Estimating The Infection Fatality Rate Among Symptomatic COVID-19 Cases In The United States

Cohort study of hospitalized patients with COVID-19 at Brazilian tertiary-care hospital: Occurrence of adverse events and mortality

INTRODUCTION

Patients hospitalized with COVID-19 are often submitted to invasive procedures and they are at risk for hospital-associated adverse events.

OBJECTIVES

To evaluate the epidemiological and clinical aspects of patients hospitalized with COVID-19; the occurrence of adverse events and the risk factors for mortality.

METHOD

Cohort study that included adult patients hospitalized with a diagnosis of SARSCoV-2 infection, at the tertiary University Hospital of UNICAMP from March 2020 to March 2021. Patients were identified through the hospital epidemiological surveillance system and followed until discharge or death. Descriptive, comparative, and logistic regression analysis was performed.

RESULTS

650 adult patients were included. The main adverse events identified were nosocomial infections (31.5%), renal failure (33.8%), thromboembolic and vascular events (12.6%). Mortality was higher among those with bloodstream infections (30.2% vs. 8.6%; p < 0.0001), ventilator-associated pneumonia (VAP, 52.5% vs. 12.3%; p < 0.0001), catheter associated urinary infection (27.3% vs. 7.2%; p < 0.0001); thromboembolic and vascular events (23.0% vs. 9.9%; p < 0.0001) and renal failure (81.3% vs. 20.9%; p < 0.0001). Klebsiella pneumoniae (15.6%), Pseudomonas aeruginosa (14.4%), Enterococcus faecalis (8.6%) were the most isolated bacteria. Logistic regression analysis identified age, (RR = 1.03; 95% CI 1.02 to 1.05); ICU admission (RR = 3.06; 95% CI 1.59 to 5.87), vasoactive drug use (RR = 3.1; 95% CI 1.79 to 4.82); renal failure (RR = 7.76; 95% CI 4.54 to 13.26); and VAP (RR = 2.2; 95% CI 1.23 to 3.96), independently associated with mortality.

CONCLUSION

adverse events have an important impact on the evolution of patients with COVID-19, reinforcing the need for optimized prevention and control measures as an essential part of care for these patients.

Fiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analytics

Fiscal responses to the COVID-19 crisis have varied a lot across countries. Using a panel of 127 countries over two separate subperiods between 2020 and 2021, this paper seeks to determine the extent that fiscal responses contributed to the spread and containment of the disease. The study first documents that rich countries, which had the largest total and health-related fiscal responses, achieved the lowest fatality rates, defined as the ratio of COVID-related deaths to cases, despite having the largest recorded numbers of cases and fatalities. The next most successful were less developed economies, whose smaller total fiscal responses included a larger health-related component than emerging market economies. The study used a promising big data analytics technology, the random forest algorithm, to determine which factors explained a country's fatality rate. The findings indicate that a country's fatality ratio over the next period can be almost entirely predicted by its economic development level, fiscal expenditure (both total and health-related), and initial fatality ratio. Finally, the study conducted a counterfactual exercise to show that, had less developed economies implemented the same fiscal responses as the rich (as a share of GDP), then their fatality ratios would have declined by 20.47% over the first period and 2.59% over the second one.

COVID-19 case-fatality variations with application to the Middle East countries

During a pandemic outbreak, it is important for health officials to know the proportions of deaths among infected individuals and to understand how these proportions change overtime, to accurately predict the impact of the pandemic and to implement effectively new intervention policies and health protocols and to adjust them accordingly. However, most studies where efforts have been made to estimate accurately the case fatality rates did not address the issue of measuring the dynamics of the pandemic deadliness during its course. Daily data on COVID-19 cases and deaths were collected from selected MENA countries. In this paper, two new measures of the pandemic fatality are developed based on the estimated time it takes hospitalized infected patients to eventually die from the disease. The first measure assigns COVID-19 deaths to its most significant lagged number of cases based on a fixed-effects panel data model. The second fatality measure relates pandemic deaths and cases based on their respective change points. The results find notable variations of the pandemic lethality between the Middle East countries, likely due to the difference in the quality of health care. Although crude case-fatality rate does not identify the pandemic lethality variations during the ongoing of the disease, this paper develops two novel measures for COVID-19 case fatality which can identify the dynamics and the variations of the pandemic deadliness.

Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties

INTRODUCTION

This study aimed to examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate.

METHODS

This study proposes 3 multiscale dimensions of spatial process, including level of influence (the percentage of population affected by a certain determinant across the entire area), scalability (the spatial process of a determinant into global, regional, and local process), and specificity (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021, and the analyses were conducted in May 2022.

RESULTS

The results suggest the following: (1) Percentage of Republican votes in the 2020 presidential election is a primary influencer because 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) Demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) The spatial process underlying fully vaccinated rates is largely local.

CONCLUSIONS

The findings challenge the 1-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.

Benchmarking of COVID-19 testing facilities: a case in the Philippines

Purpose

With the aggressive movement towards testing for COVID-19 across the globe, this study aims to shed light on how testing facilities perform in an operational perspective.

Design/methodology/approach

With 102 testing facilities in the Philippines, the relative efficiencies of each facility are quantified using a data envelopment analysis technique. Afterwards, a best-worst method was conducted to assign priority weights to each testing facility.

Findings

Results show that the proposed approach effectively prioritizes testing facilities that most likely have high utilization.

Research limitations/implications

The findings in this study would be significant to the literature in a number of respects. For one, it reveals results that would stimulate the interest among scholars in a wide variety of disciplines such as management, data mining, policymaking, decision science and epidemiology, among others.

Originality/value

This study differs from previous works in a number of respects, particularly, in that to the best of the authors’ knowledge, this is the first study to examine the relative efficiencies of COVID-19 testing facilities.

Mitigation Planning and Policies Informed by COVID-19 Modeling: A Framework and Case Study of the State of Hawaii

In the face of great uncertainty and a global crisis from COVID-19, mathematical and epidemiologic COVID-19 models proliferated during the pandemic. Yet, many models were not created with the explicit audience of policymakers, the intention of informing specific scenarios, or explicit communication of assumptions, limitations, and complexities. This study presents a case study of the roles, uses, and approaches to COVID-19 modeling and forecasting in one state jurisdiction in the United States. Based on an account of the historical real-world events through lived experiences, we first examine the specific modeling considerations used to inform policy decisions. Then, we review the real-world policy use cases and key decisions that were informed by modeling during the pandemic including the role of modeling in informing planning for hospital capacity, isolation and quarantine facilities, and broad public communication. Key lessons are examined through the real-world application of modeling, noting the importance of locally tailored models, the role of a scientific and technical advisory group, and the challenges of communicating technical considerations to a public audience.

