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

We aimed to estimate, albeit crudely and provisionally, national, regional, and global proportions of respective populations that have been infected with SARS-CoV-2, 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 worlds 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 1,000 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), the infection severity rate was 6.27 (95% CI: 6.18-6.37), and the infection criticality rate was 2.26 (95% CI: 2.24-2.28). If left unchecked with no interventions, the pandemic would eventually cause 8.18 million (95% CI: 7.30-9.18) deaths, 163.67 million (95% CI: 148.12-179.51) acute-care hospitalizations, 33.01 million (95% CI: 30.52-35.70) ICU hospitalizations, 50.23 million (95% CI: 46.24-54.67) severe cases, and 17.62 million (95% CI: 16.36-18.97) critical cases. The global population remains far below the herd immunity threshold and at risk of repeated waves of infection. Global epidemiology reveals immense regional variation in infection exposure and morbidity and mortality rates.

Epidemiological differences in the impact of COVID-19 vaccination in the United States and China

BackgroundThe objective of this study was to forecast the impact of COVID-19 vaccination in the United States (US) and China, two countries at different epidemic phases. MethodsA mathematical model describing SARS-CoV-2 transmission and disease progression was used to investigate vaccine impact. Impact was assessed both for a vaccine that prevents infection (VES = 95%) and a vaccine that prevents only disease (VEP = 95%). ResultsFor VES = 95% and gradual easing of restrictions, vaccination in the US reduced the peak incidence of infection, disease, and death by >55% and cumulative incidence by >32%, and in China by >77% and >65%, respectively. Nearly three vaccinations were needed to avert one infection in the US, but only one was needed in China. For VEP = 95%, benefits of vaccination were half those for VES = 95%. In both countries, the impact of vaccination was substantially enhanced with rapid scale-up, vaccine coverage >50%, and slower or no easing of restrictions, particularly in the US. ConclusionsCOVID-19 vaccination can flatten, delay, and/or prevent future epidemic waves. However, vaccine impact is destined to be heterogeneous across countries because of an underlying "epidemiologic inequity" that reduces benefits for countries already at high incidence, such as the US. Despite 95% efficacy, actual vaccine impact could be meager in such countries, if vaccine scale-up is slow, acceptance of the vaccine is poor, or restrictions are eased prematurely. One Sentence SummaryVaccine impact will be heterogeneous across countries disadvantaging countries at high incidence. This heterogeneity can be alleviated with rapid vaccination scale-up and limited easing of restrictions.

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

BackgroundThis study aimed to estimate the age-stratified and overall morbidity and mortality rates of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection based on an analysis of the pervasive SARS-CoV-2 epidemic in Qatar, a country with <9% of the population being [≥]50 years of age. MethodsInfection disease outcomes were investigated using a Bayesian approach applied to an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression in the population. The model was fitted to infection and disease time-series and age-stratified data. Two separate criteria for classifying morbidity were used: one based on actual recorded hospital admission (acute-care or intensive-care-unit hospitalization) and one based on clinical presentation as per World Health Organization classification of disease severity or criticality. ResultsAll outcomes showed very strong age dependence, with low values for those <50 years of age, but rapidly growing rates for those [≥]50 years of age. The strong age dependence was particularly pronounced for infection criticality rate and infection fatality rate. Infection acute-care and intensive-care-unit bed hospitalization rates were estimated at 13.10 (95% CI: 12.82-13.24) and 1.60 (95% CI: 1.58-1.61) per 1,000 infections, respectively. Infection severity and criticality rates were estimated at 3.06 (95% CI: 3.01-3.10) and 0.68 (95% CI: 0.67-0.68) per 1,000 infections, respectively. Infection fatality rate was estimated at 1.85 (95% CI: 1.74-1.95) per 10,000 infections. ConclusionsSARS-CoV-2 severity and fatality in Qatar was not high and demonstrated a very strong age dependence with <4 infections in every 1,000 being severe or critical and <2 in every 10,000 being fatal. Epidemic expansion in nations with young populations may lead to lower disease burden than previously thought.

Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19

BackgroundMathematical modeling constitutes an important tool for planning robust responses to epidemics. This study was conducted to guide the Qatari national response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic. The study investigated the time course of the epidemic, forecasted healthcare needs, predicted the impact of social and physical distancing restrictions, and rationalized and justified easing of restrictions. MethodsAn age-structured deterministic model was constructed to describe SARS-CoV-2 transmission dynamics and disease progression throughout the population. ResultsThe enforced social and physical distancing interventions flattened the epidemic curve, reducing the peaks for incidence, prevalence, acute-care hospitalization, and intensive care unit (ICU) hospitalizations by 87%, 86%, 76%, and 78%, respectively. The daily number of new infections was predicted to peak at 12,750 on May 23, and active-infection prevalence was predicted to peak at 3.2% on May 25. Daily acute-care and ICU-care hospital admissions and occupancy were forecast accurately and precisely. By October 15, 2020, the basic reproduction number R0 had varied between 1.07-2.78, and 50.8% of the population were estimated to have been infected (1.43 million infections). The proportion of actual infections diagnosed was estimated at 11.6%. Applying the concept of Rt tuning, gradual easing of restrictions was rationalized and justified to start on June 15, 2020, when Rt declined to 0.7, to buffer the increased interpersonal contact with easing of restrictions and to minimize the risk of a second wave. No second wave has materialized as of October 15, 2020, five months after the epidemic peak. ConclusionsUse of modeling and forecasting to guide the national response proved to be a successful strategy, reducing the toll of the epidemic to a manageable level for the healthcare system.

Analyzing inherent biases in SARS-CoV-2 PCR and serological epidemiologic metrics

BackgroundProspective observational data show that infected persons with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain polymerase chain reaction (PCR) positive for a prolonged duration, and that detectable antibodies develop slowly with time. We aimed to analyze how these effects can bias key epidemiological metrics used to track and monitor SARS-CoV-2 epidemics. MethodsAn age-structured mathematical model was constructed to simulate progression of SARS-CoV-2 epidemics in populations. PCR testing to diagnose infection and cross-sectional surveys to measure seroprevalence were also simulated. Analyses were conducted on simulated outcomes assuming a natural epidemic time course and an epidemic in presence of interventions. ResultsThe prolonged PCR positivity biased the epidemiological measures. There was a lag of 10 days between the true epidemic peak and the actually-observed peak. Prior to epidemic peak, PCR positivity rate was 2-fold higher than that based only on current active infection, and half of those tested positive by PCR were in the prolonged PCR positivity stage after infection clearance. Post epidemic peak, PCR positivity rate poorly predicted true trend in active infection. Meanwhile, the prolonged PCR positivity did not appreciably bias estimation of the basic reproduction number R0. The time delay in development of detectable antibodies biased measured seroprevalence. The actually-observed seroprevalence substantially underestimated true prevalence of ever infection, with the underestimation being most pronounced around epidemic peak. ConclusionsCaution is warranted in interpreting PCR and serological testing data, and any drawn inferences need to factor the effects of the investigated biases for an accurate assessment of epidemic dynamics.

