Category: Covid-19

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COVID-19 report details racial inequities, in health care and beyond - NJ Spotlight News

We apply our algorithms to a few case studies in epidemiology: Influence epidemics and COVID-19. We do emphasize that the techniques are general and can be applied to any system that experience a drastic change in its fundamental behavior.

As first example for showing our prediction methodology, we use the set of data associated with influenza epidemics. Clearly, not driven by an underlying deterministic dynamical system, the influenza time series exhibits substantial regularity in that it occurs typically during the winter months, thus enabling coarse-grained prediction of the type we will see a very small number of cases of influenza occurring in summer months. However, predicting the number of influenza cases accurately is a notoriously hard problem20, exacerbated by the possibility that a vaccine designed in a particular year does not effectively protect against infection. Moreover, the H1N1 pandemic that occurred in 2009 is an example of a Black Swan event.

The World Health Organizations FluNet is a global web-based tool for influenza virological surveillance. FluNet makes publicly available data on the number of specimens with the detected influenza viruses of type A and type B. The data have been collected from different countries, starting with the year 1997, and are updated weekly by the National Influenza Centers (NICs) of the Global Influenza Surveillance and Response System (GISRS) and other national influenza reference laboratories, collaborating actively with GISRS. We use the weekly reported data for different countries, which consist of the number of received specimens in the laboratories, the distribution of the number of specimens with confirmed viruses of type A.

The Koopman Mode Decomposition was used in the context of analyzing the dynamics of the flu epidemic from differentGoogle Fludata in21. We remark that the authors of that paper have not attempted prediction, and have analyzed only stationary modese.g. the yearly cycles, thus making the papers goals quite different from the nonstationary prediction pursued here.

We first compare the global and the local prediction algorithms. The KMD is computed using active windows of size ({textsf{w}}= 312), and the (208 times 104) HankelTakens matrices. In Fig.reff1a, we show the performances of both algorithms, using the learning data from the window April 2003April 2009 (shadowed rectangle). In the global prediction algorithm the dynamics is predicted for 104 weeks ahead. The first type of failure in the global prediction algorithm and forecasting appears after the Black Swan event occurred in the years 2009 and 2010. This is recognized by the algorithm, so that it adapts by using the smallest learning span and, with this strategy, it allows for reasonably accurate forecasting, at least for shorter lead times. This data, in addition to those from Supplementary Information section S2.4 show the benefits of monitoring the prediction error and switching to local prediction. The initial HankelTakens matrix is (3times 2), and the threshold for the local prediction relative error in Supplementary Information Algorithm S4 is 0.005.

Influenza data (USA). (a) The data are collected in the window April 2003April 2009 (shadowed rectangle) and then the dynamics is predicted for 104 weeks ahead. The local prediction algorithm recovers the prediction capability by forgetting the old data and using narrower learning windows. The local prediction algorithm delivers prediction for one week ahead. (b) The active window (shadowed rectangle) is July 2004July 2010, and the dynamics is predicted for 104 weeks ahead. The global prediction fails due to the Black Swan data in the learning window. (Some predicted values were even negative; those were replaced with zeros.) The global prediction algorithm recovers after the retouching the Black Swan event data, which allows for using big learning window. Compare with positions of the corresponding colored rectangles in Fig.2.

Next, we introduce an approach that robustifies the global algorithm in the presence of disturbances in the data, including the missing data scenario. We use the data window July 2004July 2010, which contains a Black Swan event in the period 20092010. As shown in Fig.1b, the learned KMD failed to predict the future following the active training window. This is expected because the perturbation caused by the Black Swan event resulted in the computed Ritz pairs that deviated from the precedent ones (from a learning window before disturbance), and, moreover, with most of them having large residuals. This can be seen as a second type of failure in the global prediction.

The proposed Black Swan event detecting device, built in the prediction algorithm (see Supplementary Information Algorithm S3), checks for this anomalous behaviour of the Ritz values and pinpoints the problematic subinterval. Then, the algorithm replaces the corresponding supplied data with the values obtained as predictions based on the time interval preceding the Black Swan event. Figure1b shows that such a retouching of the disturbance allows for a reasonable global prediction.

Note that in a realistic situation, global predictions of this kind will trigger response from authorities and therefore prevent its own accuracy and induce loss of confidence, whereas local prediction mechanisms need to be deployed again.

