Infection Fatality Ratio Research Articles

Spatial analysis on influence of elevation to coronavirus disease-19 infection fatality ratio in Bandung Regency and Bandung City

Abstract Bandung Metropolitan Area located on elevation span between 666 masl (Sub-district of Gedebage) to 2608 masl (Kendang Mountain, Sub-district of Kertasari). Bandung Metropolitan Area consist of Bandung City, Cimahi City, Bandung Regency, West Bandung Regency, and Sumedang Regency. Within Coronavirus Disease 2019 (COVID 19) pandemic, Bandung City and Bandung Regency are among the top on COVID 19 cases statistic. Despite of that, Infection Fatality Ratio between region units has significant differences. Aims of this research was to analyze correlation between elevation factor and the latest COVID 19 Infection Fatality Ratio in case-study area. Methods applied on this research was non-experimental quantitative research by comparative analysis type based on COVID 19 cases statistic. Therefore, Techniques used was spatial analysis based on case-study area mapping. Result of this research shows that there was correlation between elevation toward Infection Fatality Ratio, in which Bandung City with average elevation 723 masl records 0,08% on fatality ratio, while Bandung Regency with average elevation 891 masl records 0,01% on fatality ratio. On district level, both Sub-district with highest elevation in Bandung City and Bandung Regency, which were Sub-district of Sukasari (850 masl) and Sub-district of Rancabali (1600 masl) records 0% on Infection Fatality Ratio.

Estimating geographic variation of infection fatality ratios during epidemics

ObjectivesWe aim to estimate geographic variability in total numbers of infections and infection fatality ratios (IFR; the number of deaths caused by an infection per 1,000 infected people) when the availability and quality of data on disease burden are limited during an epidemic. MethodsWe develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing. We demonstrate the robustness, accuracy, and precision of this framework, and apply it to the United States (U.S.) COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs. ResultsThe estimators for the numbers of infections and IFRs showed high accuracy and precision; for instance, when applied to simulated validation data sets, across counties, Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928, respectively, and they showed strong robustness to model misspecification. Applying the county-level estimators to the real, unsimulated COVID-19 data spanning April 1, 2020 to September 30, 2020 from across the U.S., we found that IFRs varied from 0 to 44.69, with a standard deviation of 3.55 and a median of 2.14. ConclusionsThe proposed estimation framework can be used to identify geographic variation in IFRs across settings.

Riding high: seroprevalence of SARS-CoV-2 after 4 pandemic waves in Manitoba, Canada, April 2020–February 2022

BackgroundCanada is emerging from the largest SARS-CoV-2 Omicron wave to date, with over 3.3 million confirmed cases. Unfortunately, PCR confirmed cases illuminate only a small portion of infections in the community and underestimate true disease burden. Population based seroprevalence studies, which measure antibody levels against a virus can more accurately estimate infection rates in the community and identify geographical and epidemiological trends to inform public health responses.MethodsThe Manitoba COVID-19 Seroprevalence (MCS) study is a population-based cross-sectional study to assess the prevalence of SARS-CoV-2 antibodies across the province. Residual convenience specimens (n = 14,901) were tested for anti-SARS-CoV-2 nucleocapsid and spike IgG antibodies from April 1, 2020 to February 31, 2022. We estimated the monthly and cumulative prevalence using an exponential decay model, accounting for population demographics, sensitivity/specificity, and antibody waning. This approach generated estimates of natural infection as well as total antibody including vaccine-induced immunity within the community.FindingsAfter four waves of the pandemic, 60.1% (95%CI-56.6–63.7) of Manitobans have generated SARS-CoV-2 antibodies due to natural exposure independent of vaccination. Geographical analysis indicates a large portion of provincial prevalence stems from increased transmission in the Northern (92.3%) and Southern (71.8%) regional health authorities. Despite the high mortality rates reported by Manitoba, infection fatality ratios (IFR) peaked at 0.67% and declined to 0.20% following the Omicron wave, indicating parity with other national and international jurisdictions. Manitoba has achieved 93.4% (95%CI- 91.5–95.1) total antibody when including vaccination.InterpretationOur data shows that more than 3 in 5 Manitobans have been infected by SARS-CoV-2 after four waves of the pandemic. This study also identifies key geographical and age specific prevalence rates that have contributed greatly to the overall severity of the pandemic in Manitoba and will inform jurisdictions considering reduction of public health measures.

