Estimation of SARS-CoV-2 mortality during the early stages of an epidemic: A modeling study in Hubei, China, and six regions in Europe

Estimated number of lives directly saved by COVID-19 vaccination programmes in the WHO European Region from December, 2020, to March, 2023: a retrospective surveillance study

BACKGROUND

By March, 2023, 54 countries, areas, and territories (hereafter CAT) in the WHO European Region had reported more than 2·2 million COVID-19-related deaths to the WHO Regional Office for Europe. Here, we estimated how many lives were directly saved by vaccinating adults in the WHO European Region from December, 2020, to March, 2023.

METHODS

In this retrospective surveillance study, we estimated the number of lives directly saved by age group, vaccine dose, and circulating variant-of-concern (VOC) period, regionally and nationally, using weekly data on COVID-19 mortality and infection, COVID-19 vaccination uptake, and SARS-CoV-2 virus characterisations by lineage downloaded from The European Surveillance System on June 11, 2023, as well as vaccine effectiveness data from the literature. We included data for six age groups (25-49 years, 50-59 years, ≥60 years, 60-69 years, 70-79 years, and ≥80 years). To be included in the analysis, CAT needed to have reported both COVID-19 vaccination and mortality data for at least one of the four older age groups. Only CAT that reported weekly data for both COVID-19 vaccination and mortality by age group for 90% of study weeks or more in the full study period were included. We calculated the percentage reduction in the number of expected and reported deaths.

FINDINGS

Between December, 2020, and March, 2023, in 34 of 54 CAT included in the analysis, COVID-19 vaccines reduced deaths by 59% overall (CAT range 17-82%), representing approximately 1·6 million lives saved (range 1·5-1·7 million) in those aged 25 years or older: 96% of lives saved were aged 60 years or older and 52% were aged 80 years or older; first boosters saved 51% of lives, and 60% were saved during the Omicron period.

INTERPRETATION

Over nearly 2·5 years, most lives saved by COVID-19 vaccination were in older adults by first booster dose and during the Omicron period, reinforcing the importance of up-to-date vaccination among the most at-risk individuals. Further modelling work should evaluate indirect effects of vaccination and public health and social measures.

FUNDING

US Centers for Disease Control and Prevention.

Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models

Compartmental models that describe infectious disease transmission across subpopulations are central for assessing the impact of non-pharmaceutical interventions, behavioral changes and seasonal effects on the spread of respiratory infections. We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laboratory-confirmed cases. For the third feature, we implemented three methods: Brownian motion, B-splines, and approximate Gaussian processes (aGP). We compared their performance in terms of the number of effective samples per second, and the error and sharpness in estimating the time-varying transmission rate over a selection of ordinary differential equation solvers and tuning parameters, using simulated seroprevalence and laboratory-confirmed case data. Even though all methods could recover the time-varying dynamics in the transmission rate accurately, we found that B-splines perform up to four and ten times faster than Brownian motion and aGPs, respectively. We validated the B-spline model with simulated age-stratified data. We applied this model to 2020 laboratory-confirmed SARS-CoV-2 cases and two seroprevalence studies from the canton of Geneva. This resulted in detailed estimates of the transmission rate over time and the case ascertainment. Our results illustrate the potential of the presented workflow including stratified transmission to estimate age-specific epidemiological parameters. The workflow is freely available in the R package HETTMO, and can be easily adapted and applied to other infectious diseases.

COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina

We document the evolution and use of the stochastic agent-based COVID-19 simulation model (COVSIM) to study the impact of population behaviors and public health policy on disease spread within age, race/ethnicity, and urbanicity subpopulations in North Carolina. We detail the methodologies used to model the complexities of COVID-19, including multiple agent attributes (i.e., age, race/ethnicity, high-risk medical status), census tract-level interaction network, disease state network, agent behavior (i.e., masking, pharmaceutical intervention (PI) uptake, quarantine, mobility), and variants. We describe its uses outside of the COVID-19 Scenario Modeling Hub (CSMH), which has focused on the interplay of nonpharmaceutical and pharmaceutical interventions, equitability of vaccine distribution, and supporting local county decision-makers in North Carolina. This work has led to multiple publications and meetings with a variety of local stakeholders. When COVSIM joined the CSMH in January 2022, we found it was a sustainable way to support new COVID-19 challenges and allowed the group to focus on broader scientific questions. The CSMH has informed adaptions to our modeling approach, including redesigning our high-performance computing implementation.

Dynamic parameterization of a modified SEIRD model to analyze and forecast the dynamics of COVID-19 outbreaks in the United States

The rapid spread of the numerous outbreaks of the coronavirus disease 2019 (COVID-19) pandemic has fueled interest in mathematical models designed to understand and predict infectious disease spread, with the ultimate goal of contributing to the decision making of public health authorities. Here, we propose a computational pipeline that dynamically parameterizes a modified SEIRD (susceptible-exposed-infected-recovered-deceased) model using standard daily series of COVID-19 cases and deaths, along with isolated estimates of population-level seroprevalence. We test our pipeline in five heavily impacted states of the US (New York, California, Florida, Illinois, and Texas) between March and August 2020, considering two scenarios with different calibration time horizons to assess the update in model performance as new epidemiologic data become available. Our results show a median normalized root mean squared error (NRMSE) of 2.38% and 4.28% in calibrating cumulative cases and deaths in the first scenario, and 2.41% and 2.30% when new data are assimilated in the second scenario, respectively. Then, 2-week (4-week) forecasts of the calibrated model resulted in median NRMSE of cumulative cases and deaths of 5.85% and 4.68% (8.60% and 17.94%) in the first scenario, and 1.86% and 1.93% (2.21% and 1.45%) in the second. Additionally, we show that our method provides significantly more accurate predictions of cases and deaths than a constant parameterization in the second scenario (p < 0.05). Thus, we posit that our methodology is a promising approach to analyze the dynamics of infectious disease outbreaks, and that our forecasts could contribute to designing effective pandemic-arresting public health policies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00366-023-01816-9.

Health-related quality of life before and during the COVID-19 pandemic in Switzerland: a cross-sectional study

INTRODUCTION

The COVID-19 pandemic forced people to give up their daily routines and adjust to new circumstances. This might have affected health-related quality of life (HRQOL). We aimed to compare HRQOL during the first COVID-19 wave in 2020 to HRQOL before the pandemic and to identify determinants of HRQOL during the pandemic in Switzerland.

METHODS

We conducted a cross-sectional online survey during the pandemic (between May and July 2020; CoWELL sample; convenience sample). Before the pandemic (2015-2016), we had conducted a cross-sectional paper-based survey among a representative random sample of the Swiss general population (SGP sample). In both samples, we assessed physical and mental HRQOL (Short Form-36) and socio-demographic characteristics. In the CoWELL sample, we additionally assessed health- and COVID-19-related characteristics. Data were analysed using linear regressions.

RESULTS

The CoWELL sample included 1581 participants (76% women; mean age = 43 years, SD = 14 years) and the SGP sample 1209 participants (58% women, mean age = 49 years, SD = 15 years). Adjusted for sex, age, and education, the CoWELL sample reported higher physical HRQOL (PCS, +5.8 (95% CI: 5.1, 6.6), p < 0.001) and lower mental HRQOL (MCS, -6.9 (-7.8, -6.0), p < 0.001) than the SGP sample. In the CoWELL sample, especially persons with lower health literacy, who had no support network or who have had COVID-19, reported lower HRQOL.

