23% for depression ,5. AlthoThere is growing recognition that technology-based interventions for depression and anxiety bypass logistical barriers for pregnant and postpartum women -14. FurtUntreated perinatal depression and anxiety are precipitants and consequences of family conflict that worsens these symptoms -18. SincThis study was conducted during the COVID-19 pandemic and explored the feasibility and preliminary effectiveness of a Family-Based Collaborative Care Model (FBCCM) to address perinatal depression and anxiety. The FBCCM includes following two components: (1) a video-delivered family therapy treatment for depressed and anxious perinatal women receiving obstetrics care in rural clinics and (2) training for infant care providers on addressing the parenting needs of depressed and anxious mothers. The FBCCM uses technology to increase access to treatment for perinatal depression and anxiety and training for infant care providers in rural areas.This paper has 2 objectives. The first objective is to present findings on the feasibility of the FBCCM for use with families and infant care providers. The second objective is to present findings on the preliminary effectiveness of the video-delivered family therapy treatment in reducing maternal depressive symptoms and anxiety symptoms, and family conflict. We hypothesized that (1) perinatal women will report significant reductions in depressive symptoms and anxiety symptoms from baseline to the 6-month follow-up, and (2) families will report a significant reduction in conflict from baseline to the 6-month follow-up.This implementation effectiveness hybrid trial, without a comparison group, tested both clinical interventions and implementation strategies . The firThis study has been approved by an institutional review board (IRB) at an academic medical center in New England (02000646).Our research on perinatal depression guided the development of the FBCCM that includes 2 components. The first component is a video-delivered family therapy treatment (Resilience Enhancement Skills Training [REST]) -26 that The results of the preliminary pilot study of REST with home-visited families showed that it significantly reduced maternal depressive symptoms and family conflict . AlthougThe first author delivered REST to families in this study using Vidyo  and CiscThe second component of the FBCCM is a 1-hour video-delivered training session provided by the second author to infant care providers on addressing the parenting needs of depressed and anxious mothers within the context of well-child visits. The training was offered during lunchtime to increase the likelihood of provider attendance. The training used elements of the second author’s guide on addressing maternal depression in primary care , cited bPatient Health Questionnaire-2item [Generalized Anxiety Disorder-2 item [Patient Health Questionnaire-2item scores of ≥2 [Generalized Anxiety Disorder-2 item scores of ≥3 [The study population included mothers at least 18 years of age in any trimester of pregnancy and up to three months postpartum, and their adult family members receiving perinatal care in 2 participating obstetrics clinics in rural regions of New England. This study included a 2-phase eligibility screen process for mothers. Mothers routinely complete the e-2item  and Gener-2 item ,39 in thes of ≥2  or GenerThe first author called mothers within 48 hours of receipt of their requests to provide more details on the study and ask if they wanted to proceed with the phase 2 eligibility screen. The first author secured electronic consent for the phase 2 eligibility screen from mothers and administered the measures. Each mother was asked to select a “family member” (defined as her adult relative or current intimate partner) with whom she had conflict who could potentially participate with her in the study. The Family Environment Scale-Conflict (FES-C) subscale  was admiInfant care providers, willing physicians, resident physicians, and nurse practitioners who provided infant care at participating pediatric and family medicine clinics were eligible for study participation. The second author used video communication technology (Zoom) to deliver presentations to infant care providers that included an overview of the study goals, study participation, and description of the FBCCM (infant care provider training and family therapy treatment) potential benefits and risks. Infant care providers were informed that mothers, some of whom were served by their clinics, would be recruited for study participation and this confidential information could only be shared with them by the mother. Infant care providers were informed that the training would be provided to help mothers who are participating in the study and any other mothers with depression and anxiety who are served in their clinics.The first author secured electronic consent of the IRB-approved consent form for the phase 2 eligibility screen from 89 willing mothers. Of these 89 screened mothers, 29 of them were ineligible . The first author referred ineligible mothers to appropriate services in the community. Of