COVID-19-related mortality and hospital admissions in the VIVALDI study cohort: October 2020 to March 2023

Long-term-care facilities (LTCFs) were heavily affected by COVID-19 early in the pandemic, but the impact of the virus has reduced over time with vaccination campaigns and build-up of immunity from prior infection. To evaluate the mortality and hospital admissions associated with SARS-CoV-2 in LTCFs in England over the course of the VIVALDI study, from October 2020 to March 2023. We included residents aged ≥65 years from participating LTCFs who had available follow-up time within the analysis period. We calculated incidence rates (IRs) of COVID-19-linked mortality and hospital admissions per calendar quarter, along with infection fatality ratios (IFRs, within 28 days) and infection hospitalization ratios (IHRs, within 14 days) following positive SARS-CoV-2 test. A total of 26,286 residents were included, with at least one positive test for SARS-CoV-2 in 8513 (32.4%). The IR of COVID-19-related mortality peaked in the first quarter (Q1) of 2021at 0.47 per 1000 person-days (1 kpd) (around a third of all deaths), in comparison with 0.10 per 1 kpd for Q1 2023 which had a similar IR of SARS-CoV-2 infections. There was a fall in observed IFR for SARS-CoV-2 infections from 24.9% to 6.7% between these periods, with a fall in IHR from 12.1% to 8.8%. The population had high overall IRs for mortality for each quarter evaluated, corresponding to annual mortality probability of 28.8-41.3%. Standardized real-time monitoring of hospitalization and mortality following infection in LTCFs could inform policy on the need for non-pharmaceutical interventions to prevent transmission.

Morbidity and mortality burden of COVID-19 in rural Madagascar: results from a longitudinal cohort and nested seroprevalence study.

Three years into the pandemic, there remains significant uncertainty about the true infection and mortality burden of COVID-19 in the World Health Organization Africa region. High quality, population-representative studies in Africa are rare and tend to be conducted in national capitals or large cities, leaving a substantial gap in our understanding of the impact of COVID-19 in rural, low-resource settings. Here, we estimated the spatio-temporal morbidity and mortality burden associated with COVID-19 in a rural health district of Madagascar until the first half of 2021. We integrated a nested seroprevalence study within a pre-existing longitudinal cohort conducted in a representative sample of 1600 households in Ifanadiana District, Madagascar. Socio-demographic and health information was collected in combination with dried blood spots for about 6500 individuals of all ages, which were analysed to detect IgG and IgM antibodies against four specific proteins of SARS-CoV-2 in a bead-based multiplex immunoassay. We evaluated spatio-temporal patterns in COVID-19 infection history and its associations with several geographic, socio-economic and demographic factors via logistic regressions. Eighteen percent of people had been infected by April-June 2021, with seroprevalence increasing with individuals' age. COVID-19 primarily spread along the only paved road and in major towns during the first epidemic wave, subsequently spreading along secondary roads during the second wave to more remote areas. Wealthier individuals and those with occupations such as commerce and formal employment were at higher risk of being infected in the first wave. Adult mortality increased in 2020, particularly for older men for whom it nearly doubled up to nearly 40 deaths per 1000. Less than 10% of mortality in this period would be directly attributed to COVID-19 deaths if known infection fatality ratios are applied to observed seroprevalence in the district. Our study provides a very granular understanding on COVID-19 transmission and mortality in a rural population of sub-Saharan Africa and suggests that the disease burden in these areas may have been substantially underestimated.

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

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. 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. 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). 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.

