title: Social heterogeneity and the COVID-19 lockdown in a multi-group SEIR model

abstract: The goal of the lockdown is to mitigate and if possible prevent the spread of an epidemic. It consists in reducing social interactions. This is taken into account by the introduction of a factor of reduction of social interactions q, and by decreasing the transmission coefficient of the disease accordingly. Evaluating q is a difficult question and one can ask if it makes sense to compute an average coefficient q for a given population, in order to make predictions on the basic reproduction rate [R]0, the dynamics of the epidemic or the fraction of the population that will have been infected by the end of the epidemic. On a very simple example, we show that the computation of [R]0 in a heterogeneous population is not reduced to the computation of an average q but rather to the direct computation of an average coefficient [R]0. Even more interesting is the fact that, in a range of data compatible with the Covid-19 outbreak, the size of the epidemic is deeply modified by social heterogeneity, as is the height of the epidemic peak, while the date at which it is reached mainly depends on the average [R]0 coefficient. This paper illustrates more technical results that can be found in [4], with new numerical computations. It is intended to draw the attention on the role of heterogeneities in a population in a very simple case, which might be difficult to apprehend in more realistic but also more complex models.


title: Pitfalls and solutions in case fatality risk estimation - A multi-country analysis on the role of demographics, surveillance, time lags between reporting and death and healthcare system capacity on COVID-19

abstract:
European countries report large differences in COVID-19 case fatality risk (CFR) and high variation over the year. CFR estimates may both depend on the method used for estimation and of country-specific characteristics. While crude methods simply use cumulative total numbers of cases and deaths, the CFR can be influenced by the demographic characteristics of the cases, case detection rates, time lags between reporting of infections and deaths and infrastructural characteristics, such as healthcare capacities.

We used publicly available weekly data from the national health authorities of Germany, Italy, France and Spain on case and death numbers by age group connected to COVID-19 for the year 2020. We propose to use smoothed data of national weekly test rates for case adjustment and investigated the impact of different time lags from case reporting to death on the estimation of the CFR. Finally, we described the association between case fatality and the demand for hospital beds for COVID-19, taking into account national hospital bed capacities.

Crude CFR estimates differ considerably between the four study countries with end-of-year values of approximately 1.9%, 3.5%, 2.5% and 2.7% for Germany, Italy, France and Spain, respectively. Age-adjustment reduces the differences considerably, resulting in values of 1.61%, 2.4% and 2% for Germany, Italy and Spain, respectively. Frances age-specific data was restricted to hospitalised cases only and is therefore not comparable in that regard. International crude International CFR time series show smaller differences when adjusting for demographics of the cases or the test rates. Curves adjusted for age structure, testing or time lags show smaller variance over the year and a smaller degree of non-stationarity. The data does not suggest any connection of CFRs to hospital capacities for the four countries under study.

category: epidemiology


title: A Novel Smart City Based Framework on Perspectives for application of Machine Learning in combatting COVID-19

abstract: The spread of COVID-19 across the world continues as efforts are being made from multi-dimension to curtail its spread and provide treatment. The COVID-19 triggered partial and full lockdown across the globe in an effort to prevent its spread. COVID-19 causes serious fatalities with United States of America recording over 3,000 deaths within 24 hours, the highest in the world for a single day and as of October 2020 has recorded a total of 270,642 death toll. In this paper, we present a novel framework which intelligently combines machine learning models and internet of things (IoT) technology specific in combatting COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology in interacting with a population and its environment with the aim of curtailing COVID-19. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store and analyze data using machine learning algorithms. These algorithms are able to detect, prevent, and trace the spread of COVID-19, and provide better understanding of the virus in smart cities. Similarly, the study outlined case studies on the application of machine learning to help in the fight against COVID-19 in hospitals across the world. The framework proposed in the study is a comprehensive presentation on the major components needed for an integration of machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a point for generating research interests which will yield outcomes capable of been integrated to form an improved framework.

category: health informatics


title: A structured model for COVID-19 spread: modelling age and healthcare inequities

abstract: We use a stochastic branching process model, structured by age and level of healthcare access, to look at the heterogeneous spread of COVID-19 within a population. We examine the effect of control scenarios targeted at particular groups, such as school closures or social distancing by older people. Although we currently lack detailed empirical data about contact and infection rates between age groups and groups with different levels of healthcare access within New Zealand, these scenarios illustrate how such evidence could be used to inform specific interventions. We find that an increase in the transmission rates amongst children from reopening schools is unlikely to significantly increase the number of cases, unless this is accompanied by a change in adult behaviour. We also find that there is a risk of undetected outbreaks occurring in communities that have low access to healthcare and that are socially isolated from more privileged communities. The greater the degree of inequity and extent of social segregation, the longer it will take before any outbreaks are detected. Well-established evidence for health inequities, particularly in accessing primary healthcare and testing, indicates that Maori and Pacific peoples are at higher risk of undetected outbreaks in Aotearoa New Zealand. This highlights the importance of ensuring that community needs for access to healthcare, including early proactive testing, rapid contact tracing, and the ability to isolate, are being met equitably. Finally, these scenarios illustrate how information concerning contact and infection rates across different demographic groups may be useful in informing specific policy interventions.


title: Normal Childhood Brain Growth and a Universal Sex and Anthropomorphic Relationship to Cerebrospinal Fluid

abstract:
ObjectThe study of brain size and growth has a long and contentious history, yet normal brain volume development has yet to be fully described. In particular, the normal brain growth and cerebrospinal fluid (CSF) accumulation relationship is critical to characterize because it is impacted in numerous conditions of early childhood where brain growth and fluid accumulation are affected such as infection, hemorrhage, hydrocephalus, and a broad range of congenital disorders. This study aims to describe normal brain volume growth, particularly in the setting of cerebrospinal fluid accumulation.

MethodsWe analyzed 1067 magnetic resonance imaging (MRI) scans from 505 healthy pediatric subjects from birth to age 18 to quantify component and regional brain volumes. The volume trajectories were compared between the sexes and hemispheres using Smoothing Spline ANOVA. Population growth curves were developed using Generalized Additive Models for Location, Scale, and Shape.

ResultsBrain volume peaked at 10-12 years of age. Males exhibited larger age-adjusted total brain volumes than females, and body size normalization procedures did not eliminate this difference. The ratio of brain to CSF volume, however, revealed a universal age-dependent relationship independent of sex or body size.

ConclusionsThese findings enable the application of normative growth curves in managing a broad range of childhood disease where cognitive development, brain growth, and fluid accumulation are interrelated.

category: neurology


title: Effects of Age and Knee Osteoarthritis on the Modular Control of Walking: A Pilot Study

abstract: Older adults and individuals with knee osteoarthritis (KOA) often exhibit reduced locomotor function and altered muscle activity. Identifying age- and KOA-related changes to the modular control of gait may provide insight into the neurological mechanisms underlying reduced walking performance in these populations. The purpose of this pilot study was to determine if the modular control of walking differs between younger and older adults without KOA and adults with end-stage KOA. Kinematic, kinetic, and electromyography data were collected from ten younger (23.5 {+/-} 3.1 years) and ten older (63.5 {+/-} 3.4 years) adults without KOA and ten adults with KOA (64.0 {+/-} 4.0 years) walking at their self-selected speed. Separate non-negative matrix factorizations of 500 bootstrapped samples determined the number of modules required to reconstruct each participants electromyography. The number of modules required in the younger adults (3.2 {+/-} 0.4) was greater than in the individuals with KOA (2.3 {+/-} 0.7; p = 0.002), though neither cohorts required number of modules differed significantly from the unimpaired older adults (2.7 {+/-} 0.5; p [≥] 0.113). A significant association between module number and walking speed was observed (r = 0.532; p = 0.003) and individuals with KOA walked significantly slower (0.095 {+/-} 0.21 m/s) than younger adults (1.24 {+/-} 0.15 m/s; p = 0.005). Individuals with KOA also exhibited altered module activation patterns and composition (which muscles are associated with each module) compared to unimpaired adults. These findings suggest aging alone may not significantly alter modular control; however, the combined effects of knee osteoarthritis and aging may together impair the modular control of gait.


title: Comorbid chronic pain and depression: Shared risk factors and differential antidepressant effectiveness

abstract: The bidirectional relationship between depression and chronic pain is well recognized, but their clinical management remains challenging. Here we characterize the shared risk factors and outcomes for their comorbidity in the Australian Genetics of Depression cohort study (N=13,839). Participants completed online questionnaires about chronic pain, psychiatric symptoms, comorbidities, treatment response and general health. Logistic regression models were used to examine the relationship between chronic pain and clinical and demographic factors. Cumulative linked logistic regressions assessed the effect of chronic pain on treatment response for ten different antidepressants. Chronic pain was associated with an increased risk of depression (OR=1.86 [1.37-2.54]), recent suicide attempt (OR=1.88[1.14-3.09]), higher use of tobacco (OR=1.05 [1.02-1.09]) and misuse of painkillers (e.g., opioids; OR=1.31 [1.06-1.62]). Participants with comorbid chronic pain and depression reported fewer functional benefits from antidepressant use and lower benefits from sertraline (OR=0.75[0.68-0.83]), escitalopram (OR=0.75[0.67-0.85]) and venlafaxine (OR=0.78[0.68-0.88]) when compared to participants without chronic pain. Furthermore, participants taking sertraline (OR=0.45[0.30-0.67]), escitalopram (OR=0.45[0.27-0.74]) and citalopram (OR=0.32[0.15-0.67]) specifically for chronic pain (among other indications) reported lower benefits compared to other participants taking these same medications but not for chronic pain. These findings reveal novel insights into the complex relationship between chronic pain and depression. Treatment response analyses indicate differential effectiveness between particular antidepressants and poorer functional outcomes for these comorbid conditions. Further examination is warranted in targeted interventional clinical trials, which also include neuroimaging genetics and pharmacogenomics protocols. This work will advance the delineation of disease risk indicators and novel aetiological pathways for therapeutic intervention in comorbid pain and depression as well as other psychiatric comorbidities.


