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Our take —

Authors used a specialized Susceptible-Exposed-Infected-Removed (SEIR) model to estimate the true extent of the COVID-19 epidemic in Wuhan, China over the course of five time periods between January 1, 2020 through March 8, 2020. Results estimated the effective reproductive number decreased from 3.84 during the first period to 0.28 during the final period. Authors also determined an overall ascertainment rate of 0.13, indicating a high proportion of asymptomatic or mild-symptomatic cases. This study used laboratory-confirmed cases from Wuhan and results were robust to extensive sensitivity analyses.

Study design

Modeling/Simulation

Study population and setting

Authors developed a Susceptible (S), Exposed (E), Presymptomatic infectious (P), unAscertained infectious (A), Isolated (H), and Removed (R) (SAPHIRE) model, an extension of the traditional Susceptible-Exposed-Infected-Recovered model, to estimate the true extent of the COVID-19 epidemic in Wuhan, China. Using 32,583 laboratory-confirmed cases from Wuhan, authors ran the model across five time periods in 2020 (January 1 to 9; January 10 to 22; January 23 to February 1; February 2 to 16; and February 17 to March 8), defined by different events (e.g., Chinese New Year) and the implementation of intervention strategies (e.g., the cordons sanitaire). Authors estimated Re (the effective reproductive number, the reproductive number after the implementation of interventions and community transmission has occurred) for each time period and total cumulative cases across all time periods using these data. Authors also projected what the total cumulative cases would have been assuming the incidence trend from each time period continued uninterrupted until March 8, 2020 (i.e., assuming none or with no additional interventions than what occurred in each respective time period). Authors used confirmed cases exported from Wuhan to Singapore to estimate the ascertainment rate (the percentage of total cases that are reported and confirmed).

Summary of Main Findings

The Re in each time period was 3.54 (95% CrI: 3.40-3.67), 3.32 (3.19-3.44), 1.18 (1.11-1.25), 0.51 (0.47-0.54), and 0.28 (0.23-0.33), respectively from first to fifth/last. Authors credit the significant decrease in Re to the wide-spread and multi-level public health interventions implemented in Wuhan. Overall, authors estimated that a total of 249,187 (95% CrI: 198,412-307,062) cases (including unascertained cases) occurred when they fit the data across all five time periods; this was notably lower than when authors fit the data according to time period trends, which was estimated to be up to 6,302,694 (6,275,508-6,327,520) when the trend from the second time period was assumed. The model projected the number of daily infections (including unascertained cases) peaked on February 2, 2020 at 55,879 (43,582-69,571). Regardless of time period, case ascertainment rates were low: 0.15 (95% CrI: 0.13-0.17), 0.15 (0.13-0.17), 0.14 (0.11-0.17), 0.10 (0.08-0.12), 0.16 (0.13-0.21), respectively, from first to fifth/last, and 0.13 (0.11-0.16) overall. Due to the high proportion of presymptomatic and unascertained cases, authors estimated the probability of case resurgence could be as high as 97% and would occur approximately one month after the removal of control measures, assuming they were lifted 14 days after the first day of zero ascertained cases.

Study Strengths

Authors used laboratory-confirmed cases, ensuring that false clinical diagnoses did not bias results. Authors validated assumed parameters and estimation methods before running the full simulations; authors determined the model was able to make accurate estimations. The decreases observed in Re between time periods and the low proportion of unascertained cases were robust to sensitivity analyses.

Limitations

Delays in laboratory reporting may have resulted in an underestimation of the ascertainment rate, which would also overestimate the proportion of asymptomatic cases and the effect these cases would have on potential resurgence. Authors also excluded clinically confirmed cases without laboratory confirmation, which may have had similar effects on results. Authors assumed homogeneous transmission between heterogeneous populations; rates of transmission have been shown to vary between groups of differing ages, sexes, race/ethnicities, and geographic locations. Control measures were evaluated as a whole, so the effects of individual interventions on Re are not available.