Reporting dinaciclib and theodrenaline as a multitargeted inhibitor against SARS-CoV-2: an in-silico study

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is one of the rapid spreading coronaviruses that belongs to the Coronaviridae family. The rapidly evolving nature of SARS-CoV-2 results in a variety of variants with a capability of evasion to existing therapeutics and vaccines. So, there is an imperative need to discover potent drugs that can able to disrupt the function of multiple drug targets to tackle the SARS-CoV-2 menace. Here in this study, we took the different targets of SARS-CoV-2 prepared in the Schrodinger maestro. The library of the DrugBank database is screened against the selected crucial targets. Our molecular docking, Molecular Mechanics/Generalized Born Surface Area (MMGBSA), and molecular dynamics simulation studies led to identifying dinaciclib and theodrenaline as potential drugs against multiple drug targets: main protease, NSP15-endoribonuclease and papain-like-protease, of SARS-CoV-2. Dinaciclib with papain-like protease and NSP15-endoribonuclease show the docking score of -7.015 and -8.737, respectively, while the theodrenaline with NSP15-endoribonuclease and main protease produced the docking score of -8.507 and -7.289, respectively. Furthermore, the binding free energy calculations with MM/GBSA and molecular dynamics simulation studies of the complexes confirm the reliability of the drugs. The selected drugs are capable of binding to multiple targets simultaneously, thus withstanding their activity of target disruption in different variants of SARS-CoV-2. Although, the repurposed drugs are showing potent activity, but may need further in-vitro and in-vivo validations.Communicated by Ramaswamy H. Sarma.

How COVID-19 induced panic on stock price and green finance markets: global economic recovery nexus from volatility dynamics

This paper investigates the effect of different categories of essential COVID-19 data from 2020 to 2021 towards stock price dynamics and options markets. It applied the hypothetical method in which investors develop depression based on the understanding suggested by various green finance divisions. Furthermore, additional elements like panic, sentiment, and social networking sites may impact the attitude, size, and direction of green finance, subsequently impacting the security prices. We created new emotion proxies based on five groups of information, namely COVID-19, marketplace, lockdown, banking sector, and government relief using Google search data. The results show that (1) if the proportional number of traders' conduct exceeds the stock market, the effect of sentimentality indexes on jump volatility is expected to change; (2) the volatility index component jump radically increases with the COVID-19 index, city and market lockdown index, and banking index; and (3) expanding the COVID-19 index gives rise to the stock market index. Moreover, all indexes decreased in jump volatility but only after 5 days. These findings comply with the hypotheses proposed by our model.

Association of anti-contagion policies with the spread of COVID-19 in United States

BACKGROUND

The outbreak of a novel coronavirus, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, raised worldwide concern. The present study investigates the association between anti-contagion policies and the spread of COVID-19 across the United States.

DESIGN AND METHODS

We selected the most frequently implemented COVID-19 anti-contagion policies in all the U.S. states issued from 29 February 2020. Accordingly, we modified an epidemiological model and combined it with a comprehensive statistical analysis to evaluate the policies' individual and overall likely impact.

RESULTS

For the first time, a novel index, evaluates the associations between policy implementation and COVID-19 spread at both statewide and national levels. Our results indicate that governmental policies requiring mask use, businesses social distancing, and quarantining travelers may be most effective for controlling COVID-19 spread. Simultaneously, widespread orders like school closure and safer-at-home that can be particularly disruptive to the economy and social fabric of society may be unnecessary given their lack of association with reducing infection.

CONCLUSIONS

The absence of any COVID-19 vaccines during the first several months of its pandemic necessitated using governmental policies to help stop the spread of this disease. Our index showed the association between implemented policies and COVID-19 spread, highlighting the specific policies with the greatest association - mandatory quarantine upon entering a state, businesses implementing social distancing, and mandatory mask use - and those with less association like school closure and safer-at-home orders. This study provided evidence to inform policy choices for the current global crisis and future pandemics.

Clinical informatics solutions in COVID-19 pandemic: Scoping literature review

Background

The global outbreak of COVID-19 (coronavirus disease 2019) disease has highlighted the importance of disease monitoring, diagnosing, treating, and screening. Technology-based instruments could efficiently assist healthcare systems during pandemics by allowing rapid and widespread transfer of information, real-time tracking of data transfer, and virtualization of meetings and patient visits. Therefore, this study was conducted to investigate the applications of clinical informatics (CI) during the COVID-19 outbreak.

Methods

A comprehensive search was performed on Medline and Scopus databases in September 2020. Eligible studies were selected based on the inclusion and exclusion criteria. The extracted data from the studies reviewed were about study sample, study type, objectives, clinical informatics domain, applied method, sample size, outcomes, findings, and conclusion. The risk of bias was evaluated in the studies using appropriate instruments based on the type of each study. The selected studies were then subjected to thematic synthesis.

Results

In this review study, 72 out of 2716 retrieved articles met the inclusion criteria for full-text analysis. Most of the articles reviewed were done in China and the United States of America. The majority of the studies were conducted in the following CI domains: prediction models (60%), telehealth (36%), and mobile health (4%). Most of the studies in telehealth domain used synchronous methods, such as online and phone- or video-call consultations. Mobile applications were developed as self-triage, self-scheduling, and information delivery tools during the COVID-19 pandemic. The most common types of prediction models among the reviewed studies were neural network (49%), classification (42%), and linear models (4.5%).

Conclusion

The present study showed clinical informatics applications during COVID-19 and identified current gaps in this field. Health information technology and clinical informatics seem to be useful in assisting clinicians and managers to combat COVID-19. The most common domains in clinical informatics for research on the COVID-19 crisis were prediction models and telehealth. It is suggested that future researchers conduct scoping reviews to describe and analyze other levels of medical informatics, including bioinformatics, imaging informatics, and public health informatics.

A Programmatic Response, Including Bamlanivimab or Casirivimab-imdevimab Administration, Reduces Hospitalization and Death in COVID-19 Positive Abdominal Transplant Recipients

BACKGROUND

(COVID-19) has resulted in significant morbidity and mortality in solid organ transplant recipients. In December 2020, at the peak of the Los Angeles outbreak, our center rapidly implemented a protocol to improve outpatient management and provide bamlanivimab or casirivimab-imdevimab [COVID monoclonal antibody (mAb) therapies] to all eligible COVID-19 positive liver and kidney transplant recipients.

METHODS

A retrospective review of all abdominal organ transplant recipients who were COVID-19 polymerase chain reaction positive between February 2020 and February 2021 from our center was performed. Patient demographics, COVID-19 treatments, hospitalizations, and survival were reviewed. Patients were considered eligible for COVID mAb therapy if they met outpatient criteria at the time of diagnosis.

RESULTS

In the study period, 121 patients in the kidney transplant recipients group (KG) and 105 patients in the liver or combined liver/kidney transplant recipients group (LG) were COVID-19 polymerase chain reaction positive. Hospitalization rates were similar for the KG (45%) versus LG (35%) (P = 0.20), but mortality was higher for the KG (22%) when compared to LG (10%) (P = 0.02). Our programmatic response, including outpatient COVID mAb therapies, reduced hospitalizations (P = 0.01) and deaths (P = 0.01). Ninety-four KG and 87 LG patients were identified as potentially eligible for COVID mAb therapy, and 17 KG and 17 LG patients were treated. COVID mAb therapies reduced hospitalization from 32% to 15% (P = 0.045) and eliminated mortality (13% versus 0%, P = 0.04).

CONCLUSIONS

An aggressive approach including outpatient COVID mAb therapy in the COVID positive abdominal organ transplant recipients significantly decreased hospitalization and death. Early outpatient intervention for COVID-19 disease in transplant patients should be considered where possible.

Associations of the COVID-19 pandemic with older individuals' healthcare utilization and self-reported health status: a longitudinal analysis from Singapore

BACKGROUND

The COVID-19 pandemic has challenged the capacity of healthcare systems around the world and can potentially compromise healthcare utilization and health outcomes among non-COVID-19 patients.