title: Decentralized governance may lead to higher infection levels and sub-optimal releases of quarantines amid the COVID-19 pandemic

abstract: The outbreak of the novel Coronavirus (COVID-19) has led countries worldwide to administer quarantine policies. However, each country or state decides independently what mobility restrictions to administer within its borders, while aiming to maximize its own citizens welfare. Since individuals travel between countries and states, the policy in one country affects the infection levels in other countries. Therefore, major question is whether the policies dictated by multiple governments could be efficient. Here we focus on the decision regarding the timing of releasing quarantines, which were common during the first year of the pandemic. We consider a game-theoretical epidemiological model in which each government decides when to switch from a restrictive to a non-restrictive quarantine and vice versa. We show that, if travel between countries is frequent, then the policy dictated by multiple governments is sub-optimal. But if international travel is restricted, then the policy may become optimal.

category: health policy


title: Outdoor PM2.5 Concentration and Rate of Change in COVID-19 Infection in Provincial Capital Cities in China

abstract: Motivated by earlier findings that exposure to daily outdoor PM2.5 (P) may increase the risk of influenza infection, our study examines if immediate exposure to outdoor P will modify the rate of change in the daily number of COVID-19 infections (R), for (1) the high infection provincial capital cities in China and (2) Wuhan, China, using regression modelling. A multiple linear regression model was constructed to model the statistical relationship between P and R in China and in Wuhan, from 1 January to 20 March 2020. We carefully accounted for potential key confounders and addressed collinearity. The causal relationship between P and R, and the interaction effect between key variables were investigated. A causal relationship between P and R across the high infection provincial capital cities in China was established via matching. A higher P resulted in a higher R in China. A 10 {micro}g/m3 increase in P gave a 1.5% increase in R (p < 0.001). An interaction analysis between P and absolute humidity (AH) showed a statistically significant negative relationship between P x AH and R (p < 0.05). When AH was $ 5.8 g/m3, a higher P and AH gave a higher R. In contrast, when AH [≥] 5.8 g/m3, the effect of a higher P was counteracted by the effect of a higher AH, resulting in a lower R. Given that P can exacerbate R, we recommend the installation of air purifiers and better air ventilation to reduce the effect of P on R. Further, given the increasing discussions/observations that COVID-19 can be airborne, we highly recommend the wearing of surgical masks to keep one from contracting COVID-19 via the viral-particulate transmission pathway.


title: Analysis of Crowdsourced Metformin Tablets from Individuals Reveals Widespread Contamination with N-Nitrosodimethylamine (NDMA) and N,N-Dimethylformamide (DMF) in the United States

abstract: Reports of metformin drug products contaminated with unacceptable levels of the probable human carcinogen N-Nitrosodimethylamine (NDMA) prompted a national sampling of post-market metformin drug products in early 2020. To broadly sample the United States market and minimize supply chain bias, metformin medication samples were crowdsourced directly from individuals across many states. 155 samples were received, and liquid chromatography-high resolution mass spectrometry tests for a panel of nitrosamines and N,N-Dimethylformamide (DMF) revealed significant levels of NDMA and DMF that relate to formulation. 49% of all medication samples contained detectable levels of NDMA and, when scaled to maximum daily tablet dose, 16% of all medication samples contained NDMA levels exceeding the United States Food and Drug Administration acceptable daily intake (ADI) limit. The highest NDMA detection from the tested samples was 748 ng per 500 mg tablet, which, when scaled to a common 2000 mg per day dosage regimen, is 31 times the ADI limit. The presence of N,N-Dimethylformamide (DMF) across 74% of the sampled metformin products is concerning given its same carcinogenicity categorization as NDMA and proposed role in formation of NDMA. Results underscore the need for continued surveillance of product quality, recalls of tainted medications, and investigation of metformin manufacturing practices.

category: pharmacology and therapeutics


title: Death, Demography and the Denominator: Age-Adjusted Influenza-18 Mortality in Ireland

abstract: Using the Irish experience of the Spanish flu, we demonstrate that pandemic mortality statistics are sensitive to the demographic composition of a country. We build a population database for Irelands 32 counties with vital statistics on births, ageing, migration and deaths. We show how age-at-death statistics in 1918 and 1919 should be used to construct the age-adjusted mortality statistics necessary to make comparisons across time and space. We conclude that studies of the economic consequences of Influenza-18 and Covid-19 must better control for demographic factors if they are to yield useful policy-relevant results. For example, while Northern Ireland has a higher crude death rate in the current pandemic, it also has an older population; age-adjusted mortality paints a very different picture.

category: health economics


title: Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

abstract: Coronary artery disease (CAD) is the leading cause of death worldwide, constituting more than one-fourth of global mortalities every year. Accurate semantic segmentation of each artery using invasive coronary angiography (ICA) is important for stenosis assessment and CAD diagnosis. However, due to the morphological similarity among different types of arteries, it is challenging for deep-learning-based models to generate semantic segmentation with an end-to-end approach. In this paper, we propose a multi-step semantic segmentation algorithm based on the analysis of arterial segments extracted from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlying arterial topology, position and pixel features, we construct a powerful coronary artery segment classifier based on support vector machine. Each arterial segment is classified into left coronary artery (LCA), left anterior descending (LAD) and other types of arterial segments. We tested the proposed method on a dataset with 225 ICAs and achieved artery classification accuracy of 70.33%. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs.

category: cardiovascular medicine


title: Wading through Molasses: A qualitative examination of the experiences, perceptions, attitudes, and knowledge of Australian medical practitioners regarding medical billing

abstract:
BackgroundMedical billing errors and fraud have been described as one of the last "great unreduced healthcare costs," with some commentators suggesting measurable average losses from this phenomenon are 7% of total health expenditure. In Australia, it has been estimated that leakage from Medicare caused by non-compliant medical billing may be 10-15% of the schemes total cost. Despite a growing body of international research, mostly from the U.S, suggesting that rather than deliberately abusing the health financing systems they operate within, medical practitioners may be struggling to understand complex and highly interpretive medical billing rules, there is a lack of research in this area in Australia. The aim of this study was to address this research gap by examining the experiences of medical practitioners through the first qualitative study undertaken in Australia, which may have relevance in multiple jurisdictions.

MethodThis study interviewed 27 specialist and general medical practitioners who claim Medicare reimbursements in their daily practice. Interviews were recorded, transcribed, and analysed using thematic analysis.

ResultsThe qualitative data revealed five themes including inadequate induction, poor legal literacy, absence of reliable advice and support, fear and deference, and unmet opportunities for improvement.

ConclusionThe qualitative data presented in this study suggest Australian medical practitioners are ill-equipped to manage their Medicare compliance obligations, have low levels of legal literacy and desire education, clarity and certainty around complex billing standards and rules. Non-compliant medical billing under Australias Medicare scheme is a nuanced phenomenon that may be far more complex than previously thought and learnings from this study may offer important insights for other countries seeking solutions to the phenomenon of health system leakage. Strategies to address the barriers and deficiencies identified by participants in this study will require a multi-pronged approach. The data suggest that the current punitive system of ensuring compliance by Australian medical practitioners is not fit for purpose.


title: Digital proximity tracing on empirical contact networks for pandemic control

abstract: Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15-20 minutes and closer than 2-3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.

category: infectious diseases


title: Computerized physical and cognitive training improves functional architecture of the brain in adults with Down Syndrome: a longitudinal network science EEG study

abstract:
Understanding the neuroplastic capacity of people with Down Syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brains intrinsic cortical networks. Brain activity of 12 PwDS before and after a ten-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left to right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying DS processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.

