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

The COVID-19 pandemic: the public health reality

The coronavirus disease (COVID-19), while mild in most cases, has nevertheless caused significant mortality. The measures adopted in most countries to contain it have led to colossal social and economic disruptions, which will impact the medium- and long-term health outcomes for many communities. In this paper, we deliberate on the reality and facts surrounding the disease. For comparison, we present data from past pandemics, some of which claimed more lives than COVID-19. Mortality data on road traffic crashes and other non-communicable diseases, which cause more deaths each year than COVID-19 has so far, is also provided. The indirect, serious health and social effects are briefly discussed. We also deliberate on how misinformation, confusion stemming from contrasting expert statements, and lack of international coordination may have influenced the public perception of the illness and increased fear and uncertainty. With pandemics and similar problems likely to re-occur, we call for evidence-based decisions, the restoration of responsible journalism and communication built on a solid scientific foundation.

The effect of control measures on COVID-19 transmission in Italy: Comparison with Guangdong province in China

BACKGROUND

COVID-19 has spread all around the world. Italy is one of the worst affected countries in Europe. Although there is a trend of relief, the epidemic situation hasn't stabilized yet. This study aims to investigate the dynamics of the disease spread in Italy and provide some suggestions on containing the epidemic.

METHODS

We compared Italy's status at the outbreak stage and control measures with Guangdong Province in China by data observation and analysis. A modified autonomous SEIR model was used to study the COVID-19 epidemic and transmission potential during the early stage of the outbreak in Italy. We also utilized a time-dependent dynamic model to study the future disease dynamics in Italy. The impact of various non-pharmaceutical control measures on epidemic was investigated through uncertainty and sensitivity analyses.

RESULTS

The comparison of specific measures implemented in the two places and the time when the measures were initiated shows that the initial prevention and control actions in Italy were not sufficiently timely and effective. We estimated parameter values based on available cumulative data and calculated the basic reproduction number to be 4.32 before the national lockdown in Italy. Based on the estimated parameter values, we performed numerical simulations to predict the epidemic trend and evaluate the impact of contact limitation, detection and diagnosis, and individual behavior change due to media coverage on the epidemic.

CONCLUSIONS

Italy was in a severe epidemic status and the control measures were not sufficiently timely and effective in the beginning. Non-pharmaceutical interventions, including contact restrictions and improvement of case recognition, play an important role in containing the COVID-19 epidemic. The effect of individual behavior changes due to media update of the outbreak cannot be ignored. For policy-makers, early and strict blockade measures, fast detection and improving media publicity are key to containing the epidemic.

Patient characteristics and admitting vital signs associated with coronavirus disease 2019 (COVID-19)-related mortality among patients admitted with noncritical illness

OBJECTIVE

To determine risk factors for mortality among COVID-19 patients admitted to a system of community hospitals in the United States.

DESIGN

Retrospective analysis of patient data collected from the routine care of COVID-19 patients.

SETTING

System of >180 acute-care facilities in the United States.

PARTICIPANTS

All admitted patients with positive identification of COVID-19 and a documented discharge as of May 12, 2020.

METHODS

Determination of demographic characteristics, vital signs at admission, patient comorbidities and recorded discharge disposition in this population to construct a logistic regression estimating the odds of mortality, particular for those patients characterized as not being critically ill at admission.

RESULTS

In total, 6,180 COVID-19+ patients were identified as of May 12, 2020. Most COVID-19+ patients (4,808, 77.8%) were admitted directly to a medical-surgical unit with no documented critical care or mechanical ventilation within 8 hours of admission. After adjusting for demographic characteristics, comorbidities, and vital signs at admission in this subgroup, the largest driver of the odds of mortality was patient age (OR, 1.07; 95% CI, 1.06-1.08; P < .001). Decreased oxygen saturation at admission was associated with increased odds of mortality (OR, 1.09; 95% CI, 1.06-1.12; P < .001) as was diabetes (OR, 1.57; 95% CI, 1.21-2.03; P < .001).

CONCLUSIONS

The identification of factors observable at admission that are associated with mortality in COVID-19 patients who are initially admitted to non-critical care units may help care providers, hospital epidemiologists, and hospital safety experts better plan for the care of these patients.