the 60 eligible mothers, 21 of them decided not to enroll in the study due to busy schedules.The first author secured electronic consent of the IRB-approved consent form for study enrollment from 39 willing mothers and their 29 willing family members. It is important to note that 10 mothers enrolled in the study without family members, and we plan to prepare a separate paper with the data on this subgroup of the sample. Of the 29 families who enrolled in the study, 3 of them dropped out before the first session due to busy schedules.Infant care providers from 5 participating clinics were invited to attend the training. The second author secured electronic consent of the IRB-approved consent form for study enrollment from 9 willing infant care providers to complete a survey prior to the training. The 9 infant care providers were from 3 of the clinics.Participants completed a web-based baseline questionnaire in research electronic data capture . The matThe first author calculated the number of REST sessions attended by each participant and the number of families who completed REST. The second author recorded the number of infant care providers who attended the training.The first author closely monitored maternal depressive and anxiety symptoms (safety) and family conflict (tolerability) during the treatment phase. Mothers completed the BDI-II  after seMothers and family members completed web-based measures in research electronic data capture at baseline, postintervention , and 3-month and 6-month follow-ups.The STAI-S  was usedThe BDI-II  was usedPerceived Hostility Survey Ages 18+ (PHS) [The NIH Toolbox 8+ (PHS)  was used8+ (PHS) . It incl8+ (PHS) . PHS sco8+ (PHS) . Each reUnivariate statistics were used to characterize families at baseline and infant care providers.Ratios for actual to expected number of sessions (10 sessions) were calculated for each family in REST. Means and SDs were calculated for sessions attended by mothers and family members. High retention was defined as families attending ≥80% of sessions. The number of infant care providers who attended the training session was calculated.For safety of REST for mothers, means and SDs were calculated for the BDI-II and the STAI-S from baseline through each clinical monitoring time point . Wilcoxon signed rank tests were used to assess changes in BDI-II and STAI-S scores from baseline through session number 8. For tolerability of REST for families, means and SDs were calculated for the FES-C from baseline through each clinical monitoring time point (session numbers 4 and 8). Maternal and family member scores were calculated separately for the FES-C at each clinical monitoring time point. Wilcoxon signed rank tests were used to assess changes in FES-C scores from baseline through session number 8 for mothers and family members separately.P=.008. Evidence of effectiveness will be demonstrated at the 6-month follow-up by significant reductions in maternal depressive symptoms on the BDI-II and maternal anxiety symptoms on the STAI-S.Given the small sample size of mothers, the Friedman test was used to assess changes in maternal depressive symptoms (BDI-II scores) and maternal anxiety symptoms (STAI-S scores) from baseline to the follow-up time points . For significant results, post hoc pairwise comparison analyses were conducted using the Wilcoxon signed rank test with a Bonferroni correction applied, resulting in a significance level set at P=.008. Evidence of effectiveness will be demonstrated at the 6-month follow-up by significant reductions in family conflict on the PHS.Family conflict was analyzed in mothers and their family members separately. Given the small sample size of mothers and small sample size of family members, the Friedman test was used to assess changes (PHS scores) from baseline to the follow-up time points . For significant results, post hoc pairwise comparison analyses were conducted using the Wilcoxon signed rank test with a Bonferroni correction applied, resulting in a significance level set at Of the 26 families who started REST, 24 families contributed follow-up data. The baseline characteristics for these 24 families are included in The characteristics for the 9 infant care providers are included in Of the 26 families who started REST, 2 families dropped out prior to the third session due to busy schedules. One family attended 5 sessions, reported they resolved their conflict, and they did not need more sessions. Nearly all mothers  attended all 10 sessions. On average, mothers attended 9.79 (SD 1.02) sessions. A few family members had to miss sessions due to work commitments. On average, family members attended 9.42 (SD 1.28) sessions. Overall, 83% of participants attended all 10 sessions.The training on addressing maternal depression and anxiety in well-child visits was offered to infant care providers at 5 participating clinics. A total of 31 infant care providers attended the training.Z=–3.95, P<.001) and a significant reduction in anxiety symptoms  from baseline through session 8. No mothers required referrals to intensive mental health services. The results indicate that REST is safe for mothers. Mothers reported a significant reduction in depressive symptoms  and their family members also reported a significant reduction in family conflict on the FES-C  from baseline through session number 8. The results indicate that REST is well tolerated by families. No families were removed from the study due to increased family conflict on the FES-C or domestic violence. Mothers reported a significant reduction in family conflict on the FES-C  were lost to follow-up after completing the postintervention measure.