Seroprevalence of SARS-CoV-2(Covid-19) Antibody Among Blood Donors in a Tertiary Care Centre in South India

Introduction The novel severe acute respiratory syndrome virus 2 (SARS-CoV-2), which is responsible Coronavirus disease (COVID-19), spread worldwide from China, causing a pandemic from late December 2019. Due to the high proportion of asymptomatic or mild infections (approximately 80%), data restricted to laboratory-confirmed cases do not capture the true extent of the spread or burden of the virus, or its infection-fatality ratio. Therefore, serological detection of specific antibodies against SARS-CoV-2 can better estimate the true number of infections. The current study aims to estimate the seroprevalence of SARS-CoV-2 antibodies among the whole blood donors without any prior COVID-19 history or symptoms. Objective To determine seroprevalence of SARS-CoV-2 (COVID-19) antibody (IgG and IgM) among asymptomatic healthy blood donors. Methods This was a cross sectional study conducted between March and July, 2021 among 300 blood donors without any prior COVID-19 history or symptoms who came to a tertiary care, multispecialty hospital in south India. Any donor who had recently travelled abroad or donors who had received COVID-19 vaccine are excluded from the study. 3 ml venous blood was drawn in EDTA tube from participants and was tested by “Access SARS CoV-2 IgG assay” and “Access SARS CoV-2 IgM assay” by UniCel DxI 800 Immunoassay analyzer (Beckman coulter). The Access SARS CoV-2 IgG assay and the Access SARS Cov-2 IgM assay detect antibodies to the Receptor Binding Domain (RBD) of the Spike Protein. Result was reported as Reactive if Signal/Cut-off (S/CO)>1.0 and non-Reactive if S/CO <1. Data was collected and entered into excel sheets and was analyzed by using the software SPSS version 25. Results A total of 300 healthy blood donors were included. The study reported seroprevalence of 15.3% for IgG and 4.3% for IgM (95%CI) among asymptomatic whole blood donors. No significant difference was observed across age groups, diet, BMI, ABO/Rh blood type or Ayurveda/homeo immune medicine intake with respect to IgG and IgM reactivity. Conclusion 15% of blood donors were seroconverted for COVID-19 during second wave. This reflects widespread seroprevalence in the adult population. Real-time seroprevalence studies will help to know the herd immunity among the blood donors which will assist in knowing the COVID-19 transmission dynamics and distribution of immunity levels at a particular point in time.

Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England

As the SARS-CoV-2 pandemic progressed, distinct variants emerged and dominated in England. These variants, Wildtype, Alpha, Delta, and Omicron were characterized by variations in transmissibility and severity. We used a robust mathematical model and Bayesian inference framework to analyse epidemiological surveillance data from England. We quantified the impact of non-pharmaceutical interventions (NPIs), therapeutics, and vaccination on virus transmission and severity. Each successive variant had a higher intrinsic transmissibility. Omicron (BA.1) had the highest basic reproduction number at 8.4 (95% credible interval (CrI) 7.8-9.1). Varying levels of NPIs were crucial in controlling virus transmission until population immunity accumulated. Immune escape properties of Omicron decreased effective levels of immunity in the population by a third. Furthermore, in contrast to previous studies, we found Alpha had the highest basic infection fatality ratio (3.0%, 95% CrI 2.8-3.2), followed by Delta (2.1%, 95% CrI 1.9–2.4), Wildtype (1.2%, 95% CrI 1.1–1.2), and Omicron (0.7%, 95% CrI 0.6-0.8). Our findings highlight the importance of continued surveillance. Long-term strategies for monitoring and maintaining effective immunity against SARS-CoV-2 are critical to inform the role of NPIs to effectively manage future variants with potentially higher intrinsic transmissibility and severe outcomes.