Author SummaryThe effects of cognitive and physical training on the neuroplasticity attributes of people with and without cognitive impairment have been well documented via neurophysiological evaluations and network science indices. However, there is still insufficient evidence for people with Down Syndrome (PwDS). We investigated the effects of a combinational training protocol on the brain network organization of 12 adult PwDS using EEG and network indices coupled with tests assessing their cognitive and physical capacity. We report evidence of adaptational neuroplastic effects, pointing to a transitional state towards a healthier organization with an increased ability to integrate and segregate information. Our findings underline the ability of the DS brain to respond to the cognitive demands of external stimuli, reflecting the possibility of developing independent-living skills.

category: rehabilitation medicine and physical therapy


title: Resting-state network plasticity induced by music therapy after traumatic brain injury

abstract: Traumatic brain injury (TBI) is characterized by a complex pattern of abnormalities in resting-state functional connectivity (rsFC) and network dysfunction, which can potentially be ameliorated by rehabilitation. In our previous randomized controlled trial, we found that a 3-month neurological music therapy intervention enhanced executive function (EF) and increased grey matter volume in the right inferior frontal gyrus (IFG) in patients with moderate-to-severe TBI (N=40). Extending this study, we performed longitudinal rsFC analyses of resting-state fMRI data using a ROI-to-ROI approach assessing within-network and between-network rsFC in the frontoparietal (FPN), dorsal attention (DAN), default mode (DMN), and salience (SAL) networks, which all have been associated with cognitive impairment after TBI. We also performed a seed-based connectivity analysis between the right IFG and whole-brain rsFC. The results showed that neurological music therapy increased the coupling between the FPN and DAN as well as between these networks and primary sensory networks. By contrast, the DMN was less connected with sensory networks after the intervention. Similarly, there was a shift towards a less connected state within the FPN and SAL networks, which are typically hyperconnected following TBI. Improvements in EF were correlated with rsFC within the FPN and between the DMN and sensorimotor networks. Finally, in the seed-based connectivity analysis, the right IFG showed increased rsFC with the right inferior parietal and left frontoparietal (Rolandic operculum) regions. Together, these results indicate that the rehabilitative effects of neurological music therapy after TBI are underpinned by a pattern of within- and between-network connectivity changes in cognitive networks as well as increased connectivity between frontal and parietal regions associated with music processing.


title: Impact of Covid-19 social distancing measures on future incidence of invasive pneumococcal disease in England and Wales - a mathematical modelling study

abstract:
In January 2020, the United Kingdom moved to a 1+1 schedule for the 13-valent pneumococcal conjugate vaccine (PCV13) with a single priming dose at 3 months and a 12month booster. We modelled the impact on invasive pneumococcal disease (IPD) out to 2030/31 of reductions in PCV13 coverage and population mixing associated with restrictions on non-essential health care visits and social distancing measures introduced in 2020/21 to reduce SARS-CoV-2 transmission.

Using an existing model of pneumococcal transmission in England and Wales we simulated the impact of a 40% reduction in coverage and a 40% reduction in mixing between and within age-groups during two lockdowns in spring 2020 and autumn/winter 2020/21. More and less extreme reductions in coverage and mixing were explored in a sensitivity analysis.

Predicted annual numbers of IPD cases under different coverage and mixing reduction scenarios with uncertainty intervals (UI) generated from minimum and maximum values of the model predictions using 500 parameter sets.

The model predicted that any increase in IPD cases resulting from a reduction in PCV13 coverage would be more than offset by a reduction in pneumococcal transmission due to social distancing measures and that overall reductions in IPD cases will persist for a few years after resumption of normal mixing. The net reduction in cumulative IPD cases over the five epidemiological years from July 2019 was predicted to be 13,494 (UI 12,211, 14,676) all ages. Similar results were obtained in the sensitivity analysis.

COVID-19 lockdowns are predicted to have had a profound effect on pneumococcal transmission resulting in a reduction in pneumococcal carriage prevalence and IPD incidence for up to five years after the end of the lockdown period. Carriage studies will be informative in confirming the predicted impact of the lockdown measures after they have been lifted.


title: Effects of adiposity on the human plasma proteome: Observational and Mendelian randomization estimates

abstract: Variation in adiposity is associated with cardiometabolic disease outcomes, but the mechanisms leading from this exposure to disease are unclear. This study aimed to estimate effects of adiposity, proxied by body mass index (BMI), on 3,622 unique plasma proteins measured by the SomaLogic platform in 2,737 healthy participants from the INTERVAL study of UK blood donors. We conducted both observational and Mendelian randomization analyses where we used a genetic risk score for BMI as an instrument to estimate effects of BMI on protein levels. Our results suggest that BMI has a broad impact on the human plasma proteome, with estimated effects of BMI appearing strongest on proteins including circulating leptin, sex hormone-binding globulin and fatty acid-binding protein-4. We also provide evidence that proteins most altered by BMI are enriched for genes involved in cardiovascular disease. Altogether, these results help to focus attention onto new potential proteomic signatures of obesity-related disease.


title: The Impact of Management on Hospital Performance

abstract: There is a prevailing popular belief that expenditure on management by healthcare providers is wasteful, diverts resources from patient care, and distracts medical and nursing staff from getting on with their jobs. There is little existing evidence to support this narrative or counter-claims. We explore the relationship between management and public sector hospital performance using a fixed effects empirical econometric specification on a panel data set consisting of all 129 non-specialist acute National Health Service (NHS) hospitals in England for the financial years 2012/13 to 2018/19. Measures of managerial input and quality of management practice are constructed from NHS Electronic Staff Records and NHS Staff Survey data. Hospital accounts and Hospital Episode Statistics data are used to construct five measures of financial performance and of timely and high quality care. We find no evidence of association either between quantity of management and management quality nor directly between quantity of management and any of our measures of hospital performance. However, there is some evidence that higher quality management is associated with better performance. NHS managers have limited discretion in performing their managerial functions, being tightly circumscribed by official guidance, targets, and other factors outside their control. Given these constraints, our findings are unsurprising.


title: THE TIME TO OFFER TREATMENT FOR COVID-19

abstract:
BACKGROUNDThe spread of COVID-19 from Wuhan China, has been alarmingly rapid. Epidemiologic techniques succeeded in containing the disease in China, but efforts have not been as successful in the rest of the World, with a total of 29,155,581 confirmed cases of COVID-19, including 926,544 deaths worldwide as of September 15, 2020. Projections are for continued new infections and deaths if no effective therapeutic interventions can be initiated over the next several months. We performed a systematic review to determine the potential time course for development of treatments and vaccines, focusing on availability now and continuing in the last half of 2020.

METHODS

Clinical TrialsWe reviewed up-to-date information from several sources to identify potential treatments for COVID-19: The Reagan-Udall Expanded Access Navigator COVID-19 Treatment Hub was used to track the efforts of companies to develop agents. We focused on trials completed as of September 1, 2020 on identified agents We used several different sources: (A) covid-trials.org, then validated results on (B) clinicaltrials.gov and the (C) World Health Organizations International Clinical Trials Registry Platform (WHO ICTRP). We excluded studies which were clearly observational, with no randomization, control, or comparison group. We further set a cutoff of 100 for numbers of subjects, since smaller trial size could lack statistical power to establish superiority of the intervention over the control.

PublicationsWe searched for published trial results on pubmed.gov and on medRxiv, the preprint server, and used a targeted Google search to find announcements of unpublished trial results

RESULTS

Clinical Trials in RecruitmentAs of our cutoff date of April 1, 2020, we found 409 trials meeting our minimum requirement of 100 subjects. The WHO Solidarity megatrial for hospitalized patients was launched in over 100 countries, actively comparing hydroxychloroquine (HCQ), lopanovir/ritonavir (LPV/r) alone and in combination with interferon beta-1, and remdesivir. The LPV/r alone and HCQ arms have already been discontinued. Of these, only 9 were conducted on outpatients. A few vaccine trials are hoping to complete Phase 3 enrollment by the end of the third quarter 2020, but a prolonged follow-up of patients will likely be required.

Clinical trials CompletedAs of September 1, 2020, there were 231 trials reporting completion, Of these, only 59 studies enrolled 100 or more subjects. There were 34 trials in hospitalized patients, 9 directed at outpatients, and 8 prevention studies,

Published DataAs of September 1, 2020 we found 70 publications reporting findings in human studies on 13 classes of drugs and on 6 vaccines. There were 33 randomized placebo or active control studies; the rest were retrospective observational. Only seven publications dealt with outpatient care, the rest all in hospitalized patients.

Available TreatmentsAt this time, remdesivir and convalescent plasma have been granted emergency use authorization in the U.S.A., solely for hospitalized patients. There is also support for glucocorticoid treatment of the COVID-19 respiratory distress syndrome. No treatments or prophylaxis are offered for outpatients.

CONCLUSIONCOVID-19 is propagated primarily by infected ambulatory individuals. There have been no options brought forward for prevention and non-hospital treatment with only a few randomized, controlled outpatient studies expected to yield results in time to impact on the continuing pandemic by the end of 2020. It will be necessary for public health authorities to make hard decisions, with limited data, to prevent the continued spread of the disease. The choices will be hardest when dealing with possible early release of safe and effective vaccines which would, of course, be of greatest benefit to the Worlds population.


title: Mycotoxin exposure biomonitoring in breastfed and non-exclusively breastfed Nigerian children

abstract: A multi-specimen, multi-mycotoxin approach involving ultra-sensitive LC-MS/MS analysis of breast milk, complementary food and urine was applied to examine mycotoxin co-exposure in 65 infants, aged 1-18 months, in Ogun state, Nigeria. Aflatoxin M1 was detected in breast milk (4/22 (18%)), while six other classes of mycotoxins were quantified; including dihydrocitrinone (6/22 (27%); range: 14.0-59.7ng/L) and sterigmatocystin (1/22 (5%); 1.2ng/L) detected for the first time. Seven distinct classes of mycotoxins including aflatoxins (9/42 (21%); range: 1.0- 16.2{micro}g/kg) and fumonisins (12/42 (29%); range: 7.9-194{micro}g/kg) contaminated complementary food. Mycotoxins covering seven distinct classes with diverse structures and modes of action were detected in 64/65 (99%) of the urine samples, demonstrating ubiquitous exposure. Two aflatoxin metabolites (AFM1 and AFQ1) and FB1 were detected in 6/65 (9%), 44/65 (68%) and 17/65 (26%) urine samples, respectively. Mixtures of mycotoxin classes were common, including 22/22 (100%), 14/42 (33%) and 56/65 (86%) samples having 2-6, 2-4, or 2-6 mycotoxins present, for breast milk, complementary food and urine, respectively. Aflatoxin and/or fumonisin was detected in 4/22 (18%), 12/42 (29%) and 46/65 (71%) for breast milk, complimentary foods and urine, respectively. Furthermore, the detection frequency, mean concentrations and occurrence of mixtures were typically greater in urine of non-exclusively breastfed compared to exclusively breastfed infants. The study provides novel insights into mycotoxin co-exposures in early-life. Albeit a small sample set, it highlights transition to higher levels of infant mycotoxin exposure as complementary foods are introduced, providing impetus to mitigate during this critical early-life period and encourage breastfeeding.