χ23,22=27.26, P<.001). There were statistically significant reductions in maternal depressive symptoms from baseline to postintervention , baseline to the 3-month follow-up , and baseline to the 6-month follow-up . Although the significant reduction in maternal depressive symptoms was sustained at the 6-month follow-up, there were no significant differences in maternal BDI-II scores from postintervention to the 3-month follow-up , postintervention to the 6-month follow-up , and the 3-month follow-up to the 6-month follow-up .χ23,22=25.29, P<.001). There were statistically significant reductions in maternal anxiety symptoms from baseline to postintervention , baseline to the 3-month follow-up , and baseline to the 6-month follow-up . Although the significant reduction in maternal anxiety symptoms was sustained at the 6-month follow-up, there were no significant differences in maternal STAI-S scores from postintervention to the 3-month follow-up , postintervention to the 6-month follow-up , and the 3-month follow-up to the 6-month follow-up .Mothers reported a significant decrease in anxiety symptoms over time . There were statistically significant reductions in mothers’ PHS scores from baseline to postintervention , baseline to the 3-month follow-up , and baseline to the 6-month follow-up . Although the significant reduction in maternal reported family conflict was sustained at the 6-month follow-up, there were no significant differences in maternal PHS scores from postintervention to the 3-month follow-up , postintervention to the 6-month follow-up , and the 3-month follow-up to the 6-month follow-up .χ23,21=17.65, P<.001). There were statistically significant reductions in family member reported conflict from baseline to postintervention , baseline to the 3-month follow-up , and baseline to the 6-month follow-up . Although the significant reduction in family member reported conflict was sustained at the 6-month follow-up, there were no significant differences in family member PHS scores from postintervention to the 3-month follow-up , postintervention to the 6-month follow-up , and the 3-month follow-up to the 6-month follow-up .Family members also reported a significant decrease in conflict with mothers over time (P<.001) and a significant reduction in anxiety symptoms (P<.001) from baseline to the 6-month follow-up. This study is the first to explore REST’s potential effectiveness in reducing maternal anxiety. We plan to conduct more research to determine REST’s effectiveness in reducing maternal anxiety.The findings from this study support the feasibility and preliminary effectiveness of REST in reducing maternal depressive symptoms, maternal anxiety symptoms, and family conflict from baseline through the 6-month follow-up. Mothers reported a significant reduction in depressive symptoms (P<.001) from baseline to the 6-month follow-up. Family members reported a significant reduction in conflict with mothers from baseline to the 6-month follow-up (P=.007). More studies are needed to show that REST is an effective treatment, especially in ethnically diverse families. A preliminary pilot study of REST showed that it was feasible and acceptable for socioeconomically disadvantaged families [Mothers reported a significant reduction in conflict with their family members  of them reported that mothers are screened for depression at their clinics, and 22% (n=2) of them reported that mothers are screened for anxiety at their clinics. Although the infant care providers acknowledged that it is important to screen mothers for anxiety; the lack of available treatment resources in rural regions has likely inhibited clinic managers’ decisions to require routine screening procedures for maternal depression and anxiety. Regardless of clinic decisions to implement routine screening procedures, mothers may still disclose depressive symptoms and anxiety symptoms during well-child visits and infant care providers need to know how to best help them. This study offered these providers convenient and easily accessible training on addressing the parenting needs of depressed and anxious mothers.The current findings for REST can be compared to those of previous research . The feaThe characteristics of the infant care providers who completed the questionnaire prior to the training are similar to those who participated in the second author’s previous research . The stuThis study is not without limitations. First, the sample sizes were small for families and infant care providers. For this reason, the findings should be interpreted with caution. Second, few ethnically diverse families and infant care providers participated in the study. Thus, the results may not be generalizable to more diverse populations. Although rural areas of the United States are less ethnically diverse than urban areas , we planThis paper included findings that support the feasibility of the FBCCM and preliminary effectiveness of the family therapy treatment component of this model. These findings are important in justifying a larger study. Our future research will focus on testing the impact of the FBCCM in improving outcomes for families and infant care providers.