The real and imaginary parts of Ritz values with residuals bellow (eta _r=0.075) for sliding active windows. The color intensity of eigenvalues indicates the amplitudes of the corresponding modes. Pink rectangles mark ends of training windows with no acceptable Ritz values. Note how the unstable eigenvalues ((Re (lambda )>0)) impact the prediction performance, and how the retouching moves them towards neutral/stablethis is shown in the yellow rectangle in panels (a) and (c). Also influenced by the disturbance are the eigenvalues in the light blue rectangles in panels (a), (b); retouching moves the real parts of eigenvalues towards neutral/stable and rearranges them in a lattice-like structure22, as shown in panels (c), (d). Compare with Fig.1b.

We now discuss the effect of the Black Swan event and its retouching to the computed eigenvalues and eigenvectors. We have observed that, as soon as a disturbance starts entering the training windows, the Ritz values start exhibiting atypical behavior, e.g. moving deeper into the right half plane (i.e. becoming more unstable), and having larger residuals because the training data no longer represent the Krylov sequence of the underlying Koopman operator.

This is illustrated in the panels (a) and (b) in Fig.2, which show, for the sliding training windows, the real and the imaginary parts of those eigenvalues for which the residuals of the associated eigenvectors are smaller than (eta _r=0.075). Note the absence of such eigenvalues in time intervals that contain the disturbance caused by the Black Swan event.

On the other hand, the retouching technique that repairs the distorted training data restores the intrinsic dynamics over the entire training window. The distribution of the relevant eigenvalues becomes more consistent, and the prediction error decreases, see panels (c) and (d) in Fig.2, and in Supplementary Information Figure S16.

Our proposed retouching procedure relies on detecting anomalous behavior of the Ritz values; a simple strategy of monitoring the spectral radius of active windows (absolutely largest Ritz value extracted from the data in that window) is outlined in Supplementary Information. Note that this can also be used as a litmus test for switching to the local prediction algorithm. In Supplementary Information, we provide further examples, with the influenza data, that confirm the usefulness of the retouching procedure. In general, this procedure can also be adapted to the situation when the algorithm receives a signal that the incoming data is missing or corrupted.

The second set of data we consider is that associated with the ongoing COVID-19 pandemic. Because the virus is new, the whole event is, in a sense, a Black Swan. However, as we show below, the prediction approach advanced here is capable of adjusting quickly to the new incoming, potentially sparse data and is robust to inaccurate reporting of cases.

At the beginning of the spread of COVID-19, we have witnessed at moments rather chaotic situation in gaining the knowledge on the new virus and the disease. The development of COVID-19 diagnostic tests made tracking and modeling feasible, but with many caveats: the data itself is clearly not ideal, as it depends on the reliability of the tests, testing policies in different countries (triage, number of tests, reporting intervals, reduced testing during the weekends), contact tracing strategies, using surveillance technology, credit card usage and phone contacts tracking, the number of asymptomatic transmissions etc. Many different and unpredictable exogenous factors can distort it. So, for instance the authors of23 comment at https://ourworldindata.org/coronavirus-testing that e.g. The Netherlands, for instance, makes it clear that not all labs were included in national estimates from the start. As new labs get included, their past cumulative total gets added to the day they begin reporting, creating spikes in the time series. For a prediction algorithm, this creates a Black Swan event that may severely impair prediction skills, see sectionRetouching the Black Swan event data.

This poses challenging problems to the compartmental type models of (SIR, SEIR) which in order to be useful in practice have to be coupled with data assimilation to keep adjusting the key parameters, see e.g.24. Our technique of retouching (sectionRetouching the Black Swan event data) can in fact be used to assist data assimilation by detecting Black Swan disturbance and thus to avoid assimilating disturbance as normal.

In the KMD based framework, the changes in the dynamics are automatically assimilated on-the-fly by recomputing the KMD using new (larger or shifted) data snapshot windows. This is different from the compartmental type models of infectious diseases, most notably in the fact that the procedure presented here does not assume any model and, moreover, that it is entirely oblivious to the nature of the underlying process.

As a first numerical example, we use the reported cumulative daily cases in European countries. In Supplementary Information section S1.5, we use this data for a detailed worked example that shows all technical details of the method. This is a good test case for the methodusing the data from different countries in the same vector observable poses an additional difficulty for a data driven revealing of the dynamics, because the countries independently and in an uncoordinated manner impose different restrictions, thus changing the dynamics on local levels. For instance, at the time of writing these lines, a new and seemingly more infectious strain of the virus circulating in some parts of London and in south of England prompted the UK government to impose full lockdown measures in some parts of the United Kingdom. Many European countries reacted sharply and immediately suspended the air traffic with the UK.