category: public and global health


title: Cortical re-organization after traumatic brain injury elicited using functional electrical stimulation therapy: A case report

abstract: Functional electrical stimulation therapy (FEST) can improve motor function after neurological injuries. However, little is known about cortical changes after FEST and weather it can improve motor function after traumatic brain injury (TBI). Our study examined cortical changes and motor improvements in one male participant with chronic TBI suffering from mild motor impairment affecting the right upper-limb during 3-months of FEST and during 3-months follow-up. In total, 36 sessions of FEST were applied to enable upper-limb grasping and reaching movements. Short-term assessments carried out using transcranial magnetic stimulation (TMS) showed reduced cortical silent period (CSP), indicating cortical and/or subcortical inhibition after each intervention. At the same time, no changes in motor evoked potentials (MEPs) were observed. Long-term assessments showed increased MEP corticospinal excitability after 12-weeks of FEST, which seemed to remain during both follow-ups, while no changes in CSP were observed. Similarly, long-term assessments using TMS mapping showed larger hand MEP area in the primary motor cortex (M1) after 12-weeks of FEST as well as during both follow-ups. Corroborating TMS results, functional magnetic resonance imaging (fMRI) data showed M1 activations increased during hand grip and finger pinch tasks after 12-weeks of FEST, while gradual reduction of activity compared to after the intervention was seen during follow-ups. Widespread changes were seen not only in the M1, but also sensory, parietal rostroventral, supplementary motor, and premotor areas in both contralateral and ipsilateral hemispheres, especially during the finger pinch task. Drawing test performance showed improvements after the intervention and during follow-ups. Our findings suggest that task-specific and repetitive FEST can effectively increase cortical activations by integrating voluntary motor commands and sensorimotor network through FES. Overall, our results demonstrated cortical re-organization and improved fine motor function in an individual with chronic TBI after FEST.


title: Clinical identification of malignant pleural effusions

abstract:
INTRODUCTIONPleural effusions frequently signal disseminated cancer. Diagnostic markers of pleural malignancy at presentation that would assess cancer risk and would streamline diagnostic decisions remain unidentified. The objective of the present study was to identify and validate predictors of malignant PE at patient presentation.

MethodsA consecutive cohort of 323 patients with PE from different etiologies was recruited between 2013-2017 and was retrospectively analyzed. Data included history, chest X-ray, and blood/pleural fluid cell counts and biochemistry. Group comparison, receiver-operator characteristics, unsupervised hierarchical clustering, binary logistic regression, and random forests were used to develop the malignant pleural effusion detection (MAPED) score. MAPED was validated in an independent retrospective UK cohort (n = 238).

ResultsFive variables showed significant diagnostic power and were incorporated into the 5-point MAPED score. Age > 55 years, effusion size > 50% of the most affected lung field, pleural neutrophil count < 2,500/mm3, effusion protein > 3.5 g/dL, and effusion lactate dehydrogenase > 250 U/L, each scoring one point, predicted underlying cancer with area under curve = 0.819 (P < 10-15) in the derivation cohort and 0.723 (P = 3 *10-9) in the validation cohort. Interestingly, MAPED correctly identified 33/42(79%) of cytology-negative patients that indeed had cancer.

ConclusionThe MAPED score identifies malignant pleural effusions with satisfactory accuracy and can be used complimentary to cytology to streamline diagnostic procedures.

category: oncology


title: Perfluoroalkyl substances are increased in patients with late-onset ulcerative colitis and induce intestinal barrier defects ex vivo in murine intestinal tissue

abstract:
BackgroundEnvironmental factors are strongly implicated in late-onset of inflammatory bowel disease. Here, we investigate whether high exposure to perfluoroalkyl substances correlates with (1) late-onset inflammatory bowel disease, and (2) disturbances of the bile acid pool. We further explore the effect of the specific perfluoroalkyl substance perfluoronoctanoic acid on intestinal barrier function in murine tissue.

MethodsSerum levels of perfluoroalkyl substances and bile acids were assessed by ultra-performance liquid chromatography coupled to a triple-quadrupole mass spectrometer in matched samples from patients with ulcerative colitis (n=20) and Crohns disease (n=20) diagnosed at the age of [≥]55 years. Age and sex-matched blood donors (n=20), were used as healthy controls. Ex vivo Ussing chamber experiments were performed to assess the effect of perfluoronoctanoic acid on ileal and colonic murine tissue (n=9).

ResultsThe total amount of perfluoroalkyl substances was significantly increased in patients with ulcerative colitis compared to healthy controls and patients with Crohns disease (p<0.05). Ex vivo exposure to perfluoronoctanoic acid induced a significanlty altered ileal and colonic barrier function. The distribution of bile acids, as well as the correlation pattern between (1) perfluoroalkyl substances and (2) bile acids, differed between patient and control groups.

ConclusionOur results demonstrate that perfluoroalkyl substances levels are increased in patients with late-onset ulcerative colitis and may contribute to the disease by inducing a dysfunctional intestinal barrier.

category: gastroenterology


title: Characteristics and stability of sensorimotor activity driven by isolated-muscle group activation in a human with tetraplegia

abstract:
Understanding cortical movement representations and their stability can shed light on robust brain-machine interface (BMI) approaches to decode these representations without frequent recalibration. Here, we characterize the spatial organization (somatotopy) and stability of the bilateral sensorimotor map of forearm muscles in an incomplete-high spinal-cord injury study participant implanted bilaterally in the primary motor and sensory cortices with Utah microelectrode arrays (MEAs).

We built the map by recording multiunit activity (MUA) and surface electromyography (EMG) as the participant executed (or attempted) contractions of 2 wrist muscles on each side of the body. To assess stability, we repeatedly mapped and compared left--wrist--extensor-related activity throughout several sessions, comparing somatotopy of active electrodes and neural signals both at the within-electrode (multiunit) and cross-electrode (network) levels.

Body maps showed significant activation in motor and sensory cortical electrodes, with fractured, intermixed representations of both intact and paralytic muscles. Within electrodes, firing strength stability decreased with time, with higher stability observed in sensory cortex than in motor, and in the contralateral hemisphere than in the ipsilateral. However, we observed no differences at network level, and no evidence of decoding instabilities for wrist EMG, either across timespans of hours or days, or across recording area. These results demonstrate first-time construction of a bilateral human sensorimotor map with MEAs. Further, while map stability differs between brain area and hemisphere at multiunit/electrode level, these differences are nullified at ensemble level.


title: Assessment of neuropsychological function in brain tumour treatment: A comparison of traditional neuropsychological assessment with app-based cognitive screening

abstract:
BackgroundGliomas are typically considered to cause relatively few neurological impairments. However, cognitive difficulties can arise, for example during treatment, with potential detrimental effects on quality of life. Accurate, reproducible, and accessible cognitive assessment is therefore vital in understanding the effects of both tumour and treatments. Our aim is to compare traditional neuropsychological assessment with an app-based cognitive screening tool in patients with glioma before and after surgical resection. Our hypotheses were that cognitive impairments would be apparent, even in a young and high functioning cohort, and that app-based cognitive screening would complement traditional neuropsychological assessment.

MethodsSeventeen patients with diffuse gliomas completed a traditional neuropsychological assessment and an app-based touchscreen tablet assessment (OCS-BRIDGE) pre- and post-operatively. The app assessment was also conducted at 3- and 12-month follow-up. Impairment rates, mean performance, and pre- and post-operative changes were compared using standardized Z-scores.

ResultsApproximately 2-3 hours of traditional assessment indicated an average of 2.88 cognitive impairments per patient, whilst the 30-minute screen indicated 1.18. As might be expected, traditional assessment using multiple items across the difficulty range proved more sensitive than brief screening measures in areas such as memory and attention. However, the capacity of the screening app to capture reaction times enhanced its sensitivity, relative to traditional assessment, in the area of non-verbal function. Where there was overlap between the two assessments, for example digit span tasks, the results were broadly equivalent.

ConclusionsCognitive impairments were common in this sample and app-based screening complemented traditional neuropsychological assessment. Implications for clinical assessment and follow-up are discussed.

category: psychiatry and clinical psychology


title: Development and implementation of a customised rapid syndromic diagnostic test for severe pneumonia

abstract:
BackgroundMicrobial cultures for the diagnosis of pneumonia take several days to return a result, and are frequently negative, compromising antimicrobial stewardship. The objective of this study was to establish the performance of a syndromic molecular diagnostic approach, using a custom TaqMan array card (TAC) covering 52 respiratory pathogens, and assess its impact on antimicrobial prescribing.

MethodsThe TAC was validated against a retrospective multi-centre cohort of broncho-alveolar lavage samples. The TAC was assessed prospectively in patients undergoing investigation for suspected pneumonia, with a comparator cohort formed of patients investigated when the TAC laboratory team were unavailable.

Co-primary outcomes were sensitivity compared to conventional microbiology and, for the prospective study, time to result. Metagenomic sequencing was performed to validate findings in prospective samples. Antibiotic free days (AFD) were compared between the study cohort and comparator group.