Introduction: The fact that SARS-CoV-2, the coronavirus that caused COVID-19, can translocate within days of infection to the brain and heart and that the virus can survive for months is well established. However, studies have not investigated the crosstalk between the brain, heart, and lungs regarding microbiota that simultaneously co-inhabit these organs during COVID-19 illness and subsequent death. Given the significant overlap of cause of death from or with SARS-CoV-2, we investigated the possibility of a microbial fingerprint regarding COVID-19 death.Methods: In the current study, the 16S rRNA V4 region was amplified and sequenced from 20 COVID-19-positive and 20 non-COVID-19 cases. Nonparametric statistics were used to determine the resulting microbiota profile and its association with cadaver characteristics. When comparing non-COVID-19 infected tissues versus those infected by COVID-19, there is statistical differences (p < 0.05) between organs from the infected group only.Results: When comparing the three organs, microbial richness was significantly higher in non-COVID-19-infected tissues than infected. Unifrac distance metrics showed more variance between control and COVID-19 groups in weighted analysis than unweighted; both were statistically different. Unweighted Bray-Curtis principal coordinate analyses revealed a near distinct two-community structure: one for the control and the other for the infected group. Both unweighted and weighted Bray-Curtis showed statistical differences. Deblur analyses demonstrated Firmicutes in all organs from both groups.Discussion: Data obtained from these studies facilitated the defining of microbiome signatures in COVID-19 decedents that could be identified as taxonomic biomarkers effective for predicting the occurrence, the co-infections involved in its dysbiosis, and the evolution of the virus. A human corpse subsists as a specialized disturbance habitat that selects for a distinct thanatomicrobiome structure capable of decomposing the host depending on the cause of death and abiotic and biotic factors surrounding the death  interacts with olecules . Likewisolecules . Normal olecules .postmortem microbial fingerprint characterization be used in medicolegal investigations to link physical evidence to the criminal or victim  of our study is that there are no differences in the microbiome of COVID-19 and non-COVID-19 cadavers. Thus, the HA  is that there are differences in the microbiome of COVID-19 and non-COVID-19 cadavers. To test the alternative hypothesis, we used a two-pronged strategy to first, establish an in vitro model of detection of microbial diversity in brain, heart, and lung tissue microbiota from 40 human cadavers, half of them infected with SARS-CoV-2.The distinct thanatomicrobiome profiles in organs from human cadavers that died of or with SARS-CoV-2 may help guide the application of forensic microbiology tools to establish the real cause of death. The Hp-values for multiple comparisons with the FDR algorithm to control the number of false discoveries in those tests that result in a discovery . It has greater ability  to find truly significant results.The chi-squared test was used to determine if the deviations in the data are caused by sampling or experimental error. Deviations may be caused by random chance or if there are differences in our data that are due to a statistically significant difference. If they were caused by a statistically significant difference, it gives evidence to support or reject our hypothesis. Generally, when approaching a chi square test, you start with the null hypothesis that there is no difference in the data; that is, that any differences are in fact due to sampling errors, experimental errors, or by chance errors. So, by calculating chi-squared values generate statistical support for whether to reject or accept the hypothesis. The second approach involved adjusting all The results demonstrated that microbial profiles vary significantly during COVID-19 infection as the corpse decomposes. When comparing non-COVID-19 infected samples versus COVID-19 infected samples, there is statistical difference between the brain, heart, and lung from the infected group only. Among the three organs, microbial richness was significantly higher in the non-COVID-19-infected tissues compared to the infected tissues. Further, Unifrac distance analyses demonstrated that there was more variance between control and COVID-19 groups in weighted analysis than unweighted; both were