OBJECTIVES

To examine the associations of the COVID-19 pandemic with healthcare utilization, out-of-pocket medical costs, and perceived health among middle-aged and older individuals in Singapore.

METHOD

Utilizing data collected from a monthly panel survey, a difference-in-differences approach was used to characterize monthly changes of healthcare use and spending and estimate the probability of being diagnosed with a chronic condition and self-reported health status before and during the COVID-19 outbreak in 2020.

SUBJECTS

Data were analyzed from 7569 nationally representative individuals from 2019 January and 2020 December.

MEASURES

Healthcare utilization and healthcare spending by medical service categories as well as self-reported health status.

RESULTS

Between January and April 2020 (the first peak period of COVID-19 in Singapore), doctor visits decreased by 30%, and out-of-pocket medical spending decreased by 23%, mostly driven by reductions in inpatient and outpatient care. As a result, the probability of any diagnosis of chronic conditions decreased by 19% in April 2020. The decreased healthcare utilization and spending recovered after lifting the national lockdown in June, 2020 and remained similar to the pre-pandemic level through the rest of 2020.

CONCLUSIONS

Middle-aged and older Singaporeans' healthcare utilization and the diagnosis of chronic conditions substantially decreased during the first peak period of the COVID-19 outbreak. Further studies to track the longer-term health effect of the pandemic among non-COVID-19 patients are warranted.

Factors associated with COVID-19 vaccination intentions among adults in the deep South

The recent approval of several COVID-19 vaccines signals progress toward controlling the pandemic. Although social distancing and masking have been effective, vaccines are an important additional measure of protection to reduce COVID-19 spread. Adequate uptake is essential to reach herd immunity, estimated to be approximately 67%. However, vaccine hesitancy, the fast-tracked nature of the COVID-19 vaccines, and misinformation circulating through various forms of media have contributed to lower vaccination intention than desired. The current research study developed an online survey conducted via Facebook to explore the attitudes and perceptions of adult Alabama residents about COVID-19 and the COVID-19 vaccines. Of the 3,781 respondents, only 44.3% reported intent to receive a vaccine, with a large proportion reporting they were unsure (28.1%). Lack of intention to vaccinate was associated with low educational attainment, low COVID-19 knowledge levels, low income, and African American race. The current survey also explored participants’ influenza vaccine behavior as this information can also be used to inform successful COVID-19 vaccine distribution. Of the respondents, 56% report receiving the yearly influenza vaccine and the majority receive it at a pharmacy or healthcare provider office. This informs likely successful locations for COVID-19 vaccine distribution. Appropriate education targeted to populations most likely to refuse COVID-19 vaccination is essential to promote uptake. The information collected from the current study should be utilized to inform effective and efficient vaccine distribution strategies.

The societal economic value of COVID-19 vaccines in the United States

AIMS

The COVID-19 pandemic has claimed the lives of more than 800,000 people in the United States (US) and has been estimated to carry a societal cost of $16 trillion over the next decade. The availability of COVID-19 vaccines has had a profound effect on the trajectory of the pandemic, with wide-ranging benefits. We aimed to estimate the total societal economic value generated in the US from COVID-19 vaccines.

METHODS

We developed a population-based economic model informed by existing data and literature to estimate the total societal value generated from COVID-19 vaccines by avoiding COVID-19 infections as well as resuming social and economic activity more quickly. To do this, we separately estimated the value generated from life years saved, healthcare costs avoided, quality of life gained, and US gross domestic product (GDP) gained under a range of plausible assumptions.

RESULTS

Findings from our base case analysis suggest that from their launch in December 2020, COVID-19 vaccines were projected to generate $5.0 trillion in societal economic value for the US from avoided COVID-19 infections and resuming unrestricted social and economic activity more quickly. Our scenario analyses suggest that the value could range between $1.8 and $9.9 trillion. Our model indicates that the most substantial sources of value are derived from reduction in prevalence of depression ($1.9 trillion), gains to US GDP ($1.4 trillion), and lives saved from fewer COVID-19 infections ($1.0 trillion).

LIMITATIONS

Constructed as a projection from December 2020, our model does not account for the Delta or future variants, nor does it account for improvements in COVID-19 treatment.

CONCLUSIONS

The magnitude of economic benefit from vaccination highlights the need for coordinated policy decisions to support continued widespread vaccine uptake in the US.

COVID-19 mortality rate and its associated factors during the first and second waves in Nigeria

COVID-19 mortality rate has not been formally assessed in Nigeria. Thus, we aimed to address this gap and identify associated mortality risk factors during the first and second waves in Nigeria. This was a retrospective analysis of national surveillance data from all 37 States in Nigeria between February 27, 2020, and April 3, 2021. The outcome variable was mortality amongst persons who tested positive for SARS-CoV-2 by Reverse-Transcriptase Polymerase Chain Reaction. Incidence rates of COVID-19 mortality was calculated by dividing the number of deaths by total person-time (in days) contributed by the entire study population and presented per 100,000 person-days with 95% Confidence Intervals (95% CI). Adjusted negative binomial regression was used to identify factors associated with COVID-19 mortality. Findings are presented as adjusted Incidence Rate Ratios (aIRR) with 95% CI. The first wave included 65,790 COVID-19 patients, of whom 994 (1∙51%) died; the second wave included 91,089 patients, of whom 513 (0∙56%) died. The incidence rate of COVID-19 mortality was higher in the first wave [54∙25 (95% CI: 50∙98-57∙73)] than in the second wave [19∙19 (17∙60-20∙93)]. Factors independently associated with increased risk of COVID-19 mortality in both waves were: age ≥45 years, male gender [first wave aIRR 1∙65 (1∙35-2∙02) and second wave 1∙52 (1∙11-2∙06)], being symptomatic [aIRR 3∙17 (2∙59-3∙89) and 3∙04 (2∙20-4∙21)], and being hospitalised [aIRR 4∙19 (3∙26-5∙39) and 7∙84 (4∙90-12∙54)]. Relative to South-West, residency in the South-South and North-West was associated with an increased risk of COVID-19 mortality in both waves. In conclusion, the rate of COVID-19 mortality in Nigeria was higher in the first wave than in the second wave, suggesting an improvement in public health response and clinical care in the second wave. However, this needs to be interpreted with caution given the inherent limitations of the country's surveillance system during the study.

Resource allocation for different types of vaccines against COVID-19: Tradeoffs and synergies between efficacy and reach

Objective

Vaccine shortage and supply-chain challenges have caused limited access by many resource-limited countries during the COVID-19 pandemic. One of the primary decisions for a vaccine-ordering decision-maker is how to allocate the limited resources between different types of vaccines effectively. We studied the tradeoff between efficacy and reach of the two vaccine types that become available at different times.

Methods

We extended a Susceptible-Infected-Recovered-Deceased (SIR-D) model with vaccination, ran extensive simulations with different settings, and compared the level of infection attack rate (IAR) under different reach ratios between two vaccine types under different resource allocation decisions.

Results

We found that when there were limited resources, allocating resources to a vaccine with high efficacy that became available earlier than a vaccine with lower efficacy did not always lead to a lower IAR, particularly if the former could vaccinate less than 42.5% of the population (with the selected study parameters) who could have received the latter. Sensitivity analyses showed that this result stayed robust under different study parameters.