DISCUSSION

Aspects unique to the COVID-19 pandemic affected HRQOL. Vulnerable persons such as those having had COVID-19, less support opportunities, and with lower health literacy are especially prone to impaired HRQOL during the COVID-19 pandemic.

Continuous population-level monitoring of SARS-CoV-2 seroprevalence in a large European metropolitan region

Effective public health measures against SARS-CoV-2 require granular knowledge of population-level immune responses. We developed a Tripartite Automated Blood Immunoassay (TRABI) to assess the IgG response against three SARS-CoV-2 proteins. We used TRABI for continuous seromonitoring of hospital patients and blood donors (n = 72'250) in the canton of Zurich from December 2019 to December 2020 (pre-vaccine period). We found that antibodies waned with a half-life of 75 days, whereas the cumulative incidence rose from 2.3% in June 2020 to 12.2% in mid-December 2020. A follow-up health survey indicated that about 10% of patients infected with wildtype SARS-CoV-2 sustained some symptoms at least twelve months post COVID-19. Crucially, we found no evidence of a difference in long-term complications between those whose infection was symptomatic and those with asymptomatic acute infection. The cohort of asymptomatic SARS-CoV-2-infected subjects represents a resource for the study of chronic and possibly unexpected sequelae.

Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases

Simulating the spread of infectious diseases in human communities is critical for predicting the trajectory of an epidemic and verifying various policies to control the devastating impacts of the outbreak. Many existing simulators are based on compartment models that divide people into a few subsets and simulate the dynamics among those subsets using hypothesized differential equations. However, these models lack the requisite granularity to study the effect of intelligent policies that influence every individual in a particular way. In this work, we introduce a simulator software capable of modeling a population structure and controlling the disease's propagation at an individualistic level. In order to estimate the confidence of the conclusions drawn from the simulator, we employ a comprehensive probabilistic approach where the entire population is constructed as a hierarchical random variable. This approach makes the inferred conclusions more robust against sampling artifacts and gives confidence bounds for decisions based on the simulation results. To showcase potential applications, the simulator parameters are set based on the formal statistics of the COVID-19 pandemic, and the outcome of a wide range of control measures is investigated. Furthermore, the simulator is used as the environment of a reinforcement learning problem to find the optimal policies to control the pandemic. The obtained experimental results indicate the simulator's adaptability and capacity in making sound predictions and a successful policy derivation example based on real-world data. As an exemplary application, our results show that the proposed policy discovery method can lead to control measures that produce significantly fewer infected individuals in the population and protect the health system against saturation.

Direct and indirect effects of the COVID-19 pandemic on mortality in Switzerland

The direct and indirect impact of the COVID-19 pandemic on population-level mortality is of concern to public health but challenging to quantify. Using data for 2011-2019, we applied Bayesian models to predict the expected number of deaths in Switzerland and compared them with laboratory-confirmed COVID-19 deaths from February 2020 to April 2022 (study period). We estimated that COVID-19-related mortality was underestimated by a factor of 0.72 (95% credible interval [CrI]: 0.46-0.78). After accounting for COVID-19 deaths, the observed mortality was -4% (95% CrI: -8 to 0) lower than expected. The deficit in mortality was concentrated in age groups 40-59 (-12%, 95%CrI: -19 to -5) and 60-69 (-8%, 95%CrI: -15 to -2). Although COVID-19 control measures may have negative effects, after subtracting COVID-19 deaths, there were fewer deaths in Switzerland during the pandemic than expected, suggesting that any negative effects of control measures were offset by the positive effects. These results have important implications for the ongoing debate about the appropriateness of COVID-19 control measures.

Optimal control analysis of a multigroup SEAIHRD model for COVID-19 epidemic

The COVID-19 pandemic has threatened public health and caused substantial economic loss to most countries worldwide. A multigroup susceptible-exposed-asymptomatic-infectious-hospitalized-recovered-dead (SEAIHRD) compartment model is first constructed to model the spread of the disease by dividing the population into three age groups: young (aged 0-19), prime (aged 20-64), and elderly (aged 65 and over). Then, we develop a free terminal time, partially fixed terminal state optimal control problem to minimize deaths and costs associated with hospitalization and the implementation of different control strategies. And the optimal strategies are derived under different assumptions about medical resources and vaccination. Specifically, we explore optimal control strategies for reaching herd immunity in the COVID-19 outbreak in a free terminal time situation to evaluate the effect of nonpharmaceutical interventions (NPIs) and vaccination as control measures. The transmission rate of SARS-CoV-2 is calibrated by using real data in the United States at the early stage of the epidemic. Through numerical simulation, we conclude that the outbreak of COVID-19 can be contained by implementing appropriate control of the prime age population and relatively strict control measures for young and elderly populations. Within a specific period, strict control measures should be implemented before the vaccine is marketed.

Evolution of antibody profiles against SARS-CoV-2 in experienced and naïve vaccinated elderly people

Introduction

The third dose of the COVID-19 vaccine is especially necessary in people over 65 years of age due to their lower immune response.

Methods

We designed a multicentre, prospective observational study including 98 people ≤65 years old who lived in two nursing homes in Valladolid, Spain. One of the groups had previous experience with SARS-CoV-2 (n=68;69.4%) and the other was naïve (n=30;30.6%). We evaluated the response to the three doses of the Comirnaty vaccine and the dynamics of antibodies during 5 consecutive serum samplings: 2 after the first two doses of vaccination, one three months after the first dose, another at 6 months and the last one month after the third dose. IgG antibodies against SARS-CoV-2 S1, RBD and N antigens were analysed.

Results

Both groups increased the level of Abs against S1 and RBD, but the experienced group showed a 130-fold higher humoral response due to hybrid immunisation (infection+vaccination). The response to vaccination with Comirnaty against COVID-19 was higher in those ≤65 years with previous experience than those who were naïve. However, the amount of antibodies against S1 and RBD equalised at 6 months. After the third dose, both groups raised the amount of antibodies to a similar level. The reinfections suggested by the analysis of antibodies against N were frequent in both groups.

Discussion

The third dose showed a clear benefit for elderly people, with the reinforcement of the antibody levels after the decline suffered after six months of the first two doses.

COVID-19 contagion forecasting framework based on curve decomposition and evolutionary artificial neural networks: A case study in Andalusia, Spain

Many types of research have been carried out with the aim of combating the COVID-19 pandemic since the first outbreak was detected in Wuhan, China. Anticipating the evolution of an outbreak helps to devise suitable economic, social and health care strategies to mitigate the effects of the virus. For this reason, predicting the SARS-CoV-2 transmission rate has become one of the most important and challenging problems of the past months. In this paper, we apply a two-stage mid and long-term forecasting framework to the epidemic situation in eight districts of Andalusia, Spain. First, an analytical procedure is performed iteratively to fit polynomial curves to the cumulative curve of contagions. Then, the extracted information is used for estimating the parameters and structure of an evolutionary artificial neural network with hybrid architectures (i.e., with different basis functions for the hidden nodes) while considering single and simultaneous time horizon estimations. The results obtained demonstrate that including polynomial information extracted during the training stage significantly improves the mid- and long-term estimations in seven of the eight considered districts. The increase in average accuracy (for the joint mid- and long-term horizon forecasts) is 37.61% and 35.53% when considering the single and simultaneous forecast approaches, respectively.