In the first numerical experiment, we use two datasets from the time period February 29 to November 19. and consider separately two sets of countries: Germany, France and the UK in the first, and Germany, France, UK, Denmark, Slovenia, Czechia, Slovakia and Austria in the second. The results for a particular prediction interval are given in Figs.3 and 4. For more examples and discussion how the prediction accuracy depends on the Government Response Stringency Index (GRSI25,26) see Supplementary Information section S1.5.

Prediction of COVID-19 cases (35 days ahead, starting July 11) for Germany, France and United Kingdom. Left panel: The HankelTakens matrix ({mathbb {H}}) is (282 times 172), the learning data consists of ({textbf{h}}_{1:40}). The KMD uses 39 modes. Middle panel: The matrix ({mathbb {H}}) is (363 times 145), the learning data is ({textbf{h}}_{1:13}). The KMD uses 12 modes. Right panel: The KoopmanRitz values corresponding to the first (magenta circles) and the middle (blue plusses) panel. Note how the three rightmost values nearly match.

Prediction errors and KMD spectrum of COVID-19 cases (28 days ahead, starting July 11) for Germany, France, United Kingdom, Denmark, Slovenia, Czechia, Slovakia and Austria. Left panel: The HankelTakens matrix ({mathbb {H}}) is (752 times 172), the learning data consists of ({textbf{h}}_{1:40}). The KMD uses 39 modes. Middle panel: The matrix ({mathbb {H}}) is (968 times 145), the learning data is ({textbf{h}}_{1:13}). The KMD uses 12 modes. Right panel: The KoopmanRitz values corresponding to the first two computations in Fig.3 (magenta circles and blue pluses, respectively) and the the first two panels in this Figure (orange x-es and cyan squares, respectively). Note how the corresponding KoopmanRitz values nearly match for all cases considered.

In the above examples, the number of the computed modes was equal to the dimension of the subspace of spanned by the training snapshots, so that the KMD of the snapshots themselves was accurate up to the errors of the finite precision arithmetic. In general, that will not be the case, and the computed modes will span only a portion the training subspace, meaning that the KMD of the snapshots might have larger representation error. (Here we refer the reader to Supplementary Information section S1.3, where all technical details are given.) This fact has a negative impact to the extrapolation forward in time and the problem can be mitigated by giving more importance to reconstruction of more recent weights. This is illustrated in Figs.5 and6, where the observables are the raw data (reported cases) for Germany, extended by a two additional sequence of filtered (smoothened) values.

Prediction experiment with data from Germany. Left panel: the computed residuals for the computed 102 Koopman Ritz pairs (extracted from a subspace spanned by 132 snapshots ({textbf{h}}_{1:132})). Note that all residuals are small. The corresponding Ritz values are shown in the first panel in Fig.6. Middle panel: KMD reconstruction error for ({textbf{h}}_{1:132}) and the error in the predicted values ({textbf{h}}_{133:160}) (encircled with ({circ })). The reconstruction is based on the coefficients ((alpha _j)_{j=1}^r=mathrm {argmin }_{alpha _j}sum _{k} Vert {textbf{h}}_k - sum _{j=1}^{r} lambda _j^{k}alpha _j {textbf{v}}_jVert _2^2). Right panel: Prediction errors for the period October 11November 7.

Prediction experiment with DS3 with data from Germany. Left panel: the computed 102 Koopman Ritz values (extracted from a subspace spanned by 132 snapshots ({textbf{h}}_{1:132})). The corresponding residuals are shown in the first panel in Fig.5. Middle panel: KMD reconstruction error for ({textbf{h}}_{1:132}) and the error in the predicted values ({textbf{h}}_{133:160}) (encircled with ({circ })). The reconstruction is based on the coefficients ((alpha _j)_{j=1}^r=mathrm {argmin }_{alpha _j}sum _{k} w_k^2 Vert {textbf{h}}_k - sum _{j=1}^{r} lambda _j^{k}alpha _j {textbf{v}}_jVert _2^2). Right panel: Prediction errors for the period October 11 November 7. Compare with the third graph in Fig.5.

The figures illustrate an important point in prediction methodology, that we emphasized in the introduction: a longer dataset and a better data reconstruction ability (i.e. interpolation) does not necessarily lead to better prediction. Namely, weighting more recent data more heavily produces better prediction results. This was already observed in27 for the case of traffic dynamics, and the method we present here can be used to optimize the prediction ability.

We have deployed the algorithm to assess the global and United States evolution of the COVID-19 pandemic. The evolution of the virus is rapid, and Black Swans in the sense of new cases in regions not previously affected appear with high frequency. Despite that, the Koopman Mode Decomposition based algorithm performed well.