Conclusions

Our results showed that a vaccine with lower resource requirements (wider reach) can significantly contribute to reducing IAR, even if it becomes available later in the pandemic, compared to a higher efficacy vaccine that becomes available earlier but requires more resources. Limited resource in vaccine distribution is significant challenge in many parts of the world that needs to be addressed to improve the global access to life-saving vaccines. Understanding the tradeoffs between efficacy and reach is critical for resource allocation decisions between different vaccine types for improving health outcomes.

Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

The first year of COVID-19 in Italy: incidence, lethality, and health policies

BACKGROUND

The novel coronavirus disease is an ongoing pandemic that started in China in December 2019. This paper is aimed at estimating the first two infections waves in Italy in relation to adopted health policies.

DESIGN AND METHODS

We moved deaths of the Italian COVID-19 registry from recorded to infection date by the weighted moving average. We considered two infection fatality ratios related to the effective or saturated health system, we estimated the likely incidence curve from the resulting deaths and evaluated the curve shape before and after the national health policies.

RESULTS

From the 24th of February 2020 to the 7th of February 2021, we estimated 6,664,655 (4,639,221-9,325,138) cases distributed on two waves. Suitable daily infection fatality rates were 2.53% within the first wave and 1.15% within the second one. The first wave (February-July 2020) had its peak on the 14th of March 2020 (26,575). The second wave (August 2020-February 2021) was fatter with the peak on the 12th of November (60,425) and a hump in December before decreasing to 26,288 at the end. Adopted health policies were followed by changes in the curve rate.

CONCLUSION

Tracing infection contacts and quarantining asymptomatic people reduced virus lethality in the second wave.  Restriction on population mobility is effective within a suppression strategy, distance learning reduces contacts among families. Removal of restrictions should be implemented by sequential steps for avoiding a quick rising of incident cases. A reasonable public health daily goal to control both virus spread and lethality could be to find at least 87 cases for each death.

Significance of SARS-CoV-2 specific antibody testing during COVID-19 vaccine allocation

OBJECTIVE

To assess the value of using SARS-CoV-2 specific antibody testing to prioritize the vaccination of susceptible individuals as part of a COVID-19 vaccine distribution plan when vaccine supply is limited.

METHODS

An extended susceptible-infected-recovered (SIR) compartmental model was used to simulate COVID-19 spread when considering diagnosis, isolation, and vaccination of a cohort of 1 million individuals. The scenarios modeled represented 4 pandemic severity scenarios and various times when the vaccine becomes available during the pandemic. Eligible individuals have a probability p of receiving antibody testing prior to vaccination (p = 0, 0.25, 0.5, 0.75, and 1). The vaccine was modeled as a single dose vaccine with 90% and 70% efficacy. The value of serology testing was evaluated by comparing the infection attack rate, peak infections, peak day, and deaths.

RESULTS

The use of antibody testing to prioritize the allocation of limited vaccines reduces infection attack rates and deaths. The size of the reduction depends on when the vaccine becomes available relative to the infection peak day. The largest percentage reduction in cases and deaths occurs when the vaccine is deployed before and close to the infection peak day. The reduction in the number of cases and deaths diminishes as vaccine deployment is delayed.

CONCLUSIONS

Antibody testing as part of the vaccination plan is an effective method to maximize the benefit of a COVID-19 vaccine. Decision-makers need to consider relative timing between the infection peak day and when the vaccine becomes available.

A simple but complex enough θ -SIR type model to be used with COVID-19 real data. Application to the case of Italy

Since the start of the COVID-19 pandemic in China many models have appeared in the literature, trying to simulate its dynamics. Focusing on modeling the biological and sociological mechanisms which influence the disease spread, the basic reference example is the SIR model. However, it is too simple to be able to model those mechanisms (including the three main types of control measures: social distancing, contact tracing and health system measures) to fit real data and to simulate possible future scenarios. A question, then, arises: how much and how do we need to complexify a SIR model? We develop a θ -SEIHQRD model, which may be the simplest one satisfying the mentioned requirements for arbitrary territories and can be simplified in particular cases. We show its very good performance in the Italian case and study different future scenarios.

Reduction in Emergency Department Presentations in a Regional Health System during the Covid-19 Pandemic

INTRODUCTION

Nationally, there has been more than a 40% decrease in Emergency Department (ED) patient volume during the coronavirus disease 2019 (Covid-19) crisis, with reports of decreases in presentations of time-sensitive acute illnesses. We analyzed ED clinical presentations in a Maryland/District of Columbia regional hospital system while health mitigation measures were instituted.

METHODS

We conducted a retrospective observational cohort study of all adult ED patients presenting to five Johns Hopkins Health System (JHHS) hospitals comparing visits from March 16 through May 15, in 2019 and 2020. We analyzed de-identified demographic information, clinical conditions, and ICD-10 diagnosis codes for year-over-year comparisons.

RESULTS

There were 36.7% fewer JHHS ED visits in 2020 compared to 2019 (43,088 vs. 27,293, P<.001). Patients 75+ had the greatest decline in visits (-44.00%, P<.001). Both genders had significant decreases in volume (-41.9%, P<.001 females vs -30.6%, P<.001 males). Influenza like illness (ILI) symptoms increased year-over-year including fever (640 to 1253, 95.8%, P<.001) and shortness of breath (2504 to 2726, 8.9%, P=.002). ICD-10 diagnoses for a number of time-sensitive illnesses decreased including deep vein thrombosis (101 to 39, -61%, P<.001), acute myocardial infarction (157 to 105, -33%, P=.002), gastrointestinal bleeding (290 to 179, -38.3%, P<.001), and strokes (284 to 234, -17.6%, P=0.03).

CONCLUSION

ED visits declined significantly among JHHS hospitals despite offsetting increases in ILI complaints. Decreases in presentations of time-sensitive illnesses were of particular concern. Efforts should be taken to inform patients that EDs are safe, otherwise preventable morbidity and mortality will remain a problem.

Toward a Country-Based Prediction Model of COVID-19 Infections and Deaths Between Disease Apex and End: Evidence From Countries With Contained Numbers of COVID-19

The complexity of COVID-19 and variations in control measures and containment efforts in different countries have caused difficulties in the prediction and modeling of the COVID-19 pandemic. We attempted to predict the scale of the latter half of the pandemic based on real data using the ratio between the early and latter halves from countries where the pandemic is largely over. We collected daily pandemic data from China, South Korea, and Switzerland and subtracted the ratio of pandemic days before and after the disease apex day of COVID-19. We obtained the ratio of pandemic data and created multiple regression models for the relationship between before and after the apex day. We then tested our models using data from the first wave of the disease from 14 countries in Europe and the US. We then tested the models using data from these countries from the entire pandemic up to March 30, 2021. Results indicate that the actual number of cases from these countries during the first wave mostly fall in the predicted ranges of liniar regression, excepting Spain and Russia. Similarly, the actual deaths in these countries mostly fall into the range of predicted data. Using the accumulated data up to the day of apex and total accumulated data up to March 30, 2021, the data of case numbers in these countries are falling into the range of predicted data, except for data from Brazil. The actual number of deaths in all the countries are at or below the predicted data. In conclusion, a linear regression model built with real data from countries or regions from early pandemics can predict pandemic scales of the countries where the pandemics occur late. Such a prediction with a high degree of accuracy provides valuable information for governments and the public.

