Infection Fatality Rate Research Articles

COVID-19: What if immunity wanes?

Using a simple economic model in which social distancing reduces contagion, we study the implications of waning immunity for the epidemiological dynamics and social activity. If immunity wanes, we find that COVID‐19 likely becomes endemic and that social distancing is here to stay until the discovery of a vaccine or cure. But waning immunity does not necessarily change optimal actions on the onset of the pandemic. Decentralized equilibria are virtually independent of waning immunity until close to peak infections. For centralized equilibria, the relevance of waning immunity decreases in the probability of finding a vaccine or cure, the costs of infection (e.g., infection–fatality rate), the degree of partial immunity and the presence of other NPIs that lower contagion (e.g., quarantining and mask use). In simulations calibrated to July 2020, our model suggests that waning immunity is virtually unimportant for centralized equilibria until at least 2021. This provides vital time for individuals and policy‐makers to learn about immunity against SARS‐CoV‐2 before it becomes critical.

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.Conclusions: 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 for public health The COVID-19 pandemic posed important challenges to public health institutions. All around the world, countries experimented with severe and unprecedented measures of containment, which strongly restricted people's mobility. To provide quantitative measures about the effects of the adopted public health policies on the virus spread and lethality is an important step for controlling the pandemic.

Estimating the SARS-CoV-2 infection fatality rate by data combination: the case of Germany’s first wave

Summary Assessing the infection fatality rate (IFR) of SARS-CoV-2 in a population is a controversial issue. Due to asymptomatic courses of COVID-19, many infections remain undetected. Reported case fatality rates are therefore poor estimates of the IFR. We propose a strategy to estimate the IFR that combines official data on cases and fatalities with data from seroepidemiological studies in infection hotspots. The application of the method yields an estimate of the IFR of wild-type SARS-CoV-2 in Germany during the first wave of the pandemic of 0.83% (95% CI: [0.69%; 0.98%]), notably higher than the estimate reported in the prominent study by Streeck et al. (2020) (0.36% [0.17%; 0.77%]) and closer to that obtained from a world-wide meta analysis (0.68% [0.53%; 0.82%]), where the difference can be explained by Germany’s disadvantageous age structure. Provided that suitable data are available, the proposed method can be applied to estimate the IFR of virus variants and other regions.

Outcomes following SARS-CoV-2 infection in patients with primary and secondary immunodeficiency in the UK.

In March 2020, the United Kingdom Primary Immunodeficiency Network (UKPIN) established a registry of cases to collate the outcomes of individuals with PID and SID following SARS-CoV-2 infection and treatment. A total of 310 cases of SARS-CoV-2 infection in individuals with PID or SID have now been reported in the UK. The overall mortality within the cohort was 17.7% (n = 55/310). Individuals with CVID demonstrated an infection fatality rate (IFR) of 18.3% (n = 17/93), individuals with PID receiving IgRT had an IFR of 16.3% (n = 26/159) and individuals with SID, an IFR of 27.2% (n = 25/92). Individuals with PID and SID had higher inpatient mortality and died at a younger age than the general population. Increasing age, low pre-SARS-CoV-2 infection lymphocyte count and the presence of common co-morbidities increased the risk of mortality in PID. Access to specific COVID-19 treatments in this cohort was limited: only 22.9% (n = 33/144) of patients admitted to the hospital received dexamethasone, remdesivir, an anti-SARS-CoV-2 antibody-based therapeutic (e.g. REGN-COV2 or convalescent plasma) or tocilizumab as a monotherapy or in combination. Dexamethasone, remdesivir, and anti-SARS-CoV-2 antibody-based therapeutics appeared efficacious in PID and SID. Compared to the general population, individuals with PID or SID are at high risk of mortality following SARS-CoV-2 infection. Increasing age, low baseline lymphocyte count, and the presence of co-morbidities are additional risk factors for poor outcome in this cohort.

SARS-CoV-2 epidemic after social and economic reopening in three U.S. states reveals shifts in age structure and clinical characteristics.