In Fig.7a we show the worldwide forecast number of confirmed cases produced by the algorithm for November 13th, 2020. The forecasts were generated by utilizing the previous three days of data to forecast the next three days of data for regions with higher than 100 cases reported. The bubbles in Fig.7a are color coded according to their relative percent error. As can be observed, a majority of the forecasts fell below 15% error. The highest relative error for November 13th, 2020 was 19.8% which resulted from an absolute error of 196 cases. The mean relative percent error, produced by averaging across all locations, is 1.8% with a standard deviation of 3.36% for November 13th, 2020. Overall, the number of confirmed cases are predicted accurately and since the forecasts were available between one to three days ahead of time, local authorities could very well utilize our forecasts to focus testing and prevention measures in hot-spot areas that will experience the highest growth.

A video demonstrating the worldwide forecasts for March 25, 2020November 29, 2020 is provided in the Supplementary Information online (Fig.7a is a snapshot from that video). Lastly, it is well known that the ability to test people for the virus increased throughout the development of the pandemic and thus resulted in changes in the dynamics of reported cases. Although it is impossible for a data-driven algorithm to account for changes due to external factors, such as increased testing capabilities, it is important that the algorithm be able to adjust and relearn the new dynamics. For this reason, we encourage the reader to reference the video and note that although periods of inaccuracy due to black swan events occur, the algorithm is always able to stabilize and recover. In contrast, since this is at times a rapidly (exponentially) growing set of data, methods like naive persistence forecast do poorly.

In Fig.7b, c we show the performance of the prediction for the cumulative data for the US in March-April 2020. It is of interest to note that the global curve is obtained as a sum of local predictions shown in Fig.7a, rather than as a separate algorithm on the global data. Again, the performance of the algorithm on this nonstationary data is good.

Prediction of confirmed COVID-19 cases utilizing the publicly available COVID-19 data repository provided by Johns Hopkins. The true data ranges between March 22nd, 2020 and November 29th, 2020. We utilize the last three days of data to forecast the following three days of data. (a) Predicted conditions and prediction error worldwide on November 13. The widths of the bubbles represent the number of cases in a region; only regions with more that 100 cases are used and the bubbles are colored according to their relative percent error. (b) Comparison of true and forecast data for cumulative confirmed cases in the US for April to December 2020. The cumulative forecasts shown here were obtained by summing the forecasts of the individual locations, indicating that the region specific forecasts were sufficiently accurate for tracking the cumulative dynamics of the virus in the US. (c) Percent error for the forecasts of the cumulative confirmed cases in the US. On average the percent error is less than 5 percent and although spikes occur, which could be due to changes in testing availability, the algorithm adjusts and the error stabilizes within a short amount of time. Furthermore, Johns Hopkins provided data for around 1787 locations around the United States and we produced forecasts for each of those locations.

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A Koopman operator-based prediction algorithm and its application to COVID-19 pandemic and influenza cases ... - Nature.com

In a stunning reversal of decades of progress, global life expectancy at birth fell 1.6 years from 2019 to 2021, with 16 million of 131 million total deaths in 2020 and 2021 directly or indirectly attributable to COVID-19, reveals one of the most comprehensive studies of its kind published yesterday in The Lancet.

The Global Burden of Diseases (GBD), Injuries, and Risk Factors Study 2021 Collaborators analyzed trends in death rates and life expectancy in 204 countries and territories and 811 subnational locations from 1950 to 2021, with a focus on the 2020-2021 COVID-19 pandemic period. The data, obtained from registries, surveys, censuses, and other sources, include more than 607 billion estimates of 371 diseases and injuries and 88 risk factors.

An ongoing effort, the GBD is the largest and most comprehensive study measuring health losses in global locations over time. More than 11,000 collaborators in 160 countries contribute to the research, which is coordinated by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.

About 131 million people around the world died from any cause in 2020-2021 combined, with 15.9 million more deaths than expected due to COVID-19 infection or pandemic-related social, economic, or behavioral factors, such as delays in seeking healthcare.

Age-standardized death rates decreased 62.8% from 1950 to 2019 and then rose 5.1% in 2020-2021, but death rates among children younger than 5 years continued to fall, albeit more slowly (4.7 million in 2021, down from 5.2 million in 2019). Regional differences in child death rates, however, were stark, with one of four child pandemic deaths occurring in South Asia, and two of every four deaths occurring in sub-Saharan Africa.