Global variations in the burden of SARS-CoV-2 infection and its outcomes in pregnant women by geographical region and country’s income status: a meta-analysis

Introduction

The prevalence of COVID-19 and its impact varied between countries and regions. Pregnant women are at high risk of COVID-19 complications compared with non-pregnant women. The magnitude of variations, if any, in SARS-CoV-2 infection rates and its health outcomes among pregnant women by geographical regions and country’s income level is not known.

Methods

We performed a random-effects meta-analysis as part of the ongoing PregCOV-19 living systematic review (December 2019 to April 2021). We included cohort studies on pregnant women with COVID-19 reporting maternal (mortality, intensive care admission and preterm birth) and offspring (mortality, stillbirth, neonatal intensive care admission) outcomes and grouped them by World Bank geographical region and income level. We reported results as proportions with 95% confidence intervals (CI).

Results

We included 311 studies (2 003 724 pregnant women, 57 countries). The rates of SARS-CoV-2 infection in pregnant women varied significantly by region (p<0.001) and income level (p<0.001), with the highest rates observed in Latin America and the Caribbean (19%, 95% CI 12% to 27%; 13 studies, 38 748 women) and lower-middle-income countries (13%, 95% CI 6% to 23%; 25 studies, 100 080 women). We found significant differences in maternal and offspring outcomes by region and income level. Lower-middle-income countries reported significantly higher rates of maternal mortality (0.68%, 95% CI 0.24% to 1.27%; 3 studies, 31 136 women), intensive care admission (4.53%, 95% CI 2.57% to 6.91%; 54 studies, 23 420 women) and stillbirths (1.09%, 95% CI 0.48% to 1.88%; 41 studies, 4724 women) than high-income countries. COVID-19 complications disproportionately affected South Asia, which had the highest maternal mortality rate (0.88%, 95% CI 0.16% to 1.95%; 17 studies, 2023 women); Latin America and the Caribbean had the highest stillbirth rates (1.97%, 95% CI 0.9% to 3.33%; 10 studies, 1750 women).

Conclusion

The rates of SARS-CoV-2 infection in pregnant women vary globally, and its health outcomes mirror the COVID-19 burden and global maternal and offspring inequalities.

PROSPERO registration number

CRD42020178076.

Novel methods for estimating the instantaneous and overall COVID-19 case fatality risk among care home residents in England

The COVID-19 pandemic has had high mortality rates in the elderly and frail worldwide, particularly in care homes. This is driven by the difficulty of isolating care homes from the wider community, the large population sizes within care facilities (relative to typical households), and the age/frailty of the residents. To quantify the mortality risk posed by disease, the case fatality risk (CFR) is an important tool. This quantifies the proportion of cases that result in death. Throughout the pandemic, CFR amongst care home residents in England has been monitored closely. To estimate CFR, we apply both novel and existing methods to data on deaths in care homes, collected by Public Health England and the Care Quality Commission. We compare these different methods, evaluating their relative strengths and weaknesses. Using these methods, we estimate temporal trends in the instantaneous CFR (at both daily and weekly resolutions) and the overall CFR across the whole of England, and dis-aggregated at regional level. We also investigate how the CFR varies based on age and on the type of care required, dis-aggregating by whether care homes include nursing staff and by age of residents. This work has contributed to the summary of measures used for monitoring the UK epidemic.

During an epidemic, the case fatality risk (CFR), i.e. the probability that an individual dies after testing positive for a disease, is a key parameter informing the public health response. However, calculating the CFR is not trivial, since there are cases who may die in the future but have not died yet. Therefore, statistical methods are required to correct for the distribution of times between testing positive and dying. In this paper, we derive multiple methods, some existing and some novel, within a consistent methodological framework. This allows us to understand how these different approaches are related and their relative strengths and weaknesses. During the COVID-19 pandemic, care homes have been particularly affected, due to the high risk of COVID-19-associated mortality in the frail and elderly. We apply our CFR methods to data from English care homes to analyse changes in the care home CFR throughout the pandemic.

Containing novel SARS-CoV-2 variants at source is possible with high-intensity sequencing

Throughout the COVID-19 pandemic, control of transmission has been repeatedly thwarted by the emergence of variants of concern (VOC) and their geographic spread. Key questions remain regarding effective means of minimizing the impact of VOC, in particular the feasibility of containing them at source, in light of global interconnectedness. By analysing a stochastic transmission model of COVID-19, we identify the appropriate monitoring requirements that make containment at source feasible. Specifically, precise risk assessment informed primarily by epidemiological indicators (e.g. accumulated hospitalization or mortality reports), is unlikely prior to VOC escape. Consequently, decision makers will need to make containment decisions without confident severity estimates. In contrast, successfully identifying and containing variants via genomic surveillance is realistic, provided sequence processing and dissemination is prompt.

Correlation between human development index and its components with COVID-19 indices: a global level ecologic study

Background

Given that COVID-19 continues to spread worldwide, attempts to restrain the virus and to prevent the effects that critically ill patients with COVID-19 have on healthcare systems, has become a public health priority. This ecological study aimed to investigate the correlation between the Human Development Index (HDI) and the epidemiological indicators of COVID-19, including the cumulative incidence rate of cases, the cumulative incidence rate of death, performed COVID-19 tests per million, recovery rate, and case fatality rate.

Methods

In this ecological study, a data set was provided, which included the epidemiologic indices of COVID-19, HDI, and its components for each country. Correlation coefficients were used to determine linear correlation. Also, the scatter plots of the HDI for the studied countries based on the epidemiologic indices of COVID-19 were drawn.

Results

This study showed that HDI and its components had positive correlation with a cumulative incidence rate of cases, the cumulative incidence rate of death, and performed COVID-19 tests (p < 0.001). HDI and two of its components, including literacy and Gross National Income (GNI) components had negative correlation with case fatality rate (CFR). Also, HDI and two of its components, including literacy and life expectancy components had negative correlation with recovery rate.

Conclusion

Our study showed that the HDI and its components can affect the epidemiological status of COVID-19. As HDI increased, the cumulative incidence rate of cases, cumulative incidence rate of death, and COVID-19 tests increased as well. As HDI increased, CFR and recovery rate decreased as well. Although the HDI is higher in high-income countries, these countries may have also better reporting and surveillance systems.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

The effective reproductive number R_e is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of R_e, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated R_e dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated R_e. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent R_e estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of R_e estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Estimation and worldwide monitoring of the effective reproductive number of SARS-CoV-2

The effective reproductive number Re is a key indicator of the growth of an epidemic. Since the start of the SARS-CoV-2 pandemic, many methods and online dashboards have sprung up to monitor this number through time. However, these methods are not always thoroughly tested, correctly placed in time, or are overly confident during high incidence periods. Here, we present a method for timely estimation of Re, applied to COVID-19 epidemic data from 170 countries. We thoroughly evaluate the method on simulated data, and present an intuitive web interface for interactive data exploration. We show that, in early 2020, in the majority of countries the estimated Re dropped below 1 only after the introduction of major non-pharmaceutical interventions. For Europe the implementation of non-pharmaceutical interventions was broadly associated with reductions in the estimated Re. Globally though, relaxing non-pharmaceutical interventions had more varied effects on subsequent Re estimates. Our framework is useful to inform governments and the general public on the status of epidemics in their country, and is used as the official source of Re estimates for SARS-CoV-2 in Switzerland. It further allows detailed comparison between countries and in relation to covariates such as implemented public health policies, mobility, behaviour, or weather data.