State-level reopenings in late spring 2020 facilitated the resurgence of severe acute respiratory syndrome coronavirus 2 transmission. Here, we analyze age-structured case, hospitalization, and death time series from three states—Rhode Island, Massachusetts, and Pennsylvania—that had successful reopenings in May 2020 without summer waves of infection. Using 11 daily data streams, we show that from spring to summer, the epidemic shifted from an older to a younger age profile and that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals. Clinical case management improved from spring to summer, resulting in fewer critical care admissions and lower infection fatality rate. Attack rate estimates through 31 August 2020 are 6.2% [95% credible interval (CI), 5.7 to 6.8%] of the total population infected for Rhode Island, 6.7% (95% CI, 5.4 to 7.6%) in Massachusetts, and 2.7% (95% CI, 2.5 to 3.1%) in Pennsylvania.

SARS-CoV-2 Sero-Surveillance in Greece: Evolution over Time and Epidemiological Attributes during the Pre-Vaccination Pandemic Era.

Background: Nation-wide SARS-CoV-2 seroprevalence surveys provide valuable insights into the course of the pandemic, including information often not captured by routine surveillance of reported cases. Methods: A serosurvey of IgG antibodies against SARS-CoV-2 was conducted in Greece between March and December 2020. It was designed as a cross-sectional survey repeated at monthly intervals. The leftover sampling methodology was used and a geographically stratified sampling plan was applied. Results: Of 55,947 serum samples collected, 705 (1.26%) were found positive for anti-SARS-CoV-2 antibodies, with higher seroprevalence (9.09%) observed in December 2020. Highest seropositivity levels were observed in the “0–29” and “30–49” year age groups. Seroprevalence increased with age in the “0–29” age group. Highly populated metropolitan areas were characterized with elevated seroprevalence levels (11.92% in Attica, 12.76% in Thessaloniki) compared to the rest of the country (5.90%). The infection fatality rate (IFR) was estimated at 0.451% (95% CI: 0.382–0.549%) using aggregate data until December 2020, and the ratio of actual to reported cases was 9.59 (7.88–11.33). Conclusions: The evolution of seroprevalence estimates aligned with the course of the pandemic and varied widely by region and age group. Young and middle-aged adults appeared to be drivers of the pandemic during a severe epidemic wave under strict policy measures.

Variation in excess all-cause mortality by age, sex, and province during the first wave of the COVID-19 pandemic in Italy

Although previous evidence suggests that the infection fatality rate from COVID-19 varies by age and sex, and that transmission intensity varies geographically within countries, no study has yet explored the age-sex-space distribution of excess mortality associated with the COVID pandemic. By applying the principles of small-area estimation to existing model formulations for excess mortality, this study develops a novel method for assessing excess mortality across small populations and assesses the pattern of COVID excess mortality by province, year, week, age group, and sex in Italy from March through May 2020. We estimate that 53,200 excess deaths occurred across Italy during this time period, compared to just 35,500 deaths where COVID-19 was registered as the underlying cause of death. Out of the total excess mortality burden, 97% of excess deaths occurred among adults over age 60, and 68% of excess deaths were concentrated among adults over age 80. The burden of excess mortality was unevenly distributed across the country, with just three of Italy’s 107 provinces accounting for 32% of all excess mortality. This method for estimating excess mortality can be adapted to other countries where COVID-19 diagnostic capacity is still insufficient, and could be incorporated into public health rapid response systems.

Choosing the right COVID-19 indicator: crude mortality, case fatality, and infection fatality rates influence policy preferences, behaviour, and understanding

Individuals worldwide are overwhelmed with news about COVID-19. In times of pandemic, media alternate the usage of different COVID-19 indicators, ranging from the more typical crude mortality rate to the case fatality rate, and the infection fatality rate continuously. In this article, we used experimental methods to test whether and how the treatment of individuals with different types of information on COVID-19 is able to change policy preferences, individual and social behaviours, and the understanding of COVID-19 indicators. Results show that while the usage of the crude mortality rate proves to be more efficient in terms of supporting policy preferences and behaviours to contain the virus, all indicators suffer from a significant misunderstanding on behalf of the population.