Results128 stored samples were tested, with sensitivity of 97% (95% CI 88-100%). Prospectively 95 patients were tested by TAC, with 71 forming the comparator group. TAC returned results 51 hours (IQR 41-69 hours) faster than culture and with sensitivity of 92% (95% CI 83-98%) compared to conventional microbiology. 94% of organisms identified by sequencing were detected by TAC. There was a significant difference in the distribution of AFDs with more AFDs in the TAC group (p=0.02). TAC group were more likely to experience antimicrobial de-escalation (OR 2.9 (95%1.5-5.5).

ConclusionsImplementation of a syndromic molecular diagnostic approach to pneumonia led to faster results, with high sensitivity and impact on antibiotic prescribing.

Trial registrationThe prospective study was registered with clinicaltrials.gov NCT03996330

category: intensive care and critical care medicine


title: Solar UV B/A Radiation is Highly Effective in Inactivating SARSCoV2

abstract:
Solar UV-C photons do not reach Earths surface, but are known to be endowed with germicidal properties that are also effective on viruses. The effect of softer UV-B and UV-A photons, which copiously reach the Earths surface, on viruses are instead little studied, particularly on single-stranded RNA viruses.

Here we combine our measurements of the action spectrum of Covid-19 in response to UV light, Solar irradiation measurements on Earth during the SARS-CoV-2 pandemics, worldwide recorded Covid-19 mortality data and our "Solar-Pump" diffusive model of epidemics to show that (a) UV-B/A photons have a powerful virucidal effect on the single-stranded RNA virus Covid-19 and that (b) the Solar radiation that reaches temperate regions of the Earth at noon during summers, is sufficient to inactivate 63% of virions in open-space concentrations (1.5x103 TCID50/mL, higher than typical aerosol) in less than 2 minutes.

We conclude that the characteristic seasonality imprint displayed world-wide by the SARS-Cov-2 mortality time-series throughout the diffusion of the outbreak (with temperate regions showing clear seasonal trends and equatorial regions suffering, on average, a systematically lower mortality), might have been efficiently set by the different intensity of UV-B/A Solar radiation hitting different Earths locations at different times of the year.

Our results suggest that Solar UV-B/A play an important role in planning strategies of confinement of the epidemics, which should be worked out and set up during spring/summer months and fully implemented during low-solar-irradiation periods.


title: Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

abstract:
In the absence of pharmaceutical interventions, social distancing is being used worldwide to curb the spread of COVID-19. The impact of these measures has been inconsistent, with some regions rapidly nearing disease elimination and others seeing delayed peaks or nearly flat epidemic curves. Here we build a stochastic epidemic model to examine the effects of COVID-19 clinical progression and transmission network structure on the outcomes of social distancing interventions. Our simulations show that long delays between the adoption of control measures and observed declines in cases, hospitalizations, and deaths occur in many scenarios. We find that the strength of within-household transmission is a critical determinant of success, governing the timing and size of the epidemic peak, the rate of decline, individual risks of infection, and the success of partial relaxation measures. The structure of residual external connections, driven by workforce participation and essential businesses, interacts to determine outcomes. We suggest limited conditions under which the formation of household "bubbles" can be safe. These findings can improve future predictions of the timescale and efficacy of interventions needed to control second waves of COVID-19 as well as other similar outbreaks, and highlight the need for better quantification and control of household transmission.

Author SummarySocial distancing is the main tool used to control COVID-19, and involves reducing contacts that could potentially transmit infection with strategies like school closures, work-from-home policies, mask-wearing, or lockdowns. These measures have been applied around the world, but in situations where they have suppressed infections, the effect has not been immediate or consistent. In this study we use a mathematical model to simulate the spread and control of COVID-19, tracking the different settings of person-to-person contact (e.g. household, school, workplace) and the different clinical stages an infected individual may pass through before recovery or death. We find that there are often long delays between when strong social distancing policies are adopted and when cases, hospitalizations, and deaths peak and begin to decline. Moreover, we find that the amount of transmission that happens within versus outside the household is critical to determining when social distancing can be effective and the delay until the epidemic peak. We show how the interaction between unmitigated households spread and residual external connections due to essential activities impacts individual risk and population infection levels. These results can be used to better predict the impact of future interventions to control COVID-19 or similar outbreaks


title: Exploring the Feasibility of Using Real-World Data from a Large Clinical Data Research Network to Simulate Clinical Trials of Alzheimer's Disease

abstract: In this study, we explored the feasibility of using real-world data (RWD) from a large clinical research network to simulate real-world clinical trials of Alzheimers disease (AD). The target trial (i.e., NCT00478205) is a Phase III double-blind, parallel-group trial that compared the 23 mg donepezil sustained release with the 10 mg donepezil immediate release formulation in patients with moderate to severe AD. We followed the target trials study protocol to identify the study population, treatment regimen assignments, and outcome assessments, and to set up a number of different simulation scenarios and parameters. We considered two main scenarios: (1) a one-arm simulation: simulating a standard-of-care arm that can serve as an external control arm; and (2) a two-arm simulation: simulating both intervention and control arms with proper patient matching algorithms for comparative effectiveness analysis. In the two-arm simulation scenario, we used propensity score matching controlling for baseline characteristics to simulate the randomization process. In the two-arm simulation, higher SAE rates were observed in the simulated trials than the rates reported in original trial, and a higher SAE rate was observed in the 23mg arm than the 10 mg standard-of-care arm. In the one-arm simulation scenario, similar estimates of SAE rates were observed when proportional sampling was used to control demographic variables. In conclusion, trial simulation using RWD is feasible in this example of AD trial in terms of safety evaluation. Trial simulation using RWD could be a valuable tool for post-market comparative effectiveness studies and for informing future trials design. Nevertheless, such approach may be limited, for example, by the availability of RWD that matches the target trials of interest, and further investigations are warranted.


title: Diagnostic Model of in-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods : Algorithm Development and Validation

abstract:
BackgroundPreventing in-hospital mortality in Patients with ST-segment elevation myocardial infarction (STEMI) is a crucial step.

ObjectivesThe objective of our research was to to develop and externally validate the diagnostic model of in-hospital mortality in acute STEMI patients used artificial intelligence methods.

MethodsWe divide non-randomly the American population with acute STEMI into a training set, a test set,and a validation set. We converted the unbalanced data into balanc ed data. We used artificial intelligence methods to develop and externally validate the dia gnostic model of in-hospital mortality in acute STEMI patients. We used confusion matrix combined with the area under the receiver operating characteristic curve (AUC) to evaluat e the pros and cons of the above models.

ResultsThe strongest predictors of in-hospital mortality were age, gender, cardiogenic shock, atrial fibrillation(AF), ventricular fibrillation(VF),third degree atrioventricular block,in-hospital bleeding, underwent percutaneous coronary intervention(PCI) during hospitalization, underwent coronary artery bypass grafting (CABG) during hospitalization, hypertension history, diabetes history, and myocardial infarction history.The F2 score of logistic regression in the training set, the test set, and the validation data set were 0.81, 0.6, and 0.59 respectively.The AUC of logistic regression in the training set, the test set, and the validation data set were 0.77, 0.78, and 0.8 respectively. The diagnostic model built by logistic regression was the best.

ConclusionWe had used artificial intelligence methods developed and externally validated the diagnostic model of in-hospital mortality in acute STEMI patients.

We registered this study with WHO International Clinical Trials Registry Platform (ICTRP) (registration number: ChiCTR1900027129; registered date: 1 November 2019).


title: Diagnostic Model of in-Hospital Mortality in Patients with Acute ST-Segment Elevation Myocardial Infarction Used Artificial Intelligence Methods


title: FEAT: a Flexible, Efficient and Accurate Test Strategy for COVID-19

abstract: Early detection of COVID-19 is critical in mitigating the spread of the virus. Commonly used tests include nucleic acid detection, antibodies detection via blood testing and CT imaging. Some tests are accurate but time-consuming, while others are cheaper but less accurate. Exactly which test to use is constrained by various considerations, such as availability, cost, accuracy and efficiency. In this paper, we propose a Flexible, Efficient and Accurate Test (FEAT). FEAT is based on group testing with simple but careful design by incorporating ideas such as close contact cliques and repeated tests. FEAT could dramatically improve the efficiency and/or accuracy for any existing test. For example, for accurate but slow test such as RT-PCR, FEAT can improve efficiency by multiple times without compromising accuracy. On the other hand, for fast but inaccurate tests, FEAT can sharply lower the false negative rates (FNR) and greatly increase efficiency. Theoretical justifications are provided. We point out some scenarios where the FEAT can be effectively employed.


title: UV-C irradiation is highly effective in inactivating SARS-CoV-2 replication

abstract: The potential virucidal effects of UV-C irradiation on SARS-CoV-2 were experimentally evaluated for different illumination doses and virus concentrations (1000, 5, 0.05 MOI). At a virus density comparable to that observed in SARS-CoV-2 infection, an UV-C dose of just 3.7 mJ/cm2 was sufficient to achieve a more than 3-log inactivation without any sign of viral replication. Moreover, a complete inactivation at all viral concentrations was observed with 16.9 mJ/cm2. These results could explain the epidemiological trends of COVID-19 and are important for the development of novel sterilizing methods to contain SARS-CoV-2 infection.