Decline of onset-to-diagnosis interval and its impacts on clinical outcome of COVID-19 in China: a nation-wide observational study

Background

To quantitatively assess the impact of the onset-to-diagnosis interval (ODI) on severity and death for coronavirus disease 2019 (COVID-19) patients.

Methods

This retrospective study was conducted based on the data on COVID-19 cases of China over the age of 40 years reported through China’s National Notifiable Infectious Disease Surveillance System from February 5, 2020 to October 8, 2020. The impacts of ODI on severe rate (SR) and case fatality rate (CFR) were evaluated at individual and population levels, which was further disaggregated by sex, age and geographic origin.

Results

As the rapid decline of ODI from around 40 days in early January to < 3 days in early March, both CFR and SR of COVID-19 largely dropped below 5% in China. After adjusting for age, sex, and region, an effect of ODI on SR was observed with the highest OR of 2.95 (95% CI 2.37‒3.66) at Day 10–11 and attributable fraction (AF) of 29.1% (95% CI 22.2‒36.1%) at Day 8–9. However, little effect of ODI on CFR was observed. Moreover, discrepancy of effect magnitude was found, showing a greater effect from ODI on SR among patients of male sex, younger age, and those cases in Wuhan.

Conclusion

The ODI was significantly associated with the severity of COVID-19, highlighting the importance of timely diagnosis, especially for patients who were confirmed to gain increased benefit from early diagnosis to some extent.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07660-4.

Nonlinear modal regression for dependent data with application for predicting COVID-19

In this paper, under the stationary α-mixing dependent samples, we develop a novel nonlinear modal regression for time series sequences and establish the consistency and asymptotic property of the proposed nonlinear modal estimator with a shrinking bandwidth h under certain regularity conditions. The asymptotic distribution is shown to be identical to the one derived from the independent observations, whereas the convergence rate (n h 3 in which n is the sample size) is slower than that in the nonlinear mean regression. We numerically estimate the proposed nonlinear modal regression model by the use of a modified modal expectation-maximization (MEM) algorithm in conjunction with Taylor expansion. Monte Carlo simulations are presented to demonstrate the good finite sample (prediction) performance of the newly proposed model. We also construct a specified nonlinear modal regression to match the available daily new cases and new deaths data of the COVID-19 outbreak at the state/region level in the United States, and provide forward predictions up to 130 days ahead (from 24 August 2020 to 31 December 2020). In comparison to the traditional nonlinear regressions, the suggested model can fit the COVID-19 data better and produce more precise predictions. The prediction results indicate that there are systematic differences in spreading distributions among states/regions. For most western and eastern states, they have many serious COVID-19 burdens compared to Midwest. We hope that the built nonlinear modal regression can help policymakers to implement fast actions to curb the spread of the infection, avoid overburdening the health system and understand the development of COVID-19 from some points.

Early outcomes in adults hospitalized with severe SARS-CoV-2 infection receiving tocilizumab

BACKGROUND

Modulation of the immune system to prevent lung injury is being widely used against the new coronavirus disease (COVID-19). The primary endpoint was mortality at 7 days after tocilizumab administration. Secondary endpoints were admission to the intensive care unit, development of ARDS and respiratory insufficiency among others.

METHODS

We report the preliminary results from the Vall d'Hebron cohort study at Vall d'Hebron University Hospital, in Barcelona (Spain), including all consecutive patients who had a confirmed SARS-CoV-2 infection and who were treated with tocilizumab until March 25th.

RESULTS

82 patients with COVID-19 received at least one dose of tocilizumab. The mean (± SD) age was 59.1 (19.8) years, 63% were male, 22% were of non-Spanish ancestry, and the median (IQR) age-adjusted Charlson index at baseline was 3 (1-4) points. Respiratory failure and ARDS developed in 62 (75.6%) and 45 (54.9%) patients, respectively. Median time from symptom onset to ARDS development was 8 (5-11) days. Mortality at 7 days was 26.8%. Hazard ratio for mortality was 3.3; 95% CI, 1.3-8.5 (age-adjusted hazard ratio for mortality 2.1; 95% CI, 0.8-5.8) if tocilizumab was administered after the onset of ARDS.

CONCLUSION

Early administration of tocilizumab in patients needing oxygen supplementation may be critical to patient recovery. Our preliminary data could inform bedside decisions until more data regarding the precise timing in of initiation of the treatment with tocilizumab.

Early outcomes in adults hospitalized with severe SARS-CoV-2 infection receiving tocilizumab

Background

Methods

Results

Conclusion

Predictive Models for Forecasting Public Health Scenarios: Practical Experiences Applied during the First Wave of the COVID-19 Pandemic

Background: Forecasting the behavior of epidemic outbreaks is vital in public health. This makes it possible to anticipate the planning and organization of the health system, as well as possible restrictive or preventive measures. During the COVID-19 pandemic, this need for prediction has been crucial. This paper attempts to characterize the alternative models that were applied in the first wave of this pandemic context, trying to shed light that could help to understand them for future practical applications. Methods: A systematic literature search was performed in standardized bibliographic repertoires, using keywords and Boolean operators to refine the findings, and selecting articles according to the main PRISMA 2020 statement recommendations. Results: After identifying models used throughout the first wave of this pandemic (between March and June 2020), we begin by examining standard data-driven epidemiological models, including studies applying models such as SIR (Susceptible-Infected-Recovered), SQUIDER, SEIR, time-dependent SIR, and other alternatives. For data-driven methods, we identify experiences using autoregressive integrated moving average (ARIMA), evolutionary genetic programming machine learning, short-term memory (LSTM), and global epidemic and mobility models. Conclusions: The COVID-19 pandemic has led to intensive and evolving use of alternative infectious disease prediction models. At this point it is not easy to decide which prediction method is the best in a generic way. Moreover, although models such as the LSTM emerge as remarkably versatile and useful, the practical applicability of the alternatives depends on the specific context of the underlying variable and on the information of the target to be prioritized. In addition, the robustness of the assessment is conditioned by heterogeneity in the quality of information sources and differences in the characteristics of disease control interventions. Further comprehensive comparison of the performance of models in comparable situations, assessing their predictive validity, is needed. This will help determine the most reliable and practical methods for application in future outbreaks and eventual pandemics.

Survival among people hospitalized with COVID-19 in Switzerland: a nationwide population-based analysis

BACKGROUND

Increasing age, male sex, and pre-existing comorbidities are associated with lower survival from SARS-CoV-2 infection. The interplay between different comorbidities, age, and sex is not fully understood, and it remains unclear if survival decreases linearly with higher ICU occupancy or if there is a threshold beyond which survival falls.

METHOD

This national population-based study included 22,648 people who tested positive for SARS-CoV-2 infection and were hospitalized in Switzerland between February 24, 2020, and March 01, 2021. Bayesian survival models were used to estimate survival after positive SARS-CoV-2 test among people hospitalized with COVID-19 by epidemic wave, age, sex, comorbidities, and ICU occupancy. Two-way interactions between age, sex, and comorbidities were included to assess the differential risk of death across strata. ICU occupancy was modeled using restricted cubic splines to allow for a non-linear association with survival.