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

The relationship between prevalence of infection and severe outcomes such as hospitalisation and death changed over the course of the COVID-19 pandemic. Reliable estimates of the infection fatality ratio (IFR) and infection hospitalisation ratio (IHR) along with the time-delay between infection and hospitalisation/death can inform forecasts of the numbers/timing of severe outcomes and allow healthcare services to better prepare for periods of increased demand. The REal-time Assessment of Community Transmission-1 (REACT-1) study estimated swab positivity for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection in England approximately monthly from May 2020 to March 2022. Here, we analyse the changing relationship between prevalence of swab positivity and the IFR and IHR over this period in England, using publicly available data for the daily number of deaths and hospitalisations, REACT-1 swab positivity data, time-delay models, and Bayesian P-spline models. We analyse data for all age groups together, as well as in 2 subgroups: those aged 65 and over and those aged 64 and under. Additionally, we analysed the relationship between swab positivity and daily case numbers to estimate the case ascertainment rate of England's mass testing programme. During 2020, we estimated the IFR to be 0.67% and the IHR to be 2.6%. By late 2021/early 2022, the IFR and IHR had both decreased to 0.097% and 0.76%, respectively. The average case ascertainment rate over the entire duration of the study was estimated to be 36.1%, but there was some significant variation in continuous estimates of the case ascertainment rate. Continuous estimates of the IFR and IHR of the virus were observed to increase during the periods of Alpha and Delta's emergence. During periods of vaccination rollout, and the emergence of the Omicron variant, the IFR and IHR decreased. During 2020, we estimated a time-lag of 19 days between hospitalisation and swab positivity, and 26 days between deaths and swab positivity. By late 2021/early 2022, these time-lags had decreased to 7 days for hospitalisations and 18 days for deaths. Even though many populations have high levels of immunity to SARS-CoV-2 from vaccination and natural infection, waning of immunity and variant emergence will continue to be an upwards pressure on the IHR and IFR. As investments in community surveillance of SARS-CoV-2 infection are scaled back, alternative methods are required to accurately track the ever-changing relationship between infection, hospitalisation, and death and hence provide vital information for healthcare provision and utilisation.

SARS-CoV-2 seroepidemiology in Mongolia, 2020–2021: a longitudinal national study

The COVID-19 pandemic has global impacts but is relatively understudied in developing countries. Mongolia, a lower-middle-income country, instituted strict control measures in early 2020 and avoided widespread transmission until vaccines became available in February, 2021. Mongolia achieved its 60% vaccination coverage goal by July 2021. We investigated the distribution and determinants of SARS-CoV-2 seroprevalence in Mongolia over 2020 and2021. We performed a longitudinal seroepidemiologic study aligned with WHO's Unity Studies protocols. We collected data from a panel of 5000 individuals in four rounds between October 2020 and December 2021. We selected participants through local health centres across Mongolia by age-stratified multi-stage cluster sampling. We tested serum for the presence of total antibodies against SARS-CoV-2 receptor-binding domain, and levels of anti-SARS-CoV-2 spike IgG and neutralising antibodies. We linked participant data with national mortality, COVID-19 case, and vaccination registries. We estimated population seroprevalence and vaccine uptake, as well as unvaccinated population prior-infection prevalence. At the final round in late 2021, 82% (n=4088) of participants completed follow-up. Estimated seroprevalence increased from 1.5% (95% CI: 1.2-2.0), to 82.3% (95% CI: 79.5-84.8) between late-2020 and late-2021. At the final round an estimated 62.4% (95% CI: 60.2-64.5) of the population were vaccinated, and of the unvaccinated population 64.5% (95% CI: 59.7-69.0) had been infected. Cumulative case ascertainment in the unvaccinated was 22.8% (95% CI: 19.1%-26.9%) and the overall infection-fatality ratio was 0.100% (95% CI: 0.088-0.124). Health workers had higher odds for being COVID-19 confirmed cases at all rounds. Males (1.72 (95% CI: 1.33-2.22)) and adults aged 20 and above (12.70 (95% CI: 8.14-20.26)) had higher odds for seroconverting by mid-2021. Among the seropositive, 87.1% (95% CI: 82.3%-90.8%) had SARS-CoV-2 neutralising antibodies by late2021. Our study enabled tracking of SARS-CoV-2 serological markers in the Mongolian population over one year. We found low SARS-CoV-2 seroprevalence in 2020 and early 2021, with seropositivity increasing over a 3-month interval in 2021 due to vaccine roll out and rapid infection of most of the unvaccinated population. Despite high seroprevalence in Mongolia amongst both vaccinated and unvaccinated individuals by end-2021, the SARS-CoV-2 Omicron immune escape variant caused a substantial epidemic. World Health Organization, WHO UNITY Studies initiative, with funding by the COVID-19 Solidarity Response Fund and the German Federal Ministry of Health (BMG) COVID-19 Research and development. The Ministry of Health, Mongolia partially funded this study.