title: A Flexible Statistical Framework for Estimating Excess Mortality

abstract: Quantifying the impact of natural disasters or epidemics is critical for guiding policy decisions and interventions. When the effects of an event are long-lasting and difficult to detect in the short term, the accumulated effects can be devastating. Mortality is one of the most reliably measured health outcomes, partly due to its unambiguous definition. As a result, excess mortality estimates are an increasingly effective approach for quantifying the effect of an event. However, the fact that indirect effects are often characterized by small, but enduring, increases in mortality rates present a statistical challenge. This is compounded by sources of variability introduced by demographic changes, secular trends, seasonal and day of the week effects, and natural variation. Here we present a model that accounts for these sources of variability and characterizes concerning increases in mortality rates with smooth functions of time that provide statistical power. The model permits discontinuities in the smooth functions to model sudden increases due to direct effects. We implement a flexible estimation approach that permits both surveillance of concerning increases in mortality rates and careful characterization of the effect of a past event. We demonstrate our tools utility by estimating excess mortality after hurricanes in the United States and Puerto Rico. We use Hurricane Maria as a case study to show appealing properties that are unique to our method compared to current approaches. Finally, we show the flexibility of our approach by detecting and quantifying the 2014 Chikungunya outbreak in Puerto Rico and the COVID-19 pandemic in the United States. We make our tools available through the excessmort R package available from https://cran.r-project.org/web/packages/excessmort/.


title: Convolutional Neural Network Model to Detect COVID-19 Patients Utilizing Chest X-ray Images

abstract: This study aims to propose a deep learning model to detect COVID-19 positive cases more precisely utilizing chest X-ray images. We have collected and merged all the publicly available chest X-ray datasets of COVID-19 infected patients from Kaggle and Github, and pre-processed it using random sampling approach. Then, we proposed and applied an enhanced convolutional neural network (CNN) model to this dataset and obtained a 94.03% accuracy, 95.52% AUC and 94.03% f-measure for detecting COVID-19 positive patients. We have also performed a comparative performance between our proposed CNN model with several state-of-the-art machine learning classifiers including support vector machine, random forest, k-nearest neighbor, logistic regression, gaussian naive bayes, bernoulli naive bayes, decision tree, Xgboost, multilayer perceptron, nearest centroid and perceptron as well as deep learning and pre-trained models such as deep neural network, residual neural network, visual geometry group network 16, and inception network V3 were employed, where our model yielded outperforming results compared to all other models. While evaluating the performance of our models, we have emphasized on specificity along with accuracy to identify non-COVID-19 individuals more accurately, which may potentially facilitate the early detection of COVID-19 patients for their preliminary screening, especially in under-resourced health infrastructure with insufficient PCR testing systems and testing facilities. Moreover, this model could also be applicable to the cases of other lung infections.


title: The Role of Vitamin D in The Age of COVID-19: A Systematic Review and Meta-Analysis

abstract:
BackgroundEvidence recommends that vitamin D might be a crucial supportive agent for the immune system, mainly in cytokine response regulation against COVID-19. Hence, we carried out a systematic review and meta-analysis in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.

MethodsA systematic search was performed in PubMed, Scopus, Embase, and Web of Science up to December 18, 2020. Studies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.

ResultsTwenty-three studies containing 11901participants entered into the meta-analysis. The meta-analysis indicated that 41% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 29%-55%), and in 42% of patients, levels of vitamin D were insufficient (95% CI, 24%-63%). The serum 25-hydroxyvitamin D concentration was 20.3 ng/mL among all COVID-19 patients (95% CI, 12.1-19.8). The odds of getting infected with SARS-CoV-2 is 3.3 times higher among individuals with vitamin D deficiency (95% CI, 2.5-4.3). The chance of developing severe COVID-19 is about five times higher in patients with vitamin D deficiency (OR: 5.1, 95% CI, 2.6-10.3). There is no significant association between vitamin D status and higher mortality rates (OR: 1.6, 95% CI, 0.5-4.4).

ConclusionThis study found that most of the COVID-19 patients were suffering from vitamin D deficiency/insufficiency. Also, there is about three times higher chance of getting infected with SARS-CoV-2 among vitamin D deficient individuals and about 5 times higher probability of developing the severe disease in vitamin D deficient patients. Vitamin D deficiency showed no significant association with mortality rates in this population.


title: Changes in health promoting behavior during COVID-19 physical distancing: Utilizing WHOOP data to Examine Trends in Sleep, Activity, and Cardiovascular Health.

abstract: The COVID-19 pandemic incited unprecedented restrictions on the behavior of society. The aims of this study were to quantify changes to sleep/wake behavior and exercise behavior, as well as changes in physiological markers of health during COVID-19 physical distancing. A retrospective analysis of 5,436 US-based subscribers to the WHOOP platform (mean age = 40.25 {+/-} 11.33; 1,536 females, 3,900 males) was conducted covering the period from January 1st, 2020 through May 15th, 2020. This time period was separated into a 68-day baseline period and a 67-day physical distancing period. To provide context and allow for potential confounders (e.g., change of season), data were also extracted from the corresponding time periods in 2019. As compared to baseline, during physical distancing, all subjects fell asleep earlier (-0.25 hours), woke up later (0.48 hours), obtained more sleep (+0.35 hours) and reduced social jet lag (-0.21 hours). Contrasting sleep behavior was seen in 2019, with subjects falling asleep and waking up at a similar time (-0.01 hours; -0.05 hours), obtaining less sleep (-0.14 hours) and maintaining social jet lag (0.01 hours) in corresponding periods. Individuals exercised more intensely during physical distancing by increasing the time spent in high heart rate zones. In 2020, resting heart rate decreased (-0.9 beats per minute) and heart rate variability increased (+0.98 milliseconds) during physical distancing when compared to baseline. However, similar changes were seen in 2019, suggesting the variation may not be related to the introduction of physical distancing mandates. The findings suggest that changes in societal commitments (e.g., daily commute; working from home) during physical distancing may have resulted in changes to health-related behavior (i.e., increased exercise intensity and longer sleep duration). As the COVID-19 pandemic eases, maintenance of certain aspects of physical distancing (e.g., working from home) may allow for positive changes to sleep/wake and exercise behaviors.


title: Image-based modelling of inhaler deposition during respiratory exacerbation

abstract:
For many of the one billion sufferers of respiratory diseases worldwide, managing their disease with inhalers improves their ability to breathe. Poor disease management and rising pollution can trigger exacerbations which require urgent relief. Higher drug deposition in the throat instead of the lungs limits the impact on patient symptoms. To optimise delivery to the lung, patient-specific computational studies of aerosol inhalation can be used. How-ever in many studies, inhalation modelling does not represent an exacerbation, where the patients breath is much faster and shorter. Here we compare differences in deposition of inhaler particles (10, 4 {micro}m) in the airways of a healthy male, female lung cancer and child cystic fibrosis patient. We aimed to evaluate deposition differences during an exacerbation compared to healthy breathing with image-based healthy and diseased patient models. We found that the ratio of drug in the lower to upper lobes was 35% larger during healthy breathing than an exacerbation. For smaller particles the upper airway deposition was similar in all patients, but local deposition hotspots differed in size, location and intensity. Our results identify that image-based airways must be used in respiratory modelling. Various inhalation profiles should be tested for optimal prediction of inhaler deposition.

HighlightsO_LIRegional and local drug deposition was modelled in three patients during normal, sinusoidal inhalation and an exacerbation.
C_LIO_LILocal drug deposition changes with airway shape and inhalation profile, even when regional deposition is similar.
C_LIO_LIImage-based models were combined with highly-resolved particle tracking including particle contact and cohesion.
C_LIO_LIFluid model validated by comparing gas velocity field with in vitro experiments.
C_LI

category: respiratory medicine


title: Motivation and Cognitive Abilities as Mediators between Polygenic Scores and Psychopathology in Children

abstract:
ObjectiveFundamental questions in biological psychiatry concern the mechanisms that mediate between genetic liability and psychiatric symptoms. Genetic liability for many common psychiatric disorders often confers transdiagnostic risk to develop a wide variety of psychopathological symptoms through yet unknown pathways. We examine the psychological and cognitive pathways that might mediate the relationship between genetic liability (indexed by polygenic scores; PS) and broad psychopathology (indexed by p factor and its underlying dimensions).

MethodWe first identified which of the common psychiatric PSs (major depressive disorder [MDD], attention-deficit/hyperactivity disorder [ADHD], anxiety, bipolar, schizophrenia, autism) were associated with p factor. We then focused on three pathways: punishment sensitivity (reflected by behavioral inhibition system; BIS), reward sensitivity (reflected by behavioral activation system; BAS) and cognitive abilities (reflected by g factor based on 10 neurocognitive tasks). We applied structural equation modeling on the Adolescent Brain Cognitive Development (ABCD) dataset (n=4,814; 2,263 female children; 9-10 years old).

ResultsMDD and ADHD PSs were associated with p factor. The association between MDD PS and psychopathology was partially mediated by punishment sensitivity and cognitive abilities: proportion mediated= 22.35%. Conversely, the influence of ADHD PS on psychopathology was partially mediated by reward sensitivity and cognitive abilities: proportion mediated=30.04%. The mediating role of punishment sensitivity was specific to the emotional/internalizing. This mediating role of both reward sensitivity and cognitive abilities was focusing on the behavioral/externalizing and neurodevelopmental dimensions of psychopathology.