Value added

This study sought to describe the full spectrum of the dynamics of the COVID-19 epidemic in Wuhan using laboratory-confirmed cases from the city. Results indicated control measures significantly reduced transmission and that there was a high proportion of unascertained cases. Understanding these dynamics is critical for surveillance and control measures, and could be used to inform strategies in countries still experiencing active transmission.

Our take —

This study, covering over 17 million adults in England, confirms previously observed associations with mortality: higher risks were seen with older age, male sex, chronic comorbidities, socioeconomic deprivation, and nonwhite ethnicity. Although causality cannot be inferred for any given risk factor owing to the study design, the size of the analysis provides a striking replication of results observed elsewhere in smaller studies.

Study design

Retrospective Cohort

Study population and setting

This study analyzed electronic medical records from 17,278,392 adults aged 18 years and over from the National Health Service in England, representing approximately 40% of the adult population in England, to estimate risk factors for COVID-19 mortality. Registered active adult patients with >1 year of prior follow-up and with recorded age, sex, and “deprivation” (a measure related to poverty, derived from area of residence) were eligible for inclusion; the study period was from February 1 to May 6, 2020. Risk factors for COVID-19 mortality were assessed with Cox proportional hazards models 1) adjusted for age and sex; and 2) adjusted simultaneously for all included risk factors.

Summary of Main Findings

There were 10,926 deaths (0.06%) attributed to COVID-19 in the study population. In the fully adjusted model, the risk of mortality increased with every decade of age: relative to the reference category aged 50-59 years, the hazard ratio (HR) for death was 20.61 (95% CI: 18.72-22.70) for those aged 80 years and older. Men had a higher hazard of mortality relative to women (HR: 1.59, 95% CI: 1.53-1.65). Mortality risks for Black (HR: 1.48, 1.30 to 1.69), South Asian (HR: 1.44, 1.32-1.58), and mixed ethnicities (HR: 1.43, 1.11-1.85) were higher relative to white ethnicity. The most deprived quintile (from a geographically-defined index of socioeconomic status) had 1.80 times (1.69-1.91) the mortality hazard of the least deprived quintile. Multiple comorbidities were associated with increased mortality risk, including obesity, diabetes, chronic heart disease, chronic liver disease, kidney dysfunction, recently diagnosed cancer, and severe asthma. Although smoking and hypertension were associated with higher mortality in the age- and sex-adjusted model, they were not in the fully adjusted model. Post-hoc analyses of smoking and hypertension indicated that controlling for chronic respiratory disease (for smoking) and for diabetes and obesity (for hypertension) explained much of the changes in hazard ratios.

Study Strengths

The primary strength of this study is its size; it was able to cover a significant proportion of the adult population of England.

Limitations

Estimated hazard ratios for covariates were derived from a model that adjusted simultaneously for all covariates; results for a given risk factor cannot be interpreted causally due to possible mediation, unmeasured confounding, and collider stratification bias. There were high proportions of missing data for ethnicity (26%), obesity (22%), and hypertension (10%). Some deaths may have been missed due to misclassification. Although the dataset was very large, it may not have been representative of the population of England (e.g., only 17% of health care practices in London were included). The study does not address risk factors for non-fatal but nonetheless severe manifestations of COVID-19.

Value added

This extremely large study confirms associations seen in other studies and provides further evidence for the role of structural inequalities in COVID-19 impacts.

Our take —

This study found that for surveillance purposes, analytical sensitivity of tests are far less important than testing frequency and result turnaround time. Tests with sensitivity significantly less than the typical qPCR should be considered when they may allow for more frequent testing (e.g., every 1-3 days) and rapid results delivery (e.g., less than one day) These results will help motivate regulatory authorities on the utility of lower sensitivity tests and will help those designing surveillance programs decide between different types of tests given budget, staffing and logistic constraints.

Study design

Modeling/Simulation

Study population and setting

This study combined a model of within-host viral growth and decay with transmission models to estimate the potential impact of analytical test sensitivity (limit of detection of 1,000 vs. 100,000 RNA copies), frequency (every 1, 3, 7 or 14 days) and turnaround time (0, 1 or 2 days) on the effectiveness of a population-wide surveillance program. Individuals were assumed to self-isolate either upon receiving a positive test or upon the start of symptoms, until they were no longer infectious. Transmission simulations were conducted using two different models, with different levels of complexity, including an agent-based model with age-specific and household patterns of potentially infectious contacts based on data from New York City; and a university-like setting, where individuals mix at random and infections are constantly introduced from an outside source.