Data-driven multiscale dynamical framework to control a pandemic evolution with non-pharmaceutical interventions.

Before the availability of vaccines, many countries have resorted multiple times to drastic social restrictions to prevent saturation of their health care system, and to regain control over an otherwise exponentially increasing COVID-19 pandemic. With the advent of data-sharing, computational approaches are key to efficiently control a pandemic with non-pharmaceutical interventions (NPIs). Here we develop a data-driven computational framework based on a time discrete and age-stratified compartmental model to control a pandemic evolution inside and outside hospitals in a constantly changing environment with NPIs. Besides the calendrical time, we introduce a second time-scale for the infection history, which allows for non-exponential transition probabilities. We develop inference methods and feedback procedures to successively recalibrate model parameters as new data becomes available. As a showcase, we calibrate the framework to study the pandemic evolution inside and outside hospitals in France until February 2021. We combine national hospitalization statistics from governmental websites with clinical data from a single hospital to calibrate hospitalization parameters. We infer changes in social contact matrices as a function of NPIs from positive testing and new hospitalization data. We use simulations to infer hidden pandemic properties such as the fraction of infected population, the hospitalisation probability, or the infection fatality ratio. We show how reproduction numbers and herd immunity levels depend on the underlying social dynamics.

Estimation of disease burden and clinical severity of COVID-19 caused by Omicron BA.2 in Shanghai, February-June 2022

ABSTRACT An outbreak of COVID-19 caused by the SARS-CoV-2 Omicron BA.2 sublineage occurred in Shanghai, China from February 26 to June 30, 2022. We use official reported data retrieved from Shanghai municipal Health Commissions to estimate the incidence of infections, severe/critical infections, and deaths to assess the disease burden. By adjusting for right censoring and RT-PCR sensitivity, we provide estimates of clinical severity, including the infection fatality ratio, symptomatic case fatality ratio, and risk of developing severe/critical disease upon infection. The overall infection rate, severe/critical infection rate, and mortality rate were 2.74 (95% CI: 2.73-2.74) per 100 individuals, 6.34 (95% CI: 6.02-6.66) per 100,000 individuals and 2.42 (95% CI: 2.23-2.62) per 100,000 individuals, respectively. The severe/critical infection rate and mortality rate increased with age, noted in individuals aged 80 years or older. The overall fatality ratio and risk of developing severe/critical disease upon infection were 0.09% (95% CI: 0.09-0.10%) and 0.27% (95% CI: 0.24-0.29%), respectively. Having received at least one vaccine dose led to a 10-fold reduction in the risk of death for infected individuals aged 80 years or older. Under the repeated population-based screenings and strict intervention policies implemented in Shanghai, our results found a lower disease burden and mortality of the outbreak compared to other settings and countries, showing the impact of the successful outbreak containment in Shanghai. The estimated low clinical severity of this Omicron BA.2 epidemic in Shanghai highlight the key contribution of vaccination and availability of hospital beds to reduce the risk of death.

Modelling the first wave of COVID-19 in India.