ConclusionWe provide a better understanding of how genetic risks for MDD and ADHD confer risks for psychopathology and suggest potential prevention/intervention targets for children at-risk.


title: Reduced ICU demand with early CPAP and proning in COVID-19 at Bradford: a single centre cohort

abstract:
BackgroundGuidance in COVID-19 respiratory failure has favoured early intubation, with concerns over the use of CPAP. We adopted early CPAP and self-proning, and evaluated the safety and efficacy of this approach.

MethodsThis retrospective observational study included all patients with a positive COVID-19 PCR, and others with high clinical suspicion. Our protocol advised early CPAP and self-proning for severe cases, aiming to prevent rather than respond to deterioration. CPAP was provided outside critical care by ward staff supported by physiotherapists and an intensive critical care outreach program. Data were analysed descriptively and compared against a large UK cohort (ISARIC).

Results559 patients admitted before 1/May/20 were included. 376 were discharged alive, and 183 died. 165 patients (29.5%) received CPAP, 40 (7.2%) were admitted to critical care and 28 (5.0%) were ventilated. Hospital mortality was 32.7%, and 50% for critical care. Following CPAP, 62% of patients with S:F or P:F ratios indicating moderate or severe ARDS, who were candidates for escalation, avoided intubation. Figures for critical care admission, intubation and hospital mortality are lower than ISARIC, whilst critical care mortality is similar. Following ISARIC proportions we would have admitted 92 patients to critical care and intubated 55. Using the described protocol, we intubated 28 patients from 40 admissions, and remained within our expanded critical care capacity.

ConclusionBradfords protocol produced good results despite our population having high levels of co-morbidity and ethnicities associated with poor outcomes. In particular we avoided overloading critical care capacity. We advocate this approach as both effective and safe.

Social media summaryThe use of early CPAP and proning in COVID-19 was associated with lower critical care admissions, intubation, and mortality at Bradford compared to a large UK cohort (ISARIC WHO CCP-UK).


title: An optimal lockdown relaxation strategy for minimizing the economic effects of covid-19 outbreak

abstract: In order to recover the damage to the economy by the ongoing covid-19 pandemic, many countries consider the transition from strict lockdowns to partial lockdowns through relaxation of preventive measures. In this work, we propose an optimal lockdown relaxation strategy, which is aimed at minimizing the damage to the economy, while confining the covid-19 incidence to a level endurable by the available healthcare facilities in the country. In order to capture the transmission dynamics, we adopt the compartment models and develop the relevant optimization model, which turns out to be non-linear. We generate approximate solutions to the problem, whereas our experimentation is based on the data on the covid-19 outbreak in Sri Lanka.


title: Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans

abstract: COVID-19 pandemic has spread all over the world for months. As its transmissibility and high pathogenicity seriously threaten peoples lives, the accurate and fast detection of the COVID-19 infection is crucial. Although many recent studies have shown that deep learning based solutions can help detect COVID-19 based on chest CT scans, there lacks a consistent and systematic comparison and evaluation on these techniques. In this paper, we first build a clean and segmented CT dataset called Clean-CC-CCII by fixing the errors and removing some noises in a large CT scan dataset CC-CCII with three classes: novel coronavirus pneumonia (NCP), common pneumonia (CP), and normal controls (Normal). After cleaning, our dataset consists of a total of 340,190 slices of 3,993 scans from 2,698 patients. Then we benchmark and compare the performance of a series of state-of-the-art (SOTA) 3D and 2D convolutional neural networks (CNNs). The results show that 3D CNNs outperform 2D CNNs in general. With extensive effort of hyperparameter tuning, we find that the 3D CNN model DenseNet3D121 achieves the highest accuracy of 88.63% (F1-score is 88.14% and AUC is 0.940), and another 3D CNN model ResNet3D34 achieves the best AUC of 0.959 (accuracy is 87.83% and F1-score is 86.04%). We further demonstrate that the mixup data augmentation technique can largely improve the model performance. At last, we design an automated deep learning methodology to generate a lightweight deep learning model MNas3DNet41 that achieves an accuracy of 87.14%, F1-score of 87.25%, and AUC of 0.957, which are on par with the best models made by AI experts. The automated deep learning design is a promising methodology that can help health-care professionals develop effective deep learning models using their private data sets. Our Clean-CC-CCII dataset and source code are available at: https://github.com/HKBU-HPML/HKBU_HPML_COVID-19.


title: Spatio-temporal modelling of the first Chikungunya epidemic in an intra-urban setting: the role of socioeconomic status, environment and temperature

abstract:
Three key elements are the drivers of Aedes-borne disease: mosquito infestation, virus circulating, and susceptible human population. However, information on these aspects are not easily available in low- and middle-income countries. We analysed data on factors that influence one or more of those elements to study the first chikungunya epidemic in Rio de Janeiro city in 2016. Using spatio-temporal models, under the Bayesian framework, we estimated the association of those factors with chikungunya notified cases by neighbourhood and week. To estimate the minimum temperature effect in a non-linear fashion, we used a transfer function considering an instantaneous effect and propagation of a proportion of such effect to future times. The sociodevelopment index and the proportion of green areas were included in the model with time-varying coefficients, allowing us to explore how their associations change throughout the epidemic. There were 13627 chikungunya cases in the study period. The sociodevelopment index presented the strongest association, inversely related with the risk of cases. Such association was more pronounced in the first weeks, indicating that socioeconomically vulnerable neighbourhoods were affected first and hardest by the epidemic. The proportion of green areas effect was null for most weeks. The temperature was directly associated with the risk of chikungunya for most neighbourhoods, with different decaying patterns. The temperature effect persisted longer where the epidemic was concentrated. In such locations, interventions should be designed to be continuous and to work in the long term. We observed that the role of the covariates change over time. Therefore, time-varying coefficients should be widely incorporated when modelling Aedes-borne diseases. Our model contributed to the understanding of the spatio-temporal dynamics of an urban Aedes-borne disease introduction in a tropical metropolitan city.

Author SummaryViruses transmitted by the Aedes mosquitoes represent a major public health concern. With the abundance of the mosquito and susceptible human population, the entry of new Aedes-transmitted virus brings the risk of large epidemics. The first-ever chikungunya epidemic in Rio de Janeiro city, Brazil, happened in 2016. We used information neighbourhood information on environment, socioeconomic status, and weekly temperature, to study the disease spread within the city. Our results show that better socioeconomic status play a major role in preventing the disease, with poorer areas being affected first and harder by the epidemic. This highlights that improving socioeconomic and sanitary conditions are essential for Aedes-borne diseases prevention and control. The temperature increased the risk of chikungunya cases, and this effect persisted for longer in areas where the epidemic was concentrated. This indicates that interventions should be designed to be long-lasting in such locations. Our results contribute to understanding better the dynamics of a first urban Aedes-borne disease epidemic in a tropical metropolitan city, with the potential to help design better interventions for disease prevention and control.


title: Seroprevalence of IgG antibodies against SARS coronavirus 2 in Belgium: a serial prospective cross-sectional nationwide study of residual samples

abstract:
To assess the evolving SARS-CoV-2 seroprevalence and seroincidence related to the national lock-down in Belgium, a nationwide seroprevalence study, stratified by age, sex and region using 3000-4000 residual samples was performed during 7 periods between 30 March and 17 October 2020. Residual sera from ambulatory patients were analyzed for IgG antibodies against S1 proteins of SARS-CoV-2 with a semi-quantitative commercial ELISA. Weighted seroprevalence (overall, by age category and sex) and seroincidence during 7 consecutive periods were estimated for the Belgian population while accommodating test-specific sensitivity and specificity.

The weighted overall seroprevalence initially increased from 1.8% (95% CrI 1.0-2.6) to 5.3% (95% CrI 4.2-6.4), implying a seroincidence of 3.4% (95% CrI 2.4-4.6) between the 1st and 2nd collection period over a period of 3 weeks during the lockdown period (start lockdown mid March 2020). Thereafter, seroprevalence stabilized, however, significant decreases are observed when comparing the 3rd with the 5th and also with the 6th period resulting in negative seroincidence estimates after lockdown was lifted. We estimated for the last collection period mid October 2020 a weighted overall seroprevalence of 4.2% (95% CrI 3.1-5.2).

During lockdown, an initial small but increasing fraction of the Belgian population showed serologically detectable signs of exposure to SARS-CoV-2, which did not further increase when confinement measures eased and full lockdown was lifted.


title: Seroprevalence of IgG antibodies against SARS coronavirus 2 in Belgium - a serial prospective cross-sectional nationwide study of residual samples


title: Seroprevalence of IgG antibodies against SARS coronavirus 2 in Belgium - a serial prospective cross-sectional nationwide study of residual samples (March - October 2020)


title: Using machine learning to predict COVID-19 infection and severity risk among 4,510 aged adults: a UK Biobank cohort study

abstract:
BackgroundMany risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e., hospitalization).

MethodsAmong aged adults (69.3 {+/-} 8.6 years) in UK Biobank, COVID-19 data was downloaded for 4,510 participants with 7,539 test cases. We downloaded baseline data from 10-14 years ago, including demographics, biochemistry, body mass, and other factors, as well as antibody titers for 20 common to rare infectious diseases. Permutation-based linear discriminant analysis was used to predict COVID-19 risk and hospitalization risk. Probability and threshold metrics included receiver operating characteristic curves to derive area under the curve (AUC), specificity, sensitivity, and quadratic mean.