Two waves of COIVD-19 in Brazilian cities and vaccination impact.

Brazil has suffered two waves of Coronavirus Disease 2019 (COVID-19). The second wave, coinciding with the spread of the Gamma variant, was more severe than the first wave. Studies have not yet reached a conclusion on some issues including the extent of reinfection, the infection fatality rate (IFR), the infection attack rate (IAR) and the effects of the vaccination campaign in Brazil, though it was reported that confirmed reinfection was at a low level. We modify the classical Susceptible-Exposed-Infectious-Recovered (SEIR) model with additional class for severe cases, vaccination and time-varying transmission rates. We fit the model to the severe acute respiratory infection (SARI) deaths, which is a proxy of the COVID-19 deaths, in 20 Brazilian cities with the large number of death tolls. We evaluate the vaccination effect by a contrast of "with" vaccination actual scenario and "without" vaccination in a counterfactual scenario. We evaluate the model performance when the reinfection is absent in the model. In the 20 Brazilian cities, the model simulated death matched the reported deaths reasonably well. The effect of the vaccination varies across cities. The estimated median IFR is around 1.2%. Overall, through this modeling exercise, we conclude that the effects of vaccination campaigns vary across cites and the reinfection is not crucial for the second wave. The relatively high IFR could be due to the breakdown of medical system in many cities.

Newly Developed COVID-19 Bundle Greatly Reduces the Infection Fatality Rate for a Highly Vulnerable Nursing Home Population.

This performance improvement initiative used a bundle designed to reduce the COVID-19 infection fatality rate (IFR) by ≥33% and the new infection rate (IR) to <1% among nursing home (NH) residents over a 3-month period at a large public NH in New York City. Participants were all NH residents, newly testing COVID-19 PCR positive between March 1, 2020 and June 30, 2020. Key bundle components involved close observation of all residents with vital signs taken once/shift, including O2 saturation, frequent clinical team follow-up visits for those symptomatic, and ramped-up COVID-19 PCR testing. From April to June, average IFR was 12.3%, a 49.6% reduction from the March baseline (P < 0.05), and average new IR was 5.4%, a 29.9% reduction from baseline (P < 0.05). In the 2 follow-up months, no deaths occurred with a new IR < 1%, indicating sustained improvement. Because of its simplicity, this bundle or components of it could be readily applied elsewhere after appropriate assessment.

Trend and Epidemiological Analysis of Coronavirus in Nigeria

Nigeria is the most populous country in Africa and it’s among the countries where the novel corona virus is highly endemic. World Health Organization listed Nigeria among other 13 African countries identified as high-risk for the spread of the virus. Hence the need to observe the six geopolitical zones in Nigeria, in other to study the trend of the virus and also look at some the epidemiological measures based on the case records of adult patients above 16 years with Corona virus admitted between February 2020 and February 2021. The research work tries to analyze the epidemiological measures of the COVID-19 using the Case Fatality Rate (CFR), Infection Fatality Rate (IFR), Mortality Rate (MR) and Incidence Rate (IR) and also to analyze the trend of death across the six geopolitical zones in Nigeria. The analysis of the epidemiological measure shows a wide variation in CFR among the six geopolitical zones, it’s an indication that it disproportionately affected states like Lagos and Abuja which are the most affluent. The result from IFR shows that the North-East has the highest infection rate at 4.88% while South-West has the least at 1.53%. MR shows south west with the highest population and highest death recorded of 11% while south-East has the lowest death rate. The incidence rate was lowest in North-East at 2.3% and highest in the South-West at 18.93%, studying this incidence rate of the six geopolitical zone one can conclude that the occupation and the economic activities of the zones really influenced the rates. The trend analysis of death across the six zones shows the South East has a perfect fitted polynomial series of order four model.