RESULTS

Of 22,648 people hospitalized with COVID-19, 4785 (21.1%) died. The survival was lower during the first epidemic wave than in the second (predicted survival at 40 days after positive test 76.1 versus 80.5%). During the second epidemic wave, occupancy among all available ICU beds in Switzerland varied between 51.7 and 78.8%. The estimated survival was stable at approximately 81.5% when ICU occupancy was below 70%, but worse when ICU occupancy exceeded this threshold (survival at 80% ICU occupancy: 78.2%; 95% credible interval [CrI] 76.1 to 80.1%). Periods with higher ICU occupancy (>70 vs 70%) were associated with an estimated number of 137 (95% CrI 27 to 242) excess deaths. Comorbid conditions reduced survival more in younger people than in older people. Among comorbid conditions, hypertension and obesity were not associated with poorer survival. Hypertension appeared to decrease survival in combination with cardiovascular disease.

CONCLUSIONS

Survival after hospitalization with COVID-19 has improved over time, consistent with improved management of severe COVID-19. The decreased survival above 70% national ICU occupancy supports the need to introduce measures for prevention and control of SARS-CoV-2 transmission in the population well before ICUs are full.

A cross-sectional international study shows confidence in public health scientists predicts use of COVID-19 non-pharmaceutical interventions

BACKGROUND

We examine the antecedents of COVID-19 phone tracking applications use, social distancing, and mask use, in the United States, Hong Kong and Japan.

METHODS

We draw on online panel surveys of over 1000 respondents each in the USA, Hong Kong and Japan, using broadly representative quota sample selections. Results are tested by ordinal logistic regression for the two ordinal dependent variables and logistic regression for phone app use.

RESULTS

Confidence in public health scientists predicts use of phone tracking applications, social distancing, and mask use, albeit statistically insignificant for tracer phone application use in Hong Kong. Trust in government predicts the use of a phone tracking application. Counterintuitively, trust in government is negatively and significantly associated with mask use and social distancing in Hong Kong and Japan. Women are more likely to wear masks and practice social distancing. Government employees are more likely to use a phone tracking application, but less likely to mask and social distance. Voting and civic participation are positively associated with trust in government and confidence in public health scientists, in all three countries. There are interesting variations across all three countries on other antecedents and controls.

CONCLUSIONS

Building and maintaining confidence in public health scientists provides a key tool to manage pandemics. Credible, effectively communicative - and independent - medical and scientific leaders may be central to pandemic control success. For digital measures, trust in government and privacy protection is central. Political and social factors are important to understand successful public health policy implementation.

Reduced COVID-19 mortality linked with early antibodies against SARS-CoV-2, irrespective of age

BACKGROUND

COVID-19 pandemic has generated a million deaths worldwide. The efficiency of the immune system can modulate individual vulnerability with variable outcomes. However, the relationships between disease severity and the titer of antibodies produced against SARS-CoV-2 in non-vaccinated, recently infected subjects need to be fully elucidated.

METHODS

A total of 99 patients admitted to a COVID-unit underwent clinical assessment and measurement of serum levels of anti-spike protein (S1) IgM, and anti-nucleocapsid protein IgG. Patients were stratified according to the clinical outcome (i.e., discharged at home or in-hospital death).

RESULTS

Following hospitalization, 18 died during the hospital stay. They were older, had lymphopenia, a higher co-morbidity rate, and longer hospital stay than 81 patients who were discharged after healing. Patients in this latter group had, at hospital admittance, 7.9-fold higher serum concentration of IgM, and 2.4-fold higher IgG levels. Multivariate Cox regression models indicated age and anti-nucleocapsid protein IgG concentration at admission as independently associated with the risk of in-hospital death.

CONCLUSIONS

An efficient immunological response during the early phase of COVID-19 protects from mortality, irrespective of age. Advanced age is a critical risk factor for poor outcome in infected subjects. Further studies must explore potential therapeutic strategies able to restore a valid functional humoral immunity in elderly patients with poor antibody response during the early stage of COVID-19 infection.

A hybrid Neural Network-SEIR model for forecasting intensive care occupancy in Switzerland during COVID-19 epidemics

Anticipating intensive care unit (ICU) occupancy is critical in supporting decision makers to impose (or relax) measures that mitigate COVID-19 transmission. Mechanistic approaches such as Susceptible-Infected-Recovered (SIR) models have traditionally been used to achieve this objective. However, formulating such models is challenged by the necessity to formulate equations for plausible causal mechanisms between the intensity of COVID-19 transmission and external epidemic drivers such as temperature, and the stringency of non-pharmaceutical interventions. Here, we combined a neural network model (NN) with a Susceptible-Exposed-Infected-Recovered model (SEIR) in a hybrid model and attempted to increase the prediction accuracy of existing models used to forecast ICU occupancy. Between 1st of October, 2020 - 1st of July, 2021, the hybrid model improved performances of the SEIR model at different geographical levels. At a national level, the hybrid model improved, prediction accuracy (i.e., mean absolute error) by 74%. At the cantonal and hospital levels, the reduction on the forecast's mean absolute error were 46% and 50%, respectively. Our findings illustrate those predictions from hybrid model can be used to anticipate occupancy in ICU, and support the decision-making for lifesaving actions such as the transfer of patients and dispatching of medical personnel and ventilators.

COMMON VARIABLE IMMUNODEFICIENCY: PREDISPOSING OR PROTECTIVE FACTOR FOR SEVERE COMPLICATIONS OF COVID-19?

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The usual presentation of the disease is a common cold-like illness but it can present with more severe and sometimes fatal manifestations. Immunocompromised patients such as those with common variable immunodeficiency (CVID) also are among the infected population. A limited number of reports have been published concerning CVID patients with COVID-19. The main reported symptoms were fever, cough, dyspnea and fatigue while the median duration of illness was 19 (interquartile range 14-26.5) days. Total recovery rate was 88.4%. It is still unknown whether primary immunodeficiency interacts as a predisposing or protective factor against the severe forms of COVID-19. Substitute immunoglobulin (IG) therapy is the only treatment option for CVID. Some reports suggest that early administration of intravenous IGs or convalescent plasma infusion may positively influence the outcome of COVID-19 in these patients.

Loss of life expectancy due to respiratory infectious diseases: findings from the global burden of disease study in 195 countries and territories 1990-2017

Understanding of the patterns of and changes in mortality from respiratory infectious diseases (RID) and its contribution to loss of life expectancy (LE) is inadequate in the existing literature. With rapid sociodemographic changes globally, and the current COVID-19 pandemic, it is timely to revisit the disease burden of RID. Using the approaches of life table and cause-eliminated life table based on data from the Global Burden of Disease Study (GBD), the study analyses loss of LE due to RID in 195 countries/territories and its changes during the period 1990-2017. Results indicate that loss of LE due to RID stood at 1.29 years globally in 2017 globally and varied widely by age, gender, and geographic location, with men, elderly people, and populations in middle/low income countries/territories suffering a disproportionately high loss of LE due to RID. Additionally, loss of LE due to RID decreased remarkably by 0.97 years globally during the period 1990-2017 but increased slightly among populations older than 70 years and in many high income countries/territories. Results suggest that RID still pose a severe threat for population and public health, and that amid dramatic sociodemographic changes globally, the disease burden of RID may resurge. The study presents the first examination of the life-shortening effect of RID at the global and country/territory levels, providing new understanding of the changing disease burden of RID and shedding light on the potential consequences of the current COVID-19 pandemic.