COVID's Razor: RAS Imbalance, the Common Denominator Across Disparate, Unexpected Aspects of COVID-19

A modern iteration of Occam's Razor posits that "the simplest explanation is usually correct." Coronavirus Disease 2019 involves widespread organ damage and uneven mortality demographics, deemed unexpected from what was originally thought to be "a straightforward respiratory virus." The simplest explanation is that both the expected and unexpected aspects of COVID-19 share a common mechanism. Silent hypoxia, atypical acute respiratory distress syndrome (ARDS), stroke, olfactory loss, myocarditis, and increased mortality rates in the elderly, in men, in African-Americans, and in patients with obesity, diabetes, and cancer-all bear the fingerprints of the renin-angiotensin system (RAS) imbalance, suggesting that RAS is the common culprit. This article examines what RAS is and how it works, then from that baseline, the article presents the evidence suggesting RAS involvement in the disparate manifestations of COVID-19. Understanding the deeper workings of RAS helps one make sense of severe COVID-19. In addition, recognizing the role of RAS imbalance suggests potential routes to mitigate COVID-19 severity.

United States County-level COVID-19 Death Rates and Case Fatality Rates Vary by Region and Urban Status

COVID-19 is a global pandemic with uncertain death rates. We examined county-level population morality rates (per 100,000) and case fatality rates by US region and rural-urban classification, while controlling for demographic, socioeconomic, and hospital variables. We found that population mortality rates and case fatality rates were significantly different across region, rural-urban classification, and their interaction. All significant comparisons had p < 0.001. Northeast counties had the highest population mortality rates (27.4) but had similar case fatality rates (5.9%) compared to other regions except the Southeast, which had significantly lower rates (4.1%). Population mortality rates were highest in urban counties but conversely, case fatality rates were highest in rural counties. Death rates in the Northeast were driven by urban areas (e.g., small, East Coast states), while case fatality rates tended to be highest in the most rural counties for all regions, especially the Southwest. However, on further inspection, high case fatality rate percentages in the Southwest, as well as in overall US counties, were driven by a low case number. This makes it hard to distinguish genuinely higher mortality or an artifact of a small sample size. In summary, coronavirus deaths are not homogenous across the United States but instead vary by region and population and highlight the importance of fine-scale analysis.

Real-Time Digital Contact Tracing: Development of a System to Control COVID-19 Outbreaks in Nursing Homes and Long-Term Care Facilities

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can spread rapidly in nursing homes and long-term care (LTC) facilities. Symptoms-based screening and manual contact tracing have limitations that render them ineffective for containing the viral spread in LTC facilities. Symptoms-based screening alone cannot identify asymptomatic people who are infected, and the viral spread is too fast in confined living quarters to be contained by slow manual contact tracing processes.

OBJECTIVE

We describe the development of a digital contact tracing system that LTC facilities can use to rapidly identify and contain asymptomatic and symptomatic SARS-CoV-2 infected contacts. A compartmental model was also developed to simulate disease transmission dynamics and to assess system performance versus conventional methods.

METHODS

We developed a compartmental model parameterized specifically to assess the coronavirus disease (COVID-19) transmission in LTC facilities. The model was used to quantify the impact of asymptomatic transmission and to assess the performance of several intervention groups to control outbreaks: no intervention, symptom mapping, polymerase chain reaction testing, and manual and digital contact tracing.

RESULTS

Our digital contact tracing system allows users to rapidly identify and then isolate close contacts, store and track infection data in a respiratory line listing tool, and identify contaminated rooms. Our simulation results indicate that the speed and efficiency of digital contact tracing contributed to superior control performance, yielding up to 52% fewer cases than conventional methods.

CONCLUSIONS

Digital contact tracing systems show promise as an effective tool to control COVID-19 outbreaks in LTC facilities. As facilities prepare to relax restrictions and reopen to outside visitors, such tools will allow them to do so in a surgical, cost-effective manner that controls outbreaks while safely giving residents back the life they once had before this pandemic hit.

Variation in SARS-CoV-2 Prevalence in U.S. Skilled Nursing Facilities

OBJECTIVE

To identify county and facility factors associated with severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) outbreaks in skilled nursing facilities (SNFs).