Estimating the burden of COVID-19 in India is difficult because the extent to which cases and deaths have been undercounted is hard to assess. Here, we use a 9-component, age-stratified, contact-structured epidemiological compartmental model, which we call the INDSCI-SIM model, to analyse the first wave of COVID-19 spread in India. We use INDSCI-SIM, together with Bayesian methods, to obtain optimal fits to daily reported cases and deaths across the span of the first wave of the Indian pandemic, over the period Jan 30, 2020 to Feb 15, 2021. We account for lock-downs and other non-pharmaceutical interventions (NPIs), an overall increase in testing as a function of time, the under-counting of cases and deaths, and a range of age-specific infection-fatality ratios. We first use our model to describe data from all individual districts of the state of Karnataka, benchmarking our calculations using data from serological surveys. We then extend this approach to aggregated data for Karnataka state. We model the progress of the pandemic across the cities of Delhi, Mumbai, Pune, Bengaluru and Chennai, and then for India as a whole. We estimate that deaths were undercounted by a factor between 2 and 5 across the span of the first wave, converging on 2.2 as a representative multiplier that accounts for the urban-rural gradient. We also estimate an overall under-counting of cases by a factor of between 20 and 25 towards the end of the first wave. Our estimates of the infection fatality ratio (IFR) are in the range 0.05-0.15, broadly consistent with previous estimates but substantially lower than values that have been estimated for other LMIC countries. We find that approximately 35% of India had been infected overall by the end of the first wave, results broadly consistent with those from serosurveys. These results contribute to the understanding of the long-term trajectory of COVID-19 in India.

Seroprevalence of SARS-CoV-2-specific anti-spike IgM, IgG, and anti-nucleocapsid IgG antibodies during the second wave of the pandemic: A population-based cross-sectional survey across Kashmir, India.

BackgroundWithin Kashmir, which is one of the topographically distinct areas in the Himalayan belt of India, a total of 2,236 cumulative deaths occurred by the end of the second wave. We aimed to conduct this population-based study in the age group of 7 years and above to estimate the seropositivity and its attributes in Kashmir valley.MethodsWe conducted a community-based household-level cross-sectional study, with a multistage, population-stratified, probability-proportionate-to-size, cluster sampling method to select 400 participants from each of the 10 districts of Kashmir. We also selected a quota of healthcare workers, police personnel, and antenatal women from each of the districts. Households were selected from each cluster and all family members with age 7 years or more were invited to participate. Information was collected through a standardized questionnaire and entered into Epicollect 5 software. Trained healthcare personnel were assigned for collecting venous blood samples from each of the participants which were transferred and processed for immunological testing. Testing was done for the presence of SARS-CoV-2-specific anti-spike IgM, IgG antibodies, and anti-nucleocapsid IgG antibodies. Weighted seropositivity was estimated along with the adjustment done for the sensitivity and specificity of the test used.FindingsThe data were collected from a total of 4,229 participants from the general population within the 10 districts of Kashmir. Our results showed that 84.84% (95% CI 84.51–85.18%) of the participants were seropositive in the weighted imputed data among the general population. In multiple logistic regression, the variables significantly affecting the seroprevalence were the age group 45–59 years (odds ratio of 0.73; 95% CI 0.67–0.78), self-reported history of comorbidity (odds ratio of 1.47; 95% CI 1.33–1.61), and positive vaccination history (odds ratio of 0.85; 95% CI 0.79–0.90) for anti-nucleocapsid IgG antibodies. The entire assessed variables showed a significant role during multiple logistic regression analysis for affecting IgM anti-spike antibodies with an odds ratio of 1.45 (95% CI 1.32–1.57) for age more than 60 years, 1.21 (95% CI 1.15–1.27) for the female gender, 0.87 (95% CI 0.82–0.92) for urban residents, 0.86 (95% CI 0.76–0.92) for self-reported comorbidity, and an odds ratio of 1.16 (95% CI 1.08–1.24) for a positive history of vaccination. The estimated infection fatality ratio was 0.033% (95% CI: 0.034–0.032%) between 22 May and 31 July 2021 against the seropositivity for IgM antibodies.InterpretationDuring the second wave of the SARS-CoV-2 pandemic, 84.84% (95% CI 84.51–85.18%) of participants from this population-based cross-sectional sample were seropositive against SARS-CoV-2. Despite a comparatively lower number of cases reported and lower vaccination coverage in the region, our study found such high seropositivity across all age groups, which indicates the higher number of subclinical and less severe unnoticed caseload in the community.

Effects of infection fatality ratio and social contact matrices on vaccine prioritization strategies.