The adaptation of SARS-CoV-2 to humans

The process of adaptation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to humans probably had started decades ago, when its ancestor diverged from the bat coronavirus. The adaptive process comprises strategies the virus uses to overcome the respiratory tract defense barriers and replicate and shed in the host cells. These strategies include the impairment of interferon production, hiding immunogenic motifs, avoiding viral RNA detection, manipulating cell autophagy, triggering host cell death, inducing lymphocyte exhaustion and depletion, and finally, mutation and escape from immunity. In addition, SARS-CoV-2 employs strategies to take advantage of host cell resources for its benefits, such as inhibiting the ubiquitin-proteasome system, hijacking mitochondria functions, and usage of enhancing antibodies. It may be anticipated that as the tradeoffs of adaptation progress, the virus destructive burden will gradually subside. Some evidence suggests that SARS-CoV-2 will become part of the human respiratory virome, as had occurred with other coronaviruses, and coevolve with its host.

Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion

The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as “dynamic causal modeling” (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.

Retrospective epidemiology of the SARS-CoV-2 (and COVID-19) epidemic among 27 Brazilian cities

Background

Most of the countries facing the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic are still trying to understand the dynamics of the behavior of the virus and dissemination of the new agent.

Objectives

A retrospective descriptive epidemiological study of the 26 state capitals of Brazil and its capital, Brasilia, was performed to investigate the behavior of the infection and disease caused by SARS-CoV-2.

Study design

The data presented were obtained from the State Health Departments and the Brazilian Ministry of Health. Seven epidemiological markers (including the incidence, mortality and case fatality rates and the growth of the epidemic measured by the ratios observed on days 30, 60 and 90) were compared for the initial 90 days of the epidemic for each city.

Results

The epidemic spread to the country within 25 days, and deaths occurred as early as nine days from initiation. The incidence and mortality rates ranged from 70 to almost 1,599/100,000 and less than 1 to 1,171/1,000,000, respectively, at the end of the 90-day period of observation. The CFR was less than two up to 12.31%. The magnitude of each marker clustered the cities in different groups. The epidemic was managed differently in each city, with differences in qualified medical services and medical preparedness to face the emergency situation.

Conclusions

Although modeling the epidemic has been a constant task, epidemiological data should be pursued to define actual information, such as the prevalence and incidence rates, to understand the unpredictable nature of this emerging infection, including the present policy of vaccination campaigns.

Bayesian workflow for disease transmission modeling in Stan

This tutorial shows how to build, fit, and criticize disease transmission models in Stan, and should be useful to researchers interested in modeling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and other infectious diseases in a Bayesian framework. Bayesian modeling provides a principled way to quantify uncertainty and incorporate both data and prior knowledge into the model estimates. Stan is an expressive probabilistic programming language that abstracts the inference and allows users to focus on the modeling. As a result, Stan code is readable and easily extensible, which makes the modeler's work more transparent. Furthermore, Stan's main inference engine, Hamiltonian Monte Carlo sampling, is amiable to diagnostics, which means the user can verify whether the obtained inference is reliable. In this tutorial, we demonstrate how to formulate, fit, and diagnose a compartmental transmission model in Stan, first with a simple susceptible-infected-recovered model, then with a more elaborate transmission model used during the SARS-CoV-2 pandemic. We also cover advanced topics which can further help practitioners fit sophisticated models; notably, how to use simulations to probe the model and priors, and computational techniques to scale-up models based on ordinary differential equations.

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.

COVID-19 Pandemic: Public Health Risk Assessment and Risk Mitigation Strategies

A newly emerged respiratory viral disease called severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is also known as pandemic coronavirus disease (COVID-19). This pandemic has resulted an unprecedented global health crisis and devastating impact on several sectors of human lives and economies. Fortunately, the average case fatality ratio for SARS-CoV-2 is below 2%, much lower than that estimated for MERS (34%) and SARS (11%). However, COVID-19 has a much higher transmissibility rate, as evident from the constant increase in the count of infections worldwide. This article explores the reasons behind how COVID-19 was able to cause a global pandemic crisis. The current outbreak scenario and causes of rapid global spread are examined using recent developments in the literature, epidemiological features relevant to public health awareness, and critical perspective of risk assessment and mitigation strategies. Effective pandemic risk mitigation measures have been established and amended against COVID-19 diseases, but there is still much scope for upgrading execution and coordination among authorities in terms of organizational leadership's commitment and diverse range of safety measures, including administrative control measures, engineering control measures, and personal protective equipment (PPE). The significance of containment interventions against the COVID-19 pandemic is now well established; however, there is a need for its effective execution across the globe, and for the improvement of the performance of risk mitigation practices and suppression of future pandemic crises.

Estimates of global SARS-CoV-2 infection exposure, infection morbidity, and infection mortality rates in 2020

We aimed to estimate, albeit crudely and provisionally, national, regional, and global proportions of respective populations that have been infected with SARS-CoV-2 in the first year after the introduction of this virus into human circulation, and to assess infection morbidity and mortality rates, factoring both documented and undocumented infections. The estimates were generated by applying mathematical models to 159 countries and territories. The percentage of the world's population that has been infected as of 31 December 2020 was estimated at 12.56% (95% CI: 11.17-14.05%). It was lowest in the Western Pacific Region at 0.66% (95% CI: 0.59-0.75%) and highest in the Americas at 41.92% (95% CI: 37.95-46.09%). The global infection fatality rate was 10.73 (95% CI: 10.21-11.29) per 10,000 infections. Globally per 1000 infections, the infection acute-care bed hospitalization rate was 19.22 (95% CI: 18.73-19.51), the infection ICU bed hospitalization rate was 4.14 (95% CI: 4.10-4.18). If left unchecked with no vaccination and no other public health interventions, and assuming circulation of only wild-type variants and no variants of concern, the pandemic would eventually cause 8.18 million deaths (95% CI: 7.30-9.18), 163.67 million acute-care hospitalizations (95% CI: 148.12-179.51), and 33.01 million ICU hospitalizations (95% CI: 30.52-35.70), by the time the herd immunity threshold is reached at 60-70% infection exposure. The global population remained far below the herd immunity threshold by end of 2020. Global epidemiology reveals immense regional variation in infection exposure and morbidity and mortality rates.

Estimated number of deaths directly averted in people 60 years and older as a result of COVID-19 vaccination in the WHO European Region, December 2020 to November 2021

Since December 2019, over 1.5 million SARS-CoV-2-related fatalities have been recorded in the World Health Organization European Region - 90.2% in people ≥ 60 years. We calculated lives saved in this age group by COVID-19 vaccination in 33 countries from December 2020 to November 2021, using weekly reported deaths and vaccination coverage. We estimated that vaccination averted 469,186 deaths (51% of 911,302 expected deaths; sensitivity range: 129,851-733,744; 23-62%). Impact by country ranged 6-93%, largest when implementation was early.

Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union

Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded.

Effect of public health interventions during the first epidemic wave of COVID-19 in Cyprus: a modelling study

BACKGROUND

Cyprus addressed the first wave of SARS CoV-2 (COVID-19) by implementing non-pharmaceutical interventions (NPIs). The aims of this study were: a) to estimate epidemiological parameters of this wave including infection attack ratio, infection fatality ratio, and case ascertainment ratio, b) to assess the impact of public health interventions and examine what would have happened if those interventions had not been implemented.