All-cause death rates were higher among males than females aged 15 years and older (21.9% vs 16.6%) in 2020-2021 than in 2019. In 2020 or 2021, excess death rates surpassed 150 deaths per 100,000 people in 80 countries and territories, while 20 countries saw negative excess death rates.

After adjustment for population age, countries such as Jordan and Nicaragua had high excess COVID-related death rates. In analyzing subnational locations not previously investigated, the South African provinces of KwaZulu-Natal and Limpopo had among the highest excess death rates and largest life expectancy decreases, while some of the lowest excess death rates were seen in countries such as Barbados, New Zealand, Antigua, and Barbuda.

Global life expectancy climbed 22.7 years from 1950 to 2021, from 49.0 to 71.7 years, but from 2019 to 2021, it dropped 1.6 years, reversing historical trends. Thirty-two countries (15.7%) saw increased life expectancy.

The worldwide population was 7.89 billion in 2021, when populations in 56 countries had peaked and began to decrease, with continued rapid population growth in many lower-income countries. Countries in sub-Saharan Africa and South Asia experienced the greatest population growth from 2020 to 2021, at 39.5% and 26.3%, respectively. The ratio of people aged 65 years and older to those younger than 15 years rose in 188 countries (92.2%) from 2000 to 2021.

"For adults worldwide, the COVID-19 pandemic has had a more profound impact than any event seen in half a century, including conflicts and natural disasters," co-first author Austin Schumacher, PhD, of the IHME, said in an IHME press release. "Life expectancy declined in 84% of countries and territories during this pandemic, demonstrating the devastating potential impacts of novel pathogens."

Declining population growth, aging populations, and the shifting of growth to lower-income countries with worse health outcomes will have social, economic, and political consequences, such as workforce shortages in areas with shrinking younger populations and resource scarcity in those with growing populations. "This is worth restating, as these issues will require significant policy forethought to address in the affected regions," Schumacher added.

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By 2022, COVID pandemic had shaved 1.6 years from global life expectancy, research reveals - University of Minnesota Twin Cities

NEW YORK (AP) The nations top public health agency is expanding a program that tests international travelers for COVID-19 and other infectious diseases.

The Centers for Disease Control and Prevention program asks arriving international passengers to volunteer to have their noses swabbed and answer questions about their travel. The program operates at six airports and on Tuesday, the CDC said it was adding two more Chicagos OHare and Miami.

Those locations should provide more information about respiratory infections coming out of South America, Africa and Asia, particularly, CDC officials said.

Miami and Chicago enable us to collect samples coming from areas of the world where global surveillance is not as strong as it used to be, said the CDCs Allison Taylor Walker. What we really need is a good view of whats happening in the world so were prepared for the next thing.

AP correspondent Shelley Adler reports airport nasal swabbing in the U.S. is expanding.

The program began in 2021, and has been credited with detecting coronavirus variants faster than other systems. The genomic testing of travelers nasal swabs has mainly been focused on COVID-19, but testing also is being done for two other respiratory viruses flu and RSV.

Participants are not notified of their results. But they are given a COVID-19 home test kit to take with them, CDC officials say.

Samples have come from more than 475,000 air travelers coming off flights from more than 135 countries, officials said.

Health officials also have been sampling wastewater that comes off international flights at a few airports. That testing is for COVID-19, but CDC officials are evaluating the possibility of monitoring wastewater for other things, Walker said.

The CDC program has a current budget of about $37 million. The agency pays two companies, Ginkgo Bioworks and XWell, to do sample collection and testing. The companies are working with CDC to grow the program to check for more than 30 different disease-causing germs.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institutes Science and Educational Media Group. The AP is solely responsible for all content.

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U.S. airport nasal swabbing expanding to Chicago and Miami - The Associated Press

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John Stockton's lawyer claims first amendment violation as basis for COVID-19 lawsuit - KXLY Spokane

SPOKANE, Wash. - Former Gonzaga basketball star John Stockton has filed a lawsuit over state sanctions on doctors who spread alleged COVID-19 disinformation.

Stockton has been an outspoken critic of the response to the COVID-19 pandemic. He was suspended from attending Gonzaga basketball games when he refused to wear a mask.

The lawsuit was filed on March 7 with COVID-19 skeptic Robert F. Kennedy Jr. among the attorneysrepresenting Stockton. A number of medical professionals including Clarkston-based ophthalmologistRichard Eggleston are also listed as plaintiffs.

Eggleston is currently the subject of a medical commission administrative proceeding in connection to a column in which he questioned whether COVID-19 exists. The lawsuit said he's been "active in trying to assert his Constitutional rights."