Summary of Main Findings

Testing daily or every three days with effective isolation after positive test results both reduced the effective reproduction number below one regardless of the test sensitivity, and as long as results were returned in fewer than two days. For weekly testing, if results were returned on the same day, a higher sensitivity test led to less than half the cases than when a low sensitivity test was used; however, even slight delays in reporting test results reduced this margin between the tests significantly. Fortnightly testing was ineffective in all cases. Results were similar across the two different models similar to the city and university settings.

Study Strengths

The study simulated a range of viral load trajectories in infected individuals, so it could explicitly relate the limit of detection to whether a case would be detected. Results were replicated across two different models and were robust to a range of assumptions about the relationship between viral load and infectiousness, and rate of imported infections.

Limitations

The study assumed that the whole population took part in the surveillance program, there was 100% compliance in self-isolation, and that tests detected all cases where the viral load was above the limit of detection. Although the supplementary material provides methods to adjust for false negatives and test refusal (which is mathematically the same as only testing a proportion of the population), numerical results were not shown. The study also assumed that a self-isolating individual could not transmit at all; however, in some settings (e.g., within households), individuals may not be able to self-isolate perfectly. The study did not consider self-isolation of contacts of confirmed cases; including this would increase effectiveness of all the surveillance programs, but the relative increase in effectiveness for each program is unknown. The maximum delay considered, 3 days, is still shorter than that currently available in many places. Although the city setting considered different types of contacts, superspreading events due to an individual having a much-greater-than-average number of contacts was not considered.

Value added

Workplaces, schools and universities are planning for reopening, and tests with different performance characteristics, cost and turnaround time are becoming available. This study helps designers of surveillance programs to understand the tradeoff between test frequency, sensitivity and turnaround time in reducing the number of cases. The number of cases with and without surveillance for different model assumptions can be calculated using an interactive web-application (https://larremorelab.github.io/covid-calculator3).

Our take —

Herd immunity is the threshold of population immunity to a disease that must be achieved in order to bring an epidemic under control and limit onward transmission in the population. In this paper, researchers demonstrated the importance of real-world assumptions like age-dependent susceptibility to infection, age-dependent probability of transmission, and age-specific contact patterns on the calculation of herd immunity thresholds. After accounting for realistic variation in age and contact patterns, they conclude that the herd immunity threshold that must be achieved to prevent a second large wave of COVID-19 may be lower than projections from models that do not account for this age-specific variability in disease transmission. However, results are not meant to be interpreted as exact values, and should be interpreted qualitatively.

Study design

Modeling/Simulation

Study population and setting

Authors used a Susceptible-Exposed-Infectious-Recovered (SEIR) mathematical model in a generic population that included six age cohorts, three social activity levels (high, normal, low), and “preventive measures” to 1) demonstrate the effectiveness of the interventions; and 2) how herd immunity to SARS-CoV-2 is affected by different population structures. Authors varied the effectiveness of the preventive measures, but did not specify explicit interventions. Assuming an R0 (basic reproductive number) of 2.5 and introduction of the virus on February 15, 2020, authors modeled differences in effectiveness and disease-induced herd immunity assuming homogenous and heterogenous (varying population mixing by age only, activity levels only, or both) populations.

Summary of Main Findings

Models demonstrated that herd immunity levels were lower among a non-homogenous population (i.e., one of the mixed population structures described above) compared to a homogenous population; mixing both age and activity levels decreased the herd immunity level by 17 percentage points (60% vs 43%). Mixing only activity levels resulted in a greater reduction in disease-induced herd immunity levels compared to mixing age groups only (46.3% vs 55.8%). Assuming preventive measures are put in place one month after virus introduction (March 15, 2020) and lifted almost two months later (June 30, 2020), preventive measures reduced the size and delayed the timing of the peak. In the scenario with the most restrictive preventive measures, lifting these resulted in a clear second wave of infections.