METHODS

A dynamic, stochastic, individual-based Susceptible-Exposed-Infected-Recovered (SEIR) model was developed to simulate COVID-19 transmission and progression in the population of the Republic of Cyprus. The model was fitted to the observed trends in COVID-19 deaths and intensive care unit (ICU) bed use.

RESULTS

By May 8th, 2020, the infection attack ratio was 0.31% (95% Credible Interval [CrI]: 0.15, 0.54%), the infection fatality ratio was 0.71% (95% CrI: 0.44, 1.61%), and the case ascertainment ratio was 33.2% (95% CrI: 19.7, 68.7%). If Cyprus had not implemented any public health measure, the healthcare system would have been overwhelmed by April 14th. The interventions averted 715 (95% CrI: 339, 1235) deaths. If Cyprus had only increased ICU beds, without any social distancing measure, the healthcare system would have been overwhelmed by April 19th.

CONCLUSIONS

The decision of the Cypriot authorities to launch early NPIs limited the burden of the first wave of COVID-19. The findings of these analyses could help address the next waves of COVID-19 in Cyprus and other similar settings.

One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut

To support public health policymakers in Connecticut, we developed a flexible county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, and estimates of important features of disease transmission and clinical progression. In this paper, we outline the model design, implementation and calibration, and describe how projections and estimates were used to meet the changing requirements of policymakers and officials in Connecticut from March 2020 to February 2021. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We calibrated this model to data on deaths and hospitalizations and developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.

Assessing relative COVID-19 mortality during the second wave: a prospective Swiss population-based study

OBJECTIVE

During the first COVID-19 wave in Switzerland, relative mortality was at least eight times higher compared with the uninfected general population. We aimed to assess sex-specific and age-specific relative mortality associated with a SARS-CoV-2 diagnosis during the second wave.

DESIGN

Prospective population-based study.

SETTING

Individuals testing positive for SARS-CoV-2 after the start of the second wave on 1 October 2020 were followed up until death or administrative censoring on 31 December 2020.

PARTICIPANTS

5 179 740 inhabitants of Switzerland in fall 2018 aged 35-95 years (without COVID-19) and 257 288 persons tested positive for SARS-CoV-2 by PCR or antigen testing during the second wave.

PRIMARY AND SECONDARY OUTCOME MEASURES

The planned outcome measure was time to death from any cause, measured from the date of a SARS-CoV-2 diagnosis or 1 October in the general population. Information on confirmed SARS-CoV-2 diagnoses and deaths was matched by calendar time with the all-cause mortality of the general Swiss population of 2018. Proportional hazards models were used to estimate sex-specific and age-specific mortality rates and probabilities of death within 60 days.

RESULTS

The risk of death for individuals tested positive for SARS-CoV-2 in the second wave in Switzerland increased at least sixfold compared with the general population. HRs, reflecting the risk attributable to a SARS-CoV-2 infection, were higher for men (1.40, 95% CI 1.29 to 1.52) and increased for each additional year of age (1.01, 95% CI 1.01 to 1.02). COVID-19 mortality was reduced by at least 20% compared with the first wave in spring 2020.

CONCLUSION

General mortality patterns, increased for men and older persons, were similar in spring and in fall. Absolute and relative COVID-19 mortality was smaller in fall.

TRIAL REGISTRATION

The protocol for this study was registered on 3 December 2020 at https://osf.io/gbd6r.

A Bayesian estimate of the early COVID-19 infection fatality ratio in Brazil based on a random seroprevalence survey

BACKGROUND

A number of estimates of the infection fatality ratio (IFR) of SARS-CoV-2 in different countries have been published. In Brazil, the fragile political situation, together with socioeconomic and ethnic diversity, could result in substantially different IFR estimates.

METHODS

We infer the IFR in Brazil in 2020 by combining three datasets. We compute the prevalence via the population-based seroprevalence survey, EPICOVID19-BR. For the fatalities we obtain the absolute number using the public Painel Coronavírus dataset and the age-relative number using the public SIVEP-Gripe dataset. The time delay between the development of antibodies and subsequent fatality is estimated via the SIVEP-Gripe dataset. We obtain the IFR for each survey stage and 27 federal states. We include the effect of fading IgG antibody levels by marginalizing over the test detectability time window.

RESULTS

We infer a country-wide average IFR (maximum posterior and 95% CI) of 1.03% (0.88-1.22%) and age-specific IFRs of 0.032% (0.023-0.041%) [< 30 years], 0.22% (0.18-0.27%) [30-49 years], 1.2% (1.0-1.5%) [50-69 years], and 3.0% (2.4-3.9%) [≥ 70 years]. We find that the fatality ratio in the country increased significantly at the end of June 2020, likely due to the increased strain on the health system.

CONCLUSIONS

Our IFR estimate is based on data and does not rely on extrapolating models. This estimate sets a baseline value with which future medications and treatment protocols may be confronted.

OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany

Mathematical models in epidemiology are an indispensable tool to determine the dynamics and important characteristics of infectious diseases. Apart from their scientific merit, these models are often used to inform political decisions and interventional measures during an ongoing outbreak. However, reliably inferring the epidemical dynamics by connecting complex models to real data is still hard and requires either laborious manual parameter fitting or expensive optimization methods which have to be repeated from scratch for every application of a given model. In this work, we address this problem with a novel combination of epidemiological modeling with specialized neural networks. Our approach entails two computational phases: In an initial training phase, a mathematical model describing the epidemic is used as a coach for a neural network, which acquires global knowledge about the full range of possible disease dynamics. In the subsequent inference phase, the trained neural network processes the observed data of an actual outbreak and infers the parameters of the model in order to realistically reproduce the observed dynamics and reliably predict future progression. With its flexible framework, our simulation-based approach is applicable to a variety of epidemiological models. Moreover, since our method is fully Bayesian, it is designed to incorporate all available prior knowledge about plausible parameter values and returns complete joint posterior distributions over these parameters. Application of our method to the early Covid-19 outbreak phase in Germany demonstrates that we are able to obtain reliable probabilistic estimates for important disease characteristics, such as generation time, fraction of undetected infections, likelihood of transmission before symptom onset, and reporting delays using a very moderate amount of real-world observations.

SARS-CoV-2 infection hospitalization, severity, criticality, and fatality rates in Qatar

The SARS-CoV-2 pandemic resulted in considerable morbidity and mortality as well as severe economic and societal disruptions. Despite scientific progress, true infection severity, factoring both diagnosed and undiagnosed infections, remains poorly understood. This study aimed to estimate SARS-CoV-2 age-stratified and overall morbidity and mortality rates based on analysis of extensive epidemiological data for the pervasive epidemic in Qatar, a country where < 9% of the population are ≥ 50 years. We show that SARS-CoV-2 severity and fatality demonstrate a striking age dependence with low values for those aged < 50 years, but rapidly growing rates for those ≥ 50 years. Age dependence was particularly pronounced for infection criticality rate and infection fatality rate. With Qatar's young population, overall SARS-CoV-2 severity and fatality were not high with < 4 infections in every 1000 being severe or critical and < 2 in every 10,000 being fatal. Only 13 infections in every 1000 received any hospitalization in acute-care-unit beds and < 2 in every 1000 were hospitalized in intensive-care-unit beds. However, we show that these rates would have been much higher if Qatar's population had the demographic structure of Europe or the United States. Epidemic expansion in nations with young populations may lead to considerably lower disease burden than currently believed.