The lawsuit names Washington Attorney General Bob Ferguson and the executive director of the Washington Medical Commission as defendants. It seeks a preliminary and permanent injunction against a policy that allows the commission to discipline doctors who promote COVID-19 misinformation.

There is no place for the government, under the guise of regulating physicians and protecting the public, to censure, restrict or sanction the content and viewpoint of the publicly expressed views of physicians on Covid or any other subject, just because the government does not like the message or thinks it is wrong," the lawsuit states. "Going back 70 years every judge and Supreme Court justice who has written on professional soapbox speech has stated that it is fully protected by the First Amendment and/or said that it cannot be the subject of government regulation or restriction."

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John Stockton sues Washington state over sanctions for COVID-19 disinformation - KHQ Right Now

News Release

Tuesday, March 12, 2024

Part of NIHs RECOVER Initiative, trials will test at least three treatments for symptoms such as fast heart rate, dizziness and fatigue.

Two phase 2 clinical trials to test the safety and effectiveness of three treatments for adults with autonomic nervous system dysfunction from long COVID have begun. The autonomic nervous system acts largely unconsciously and regulates bodily functions, such as heart rate, digestion and respiratory rate. Symptoms associated with autonomic nervous system dysfunction have been among those that patients with long COVID say are most burdensome. The trials are part of the National Institutes of Healths Researching COVID to Enhance Recovery (RECOVER) Initiative, a nationwide research program to fully understand, diagnose and treat long COVID. Other RECOVER phase 2 clinical trials testing treatments to address viral persistence and neurological symptoms, including cognitive dysfunction (like brain fog), launched in July 2023.

As a long COVID patient, I know firsthand how disruptive and frightening symptoms including rapid heart rate, dizziness and fatigue can be. Patient representatives across RECOVER have also shared that these symptoms are some of the most debilitating symptoms of long COVID, said Heather Marti, co-chair of the RECOVER National Community Engagement Group.These trials are giving me and others with long COVID hope that it will restore our health and get us back to the lives we so desire.

The two trials, collectively known as RECOVER-AUTONOMIC, are testing three potential treatments in adults who, following COVID-19, now have postural orthostatic tachycardia syndrome (POTS). An autonomic nervous system disorder, POTS is characterized by unexpected fast heart rate, dizziness, fatigue or a combination of these symptoms when a person stands up from sitting or lying down.

The trials were developed with input from people living with longCOVID, caregivers, community representatives, clinicians and scientists allwith uniqueexpertise in the field, said Gary H. Gibbons, M.D., director of the National Heart, Lung, and Blood Institute at the NIH and co-chair of RECOVER. We are grateful for their collective involvement which significantly shaped the trials and the choice of interventions.

The trials will initially examine three potential treatments:

Patients who develop POTS after havingCOVID-19 are often severely limited by their symptoms, and there are no proven effective treatments, said Christopher Granger, M.D., Duke University Medical Center, who is co-leading RECOVER-AUTONOMIC. These interventions were selected because they have shown potential benefit in treating symptoms for POTS. The theory were testing is that they might also help individuals with long COVID.

Participants will first be randomly assigned to receive either IVIG, ivabradine or a placebo. Participants will then be randomly assigned a second time to receive either coordinator-guided, non-drug care or what is considered the usual non-drug care for POTS following COVID-19, such as diet and lifestyle recommendations. RECOVER-AUTONOMIC is an adaptive clinical trial, meaning if additional potential interventions emerge, they can quickly be added and studied in the trial.

Researchers plan to enroll 380 total participants at 50 sites across the United States. Teams at the trial sites will recruit participants from their health systems and surrounding communities. The current list of sites for the trials can be found on ClinicalTrials.gov (search: NCT06305793, NCT06305806 and NCT06305780) and additional sites will be added to this list as they begin enrolling participants.

Diversity among the trial participants is a high priority for RECOVER. To support diverse and inclusive representation, study sites are chosen based on geographic location, their connections to communities, and their track records for enrolling diverse research participants.

With the launch of the RECOVER-AUTONOMIC trials, RECOVER is currently testing seven treatments across four clinical trials and continues to enroll participants. Those interested in learning more about RECOVER clinical trials should visit trials.recovercovid.org.

About RECOVER: The National Institutes of Health Researching COVID to Enhance Recovery (NIH RECOVER) Initiative brings together clinicians, scientists, caregivers, patients, and community members to understand, diagnose, and treat long COVID. RECOVER has created one of the largest and most diverse groups of long COVID study participants in the world. In addition, RECOVER clinical trials are testing potential interventions across five symptom focus areas. For more information, please visit recovercovid.org.