statistically different. Also, unweighted Bray-Curtis principal coordinate analyses showed a near distinct two-community structure: one for control and the other for the COVID-19-infected group. Both unweighted and weighted Bray-Curtis showed statistical differences. These results facilitated the defining of microbiome profiles in COVID-19 decedents that could be identified as taxonomic biomarkers effective for predicting the occurrence, dysbiosis, and evolution of the virus..To estimate β-diversity, un-weighted and weighted UniFrac distances, as well as Bray-Curtis dissimilarity, were calculated from the amplicon sequence variants (ASVs) and genera relative abundance tables were generatedS. aureus, demonstrated enrichment in all cases, specifically COVID-19-infected lungs . Controls showed higher OTU richness values in all organs than COVID-19 . Shannon index of microbial richness and evenness values in all organs than COVID-19 . Based on the Shannon diversity index, there is no statistical differences when comparing COVID-19 and control. Shannon index of microbial richness and evenness values in all organs than COVID-19   level, alpha diversity presented significant differences between organs (adjusted erences) .For unweighted UniFrac, there was relatively low variance between control and COVID-19, with only 14.2% of variance explained by PC1 Axis and 7.27% explained by PC2 Axis . For weiStaphylococcus aureus was the most abundant bacteria found in COVID-19-infected lung tissue  were detected on brain, heart, and lung tissues. Firmicutes is listed in all comparisons at the phylum level  in COVID-19 patients were due to Corynebacterium sp.  for cases in the current study is 24 h. Therefore, due to the extended PMIs, inflammatory components were undetectable in the criminal case cadavers used in this study. Taxa that proliferate in COVID-19 deaths show reduced microbial diversity and an enrichment for microorganisms that can resist inflammatory responses more effectively than others  in both the control and infected groups  and in five of the 57 episodes of nosocomial meningitis . Morganella morganii is a Gram-negative aerobe, found often as intestinal commensal. It is commonly implicated in urinary tract infections and pyogenic infections, but rarely causes CNS infections especially brain abscess.The highest percentage of bacteria in heart tissue on the genera level was Enterobacteriaceae, which occurred in both groups . From thd groups . E. coli process . EnterobClostridium species) were predominant among all organs from both groups. The detection of Clostridium species is accounted for by the Postmortem Clostridium Effect (PCE) that distinguishes the rapid proliferation of the species in decaying internal body sites  number 2020100. For deceased subjects, consent is not required, because the tissues were collected for forensic purposes, and it is not possible to contact the next of kin under such circumstances. The reference law is authorization n9/2016 of the Guarantor of Privacy, then replaced by REGULATION (EU) 2016/679 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL. The causes of death were determined by medical examiner as determined at autopsy with intestinal pneumonia as the most prevalent (47%) among the COVID-19 positive cases and head trauma as the most prevelant (30%) among the control group . Primers used for amplification (515F/806R) targeted the V4 region and contained adapters for MiSeq sequencing in conjunction with a single-index molecular barcode on the reverse primer. Resultant read pairs were demultiplexed, formulated by their molecular barcode, and combined using USEARCH v7.0.1090  and UCHIde novo clustering of OTU analysis. ASVs are matched to a reference database similar to OTUs. ASVs that do not correspond to a database are kept as unknown taxa, similar to unknown OTUs. The last step of ASV analysis is to count all the reads matching to the same taxa in the reference database or being assigned to the same ASVs or OTUs.Instead of operational taxonomic units (OTUs), denoising tools to generate sequence variants were employed. DEBLUR was the denoising tool used to analyze the number of sequencing mismatches, and the quality of the sequences were distinguished between sequencing errors and biological variants . The tooRelative abundances of taxa were recovered by mapping merged reads using the UPARSE algorithm . Box plop-values. UniFrac is a distance metric used for comparing microbial communities and is a generic test method that describes whether two or more communities have the same structure. Weighted UniFrac was used to examine quantitative differences in community structure, thereby observing the taxa abundance in the microbiome. Unweighted UniFrac was used to determine qualitative differences in the microbial community, thereby considering only presence or absence of observed taxa. All p-values are adjusted for multiple comparisons with the FDR algorithm  and weighted (quantitative) Unifrac distances metrics. These distance metrics took into account relatedness of species to calculate distance and to calculate lgorithm .