Perceived risk of infection while traveling during the COVID-19 pandemic: Insights from Columbus, OH

The COVID-19 outbreak caused major disruptions on individuals' out-of-home activities. Worldwide mandates to slow down the spread of the disease resulted in significant reductions in travel. This study analyzes the changes in individuals' travel outcomes and their risk perceptions related to exposure and specific travel modes during the COVID-19 pandemic. We use data collected through an online survey with residents of Columbus, OH from April 30 to May 7, 2020. Employing multiple generalized estimating equations (GEEs) with a logit link function, we analyze the perceived risk of infection while traveling with different modes controlling for socio-demographics. The findings show that on average individuals are more likely to find shared modes (i.e., transit, ride-hailing, carsharing) riskier as compared to individual ones (i.e., walking, autos) when it comes to COVID-19 exposure. This study also suggests that the associations between perceptions related to exposure and various travel modes vary across groups with (1) different primary mode preferences (auto users vs non-auto users (e.g., transit users, bicyclists, etc.)), and (2) different socio-demographics. For example, auto users are more likely to find shared modes such as ride-hailing or transit riskier as compared to personal car. The conclusions present recommendations for future transportation policies in the post-COVID era. These include building upon the emerging positive perceptions towards non-motorized modes as an opportunity to promote sustainable transportation as well as formulating viable solutions to address the high-risk perceptions associated with transit.

Human security as biosecurity Reconceptualizing national security threats in the time of COVID-19

Research within security studies has struggled to determine whether infectious disease (ID) represents an existential threat to national and international security. With the emergence of SARS-CoV-2 (COVID-19), it is imperative to reexamine the relationship between ID and global security. This article addresses the specific threat to security from COVID-19, asking, "Is COVID-19 a threat to national and international security?" To investigate this question, this article uses two theoretical approaches: human security and biosecurity. It argues that COVID-19 is a threat to global security by the ontological crisis posed to individuals through human security theory and through high politics, as evidenced by biosecurity. By viewing security threats through the lens of the individual and the state, it becomes clear that ID should be considered an international security threat. This article examines the relevant literature and applies the theoretical framework to a case study analysis focused on the United States.

Comparison of Clinical Features and Outcomes in Critically Ill Patients Hospitalized with COVID-19 versus Influenza

Rationale: No direct comparisons of clinical features, laboratory values, and outcomes between critically ill patients with coronavirus disease (COVID-19) and patients with influenza in the United States have been reported.

Objectives: To evaluate the risk of mortality comparing critically ill patients with COVID-19 with patients with seasonal influenza.

Methods: We retrospectively identified patients admitted to the intensive care units (ICUs) at two academic medical centers with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or influenza A or B infections between January 1, 2019, and April 15, 2020. The clinical data were obtained by medical record review. All patients except one had follow-up to hospital discharge or death. We used relative risk regression adjusting for age, sex, number of comorbidities, and maximum sequential organ failure scores on Day 1 in the ICU to determine the risk of hospital mortality and organ dysfunction in patients with COVID-19 compared with patients with influenza.

Results: We identified 65 critically ill patients with COVID-19 and 74 patients with influenza. The mean (±standard deviation) age in each group was 60.4 ± 15.7 and 56.8 ± 17.6 years, respectively. Patients with COVID-19 were more likely to be male, have a higher body mass index, and have higher rates of chronic kidney disease and diabetes. Of the patients with COVID-19, 37% identified as Hispanic, whereas 10% of the patients with influenza identified as Hispanic. A similar proportion of patients had fevers (∼40%) and lymphopenia (∼80%) on hospital presentation. The rates of acute kidney injury and shock requiring vasopressors were similar between the groups. Although the need for invasive mechanical ventilation was also similar in both groups, patients with COVID-19 had slower improvements in oxygenation, longer durations of mechanical ventilation, and lower rates of extubation than patients with influenza. The hospital mortality was 40% in patients with COVID-19 and 19% in patients with influenza (adjusted relative risk, 2.13; 95% confidence interval, 1.24–3.63; P = 0.006).

Conclusions: The need for invasive mechanical ventilation was common in patients in the ICU for COVID-19 and influenza. Compared with those with influenza, patients in the ICU with COVID-19 had worse respiratory outcomes, including longer duration of mechanical ventilation. In addition, patients with COVID-19 were at greater risk for in-hospital mortality, independent of age, sex, comorbidities, and ICU severity of illness.

Exploring the Effect of Collective Cultural Attributes on Covid-19-Related Public Health Outcomes

Infections and deaths associated with COVID-19 show a high degree of heterogeneity across different populations. A thorough understanding of population-level predictors of such outcomes is crucial for devising better-targeted and more appropriate public health preparedness measures. While demographic, economic, and health-system capacity have featured prominently in recent work, cultural, and behavioral characteristics have largely been overlooked. However, cultural differences shape both the public policy response and individuals' behavioral responses to the crisis in ways that can impact infection dynamics and key health outcomes. To address this gap, we used meta-analytic methods to explore the global variability of three public health outcomes (i.e., crude test positivity, case/infection fatality, and mortality risk) during the first wave of the pandemic. This set of analyses identified several cultural/behavioral attributes (e.g., uncertainty avoidance and long-term vs. short-term normative orientation) as independent predictors of public health outcomes after adjusting for key demographic, political, economic, and health-system-related predictors; which were robust in sensitivity analyses. In conclusion, this study clearly demonstrates that cultural attributes do in fact account for some of the global disparities in COVID-19-attributed health outcomes. As a consequence, policymakers should more explicitly consider a society's cultural attributes alongside other important parameters such as demographic characteristics and health system constraints in order to develop better tailored and more effective policy responses.

COVID-19 Pandemic Response Simulation in a Large City: Impact of Nonpharmaceutical Interventions on Reopening Society

As the novel coronavirus (COVID-19) pandemic continues to expand, policymakers are striving to balance the combinations of nonpharmaceutical interventions (NPIs) to keep people safe and minimize social disruptions. We developed and calibrated an agent-based simulation to model COVID-19 outbreaks in the greater Seattle area. The model simulated NPIs, including social distancing, face mask use, school closure, testing, and contact tracing with variable compliance and effectiveness to identify optimal NPI combinations that can control the spread of the virus in a large urban area. Results highlight the importance of at least 75% face mask use to relax social distancing and school closure measures while keeping infections low. It is important to relax NPIs cautiously during vaccine rollout in 2021.

NIH funding for vaccine readiness before the COVID-19 pandemic

Rapid development of vaccines for COVID-19 has relied on the application of existing vaccine technologies. This work examines the maturity of ten technologies employed in candidate vaccines (as of July 2020) and NIH funding for published research on these technologies from 2000–2019. These technologies vary from established platforms, which have been used successfully in approved products, to emerging technologies with no prior clinical validation. A robust body of published research on vaccine technologies was supported by 16,358 fiscal years of NIH funding totaling $17.2 billion from 2000–2019. During this period, NIH funding for published vaccine research against specific pandemic threats such as coronavirus, Zika, Ebola, and dengue was not sustained. NIH funding contributed substantially to the advance of technologies available for rapid development of COVID-19 vaccines, suggesting the importance of sustained public sector funding for foundational technologies in the rapid response to emerging public health threats.