The relationship between measures of individualism and collectivism and the impact of COVID-19 across nations

Background

The global COVID-19 pandemic has been characterized by marked variations in prevalence, mortality and case fatality across nations. The available evidence to date suggests that social factors significantly influence these variations. The sociological concepts of individualism and collectivism provide a broad explanatory framework for the study of these factors. There is evidence to suggest that cross-cultural variations in collectivism may have emerged via a process of natural selection, as a protective mechanism against infectious diseases. As a test of this hypothesis, this paper examined the association between indices of individualism and collectivism and the prevalence, mortality and case fatality rates of COVID-19 across nations.

Study design

This study was a population-level association study based on data in the public domain and from prior publications.

Methods

Data on four standard measures of individualism/collectivism were obtained from the original publications. These were correlated with estimates of the nation-wide prevalence, mortality and fatality rates for COVID-19 in 94 countries, obtained from the Johns Hopkins Medical University real-time dashboard.

Results

Individualism was positively correlated with COVID-19 prevalence, mortality and case fatality rates; conversely, measures of collectivism were negatively correlated with these parameters. The strongest association was between scores for individualism and mortality rate, and remained significant after correcting for several potential confounders.

Conclusions

These findings are consistent with the prior hypothesis of a relationship between individualism-collectivism and the impact of infectious disease across populations, and have implications in terms of social strategies aimed at minimizing the impact of COVID-19.

Twin Peaks: Covid-19 and the labor market

This paper develops a choice-theoretic equilibrium model of the labor market in the presence of a pandemic. It includes heterogeneity in productivity, age and the ability to work from home. Worker and firm behavior changes in the presence of the virus, which itself has equilibrium consequences for the infection rate. The model is calibrated to the UK and counterfactual lockdown measures are evaluated. We find a different response in both the evolution of the virus and the labor market with different lockdown policies. A laissez-faire approach results in lives lost and acts as negative shock to the economy. A lockdown policy, absent any other intervention, will reduce the lives lost but increase the economic burden. Consistent with recent evidence, we find that the economic costs from lockdown are most felt by those earning the least. Finally, we introduce a job retention scheme as implemented by the UK Government and find that it spreads the economic hardship more equitably.

Adjusting Reported COVID-19 Deaths for the Prevailing Routine Death Surveillance in India

In India, the "low mortality" narrative based on the reported COVID-19 deaths may be causing more harm than benefit. The extent to which COVID-19 deaths get reported depends on the coverage of routine death surveillance [death registration along with medical certification of cause of death (MCCD)] and the errors in MCCD. In India, the coverage of routine death surveillance is 18.1%. This is compounded by the fact that COVID-19 death reporting is focused among reported cases and the case detection ratio is low. To adjust for the coverage of routine death surveillance and errors in MCCD, we calculated a correction (multiplication) factor at national and state level to produce an estimated number of COVID-19 deaths. As on July 31, 2020, we calculated the infection fatality ratio (IFR) for India (0.58:100-1.16:100) using these estimated COVID-19 deaths; this is comparable with the IFR range in countries with near perfect routine death surveillance. We recommend the release of excess deaths data during COVID-19 (at least in states with high death registration) and post-mortem COVID-19 testing as a surveillance activity for a better understanding of under-reporting. In its absence, we should adjust reported COVID-19 deaths for the coverage of routine death surveillance and errors in MCCD. This way we will have a clear idea of the true burden of deaths and our public health response will never be inadequate. We recommend that "reported" or "estimated" is added before the COVID-19 death data and related indicators for better clarity and interpretation.

Analysis of the time course of COVID-19 cases and deaths from countries with extensive testing allows accurate early estimates of the age specific symptomatic CFR values

BACKGROUND

Knowing the true infected and symptomatic case fatality ratios (IFR and CFR) for COVID-19 is of high importance for epidemiological model projections. Early in the pandemic many locations had limited testing and reporting, so that standard methods for determining IFR and CFR required large adjustments for missed cases. We present an alternate approach, based on results from the countries at the time that had a high test to positive case ratio to estimate symptomatic CFR.

METHODS

We calculated age specific (0-69, 70-79, 80+ years old) time corrected crude symptomatic CFR values from 7 countries using two independent time to fatality correction methods. Data was obtained through May 7, 2020. We applied linear regression to determine whether the mean of these coefficients had converged to the true symptomatic CFR values. We then tested these coefficients against values derived in later studies as well as a large random serological study in NYC at that time.

RESULTS

The age dependent symptomatic CFR values accurately predicted the percentage of the population infected as reported by two random testing studies in NYC. They also were in good agreement with later studies that estimated age specific IFR and CFR values from serological studies and more extensive data sets available later in the pandemic.

CONCLUSIONS

We found that for regions with extensive testing it is possible to get early accurate symptomatic CFR coefficients. These values, in combination with an estimate of the age dependence of infection, allows symptomatic CFR values and percentage of the population that is infected to be determined in similar regions with limited testing.

Tool for Estimating the Probability of Having COVID-19 With 1 or More Negative RT-PCR Results

Early case detection and isolation of infected individuals are critical to controlling coronavirus disease 2019 (COVID-19). Reverse transcription polymerase chain reaction (RT-PCR) is considered the gold standard for the diagnosis of severe acute respiratory syndrome coronavirus 2 infection, but false negatives do occur. We built a user-friendly online tool to estimate the probability of having COVID-19 with negative RT-PCR results and thus avoid preventable transmission.

SARS-CoV-2 Infection Is at Herd Immunity in the Majority Segment of the Population of Qatar

BACKGROUND

Qatar experienced a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic that disproportionately affected the craft and manual worker (CMW) population, who comprise 60% of the total population. This study aimed to assess ever and/or current infection prevalence in this population.

METHODS

A cross-sectional population-based survey was conducted during July 26 to September 09, 2020, to assess both anti-SARS-CoV-2 positivity through serological testing and current infection positivity through polymerase chain reaction (PCR) testing. Associations with antibody and PCR positivity were identified through regression analyses.

RESULTS

The study included 2641 participants, 69.3% of whom were <40 years of age. Anti-SARS-CoV-2 positivity was 55.3% (95% CI, 53.3%-57.3%) and was significantly associated with nationality, geographic location, educational attainment, occupation, and previous infection diagnosis. PCR positivity was 11.3% (95% CI, 9.9%-12.8%) and was significantly associated with nationality, geographic location, occupation, contact with an infected person, and reporting 2 or more symptoms. Infection positivity (antibody and/or PCR positive) was 60.6% (95% CI, 58.6%-62.5%). The proportion of antibody-positive CMWs who had a prior SARS-CoV-2 diagnosis was 9.3% (95% CI, 7.9%-11.0%). Only seven infections were ever severe, and only 1 was ever critical-an infection severity rate of 0.5% (95% CI, 0.2%-1.0%).

CONCLUSIONS

Six in every 10 CMWs in Qatar have been infected, suggestive of reaching the herd immunity threshold. Infection severity was low, with only 1 in every 200 infections progressing to be severe or critical. Only 1 in every 10 infections had been previously diagnosed, which is suggestive of mostly asymptomatic or mild infections.