DESIGN

Cross‐sectional study linking county SARS‐CoV‐2 prevalence data, administrative data, state reports of SNF outbreaks, and data from Genesis HealthCare, a large multistate provider of post‐acute and long‐term care. State data are reported as of April 21, 2020; Genesis data are reported as of May 4, 2020.

SETTING AND PARTICIPANTS

The Genesis sample consisted of 341 SNFs in 25 states, including a subset of 64 SNFs that underwent universal testing of all residents. The non‐Genesis sample included all other SNFs (n = 3,016) in the 12 states where Genesis operates that released the names of SNFs with outbreaks.

MEASUREMENTS

For Genesis and non‐Genesis SNFs: any outbreak (one or more residents testing positive for SARS‐CoV‐2). For Genesis SNFs only: number of confirmed cases, SNF case fatality rate, and prevalence after universal testing.

RESULTS

One hundred eighteen (34.6%) Genesis SNFs and 640 (21.2%) non‐Genesis SNFs had outbreaks. A difference in county prevalence of 1,000 cases per 100,000 (1%) was associated with a 33.6 percentage point (95% confidence interval (CI) = 9.6–57.7 percentage point; P = .008) difference in the probability of an outbreak for Genesis and non‐Genesis SNFs combined, and a difference of 12.5 cases per facility (95% CI = 4.4–20.8 cases; P = .003) for Genesis SNFs. A 10‐bed difference in facility size was associated with a 0.9 percentage point (95% CI = 0.6–1.2 percentage point; P < .001) difference in the probability of outbreak. We found no consistent relationship between Nursing Home Compare Five‐Star ratings or past infection control deficiency citations and probability or severity of outbreak.

CONCLUSIONS

Larger SNFs and SNFs in areas of high SARS‐CoV‐2 prevalence are at high risk for outbreaks and must have access to universal testing to detect cases, implement mitigation strategies, and prevent further potentially avoidable cases and related complications. J Am Geriatr Soc 68:2167–2173, 2020.

See related Special Article by Ouslander et al. in this issue.

Effects of policies and containment measures on control of COVID-19 epidemic in Chongqing

BACKGROUND

Coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving disease that spreads through the respiratory system and is highly contagious. In March 2020, the World Health Organization declared the COVID-19 outbreak a pandemic. In China, the pandemic was controlled after 2 mo through effective policies and containment measures. Describing the detailed policies and containment measures used to control the epidemic in Chongqing will provide a reference for the prevention and control of COVID-19 in other areas of the world.

AIM

To explore the effects of different policies and containment measures on the control of the COVID-19 epidemic in Chongqing.

METHODS

Epidemiological data on COVID-19 in Chongqing were prospectively collected from January 21 to March 15, 2020. The policies and prevention measures implemented by the government during the epidemic period were also collected. Trend analysis was performed to explore the impact of the main policy measures on the effectiveness of the control of COVID-19 in Chongqing.

RESULTS

As of March 15, the cumulative incidence of COVID-19 in Chongqing was 1.84/100000 (576 cases) and the infection fatality rate was 1.04% (6/576). The spread of COVID-19 was controlled by effective policies that involved establishing a group for directing the COVID-19 epidemic control effort; strengthening guidance and supervision; ensuring the supply of daily necessities and medical supplies and equipment to residents; setting up designated hospitals; implementing legal measures; and enhancing health education. Medical techniques were implemented to improve the recovery rate and control the epidemic. Policies such as "the lockdown of Wuhan", "initiating a first-level response to major public health emergencies", and "implementing the closed management of residential communities" significantly curbed the spread of COVID-19. Optimizing the diagnosis process, shortening the diagnosis time, and constructing teams of clinical experts facilitated the provision of "one team of medical experts for each patient" treatment for severe patients, which significantly improved the recovery rate and reduced the infection fatality rate.

CONCLUSION

The prevention policies and containment measures implemented by the government and medical institutions are highly effective in controlling the spread of the epidemic and increasing the recovery rate of COVID-19 patients.