Epidemiological impact of SARS-CoV-2 vaccination: mathematical modeling analyses

BackgroundSeveral SARS-CoV-2 vaccine candidates are currently in the pipeline. This study aims to inform SARS-CoV-2 vaccine development, licensure, decision-making, and implementation by determining key preferred vaccine product characteristics and associated population-level impact. MethodsVaccination impact was assessed at various efficacies using an age-structured mathematical model describing SARS-CoV-2 transmission and disease progression, with application for China. ResultsA prophylactic vaccine with efficacy against acquisition (VES) of [≥]70% is needed to eliminate this infection. A vaccine with VES <70% will still have a major impact, and may control the infection if it reduces infectiousness or infection duration among those vaccinated who acquire the infection, or alternatively if supplemented with a moderate social-distancing intervention (<20% reduction in contact rate), or complemented with herd immunity. Vaccination is cost-effective. For a vaccine with VES of 50%, number of vaccinations needed to avert one infection is only 2.4, one severe disease case is 25.5, one critical disease case is 33.2, and one death is 65.1. Gains in effectiveness are achieved by initially prioritizing those [≥]60 years. Probability of a major outbreak is virtually zero with a vaccine with VES [≥]70%, regardless of number of virus introductions. Yet, an increase in social contact rate among those vaccinated (behavior compensation) can undermine vaccine impact. ConclusionsEven a partially-efficacious vaccine can offer a fundamental solution to control SARS-CoV-2 infection and at high cost-effectiveness. In addition to the primary endpoint on infection acquisition, developers should assess natural history and disease progression outcomes and/or proxy biomarkers, since such secondary endpoints may prove critical in licensure, decision-making, and vaccine impact.

Age could be driving variable SARS-CoV-2 epidemic trajectories worldwide

BackgroundCurrent geographic spread of documented severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections shows heterogeneity. This study explores the role of age in potentially driving differentials in infection spread, epidemic potential, and rates of disease severity and mortality across countries. MethodsAn age-stratified deterministic mathematical model that describes SARS-CoV-2 transmission dynamics was applied to 159 countries and territories with a population [≥]1 million. ResultsAssuming worst-case scenario for the pandemic, the results indicate that there could be stark regional differences in epidemic trajectories driven by differences in the distribution of the population by age. In the African Region (median age: 18.9 years), the median R0 was 1.05 versus 2.05 in the European Region (median age: 41.7 years), and the median (per 100 persons) for the infections rate was 22.5 (versus 69.0), for severe and/or critical disease cases rate was 3.3 (versus 13.0), and for death rate was 0.5 (versus 3.9). ConclusionsAge could be a driver of variable SARS-CoV-2 epidemic trajectories worldwide. Countries with sizable adult and/or elderly populations and smaller children populations may experience large and rapid epidemics in absence of interventions. Meanwhile, countries with predominantly younger age cohorts may experience smaller and slower epidemics. These predictions, however, should not lead to complacency, as the pandemic could still have a heavy toll nearly everywhere.

Characterizing key attributes of the epidemiology of COVID-19 in China: Model-based estimations

BackgroundA novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China in late 2019. The resulting disease, Coronavirus Disease 2019 (COVID-2019), soon became a pandemic. This study aims to characterize key attributes of the epidemiology of this infection in China. MethodsAn age-stratified mathematical model was constructed to describe the transmission dynamics and estimate the age-specific differences in the biological susceptibility to the infection, age-assortativeness in transmission mixing, case fatality rate (CFR), and transition in rate of infectious contacts (and reproduction number R0) following introduction of mass interventions. ResultsThe model estimated the infectious contact rate in early epidemic at 0.59 contacts per day (95% uncertainty interval (UI)=0.48-0.71). Relative to those 60-69 years of age, susceptibility to the infection was only 0.06 in those [≤]19 years, 0.34 in 20-29 years, 0.57 in 30-39 years, 0.69 in 40-49 years, 0.79 in 50-59 years, 0.94 in 70-79 years, and 0.88 in [≥]80 years. The assortativeness in transmission mixing by age was very limited at 0.004 (95% UI=0.002-0.008). Final CFR was 5.1% (95% UI=4.8-5.4%). R0 rapidly declined from 2.1 (95% UI=1.8-2.4) to 0.06 (95% UI=0.05-0.07) following onset of interventions. ConclusionAge appears to be a principal factor in explaining the patterns of COVID-19 transmission dynamics in China. The biological susceptibility to the infection seems limited among children, intermediate among young to mid-age adults, but high among those >50 years of age. There was no evidence for differential contact mixing by age, consistent with most transmission occurring in households rather than in schools or workplaces.