ResultsThe "best-fit" model for predicting COVID-19 risk achieved excellent discrimination (AUC=0.969, 95% CI=0.934-1.000). Factors included age, immune markers, lipids, and serology titers to common pathogens like human cytomegalovirus. The hospitalization "best-fit" model was more modest (AUC=0.803, 95% CI=0.663-0.943) and included only serology titers.

ConclusionsAccurate risk profiles can be created using standard self-report and biomedical data collected in public health and medical settings. It is also worthwhile to further investigate if prior host immunity predicts current host immunity to COVID-19.


title: Child wasting and concurrent stunting in low- and middle-income countries

abstract: Sustainable Development Goal 2.2.2, to end malnutrition by 2030, measures progress through elimination of child wasting, defined as weight-for-length more than 2 standard deviations below international standards. Prevailing methods to measure wasting rely on cross-sectional surveys that cannot measure onset, recovery, and persistence -- key features of wasting epidemiology that could inform preventive interventions and disease burden estimates. Here, we show through an analysis of 21 longitudinal cohorts that wasting is a highly dynamic process of onset and recovery, and incidence peaks between birth and 3 months -- far earlier than peak prevalence at 12-15 months. By age 24 months 29.2% of children had experienced at least one wasting episode, more than 5-fold higher than point prevalence (5.6%), demonstrating that wasting incidence is far higher than cross-sectional surveys suggest. Children wasted before 6 months were more likely to experience concurrent wasting and stunting (low height-for-age) later, increasing their risk of mortality. In diverse populations with seasonal rainfall, population average weight-for-length varied substantially (>0.5 z in some cohorts), with the lowest mean Z-scores during the rainiest months, creating potential for seasonally targeted interventions. Our results motivate a new focus on extending preventive interventions for wasting to pregnant and lactating mothers, and for preventive and therapeutic interventions to include children below age 6 months in addition to current targets of ages 6-59 months.


title: Early childhood linear growth faltering in low- and middle-income countries

abstract: Globally 149 million children under five are estimated to be stunted (length more than 2 standard deviations below international growth standards). Stunting, a form of linear growth faltering, increases risk of illness, impaired cognitive development, and mortality. Global stunting estimates rely on cross-sectional surveys, which cannot provide direct information about the timing of onset or persistence of growth faltering-- a key consideration for defining critical windows to deliver preventive interventions. We performed the largest pooled analysis of longitudinal studies in low- and middle-income countries to date (n=32 cohorts, 52,640 children, ages 0-24 months), allowing us to identify the typical age of linear growth faltering onset and to investigate recurrent faltering in early life. The highest incidence of stunting onset occurred from birth to age 3 months. From 0 to 15 months, less than 5% of children per month reversed their stunting status, and among those who did, stunting relapse was common. Early timing and low reversal rates emphasize the importance of preventive intervention delivery within the prenatal and early postnatal phases coupled with continued delivery of postnatal interventions through the first 1000 days of life.


title: Risk factors and impacts of child growth faltering in low- and middle-income countries

abstract: Growth faltering (low length-for-age or weight-for length) in the first 1000 days -- from conception to two years of age -- influences both short and long-term health and survival. Evidence for interventions to prevent growth faltering such as nutritional supplementation during pregnancy and the postnatal period has increasingly accumulated, but programmatic action has been insufficient to eliminate the high burden of stunting and wasting in low- and middle-income countries. In addition, there is need to better understand age-windows and population subgroups in which to focus future preventive efforts. Here, we show using a population intervention effects analysis of 33 longitudinal cohorts (83,671 children) and 30 separate exposures that improving maternal anthropometry and child condition at birth, in particular child length-at-birth, accounted for population increases by age 24 months in length-for-age Z of 0.04 to 0.40 and weight-for-length Z by 0.02 to 0.15. Boys had consistently higher risk of all forms of growth faltering than girls, and early growth faltering predisposed children to subsequent and persistent growth faltering. Children with multiple growth deficits had higher mortality rates from birth to two years than those without deficits (hazard ratios 1.9 to 8.7). The importance of prenatal causes, and severe consequences for children who experienced early growth faltering, support a focus on pre-conception and pregnancy as key opportunities for new preventive interventions.


title: Elevated levels of hoarding in ADHD: a special link with inattention

abstract: Hoarding Disorder (HD) is under recognised and under-treated. Though HD develops by early adulthood, patients present only later in life, resulting in research based largely on samples of predominantly older females. Whilst formerly associated with Obsessive-Compulsive Disorder (OCD), it is now recognised that individuals with HD often have inattention symptoms reminiscent of Attention Deficit/Hyperactivity Disorder (ADHD). Here, we investigated HD in adults with ADHD. Patients in an ADHD clinic (n=88) reported on ADHD, HD and OCD-related symptoms, and compared with age, gender and education matched controls (n=90). Findings were assessed independently in an online UK sample to verify replication using a dimensional approach (n=220). Clinically significant hoarding symptoms were found in ~20% versus 2% of ADHD and control groups, respectively, with those with hoarding being on average in their thirties and with approximately half being male. Greater hoarding severity was noted even in the remaining patients compared with controls (d=0.89). Inattention was the only significant statistical predictor of hoarding severity in patients. Similarly, inattention, alongside depression and anxiety were the greatest predictors of hoarding in the independent sample where 3.2% identified as having clinically significant hoarding. Patients with ADHD had a high frequency of hoarding symptoms, which were specifically linked to inattention. HD should be routinely assessed in individuals with ADHD, as they do not typically disclose associated difficulties, despite these potentially leading to impaired everyday functioning. Research in HD should also investigate adults with ADHD, who are younger and with a greater prevalence of males than typical HD samples.


title: An imperfect tool: contact tracing could provide valuable reductions in COVID-19 transmission if good adherence can be achieved and maintained.

abstract: Emerging evidence suggests that contact tracing has had limited success in the UK in reducing the R number across the COVID-19 pandemic. We investigate potential pitfalls and areas for improvement by extending an existing branching process contact tracing model, adding diagnostic testing and refining parameter estimates. Our results demonstrate that reporting and adherence are the most important predictors of programme impact but tracing coverage and speed plus diagnostic sensitivity also play an important role. We conclude that well-implemented contact tracing could bring small but potentially important benefits to controlling and preventing outbreaks, providing up to a 15% reduction in R, and reaffirm that contact tracing is not currently appropriate as the sole control measure.


title: A Sleep Disorder Detection Model based on EEG Cross-Frequency Coupling and Random Forest

abstract: Sleep disorders are medical disorders of a subjects sleep architecture and based on their severity, they can interfere with mental, emotional and physical functioning. The most common ones are insomnia, narcolepsy, sleep apnea, bruxism, etc. There is an increased risk of developing sleep disorders in elderly like insomnia, periodic leg movements, rapid eye movement (REM) behaviour disorders, sleep disorder breathing, etc. Consequently, their accurate diagnosis and classification are important steps towards an early stage treatment that could save the life of a patient. The Electroencephalographic (EEG) signal is the most sensitive and important biosignal, which is able to capture the brain sleep activity that is sensitive to sleep. In this study, we attempt to analyse EEG sleep activity via complementary cross-frequency coupling (CFC) estimates, which further feed a classifier, aiming to discriminate sleep disorders. We adopted an open EEG Database with recordings that were grouped into seven sleep disorders and a healthy control. The EEG brain activity from common sensors has been analysed with two basic types of cross-frequency coupling (CFC). Finally, a Random Forest (RF) classification model was built on CFC patterns, which were extracted from non-cyclic alternating pattern (CAP) epochs. Our RFCFC model achieved a 74% multiclass accuracy. Both types of CFC, phase-to-amplitude (PAC) and amplitude-amplitude coupling (AAC) patterns contribute to the accuracy of the RF model, thus supporting their complementary information. CFC patterns, in conjunction with the RF classifier proved a valuable biomarker for the classification of sleep disorders.


title: Model-based spatial-temporal mapping of opisthorchiasis in endemic countries of Southeast Asia

abstract: Opisthorchiasis is an overlooked danger to Southeast Asia. High-resolution disease risk maps are critical but havent been available for Southeast Asia. Georeferenced disease data and potential influencing factor data were collected through a systematic review of literatures and open-access databases, respectively. Bayesian spatial-temporal joint models were developed to analyze both point- and area-level disease data, within a logit regression in combination of potential influencing factors and spatial-temporal random effects. The model-based risk mapping identified areas of low, moderate and high prevalence across the study region. Even though the overall population-adjusted estimated prevalence presented a trend down, a total of 12.39 million (95% BCI: 10.10-15.06) people were estimated infected with O. viverrini in 2018 in four major endemic countries (i.e., Thailand, Laos, Cambodia, and Vietnam), highlighting the public health importance of the disease in the study region. The high-resolution risk maps provide valuable information for spatial targeting of opisthorchiasis control interventions.


title: A model for COVID-19 transmission in Connecticut

abstract: To support public health policymakers in Connecticut, we developed a county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, as well as estimates of important features of disease transmission, public behavior, healthcare response, and clinical progression of disease. In this paper, we describe a transmission model developed to meet the changing requirements of public health policymakers and officials in Connecticut from March 2020 to February 2021. We outline the model design, implementation and calibration, and describe how projections and estimates were used to support decision-making in Connecticut throughout the first year of the pandemic. We calibrated this model to data on deaths and hospitalizations, developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated time-varying epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We describe methodology for producing projections of epidemic evolution under uncertain future scenarios, as well as analytical tools for estimating epidemic features that are difficult to measure directly, such as cumulative incidence and the effects of non-pharmaceutical interventions. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.


title: One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut