Dynamics of SARS-CoV-2 infection hospitalisation and infection fatality ratios over 23 months in England

Cognitive reserve moderates the effect of COVID-19 on cognition: A systematic review and meta-analysis of individual participant data

Elucidating the factors that mitigate the effects of COVID-19 on cognitive function offers important insights for public health policy and intervention. This systematic review and individual participant data (IPD) meta-analysis assesses cognitive reserve (CR) as a potential moderator of post-COVID-19 cognitive dysfunction (PCCD). Under PRISMA-IPD guidelines, data searches were conducted via PubMed, PsycINFO, Scopus, and Embase, up to January 2023. Eligible studies included at least one cognitive assessment, CR proxy, and disease severity indicator. Of 5604 studies, 87 were eligible (10,950 COVID-19 cases; 78,305 controls), and IPD was obtained for 29 datasets (3919 COVID-19 cases; 8267 controls). Three-level random-effects meta-analyses indicated that CR had a moderate positive association (rsp =.29), and COVID-19 severity had a small negative association (rsp = -.07) with cognitive outcomes. These effects were moderated by a significant within-study interaction. Cognitive deficits following COVID-19 were 33 % smaller among high CR individuals, and 33 % greater among low CR individuals, relative to those with average CR. Population-based initiatives promoting reserve-building behaviors may alleviate the PCCD-related public health burden. REVIEW REGISTRATION: PROSPERO registration number: CRD42022360670.

COVID-19 case fatality rate and infection fatality rate from 2020 to 2023: Nationwide analysis in Austria

BACKGROUND

Comprehensive analyses of COVID-19 case fatality rates (CFRs) and infection fatality rates (IFRs) that span the entire pandemic are not yet available but critical to retrospectively evaluate the COVID-19 disease burden and its related public health policies. We used nationwide individual participant data from Austria, the continental country with the highest SARS-CoV-2 testing rate per capita, to calculate COVID-19 CFR and estimate IFR covering the entire pandemic.

METHODS

This retrospective observational study included all Austrian residents and covered the time from February 2020 to May 2023, examining CFRs overall, monthly, and during dominant SARS-CoV-2 variant periods. CFRs were calculated for the whole population and stratified according to immunization status (presence of previous vaccination and/or infection), age, gender and nursing home residency. We additionally estimated the IFRs based on estimations of undocumented infections using a test positivity model.

RESULTS

The overall CFR of 30-day COVID-19 mortality was 0.31 % but varied depending on month, with the highest being 5.9 % in April 2020 and the lowest 0.07 % in January 2022. The variant periods reflected this trend of decreasing CFR, with the highest for Wuhan-Hu-1 (2.05 %) and the lowest for BA.1 (0.08 %). Overall CFRs were particularly high in the group without any previous immunizing event (0.67 %), the elderly (85 + year group: 7.88 %) and in nursing home residents (7.92 %). Nursing home residents accounted for 30.82 % of all COVID-19 deaths while representing only 1.22 % of diagnosed infections. Total SARS-CoV-2 infections were estimated to be almost double than confirmed cases with a corresponding overall IFR of 0.16 %.

CONCLUSION

This estimation of nationwide CFR and IFR across the entirety of the SARS-CoV-2 pandemic gives crucial insights into the period-dependent variability of the severity of diagnosed COVID-19 cases and its risk factors. Our findings further underline the disproportionate severity of COVID-19 among the elderly and especially nursing home residents.

Estimating Re and overdispersion in secondary cases from the size of identical sequence clusters of SARS-CoV-2

The wealth of genomic data that was generated during the COVID-19 pandemic provides an exceptional opportunity to obtain information on the transmission of SARS-CoV-2. Specifically, there is great interest to better understand how the effective reproduction number [Formula: see text] and the overdispersion of secondary cases, which can be quantified by the negative binomial dispersion parameter k, changed over time and across regions and viral variants. The aim of our study was to develop a Bayesian framework to infer [Formula: see text] and k from viral sequence data. First, we developed a mathematical model for the distribution of the size of identical sequence clusters, in which we integrated viral transmission, the mutation rate of the virus, and incomplete case-detection. Second, we implemented this model within a Bayesian inference framework, allowing the estimation of [Formula: see text] and k from genomic data only. We validated this model in a simulation study. Third, we identified clusters of identical sequences in all SARS-CoV-2 sequences in 2021 from Switzerland, Denmark, and Germany that were available on GISAID. We obtained monthly estimates of the posterior distribution of [Formula: see text] and k, with the resulting [Formula: see text] estimates slightly lower than estimates obtained by other methods, and k comparable with previous results. We found comparatively higher estimates of k in Denmark which suggests less opportunities for superspreading and more controlled transmission compared to the other countries in 2021. Our model included an estimation of the case detection and sampling probability, but the estimates obtained had large uncertainty, reflecting the difficulty of estimating these parameters simultaneously. Our study presents a novel method to infer information on the transmission of infectious diseases and its heterogeneity using genomic data. With increasing availability of sequences of pathogens in the future, we expect that our method has the potential to provide new insights into the transmission and the overdispersion in secondary cases of other pathogens.

Mathematical Assessment of Wastewater-Based Epidemiology to Predict SARS-CoV-2 Cases and Hospitalizations in Miami-Dade County

This study presents a wastewater-based mathematical model for assessing the transmission dynamics of the SARS-CoV-2 pandemic in Miami-Dade County, Florida. The model, which takes the form of a deterministic system of nonlinear differential equations, monitors the temporal dynamics of the disease, as well as changes in viral RNA concentration in the county's wastewater system (which consists of three sewage treatment plants). The model was calibrated using the wastewater data during the third wave of the SARS-CoV-2 pandemic in Miami-Dade (specifically, the time period from July 3, 2021 to October 9, 2021). The calibrated model was used to predict SARS-CoV-2 case and hospitalization trends in the county during the aforementioned time period, showing a strong correlation between the observed (detected) weekly case data and the corresponding weekly data predicted by the calibrated model. The model's prediction of the week when maximum number of SARS-CoV-2 cases will be recorded in the county during the simulation period precisely matches the time when the maximum observed/reported cases were recorded (which was August 14, 2021). Furthermore, the model's projection of the maximum number of cases for the week of August 14, 2021 is about 15 times higher than the maximum observed weekly case count for the county on that day (i.e., the maximum case count estimated by the model was 15 times higher than the actual/observed count for confirmed cases). This result is consistent with the result of numerous SARS-CoV-2 modeling studies (including other wastewater-based modeling, as well as statistical models) in the literature. Furthermore, the model accurately predicts a one-week lag between the peak in weekly COVID-19 case and hospitalization data during the time period of the study in Miami-Dade, with the model-predicted hospitalizations peaking on August 21, 2021. Detailed time-varying global sensitivity analysis was carried out to determine the parameters (wastewater-based, epidemiological and biological) that have the most influence on the chosen response function-the cumulative viral load in the wastewater. This analysis revealed that the transmission rate of infectious individuals, shedding rate of infectious individuals, recovery rate of infectious individuals, average fecal load per person per unit time and the proportion of shed viral RNA that is not lost in sewage before measurement at the wastewater treatment plant were most influential to the response function during the entire time period of the study. This study shows, conclusively, that wastewater surveillance data can be a very powerful indicator for measuring (i.e., providing early-warning signal and current burden) and predicting the future trajectory and burden (e.g., number of cases and hospitalizations) of emerging and re-emerging infectious diseases, such as SARS-CoV-2, in a community.

Biases in Routine Influenza Surveillance Indicators Used to Monitor Infection Incidence and Recommendations for Improvement

BACKGROUND

Monitoring how the incidence of influenza infections changes over time is important for quantifying the transmission dynamics and clinical severity of influenza. Infection incidence is difficult to measure directly, and hence, other quantities which are more amenable to surveillance are used to monitor trends in infection levels, with the implicit assumption that they correlate with infection incidence.

METHODS

Here, we demonstrate, through mathematical reasoning using fundamental mathematical principles, the relationship between the incidence of influenza infections and three commonly reported surveillance indicators: (1) the rate per unit time of influenza-like illness reported through sentinel healthcare sites, (2) the rate per unit time of laboratory-confirmed influenza infections and (3) the proportion of laboratory tests positive for influenza ('test-positive proportion').

RESULTS

Our analysis suggests that none of these ubiquitously reported surveillance indicators are a reliable tool for monitoring influenza incidence. In particular, we highlight how these surveillance indicators can be heavily biassed by the following: the dynamics of circulating pathogens (other than influenza) with similar symptom profiles, changes in testing rates and differences in infection rates, symptom rates and healthcare-seeking behaviour between age-groups and through time. We make six practical recommendations to improve the monitoring of influenza infection incidence. The implementation of our recommendations would enable the construction of more interpretable surveillance indicator(s) for influenza from which underlying patterns of infection incidence could be readily monitored.

CONCLUSIONS

The implementation of all (or a subset) of our recommendations would greatly improve understanding of the transmission dynamics, infection burden and clinical severity of influenza, improving our ability to respond effectively to seasonal epidemics and future pandemics.

The Impact of Healthcare Pressures on the COVID-19 Hospitalisation Fatality Risk in England

BACKGROUND

As the impact of the SARS-CoV-2 pandemic extends into 2023 and beyond, the treatment and outcomes of infected patients continues to evolve. Unlike earlier in the pandemic there are now further infectious disease pressures placed on hospitals, which influence patient care and triage decisions.

METHODS

The manuscript uses individual patient records linked with associated hospital management information of system pressure characteristics to attribute COVID-19 hospitalisation fatality risks (HFR) to patients and hospitals, using generalised additive mixed effects models.

RESULTS

Between 01 September 2022 and 09 October 2023, the COVID-19 hospitalisation fatality risk in England was estimated as 12.71% (95% confidence interval (CI) 12.53%, 12.88%). Staff absences had  an adjusted odds ratio of 1.038 (95% CI 1.017, 1.060) associated with the HFR when accounting for patient and hospital characteristics.

INTERPRETATION

This observational research presents evidence that a range of local hospital effects can have a meaningful impact on the risk of death from COVID-19 once hospitalised and should be accounted for when reporting estimates. We show that both the patient case mix and hospital pressures impact estimates of patient outcomes.

Modelling multiplex testing for outbreak control

During the SARS-CoV-2 pandemic, polymerase chain reaction (PCR) and lateral flow device (LFD) tests were frequently deployed to detect the presence of SARS-CoV-2. Many of these tests were singleplex, and only tested for the presence of a single pathogen. Multiplex tests can test for the presence of several pathogens using only a single swab, which can allow for: surveillance of more pathogens, targeting of antiviral interventions, a reduced burden of testing, and lower costs. Test sensitivity, however, particularly in LFD tests, is highly conditional on the viral concentration dynamics of individuals. To inform the use of multiplex testing in outbreak detection it is therefore necessary to investigate the interactions between outbreak detection strategies and the differing viral concentration trajectories of key pathogens. Viral concentration trajectories are estimated for SARS-CoV-2 and Influenza A/B. Testing strategies for the first five symptomatic cases in an outbreak are then simulated and used to evaluate key performance indicators. Strategies that use a combination of multiplex LFD and PCR tests achieve; high levels of detection, detect outbreaks rapidly, and have the lowest burden of testing across multiple pathogens. Influenza B was estimated to have lower rates of detection due to its modelled viral concentration dynamics.

Surveillance bias in the assessment of the size of COVID-19 epidemic waves: a case study

OBJECTIVES

To estimate the size of COVID-19 waves using four indicators across three pandemic periods and assess potential surveillance bias.

STUDY DESIGN

Case study using data from one region of Switzerland.

METHODS

We compared cases, hospitalizations, deaths, and seroprevalence during three periods including the first three pandemic waves (period 1: Feb-Oct 2020; period 2: Oct 2020-Feb 2021; period 3: Feb-Aug 2021). Data were retrieved from the Federal Office of Public Health or estimated from population-based studies. To assess potential surveillance bias, indicators were compared to a reference indicator, i.e. seroprevalence during periods 1 and 2 and hospitalizations during the period 3. Timeliness of indicators (the duration from data generation to the availability of the information to decision-makers) was also evaluated.

RESULTS

Using seroprevalence (our reference indicator for period 1 and 2), the 2nd wave size was slightly larger (by a ratio of 1.4) than the 1st wave. Compared to seroprevalence, cases largely overestimated the 2nd wave size (2nd vs 1st wave ratio: 6.5), while hospitalizations (ratio: 2.2) and deaths (ratio: 2.9) were more suitable to compare the size of these waves. Using hospitalizations as a reference, the 3rd wave size was slightly smaller (by a ratio of 0.7) than the 2nd wave. Cases or deaths slightly underestimated the 3rd wave size (3rd vs 2nd wave ratio for cases: 0.5; for deaths: 0.4). The seroprevalence was not useful to compare the size of these waves due to high vaccination rates. Across all waves, timeliness for cases and hospitalizations was better than for deaths or seroprevalence.

CONCLUSIONS

The usefulness of indicators for assessing the size of pandemic waves depends on the type of indicator and the period of the pandemic.

Challenges in the case-based surveillance of infectious diseases

To effectively inform infectious disease control strategies, accurate knowledge of the pathogen's transmission dynamics is required. Since the timings of infections are rarely known, estimates of the infection incidence, which is crucial for understanding the transmission dynamics, often rely on measurements of other quantities amenable to surveillance. Case-based surveillance, in which infected individuals are identified by a positive test, is the predominant form of surveillance for many pathogens, and was used extensively during the COVID-19 pandemic. However, there can be many biases present in case-based surveillance indicators due to, for example test sensitivity, changing testing behaviours and the co-circulation of pathogens with similar symptom profiles. Here, we develop a mathematical description of case-based surveillance of infectious diseases. By considering realistic epidemiological parameters and situations, we demonstrate many of the potential biases in common surveillance indicators based on case-based surveillance data. Crucially, we find that many of these common surveillance indicators (e.g. case numbers, test-positive proportion) are heavily biased by circulating pathogens with similar symptom profiles. Future surveillance strategies could be designed to minimize these sources of bias and uncertainty, providing more accurate estimates of a pathogen's transmission dynamics and, ultimately, more targeted application of public health measures.

Jointly estimating epidemiological dynamics of Covid-19 from case and wastewater data in Aotearoa New Zealand

BACKGROUND

Timely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by clinical testing, healthcare-seeking behaviour or access to care.

METHODS

We construct a state-space model with observed data of levels of SARS-CoV-2 in wastewater and reported case incidence and estimate the hidden states of the effective reproduction number, R, and CAR using sequential Monte Carlo methods.

RESULTS

We analyse data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaks at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaks around 12 March 2022. We calculate that New Zealand's second Omicron wave in July 2022 is similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 is approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022.

CONCLUSIONS

Estimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time.

Deviation from the recommended schedule: optimal dosing interval for a two-dose vaccination programme

Optimizing vaccination impact during an emerging disease becomes crucial when vaccine supply is limited, and robust protection requires multiple doses. Facing this challenge during the early stages of the COVID-19 vaccine deployment, a pivotal policy question arose: whether to administer a single dose to a larger proportion of the population by deferring the second dose, or to prioritize stronger protection for a smaller subset of the population with the established dosing interval from clinical trials. Using a delay-differential model and considering waning immunity and distribution capacity, we compared these strategies. We found that the efficacy of the first dose significantly influences the impact of delaying the second dose. Even for a relatively low efficacy of the first dose, a delayed strategy may outperform vaccination with the recommended dosing interval in reducing short-term hospitalizations and deaths despite increase in infections. The optimal delay, however, depends on the specific outcome measured and timelines within which the vaccination strategy is evaluated. We found transition lines for the relative reduction of infection, hospitalization and death below which vaccination with the recommended schedule is the preferred strategy. In a realistic parameter space, our results highlight scenarios in which the conclusions of previous studies are invalid.

Differences between the true reproduction number and the apparent reproduction number of an epidemic time series

The time-varying reproduction number R(t) measures the number of new infections per infectious individual and is closely correlated with the time series of infection incidence by definition. The timings of actual infections are rarely known, and analysis of epidemics usually relies on time series data for other outcomes such as symptom onset. A common implicit assumption, when estimating R(t) from an epidemic time series, is that R(t) has the same relationship with these downstream outcomes as it does with the time series of incidence. However, this assumption is unlikely to be valid given that most epidemic time series are not perfect proxies of incidence. Rather they represent convolutions of incidence with uncertain delay distributions. Here we define the apparent time-varying reproduction number, RA(t), the reproduction number calculated from a downstream epidemic time series and demonstrate how differences between RA(t) and R(t) depend on the convolution function. The mean of the convolution function sets a time offset between the two signals, whilst the variance of the convolution function introduces a relative distortion between them. We present the convolution functions of epidemic time series that were available during the SARS-CoV-2 pandemic. Infection prevalence, measured by random sampling studies, presents fewer biases than other epidemic time series. Here we show that additionally the mean and variance of its convolution function were similar to that obtained from traditional surveillance based on mass-testing and could be reduced using more frequent testing, or by using stricter thresholds for positivity. Infection prevalence studies continue to be a versatile tool for tracking the temporal trends of R(t), and with additional refinements to their study protocol, will be of even greater utility during any future epidemics or pandemics.

Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic

To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.

Ecological and Evolutionary Insights About Emerging Infectious Diseases from the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) pandemic challenged the workings of human society, but in doing so, it advanced our understanding of the ecology and evolution of infectious diseases. Fluctuating transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) demonstrated the highly dynamic nature of human social behavior, often without government intervention. Evolution of SARS-CoV-2 in the first two years following spillover resulted primarily in increased transmissibility, while in the third year, the globally dominant virus variants had all evolved substantial immune evasion. The combination of viral evolution and the buildup of host immunity through vaccination and infection greatly decreased the realized virulence of SARS-CoV-2 due to the age dependence of disease severity. The COVID-19 pandemic was exacerbated by presymptomatic, asymptomatic, and highly heterogeneous transmission, as well as highly variable disease severity and the broad host range of SARS-CoV-2. Insights and tools developed during the COVID-19 pandemic could provide a stronger scientific basis for preventing, mitigating, and controlling future pandemics.

Estimating SARS-CoV-2 infections and associated changes in COVID-19 severity and fatality

Background

The difficulty in identifying SARS-CoV-2 infections has not only been the major obstacle to control the COVID-19 pandemic but also to quantify changes in the proportion of infections resulting in hospitalization, intensive care unit (ICU) admission, or death.

Methods

We developed a model of SARS-CoV-2 transmission and vaccination informed by official estimates of the time-varying reproduction number to estimate infections that occurred in Italy between February 2020 and 2022. Model outcomes were compared with the Italian National surveillance data to estimate changes in the SARS-CoV-2 infection ascertainment ratio (IAR), infection hospitalization ratio (IHR), infection ICU ratio (IIR), and infection fatality ratio (IFR) in five different sub-periods associated with the dominance of the ancestral lineages and Alpha, Delta, and Omicron BA.1 variants.

Results

We estimate that, over the first 2 years of pandemic, the IAR ranged between 15% and 40% (range of 95%CI: 11%-61%), with a peak value in the second half of 2020. The IHR, IIR, and IFR consistently decreased throughout the pandemic with 22-44-fold reductions between the initial phase and the Omicron period. At the end of the study period, we estimate an IHR of 0.24% (95%CI: 0.17-0.36), IIR of 0.015% (95%CI: 0.011-0.023), and IFR of 0.05% (95%CI: 0.04-0.08).

Conclusions

Since 2021, changes in the dominant SARS-CoV-2 variant, vaccination rollout, and the shift of infection to younger ages have reduced SARS-CoV-2 infection ascertainment. The same factors, combined with the improvement of patient management and care, contributed to a massive reduction in the severity and fatality of COVID-19.