HHS Long COVID Coordination: This work is a part of the National Research Action Plan, a broader government-wide effort in response to the Presidential Memorandum directing the Secretary for the Department of Health and Human Services to mount a full and effective response to long COVID. Led by Assistant Secretary for Health Admiral Rachel Levine, the Plan and its companion Services and Supports for Longer-term Impacts of COVID-19 report lay the groundwork to advance progress in the prevention, diagnosis, treatment, and provision of services for individuals experiencing long COVID.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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NIH opens long COVID trials to evaluate treatments for autonomic nervous system dysfunction - National Institutes of Health (NIH) (.gov)

Masks, constant hand washing, and isolation are the main messages to stay safe against COVID-19 in 2020, and Gov. Michelle Lujan Grisham declared a state of emergency four years agoThe life of healthcare workers instantly changed.It meant even more precautions, dorms at UNM converted into places for healthcare workers to stay. So they didn't have to go home to their families if they were worried. We created a lot of ways to have clothing when you got here that you could leave when you left or do showers, Kate Becker, the CEO at the University of New Mexico Hospital, said.As the virus became a global pandemic, hospitals were over capacity by at least 150%.We had people literally everywhere. We turned clinics into patient spaces. We turned conference rooms into patient rooms. We had patients everywhere, Becker said.After COVID-19 drive-thru testing vaccines and return for normalcy. Hospitals like UNM are dealing with more illnesses due to delayed care for things like cancer screenings. The need for higher levels of care persists even though the COVID part has slowed, Becker said.It has even changed the way some hospitals are built. We didn't have enough rooms to keep patients isolated where the air pressure was such that the virus was not going back and forth, Becker said. She added, We had not collectively experienced a pandemic in 100-plus years and we didn't know the number of people we needed to accommodate.

Masks, constant hand washing, and isolation are the main messages to stay safe against COVID-19 in 2020, and Gov. Michelle Lujan Grisham declared a state of emergency four years ago

The life of healthcare workers instantly changed.

It meant even more precautions, dorms at UNM converted into places for healthcare workers to stay.

So they didn't have to go home to their families if they were worried. We created a lot of ways to have clothing when you got here that you could leave when you left or do showers, Kate Becker, the CEO at the University of New Mexico Hospital, said.

As the virus became a global pandemic, hospitals were over capacity by at least 150%.

We had people literally everywhere. We turned clinics into patient spaces. We turned conference rooms into patient rooms. We had patients everywhere, Becker said.

After COVID-19 drive-thru testing vaccines and return for normalcy.

Hospitals like UNM are dealing with more illnesses due to delayed care for things like cancer screenings.

The need for higher levels of care persists even though the COVID part has slowed, Becker said.

It has even changed the way some hospitals are built.

We didn't have enough rooms to keep patients isolated where the air pressure was such that the virus was not going back and forth, Becker said.

She added, We had not collectively experienced a pandemic in 100-plus years and we didn't know the number of people we needed to accommodate.

Read more:

Four years since the COVID-19 pandemic - KOAT New Mexico

Bitcoin (BTC) is up nearly 2,000% versus its COVID-19 lows on the fourth anniversary of its crash to $3,600.

On March 12, 2018, BTC price action began a plunge to levels never seen again as risk assets dived worldwide.

Bitcoin hodlers have much to celebrate with BTC/USD above $70,000, but some are commemorating a grim reminder of worse times.

Exactly four years ago, the COVID-19 cross-market crash wrought havoc across risk assets and beyond, sending Bitcoin tumbling more than 50% in a single day.

As coronavirus was just beginning to spark lockdowns and other knee-jerk moves from governments, markets felt a keen sense of the economic upheaval to come.

Beginning March 12 at $7,960, BTC/USD finished at $4,830, going on to bottom at $3,860 the following day, according to data from Cointelegraph Markets Pro and TradingView.

Its comeback was arguably just as impressive just one-and-a-half months later, $10,000 had reappeared.

Everyone who bought the dip is up 1,700% since, crypto journalist Pete Rizzo wrote in part of a dedicated post on X.

Those who decided to go all in on that day are not the only COVID-19 success stories when it comes to diversifying into BTC.

United States citizens who used their first stimulus check, worth $1,200 and delivered in April 2020, to buy Bitcoin are now sitting on $12,930, per data from monitoring resource BitcoinStimulus.

A 100% stimulus deployment, originally worth $3,200, is now worth 400% more.

Perusing other data, analyst Joe Consorti noted that overall BTC balances on exchanges peaked following the March 2020 crash.