Effective strategies of vaccine prioritization are essential to mitigate the impacts of severe infectious diseases. We investigate the role of infection fatality ratio (IFR) and social contact matrices on vaccination prioritization using a compartmental epidemic model fueled by real-world data of different diseases and countries. Our study confirms that massive and early vaccination is extremely effective to reduce the disease fatality if the contagion is mitigated, but the effectiveness is increasingly reduced as vaccination beginning delays in an uncontrolled epidemiological scenario. The optimal and least effective prioritization strategies depend non-linearly on epidemiological variables. Regions of the epidemiological parameter space, in which prioritizing the most vulnerable population is more effective than the most contagious individuals, depend strongly on the IFR age profile being, for example, substantially broader for COVID-19 in comparison with seasonal influenza. Demographics and social contact matrices deform the phase diagrams but do not alter their qualitative shapes.

Estimating deaths averted and cost per life saved by scaling up mRNA COVID-19 vaccination in low-income and lower-middle-income countries in the COVID-19 Omicron variant era: a modelling study

ObjectivesWhile almost 60% of the world has received at least one dose of COVID-19 vaccine, the global distribution of vaccination has not been equitable. Only 4% of the population of...

Synergistic influence of air temperature and vaccination on COVID-19 transmission and mortality in 146 countries or regions

ObjectiveWe aimed to determine the influence of vaccination and air temperature on COVID-19 transmission and severity. MethodsThe study data in 146 countries from January 6, 2020 to July 28, 2022 were aggregated into 19,856 weeks. Country-level weekly incidence, time-varying reproduction number (Rt), mortality, and infection–fatality ratio (IFR) were compared among groups of these weeks with different vaccination rates and air temperatures. ResultsWeeks with <15 °C air temperature and 60% vaccination showed the highest incidence (mean, 604; SD, 855; 95% CI, 553–656, unit, /100,000 persons; N = 1073) and the highest rate of weeks with >1 Rt (mean, 41.6%; SD, 1.49%; 95% CI, 39.2–45.2%; N = 1090), while weeks with >25 °C and <20% showed the lowest incidence (mean, 24; SD, 75; 95% CI, 22–26; N = 5805) and the lowest rate of weeks with >1 Rt (mean, 15.3%; SD, 0.461%; 95% CI, 14.2–16.2%; N = 6122). Mortality in weeks with <15 °C (mean, 2.1; SD, 2.8; 95% CI, 2.0–2.2, unit, /100,000 persons; N = 4365) was five times of the mortality in weeks with >25 °C (mean, 0.44; SD, 1; 95% CI, 0.41–0.46; N = 7741). IFR ranged between 2% and 2.6% (SD, 1.9%–2.4%; 95% CI, 2.0–2.7%) at < 20% vaccination level, 1.8% (SD, 2%–2.2%; 95% CI, 1.7–2.0%) at 20–60% vaccination level, and 0.7%–1% (SD, 1%–1.8%; 95% CI, 0.7–1.1%) at > 60% vaccination level and at all air temperatures (all P < 0.001). ConclusionsVaccination was insufficient to mitigate the transmission since the significantly elevated weekly incidence and >1 Rt rate in weeks with high vaccination, while IFR was reduced by high vaccination. Countries with long-term low air temperature were affected by high transmission and high mortality.

Inferring the true number of SARS-CoV-2 infections in Japan

IntroductionIn Japan, as of December 31, 2021, more than 1.73 million laboratory-confirmed cases have been reported. However, the actual number of infections is likely to be under-ascertained due to the epidemiological characteristics such as mild and subclinical infections and limited testing availability in the early days of the pandemic. In this study, we infer the true number of infections in Japan between January 16, 2020, and December 31, 2021, using a statistical modelling framework that combines data on reported cases and fatalities. MethodsWe used reported COVID-19 deaths and age-specific infection fatality ratios (IFR) to impute the true number of infections. Estimates of IFR were informed from published studies and were adjusted to reflect the effects of pharmaceutical interventions, mass vaccination, and evolving variants. To account for the uncertainty in IFR, we sampled values from relevant distributions. ResultsWe estimated that as of December 31, 2021, 3.07 million (CrI: 2.05–4.24 million) people had been infected in Japan, which is 1.77 times higher than the 1.73 million reported cases. Our meta-analysis confirmed that these findings were consistent with the intermittent seroprevalence studies conducted in Japan. ConclusionsWe have estimated that a substantial number of COVID-19 infections in Japan were unreported, particularly in adults. Our approach provides a more realistic assessment of the true underlying burden of COVID-19. The results of this study can be used as fundamental components to strengthen population health control and surveillance measures.