Seroprevalence of anti SARS-CoV-2 IgG antibodies among adults in Jammu district, India: A community-based study.

Background & objectives:Serology testing is essential for immunological surveillance in the population. This serosurvey was conducted to ascertain the cumulative population immunity against SARS-CoV-2 among adults in Jammu district and to understand the association of seropositivity with sociodemographic and clinical correlates.Methods:On September 30 and October 1, 2020, a household survey was done in 20 villages/wards chosen from 10 health blocks in district Jammu, India. Demographic, clinical and exposure information was collected from 2000 adults. Serum samples were screened for IgG antibodies using COVID Kavach MERILISA kit. Tests of association were used to identify risk factors associated with IgG positivity. Crude odds ratio with 95 per cent confidence intervals (CIs) was calculated during univariate analysis followed by logistic regression.Results:Overall adjusted seroprevalence for SARS-CoV-2 was 8.8 per cent (95% CI: 8.78-8.82); it varied from 4.1 per cent in Chauki choura to 16.7 per cent Pallanwalla across 10 blocks in the district. Seropositivity was observed to be comparatively higher in 41-50 and 61-70 yr age groups, among males and in rural areas. Fever, sore throat, cough, dyspnoea, myalgias, anosmia, ageusia, fatigue, seizures, history of exposure, medical consultation, hospitalization and missing work showed significant association with seropositivity on univariate analysis. On logistic regression, only sore throat, myalgia and missing work showed significant adjusted odds of IgG positivity. Extrapolation to adult population suggested that exposure to SARS-CoV-2 was 14.4 times higher than reported cases, translating into Infection fatality rate of 0.08 per cent.Interpretation & conclusions:Since a major part of population was immunologically naive, all efforts to contain COVID-19 need to be vigorously followed while these baseline results provide an important yardstick to monitor the trends of COVID-19 and guide locally appropriate control strategies in the region.

Reduction in the Infection Fatality Rate of Omicron (B.1.1.529) Variant Compared to Previous Variants in South Africa

Reduction in the Infection Fatality Rate of Omicron (B.1.1.529) Variant Compared to Previous Variants in South Africa

Estimation of the true infection rate and infection fatality rate of coronavirus disease 2019 in each country

The True Infection Rate (TIR) in the whole population of each country and the Infection Fatality Rate (IFR) for coronavirus disease 2019 (COVID-19) are unknown although they are important parameters. We devised a simple method to infer TIR and IFR based on the open data. The prevalence rate of the Polymerase Chain Reaction (PCR) tests among the population (Examination Rate; ER) and the positive rate of PCR tests (Infection Rate; IR) for 66 countries were picked up at a website 5 times from April 10th to June 13th, 2020, and the trajectory of each country was drawn over the IR vs. ER plot. IR and ER showed a strong negative correlation for some countries, and TIR was estimated by extrapolating the regression line when the correlation coefficient was between -0.99 and -1. True/Identified Case Ratio (TICR) and IFR were also calculated using the estimated TIR. The estimated TIR well coincided with local antibody surveys. Estimated IFR took on a wide range of values up to 10%: generally high in the Western countries. The estimated IFR of Singapore was very low (0.018%), which may be related to the reported gene mutation causing the attenuation of the viral virulence.

Seroprevalence of antibodies against SARS-Cov-2 in the high impacted sub-district in Jakarta, Indonesia.

Understanding the actual prevalence of COVID-19 transmission in the community is vital for strategic responses to the pandemic. This study aims to estimate the actual infection of COVID-19 through a seroprevalence survey and to predict infection fatality rate (IFR) in Tanjung Priok, the hardest-hit sub-district by the COVID-19 in Jakarta, Indonesia. We conducted a venous blood sampling (phlebotomy) to 3,196 individuals in Tanjung Priok between Nov 23, 2020, and Feb 19, 2021 to detect their antibodies against SARS-CoV-2. Using an enumerator-administered questionnaire, we collected data on the respondents' demographic characteristics, COVID-19 test history, COVID-19 symptoms in the last 14 days, comorbidities, and protective behaviours during the last month. We employed descriptive analysis to estimate the seroprevalence and IFR. The prevalence of Antibody against SARS-CoV-2 was 28.52% (95% CI 25.44-31.81%), with the result being higher in females than males (OR 1.20; 95% CI 1.02-1.42). By the end of the data collection (February 9, 2021), the cumulative cases of COVID-19 in Tanjung Priok were reported to be experienced by 9,861 people (2.4%). Those aged 45-65 were more likely to be seropositive than 15-19 years old (OR 1.42; 95% CI 1.05-1.92). Nearly one third (31%) of the subjects who developed at least one COVID-19 symptom in the last 14 days of the data collection were seropositive. The estimated IFR was 0.08% (95% CI 0.07-0.09), with a higher figure recorded in males (0.09; 95% CI 0.08-0.10) than females (0.07; 95% CI 0.06-0.08), and oldest age group (45-65) (0.21; 95% CI 0.18-0.23) than other younger groups. An under-reporting issue was found between the estimated COVID-19 seroprevalence and the reported cumulative cases in Tanjung Priok. More efforts are required to amplify epidemiological surveillance by the provincial and local governments.

The Second Wave of COVID-19 in South and Southeast Asia and the Effects of Vaccination.

Background: By February 2021, the overall impact of coronavirus disease 2019 (COVID-19) in South and Southeast Asia was relatively mild. Surprisingly, in early April 2021, the second wave significantly impacted the population and garnered widespread international attention.Methods: This study focused on the nine countries with the highest cumulative deaths from the disease as of August 17, 2021. We look at COVID-19 transmission dynamics in South and Southeast Asia using the reported death data, which fits a mathematical model with a time-varying transmission rate.Results: We estimated the transmission rate, infection fatality rate (IFR), infection attack rate (IAR), and the effects of vaccination in the nine countries in South and Southeast Asia. Our study suggested that the IAR is still low in most countries, and increased vaccination is required to prevent future waves.Conclusion: Implementing non-pharmacological interventions (NPIs) could have helped South and Southeast Asia keep COVID-19 under control in 2020, as demonstrated in our estimated low-transmission rate. We believe that the emergence of the new Delta variant, social unrest, and migrant workers could have triggered the second wave of COVID-19.

An in-depth analysis of 10 epidemiological terminologies used in the context of COVID-19.

Aim:In this paper, we explore the contextual use of 10 epidemiological terminologies, their significance, and interpretation/misinterpretation in explaining various aspects of the 2019 novel coronavirus disease (COVID-19) pandemic.Methods and Results:We first establish the different purposes of the terms ‘pandemic’ and ‘Public Health Emergency of International Concern.’ We then discuss the confusion caused by using the ‘case fatality rate’ as opposed to ‘infection fatality rate’ during the pandemic and the uncertainty surrounding the limited usefulness of identifying someone as ‘pre-symptomatic.’ We highlight the ambiguity in the ‘positivity rate’ and the need to be able to generate data on ‘excess mortality’ during public health emergencies. We discuss the relevance of ‘association and causation’ in the context of the facemask controversy that existed at the start of the pandemic. We point out how the accepted epidemiological practice of discussing ‘herd immunity’ in the context of vaccines has been twisted to suit the political motive of a public health approach. Given that a high proportion of COVID-19 cases are asymptomatic, we go on to show how COVID-19 has blurred the lines between ‘screening/diagnosis’ and ‘quarantine/isolation,’ while giving birth to the new terminology of ‘community quarantine.’Conclusions: Applying the lessons learned from COVID-19 to better understand the above terminologies will help health professionals communicate effectively, strengthen the scientific agenda of epidemiology and public health, and support and manage future outbreaks efficiently.

SARS-CoV-2 Seroprevalence in Germany.