Related:Bitcoin has 6 months until ETF liquidity crisis New analysis

From then on, the tally on exchanges tracked by on-chain analytics firm Glassnode began a broad downtrend one which continues to this day.

It has since dropped from 17.6% of supply to 11.6% and is still falling fast, Consorti wrote in part of accompanying X comments last week.

This article does not contain investment advice or recommendations. Every investment and trading move involves risk, and readers should conduct their own research when making a decision.

Read more:

Bitcoin is up 1,800% 4 years after the 2020 COVID-19 BTC price crash - Cointelegraph

These are the faces of people who lost their lives to COVID-19: parents, pastors, doctors, teachers, firefighters people from every age group and all walks of life.

Monday marks four years since the World Health Organization declared a global pandemic. Almost 1.2 million people in the U.S. have lost their lives to the virus.

For many families, the pandemic wounds are still raw, four years later.

"We need to acknowledge COVID. We lost over a million people," said Anne Starkweather. Her husband, Chad Capule, was one of them.

He died in March 2020, and four years on, she's still processing the grief and trauma.

"It still makes me sick to this day that he's not here. I will never be the same," she said.

Washington, D.C., Maryland and Virginia local news, events and information

Capule, an IT manager from Cheverly, Maryland, got sick while traveling to a Wisconsin hospital to set up their new computer system. Three weeks later, he died there, alone in the ICU.

His wife and sisters never got the chance to say goodbye in person.

"I feel robbed and a bit angry and jealous that we could not do that," said one of his sisters, Angie Fontanilla.

His wife told us: "That breaks my heart whenever I think about it. Him being there alone and dying alone, with no one to hold his hand, no one to tell him it was OK."

It's a sentiment echoed by so many who weren't allowed inside hospitals and were forced to hold funerals virtually, for fear of spreading the virus.

"That uncertainty translated into isolation, that they could not be with their loved one as they lay dying," said Sarah Wagner, an anthropology professor at The George Washington University. "They couldn't be there to hold a hand, to press a cheek. They couldn't say those words, a final goodbye. Or if they did, they had to do so mediated by technology."

"Often people are left in the very space where their loved one had sat next to them," Wagner said. "Same bed, same room, same kitchen. They're not there, but they're also not able to bring in family and friends to kind of get through that period."

Wagner and and fellow GW anthropology professor Roy Richard Grinker are members of a research team called Rituals in the Making, which is focused on COVID death, mourning and memorialization.

Over the past four years, theyve found that silence about COVID hurts people who are still grieving.

"When there is this enveloping silence around 1,200,000 deaths, and we don't talk about it that silence doesn't make it easier for the people who are grieving, right? This mourning process is truncated," Wagner said. "The grief is being compounded."

To honor the victims and acknowledge the grief, memorials have popped up, both big and small, including a powerful public art installation outside RFK Stadium in fall 2020, and another on the National Mall the following year displaying thousands of personalized flags.

Those temporary memorials provided safe places for people to mourn and the nation to reflect.

"I was on the mall with all the flags in the wind and the sun and all these people everywhere," Grinker recalled. "And I said, 'What would a support group look like for you to help you mourn the loss of your loved one?' And she looked around and said, 'This. This is a support group for that.'"

Suzanne Firstenberg is the artist behind the moving displays titled "In America: Remember," capturing a sobering snapshot in time.

"That's one of the things about grief in America," she said. 'We have no idea who's grieving near us. One in three people experienced a COVID death of a relative, a friend, a coworker."

Fontanilla told us: "It was helpful for us, for me, in the healing process to see that we were not alone."

"So It felt like a sense of community with these other people that had also suffered," Starkweather said.

"So you had people who came and they, for the first time, felt as if, through that installation, that their loss was being recognized," Wagner said.

"It felt like the funeral they never had, or that was like a proxy gravesite that they could go to," she said.

And the work isn't over.

Firstenberg still has the 20,000 personalized flags from the 'In America' exhibition. She's working with the team at GW on the painstaking task of archiving every single one and creating a database that researchers and relatives will be able to access.

For mourners, it's a start, with an end goal of someday having a more permanent memorial for future generations to remember.

"9/11 changed the country. COVID changed the country," Fontanilla said. "It would be good to have something that people can go to, and to remember that, and to tell future generations that, 'Hey, [this is] something important and something to learn from.'"

Firstenberg has blank white flags at her Bethesda art studio, where people can go to fill them out and honor loved ones who died of COVID-19. She says any new flags will be archived and saved into the database they're building.

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I will never be the same': 4 years on, remembering the lives lost to COVID-19 - NBC Washington