Predictors of disease severity and outcome of hospitalized renal transplant recipients with COVID-19 infection: a systematic review of a globally representative sample

Introduction. COVID-19 presents a special challenge to the kidney transplant population.Methods. A systematic review of articles that examined COVID-19 in kidney transplant recipients was performed. Patients' demographics, clinical, laboratory and radiological presentations, immunosuppression modification, and COVID-19 specific management were abstracted and analyzed. COVID-19 severity was classified into mild, moderate, and severe. Disease outcome was classified by whether the patient was discharged, still hospitalized, or died.Results. 44 articles reporting individual data and 13 articles reporting aggregated data on 149 and 561 kidney transplant recipients respectively with COVID-19 from Asia, Europe and America fulfilled all inclusion and exclusion criteria. Among studies reporting case specific data, 76% of cases had severe disease. Compared to patients with mild/moderate disease, patients with severe disease had higher CRP, LDH, Ferritin, D-dimer and were more likely to have bilateral lung involvement at presentation and longer time since transplantation (P < 0.05 for all). Recipients' age, gender and comorbidities did not impact disease severity. Patients with severe disease had a more aggressive CNI reduction and more antiviral medications utilization. Outcome was reported on 145 cases, of those 34 (23%) died all with severe disease. Longer duration from transplant to disease diagnosis, hypoxia and higher LDH were associated with mortality (P < 0.05). Different immunosuppression reduction strategies, high dose parenteral corticosteroids use and various antiviral combinations did not demonstrate survival advantage. Similar finding was observed for studies reporting aggregated data.Conclusion. COVID-19 in kidney transplant patients is associated with high rate of disease severity and fatality. Higher LDH and longer time since transplantation predicted both disease severity and mortality. None of the COVID-19 specific treatment correlated with, or improved disease outcome in kidney transplant recipients.

Mortality containment vs. Economics Opening: Optimal policies in a SEIARD model

We extend the classic approach (SIR) to a SEAIRD model with policy controls. A social planner's objective reflects the trade-off between mortality reduction and GDP, featuring its perception of the value of statistical life (PVSL). We introduce realistic and drastic limitations to the control available to it. Within this setup, we explore the results of various control policies. We notably describe the joint dynamics of infection and economy in different contexts with unique or multiple confinement episodes. Compared to other approaches, our contributions are: (i) to restrict the class of functions accessible to the social planner, and in particular to impose that they remain constant over some fixed periods; (ii) to impose implementation frictions, e.g. a lag in their implementation; (iii) to prove the existence of optimal strategies within this set of possible controls; iv) to exhibit a sudden change in optimal policy as the statistical value of life is raised, from laissez-faire to a sizeable lockdown level, indicating a possible reason for conflicting policy proposals.

Risk factors for increased COVID-19 case-fatality in the United States: A county-level analysis during the first wave

The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.

Return to Work: Managing Employee Population Health During the COVID-19 Pandemic

Coronavirus disease-2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has abruptly transformed the outlook of employer health benefits plans for 2020 and 2021. Containing the spread of the virus and facilitating care of those infected have quickly emerged as immediate priorities. Employers have adjusted health benefits coverage to make COVID-19 testing and treatment accessible and remove barriers to care in order to facilitate the containment of the disease. Employers also are introducing strategies focused on testing, surveillance, workplace modifications, and hygiene to keep workforces healthy and workplaces safe. This paper is intended to provide evidence-based perspectives for self-insured employers for managing population health during the COVID-19 pandemic. Such considerations include (1) return to work practices focused on mitigating the spread of COVID-19 through safety practices, testing and surveillance; and (2) anticipating the impact of COVID-19 on health benefits and costs (including adaptations in delivery of care, social and behavioral health needs, and managing interrupted care for chronic conditions).

Simpler and faster Covid-19 testing: Strategies to streamline SARS-CoV-2 molecular assays

BACKGROUND

Detection of SARS-CoV-2 infections is important for treatment, isolation of infected and exposed individuals, and contact tracing. RT-qPCR is the "gold-standard" method to sensitively detect SARS-CoV-2 RNA, but most laboratory-developed RT-qPCR assays involve complex steps. Here, we aimed to simplify RT-qPCR assays by streamlining reaction setup, eliminating RNA extraction, and proposing reduced-cost detection workflows that avoid the need for expensive qPCR instruments.

METHOD

A low-cost RT-PCR based "kit" was developed for faster turnaround than the CDC developed protocol. We demonstrated three detection workflows: two that can be deployed in laboratories conducting assays of variable complexity, and one that could be simple enough for point-of-care. Analytical sensitivity was assessed using SARS-CoV-2 RNA spiked in simulated nasal matrix. Clinical performance was evaluated using contrived human nasal matrix (n = 41) and clinical nasal specimens collected from individuals with respiratory symptoms (n = 110).

FINDING

The analytical sensitivity of the lyophilised RT-PCR was 10 copies/reaction using purified SARS-CoV-2 RNA, and 20 copies/reaction when using direct lysate in simulated nasal matrix. Evaluation of assay performance on contrived human matrix showed 96.7-100% specificity and 100% sensitivity at ≥20 RNA copies. A head-to-head comparison with the standard CDC protocol on clinical specimens showed 83.8-94.6% sensitivity and 96.8-100% specificity. We found 3.6% indeterminate samples (undetected human control), lower than 8.1% with the standard protocol.

INTERPRETATION

This preliminary work should support laboratories or commercial entities to develop and expand access to Covid-19 testing. Software guidance development for this assay is ongoing to enable implementation in other settings. FUND: USA NIH R01AI140845 and Seattle Children's Research Institute.

Queiroz B. An indirect method to monitor the fraction of people ever infected with COVID-19: An application to the United States

The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39-2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21-1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak.

COVID-19 case fatality rates across Southeast Asian countries (SEA): a preliminary estimate using a simple linear regression model

Purpose

The purpose of this study was to determine COVID-19 preliminary case fatality rates (CFR) across Southeast Asian (SEA) countries.

Design/methodology/approach

The study accessed the data on COVID-19 accumulated cases of fatalities and infections across SEA countries from the World Health Organization (WHO) website, covering the early days of March to May 21, 2020. The approach involved the computation of the CFR using the simple linear regression model. The slope of the regression line was the estimate of the CFR at a 95% confidence interval. The study also reviewed the different approaches of the SEA countries in dealing with the pandemic.

Findings

As of May 21, 2020, Singapore, Indonesia and the Philippines were the top three SEA countries with the highest record of COVID-19 infections. Brunei had one fatality, while Cambodia, Laos, Timor-Leste and Viet Nam had nil fatalities. Indonesia and the Philippines had the highest CFR with 6.66 and 6.59%, with R2 of 97.95 and 99.43%, respectively. Singapore had the lowest CFR (0.068%) despite high infections.

Originality/value

Increased CFR in Indonesia and the Philippines suggests that COVID-19 in the two countries is rising at an alarming rate. Strict implementation of shared management approaches to control the pandemic is seen to be closely associated with the decrease of CFR.