Clinical trials in the time of a pandemic

The first rumblings about a new coronavirus spreading in China were heard in January 2020. By the end of that month, the World Health Organization, recognizing the severity of the disease and the potential for global spread, had declared a public health emergency. By February 2020, cases had been identified in multiple countries, clinical trials of treatments with some biological plausibility had begun in China, and the initial steps of vaccine development were underway. In mid-March, by which time countries around the world were experiencing rapidly increasing numbers of cases and deaths, the World Health Organization categorized the outbreak as a pandemic. This new coronavirus was designated SARS-COV-2 in recognition of its similarity to the coronavirus responsible for the severe acute respiratory syndrome outbreak in 2002–2003. The race is on to develop treatments that can mitigate the severe consequences of infection and vaccines that can prevent infection and/or diminish the severity of disease in those who do get infected. Many challenges face these development efforts. Some are similar to those faced in the past; others are new. The urgency of finding ways to treat, and ultimately prevent, the consequences of this new and potentially deadly infection has led to unprecedented focus on clinical trials.

COVID-19 Infection Fatality Rate Associated with Incidence-A Population-Level Analysis of 19 Spanish Autonomous Communities

Previous studies have found large variations in the COVID-19 infection fatality rate (IFR). This study hypothesized that IFR would be influenced by COVID-19 epidemic intensity. We tested the association between epidemic intensity and IFR using serological results from a recent large SARS-CoV-2 serosurvey (N = 60,983) in 19 Spanish regions. The infection fatality rate for Spain as a whole was 1.15% and varied between 0.13% and 3.25% in the regions (median 1.07%, IQR 0.69-1.32%). The IFR by region was positively associated with SARS-CoV-2 seroprevalence (rho = 0.54; p = 0.0162), cases/100,000 (rho = 0.75; p = 0.002), hospitalizations/100,000 (rho = 0.78; p = 0.0001), mortality/100,000 (rho = 0.77; p = 0.0001) and case fatality rate (rho = 0.49; p = 0.0327). These results suggest that the SARS-CoV-2 IFR is not fixed. The Spanish regions with more rapid and extensive spread of SARS-CoV-2 had higher IFRs. These findings are compatible with the theory that slowing the spread of COVID-19 down reduces the IFR and case fatality rate via preventing hospitals from being overrun, and thus allowing better and lifesaving care.

Trajectory of the COVID-19 pandemic: chasing a moving target

The spread of COVID-19 has already taken a pandemic form, affecting over 180 countries in a matter of three months. The full continuum of disease ranges from mild, self-limiting illness to severe progressive COVID-19 pneumonia, multiorgan failure, cytokine storm and death. Younger and healthy population is now getting affected than before. Possibilities of airborne and fecal oral routes of transmission has increased the concern. In the absence of any specific therapeutic agent for coronavirus infections, the most effective manner to contain this pandemic is probably the non-pharmacological interventions (NPIs). The damage due to the pandemic disease is multifaceted and crippling to economy, trade, and health of the citizens of the countries. The extent of damage in such scenarios is something that is beyond calculation by Gross Domestic Product rate or currency value of the country. Unfortunately, unlike many other diseases, we are still away from the target antiviral drug and vaccine for severe acute respiratory syndrome (SARS-CoV-2) infection. The prime importance of NPIs like social distancing, staying in home, work from home, self-monitoring, public awareness, self-quarantine, etc. are constantly being emphasized by CDC, WHO, health ministries of all countries and social media houses. This is time of introspection and learning from our mistakes. Countries like China and South Korea who were initially the most hit countries could contain the disease spread by liberal testing of their population, stringent quarantine of people under investigation and isolation of the positive cases. Rest of the countries need to act urgently as well to bring an immediate halt in the community transmission.

Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases

Understanding the state of the COVID-19 pandemic relies on infection and mortality data. Yet official data may underestimate the actual cases due to limited symptoms and testing capacity. We offer a simulation-based approach which combines various sources of data to estimate the magnitude of outbreak. Early in the epidemic we applied the method to Iran's case, an epicenter of the pandemic in winter 2020. Estimates using data up to March 20th, 2020, point to 916,000 (90% UI: 508 K, 1.5 M) cumulative cases and 15,485 (90% UI: 8.4 K, 25.8 K) total deaths, numbers an order of magnitude higher than official statistics. Our projections suggest that absent strong sustaining of contact reductions the epidemic may resurface. We also use data and studies from the succeeding months to reflect on the quality of original estimates. Our proposed approach can be used for similar cases elsewhere to provide a more accurate, early, estimate of outbreak state. © 2020 System Dynamics Society.