Until now, information on the spread of SARS-CoV-2 infections in Germany has been based mainly on data from the public health offices. It may be assumed that these data do not include many cases of asymptomatic and mild infection. We determined seroprevalence over the course of the pandemic in a sequential, multilocal seroprevalence study (MuSPAD). Study participants were recruited at random in seven administrative districts (Kreise) in Germany from July 2020 onward; each participant was tested at two different times 3-5 months apart. Test findings on blood samples were used to determine the missed-case rate of reported infections, the infection fatality rate (IFR), and the association between seropositivity and demographic, socio-economic, and health-related factors, as well as to evaluate the self-reported results of PCR and antigenic tests. The registration number of this study is DRKS00022335. Among non-vaccinated persons, the seroprevalence from July to December 2020 was 1.3-2.8% and rose between February and May 2021 to 4.1-13.1%. In July 2021, 35% of tested persons in Chemnitz were not vaccinated, and the seroprevalence among these persons was 32.4% (07/2021). The surveillance detection ratio (SDR), i.e., the ratio between the true number of infections estimated from seroprevalence and the actual number or reported infections, varied among the districts included in the study from 2.2 to 5.1 up to December 2020 and from 1.3 to 2.9 up to June 2021, and subsequently declined. The IFR was in the range of 0.8% to 2.4% in all regions except Magdeburg, where a value of 0.3% was calculated for November 2020. A lower educational level was associated with a higher seropositivity rate, smoking with a lower seropositivity rate. On average, 1 person was infected for every 8.5 persons in quarantine. Seroprevalence was low after the first wave of the pandemic but rose markedly during the second and third waves. The missed-case rate trended downward over the course of the pandemic.

Holistic Research Report of COVID-19 Pandemic

The SARS-CoV-2 (COVID-19) pandemic has left no one untouched, impacting the lives of billions of people. Due to the variance in socio-economic, political, and even belief systems around the world, different geographies have chosen to combat the virus in very different ways. The objective of our independent research was to identify what factors played a role in certain countries having a “better” response than others during the COVID-19 pandemic and then to relate that back to the political and cultural characteristics of those countries. We first researched coronavirus characteristics and compared them to those of previous pandemics to understand, physically, why the virus is hard to stop. Our research finds that COVID-19 has relatively “average” characteristics for infection and case fatality rates, but the difficulty lies in the time delay from more extended incubation periods and testing results turnaround times. That, along with the disproportionate effect on people with weak immune systems and pre-existing conditions, has made the virus hard to prevent without strict lockdowns of large parts of individual economies. Using this knowledge as a baseline, we analyzed what factors led to some countries adopting more effective responses than others by examining the general government response timelines of six countries: Germany, France, South Korea, Australia, the United Kingdom, and the United States. Our analysis finds that the differing effectiveness of a country's COVID-19 response can be narrowed down to three main factors: reaction time, protective policy enforcement, and testing rates. Moreover, we find that the responses often mirror the social and political climate of the country.

Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK.

We apply Bayesian inference methods to a suite of distinct compartmental models of generalised SEIR type, in which diagnosis and quarantine are included via extra compartments. We investigate the evidence for a change in lethality of COVID-19 in late autumn 2020 in the UK, using age-structured, weekly national aggregate data for cases and mortalities. Models that allow a (step-like or graded) change in infection fatality rate (IFR) have consistently higher model evidence than those without. Moreover, they all infer a close to two-fold increase in IFR. This value lies well above most previously available estimates. However, the same models consistently infer that, most probably, the increase in IFR preceded the time window during which variant B.1.1.7 (alpha) became the dominant strain in the UK. Therefore, according to our models, the caseload and mortality data do not offer unequivocal evidence for higher lethality of a new variant. We compare these results for the UK with similar models for Germany and France, which also show increases in inferred IFR during the same period, despite the even later arrival of new variants in those countries. We argue that while the new variant(s) may be one contributing cause of a large increase in IFR in the UK in autumn 2020, other factors, such as seasonality, or pressure on health services, are likely to also have contributed.