Behavioral dynamics of COVID-19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations

Effective responses to the COVID-19 pandemic require integrating behavioral factors such as risk-driven contact reduction, improved treatment, and adherence fatigue with asymptomatic transmission, disease acuity, and hospital capacity. We build one such model and estimate it for all 92 nations with reliable testing data. Cumulative cases and deaths through 22 December 2020 are estimated to be 7.03 and 1.44 times official reports, yielding an infection fatality rate (IFR) of 0.51 percent, which has been declining over time. Absent adherence fatigue, cumulative cases would have been 47 percent lower. Scenarios through June 2021 show that modest improvement in responsiveness could reduce cases and deaths by about 14 percent, more than the impact of vaccinating half of the population by that date. Variations in responsiveness to risk explain two orders of magnitude difference in per-capita deaths despite reproduction numbers fluctuating around one across nations. A public online simulator facilitates scenario analysis over the coming months. © 2021 System Dynamics Society.

Behavioral dynamics of COVID-19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations

Effective responses to the COVID-19 pandemic require integrating behavioral factors such as risk-driven contact reduction, improved treatment, and adherence fatigue with asymptomatic transmission, disease acuity, and hospital capacity. We build one such model and estimate it for all 92 nations with reliable testing data. Cumulative cases and deaths through 22 December 2020 are estimated to be 7.03 and 1.44 times official reports, yielding an infection fatality rate (IFR) of 0.51 percent, which has been declining over time. Absent adherence fatigue, cumulative cases would have been 47 percent lower. Scenarios through June 2021 show that modest improvement in responsiveness could reduce cases and deaths by about 14 percent, more than the impact of vaccinating half of the population by that date. Variations in responsiveness to risk explain two orders of magnitude difference in per-capita deaths despite reproduction numbers fluctuating around one across nations. A public online simulator facilitates scenario analysis over the coming months. © 2021 System Dynamics Society.

Estimating the cumulative rate of SARS-CoV-2 infection

Accurate estimates of the cumulative incidence of SARS-CoV-2 infection remain elusive. Among the reasons for this are that tests for the virus are not randomly administered, and that the most commonly used tests can yield a substantial fraction of false negatives. In this article, we propose a simple and easy-to-use Bayesian model to estimate the infection rate, which is only partially identified. The model is based on the mapping from the fraction of positive test results to the cumulative infection rate, which depends on two unknown quantities: the probability of a false negative test result and a measure of testing bias towards the infected population. Accumulating evidence about SARS-CoV-2 can be incorporated into the model, which will lead to more precise inference about the infection rate.

A systematic review and meta-analysis of published research data on COVID-19 infection fatality rates

An important unknown during the coronavirus disease-2019 (COVID-19) pandemic has been the infection fatality rate (IFR). This differs from the case fatality rate (CFR) as an estimate of the number of deaths and as a proportion of the total number of cases, including those who are mild and asymptomatic. While the CFR is extremely valuable for experts, IFR is increasingly being called for by policy makers and the lay public as an estimate of the overall mortality from COVID-19.

METHODS

Pubmed, Medline, SSRN, and Medrxiv were searched using a set of terms and Boolean operators on 25/04/2020 and re-searched on 14/05/2020, 21/05/2020 and 16/06/2020. Articles were screened for inclusion by both authors. Meta-analysis was performed in Stata 15.1 by using the metan command, based on IFR and confidence intervals extracted from each study. Google/Google Scholar was used to assess the grey literature relating to government reports.

RESULTS

After exclusions, there were 24 estimates of IFR included in the final meta-analysis, from a wide range of countries, published between February and June 2020. The meta-analysis demonstrated a point estimate of IFR of 0.68% (0.53%-0.82%) with high heterogeneity (p < 0.001).

CONCLUSION

Based on a systematic review and meta-analysis of published evidence on COVID-19 until July 2020, the IFR of the disease across populations is 0.68% (0.53%-0.82%). However, due to very high heterogeneity in the meta-analysis, it is difficult to know if this represents a completely unbiased point estimate. It is likely that, due to age and perhaps underlying comorbidities in the population, different places will experience different IFRs due to the disease. Given issues with mortality recording, it is also likely that this represents an underestimate of the true IFR figure. More research looking at age-stratified IFR is urgently needed to inform policymaking on this front.

COVID-19 pandemic in the United States

OBJECTIVES

The paper highlights US health policy and technology responses to the COVID-19 pandemic from January 1, 2020 - August 9, 2020.

METHODS

A review of primary data sources in the US was conducted. The data were summarized to describe national and state-level trends in the spread of COVID-19 and in policy and technology solutions.

RESULTS

COVID-19 cases and deaths initially peaked in late March and April, but after a brief reduction in June cases and deaths began rising again during July and continued to climb into early August. The US policy response is best characterized by its federalist, decentralized nature. The national government has led in terms of economic and fiscal response, increasing funding for scientific research into testing, treatment, and vaccines, and in creating more favorable regulations for the use of telemedicine. State governments have been responsible for many of the containment, testing, and treatment responses, often with little federal government support. Policies that favor economic re-opening are often followed by increases in state-level case numbers, which are then followed by stricter containment measures, such as mask wearing or pausing re-opening plans.

CONCLUSIONS

While all US states have begun to "re-open" economic activities, this trend appears to be largely driven by social tensions and economic motivations rather than an ability to effectively test and surveil populations.

Immunological Evaluation on Potential Treatment Window for Hospitalized COVID-19 Patients

Purpose

Novel coronavirus disease has become such an escalating epidemic that the exponential growth of infected patients has overloaded the health-care systems in many countries. Determination of early assessments for patients with a risk of clinical deterioration would benefit the management of COVID-19 outbreaks.

Patients and Methods

A total of 214 confirmed COVID-19 patients were enrolled from January 11th to February 11th 2020. Medical records including laboratory parameters, clinical outcomes and other characteristics of the admitted patients were analyzed retrospectively.

Results

The critical patients experienced a significantly prolonged onset–admission interval and presented with lymphopenia (r=−0.547, p=0.015) and lower albumin level (p<0.001) 6 days after symptom onset. Early admission of critical patients significantly reduced the duration of hormone therapy. Starting from 9 days of hospital stay, the reduced lymphocyte counts exhibited linear growth. Furthermore, on days 9 and 12, significant correlations were demonstrated between immunological manifestations and duration of hormone therapy in critical patients, and length of hospital stay in severe patients. In addition, the virus negative conversion rate was more significantly correlated with increased lymphocytes in critical patients.

Conclusion

Early intervention, within 6 days of symptom onset, benefited patients’ recovery from critical illness. The 9–12 days of hospital care represented a valuable window during which to evaluate the therapeutic effects on physical recovery and virus clearance.

Acceleration of plague outbreaks in the second pandemic

Historical records reveal the temporal patterns of a sequence of plague epidemics in London, United Kingdom, from the 14th to 17th centuries. Analysis of these records shows that later epidemics spread significantly faster ("accelerated"). Between the Black Death of 1348 and the later epidemics that culminated with the Great Plague of 1665, we estimate that the epidemic growth rate increased fourfold. Currently available data do not provide enough information to infer the mode of plague transmission in any given epidemic; nevertheless, order-of-magnitude estimates of epidemic parameters suggest that the observed slow growth rates in the 14th century are inconsistent with direct (pneumonic) transmission. We discuss the potential roles of demographic and ecological factors, such as climate change or human or rat population density, in driving the observed acceleration.

SARS-CoV-2 infection fatality rate among elderly retired Danish blood donors - A cross-sectional study

BACKGROUND

Despite the vast majority of individuals succumbing to infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are elderly, infection fatality rate (IFR) estimates for the age group 70 years older are still scarce. To this end we assessed SARS-CoV-2 seroprevalence among retired blood donors and combined it with national COVID-19 survey data to provide reliable population-based IFR estimates for this age group.