Modelling the effect of non-pharmaceutical interventions on COVID-19 transmission from mobility maps.

The world has faced the COVID-19 pandemic for over two years now, and it is time to revisit the lessons learned from lockdown measures for theoretical and practical epidemiological improvements. The interlink between these measures and the resulting change in mobility (a predictor of the disease transmission contact rate) is uncertain. We thus propose a new method for assessing the efficacy of various non-pharmaceutical interventions (NPI) and examine the aptness of incorporating mobility data for epidemiological modelling. Facebook mobility maps for the United Arab Emirates are used as input datasets from the first infection in the country to mid-Oct 2020. Dataset was limited to the pre-vaccination period as this paper focuses on assessing the different NPIs at an early epidemic stage when no vaccines are available and NPIs are the only way to reduce the reproduction number (). We developed a travel network density parameter to provide an estimate of NPI impact on mobility patterns. Given the infection-fatality ratio and time lag (onset-to-death), a Bayesian probabilistic model is adapted to calculate the change in epidemic development with . Results showed that the change in clearly impacted . The three lockdowns strongly affected the growth of transmission rate and collectively reduced by 78% before the restrictions were eased. The model forecasted daily infections and deaths by 2% and 3% fractional errors. It also projected what-if scenarios for different implementation protocols of each NPI. The developed model can be applied to identify the most efficient NPIs for confronting new COVID-19 waves and the spread of variants, as well as for future pandemics.

First wave of SARS-CoV-2 in Santiago Chile: Seroprevalence, asymptomatic infection and infection fatality rate

BackgroundThe first wave of SARS-CoV-2 infection in Chile occurred during the cold season reaching a peak by the end of June 2020, with 80 % of the cases concentrated in its capital, Santiago. The main objective of this study was to estimate the attack rate during this first wave of SARS-CoV-2 in a large, densely populated city with more than seven million inhabitants. Since the number of confirmed cases provides biased information due to individuals' potential self-selection, mostly related to asymptomatic patients and testing access, we measured antibodies against SARS-CoV-2 to assess infection prevalence during the first wave in the city, as well as estimate asymptomatic cases, and infection fatality ratio. To our knowledge this is one of the few population-based cross-sectional serosurvey during the first wave in a highly affected emerging country. The challenges of pandemic response in urban settings in a capital city like Santiago, with heterogeneous subpopulations and high mobility through public transportation, highlight the necessity of more accurate information regarding the first waves of new emerging diseases. MethodsFrom April 24 to June 21, 2020, 1326 individuals were sampled from a long-standing panel of household representatives of Santiago. Immunochromatographic assays were used to detect IgM and IgG antibody isotypes. ResultsSeroprevalence reached 6.79 % (95 %CI 5.58 %−8.26 %) in the first 107 days of the pandemic, without significant differences among sex and age groups; this figure indicates an attack rate 2.8 times higher than the one calculated with registered cases. It also changes the fatality rate estimates, from a 2.33 % case fatality rate reported by MOH to an estimated crude 1.00 % (CI95 % 0.97–1.03) infection fatality rate (adjusted for test performance 1.66 % [CI95 % 1.61–1.71]). Most seropositive were symptomatic (81,1 %). ConclusionsDespite the high number of cases registered, mortality rates, and the stress produced over the health system, the vast majority of the people remained susceptible to potential new epidemic waves. We contribute to the understanding of the initial spread of emerging epidemic threats. Consequently, our results provide better information to design early strategies that counterattack new health challenges in urban contexts.