METHODS

We identified 60,926 retired blood donors age 70 years or older in the rosters of three region-wide Danish blood banks and invited them to fill in a questionnaire on COVID-19 related symptoms and behaviours. Among 24,861 (40.8%) responders, we invited a random sample of 3,200 individuals for blood testing. Overall, 1,201 (37.5%) individuals were tested for SARS-CoV-2 antibodies (Wantai) and compared to 1,110 active blood donors age 17-69 years. Seroprevalence 95% confidence intervals (CI) were adjusted for assay sensitivity and specificity.

RESULTS

Among retired (age 70 years or older) and active (age 17-69 years) blood donors, adjusted seroprevalences were 1.4% (95% CI: 0.3%-2.5%) and 2.5% (95% CI: 1.3%-3.8%), respectively. Using available population data on COVID-19 related fatalities, IFRs for patients age 70 years or older and for 17-69 years were estimated at 5.4% (95% CI: 2.7%-6.4%) and 0.083% (95% CI: 0.054%-0.18%), respectively. Only 52.4% of SARS-CoV-2 seropositive retired blood donors reported having been sick since the start of the pandemic.

CONCLUSION

COVID-19 IFR in the age group above 69 years is estimated to be 65 times as high as the IFR for people age 18-69 years.

Harnessing Recent Advances in Synthetic DNA and Electroporation Technologies for Rapid Vaccine Development Against COVID-19 and Other Emerging Infectious Diseases

DNA vaccines are considered as a third-generation vaccination approach in which antigenic materials are encoded as DNA plasmids for direct in vivo production to elicit adaptive immunity. As compared to other platforms, DNA vaccination is considered to have a strong safety profile, as DNA plasmids neither replicate nor elicit vector-directed immune responses in hosts. While earlier work found the immune responses induced by DNA vaccines to be sub-optimal in larger mammals and humans, recent developments in key synthetic DNA and electroporation delivery technologies have now allowed DNA vaccines to elicit significantly more potent and consistent responses in several clinical studies. This paper will review findings from the recent clinical and preclinical studies on DNA vaccines targeting emerging infectious diseases (EID) including COVID-19 caused by the SARS-CoV-2 virus, and the technological advancements pivotal to the improved responses-including the use of the advanced delivery technology, DNA-encoded cytokine/mucosal adjuvants, and innovative concepts in immunogen design. With continuous advancement over the past three decades, the DNA approach is now poised to develop vaccines against COVID-19, as well as other EIDs.

SARS-CoV-2 Vaccine Development: An Overview and Perspectives

Coronavirus disease 2019, abbreviated as COVID-19, is caused by a new strain of coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It started in late December 2019 in Wuhan, China, and by mid-March 2020, the disease had spread globally. As of July 17, 2020, this pandemic virus has infected 13.9 million people and claimed the life of approximately 593 000 people globally, and the numbers continue to climb. An unprecedented effort is underway to develop therapeutic and prophylactic strategies against this disease. Various drugs and vaccines are undergoing rapid development, and some of these are already in phase III clinical trials. Although Russia was the first to release a vaccine by skipping phase III clinical trials, there is no evidence of large-scale clinical trials, and the safety and efficacy of the vaccine are still a concern. Nevertheless, critical lessons can be learned and data garnered for developing promising vaccines against this rapidly emerging virus or other similar pathogens in the future. In this overview, we cover the available information on the various vaccine development initiatives by different companies, the potential strategies adopted for vaccine design, and the challenges and clinical impact expected from these vaccines. We also briefly discuss the possible role of these vaccines and the specific concerns for their use in patients with pre-existing disease conditions such as cardiovascular, lung, kidney, and liver diseases, cancer patients who are receiving immunosuppressive medications, including anticancer chemotherapies, and many other sensitive populations, such as children and the elderly.

A predictive model for Covid-19 spread - with application to eight US states and how to end the pandemic

A compartmental model is proposed to predict the coronavirus 2019 (Covid-19) spread. It considers: detected and undetected infected populations, social sequestration, release from sequestration, plus reinfection. This model, consisting of seven coupled equations, has eight coefficients which are evaluated by fitting data for eight US states that make up 43% of the US population. The evolution of Covid-19 is fairly similar among the states: variations in contact and undetected recovery rates remain below 5%; however, variations are larger in recovery rate, death rate, reinfection rate, sequestration adherence and release rate from sequestration. Projections based on the current situation indicate that Covid-19 will become endemic. If lockdowns had been kept in place, the number of deaths would most likely have been significantly lower in states that opened up. Additionally, we predict that decreasing contact rate by 10%, or increasing testing by approximately 15%, or doubling lockdown compliance (from the current ~15% to ~30%) will eradicate infections in Texas within a year. Extending our fits for all of the US states, we predict about 11 million total infections (including undetected), and 8 million cumulative confirmed cases by 1 November 2020.

Association of State Stay-at-Home Orders and State-Level African American Population With COVID-19 Case Rates

Importance

To cope with the continuing coronavirus disease 2019 (COVID-19) pandemic, state and local officials need information on the effectiveness of policies aimed at curbing disease spread, as well as state-specific characteristics, like the racial mix, associated with increased risks related to the disease.

Objective

To investigate whether state-imposed stay-at-home orders (SAHOs) and the proportion of African American population in a state were associated with the state-level COVID-19 cases.

Design, Setting, and Participants

This cross-sectional study used daily, state-level data on COVID-19 cases, tests, and fatalities from the COVID Tracking Project. Data from March 1 to May 4, 2020, for all states (except Washington state) as well as the District of Columbia were used.

Exposures

The key exposure variables were state-level SAHO (1 if in place, 0 otherwise), and proportion of state population who are African American.

Main Outcomes and Measures

The primary outcome was daily cumulative COVID-19 case rates. A secondary outcome was subsequent COVID-19 fatality rates, derived using mean cumulative fatality rates 21 to 28 days after each date. Multivariate regression models were estimated.

Results

The final sample included 3023 pooled state- and day-level observations. The mean (SD) cumulative positive case rate was 103.186 (200.067) cases per 100 000 state population, the mean (SD) cumulative test rate was 744.23 (894.944) tests per 100 000 state population, and the mean (SD) subsequent cumulative fatality rate was 12.923 (21.737) deaths per 100 000 state population. There was a negative association of SAHOs with cumulative case rates (β = -1.166; 95% CI, -1.484 to -0.847; P < .001) and subsequent fatality rates (β = -0.204; 95% CI, -0.294 to -0.113; P < .001). Estimation analyses indicated that expected cumulative case rates would have been more than 200% higher and fatality rates approximately 22% higher if there were no SAHOs, as compared with SAHOs fully in place. A higher proportion of African American population was associated with higher case rates (β = 0.045; 95% CI, 0.014 to 0.077; P = .001) and fatality rates (β = 0.068; 95% CI, 0.044 to 0.091; P < .001).

Conclusions and Relevance

In this cross-sectional study, SAHOs were associated with reductions in COVID-19 case rates. These findings could help inform policy makers to address the continued COVID-19 pandemic in the US. The proportion of African American population was positively associated with COVID-19 case rates, and this state-level finding adds to evidence from existing ecological studies using county-level data on racial disparities in COVID-19 infection rates and underlines the urgency of better understanding and addressing these disparities.