Study Strengths

Authors explored different types of heterogeneous mixing, making the model adaptable to regions or locations with known age structures and activity levels.

Limitations

Although authors considered age structures and varying activity levels in their models, they did not consider more complex parameters, such as contact matrices and differences in daily contacts based on location (e.g., household vs work). Authors made several assumptions in the model that contribute to uncertainty. First, authors assumed that preventive measures would decrease all contact rates proportionally, however, most interventions target older adults and persons at highest risk. Furthermore, they assumed that these measures would be lifted simultaneously, when in reality most countries have been lifting restrictions in stages. Authors also assumed immunity to SARS-CoV-2 after infection for an “extended period of time”. However, the true duration of infection-acquired immunity is not currently known, and more work is needed in this area.

Value added

This study estimates the impact population heterogeneity can have on disease parameters, such as herd immunity, and illustrates the importance of incorporating real-world assumptions (i.e., that populations are not homogenous) when developing mathematical models.

Our take —

The study estimated the cumulative burden of hospitalization and ICU utilization for COVID-19 across 3142 US counties. It found the 315 counties estimated to be in the 90th percentile of COVID-19 hospitalizations had relatively larger populations over 60 years. 247 counties were in the 90th percentile for hospitalization-per-bed (indicating a high burden of healthcare system utilization), and 136 counties were in the 90th percentile for ICU-per-bed. These counties were scattered across the US, but primarily in the western US. Their model made a number of simplifying assumptions that limit the interpretability of their results, but the counties projected to have the greatest burden was robust to widely varying assumptions about transmission.

Study design

Ecological; Modeling/Simulation

Study population and setting

The study objective was to estimate the burden of hospitalization and ICU admissions for COVID-19 across the US in 3,142 counties as identified in the 2010 US census. The study used data from the 2018 American Community Survey to estimate the age-specific demographic distribution in each county, and the American Hospital Association 2018 annual survey to estimate the number of hospital beds and ICUs available in a given county. Counties without beds or ICUs were then distributed to the nearby counties with greater capacity. The study used a susceptible-exposed-infected-recovered (SEIR) transmission model, and assumed a constant population in the counties included. Authors compared outcomes from two models with varying assumptions about intensity of transmission: 1) a pessimistic transmission model which assumed the reproductive number was 5; 2) an optimistic model in which it was assumed to be 2.

Summary of Main Findings

The model estimated 315 counties at or above the 90th percentile of per-capita hospitalization in the optimistic scenario, and 308 were also at or above the 90th percentile in the pessimistic scenario. Counties with relatively larger populations over the age of 60 were most likely to experience more hospitalizations from COVID-19. The counties with the greatest estimated burden were, for the most part, far from urban centers. In assessing the cumulative burden of hospitalizations, 247 counties were at or above the 90th percentile in both transmission scenarios for cumulative hospitalization per bed and were scattered across the US, and were mostly from the west and the northern midwest. 136 counties were at or above the 90th percentile for ICU admission per bed to estimate ICU utilization in comparison to healthcare system capacity, though these counties were not statistically more likely to be rural.

Study Strengths

The study used two nationwide surveys in order to inform the model for each county, and was able to assume cases in areas with reduced hospital and ICU capacity sought care in nearby counties with capacity, as might happen in a real-world scenario. Many of the study’s results regarding the highest burden areas were robust to both optimistic and pessimistic transmission scenarios. They estimated cumulative hospitalization/ICU utilization in order to circumvent issues of temporality and inaccuracies in estimating a specific transmission peak.

Limitations

The study made numerous assumptions, which was necessary for their models but may reduce the applicability of the findings. For instance, their optimistic reproductive number of 2 does not take into account interventions such as shutdowns and stay-at-home orders, that may further reduce the reproductive number. They also assumed 20% of the population in each county eventually becomes infected, which may not be a reasonable assumption, given areas that are urban are likely to experience higher transmission than very rural areas with lower contact between individuals. Finally, the study used data on hospital and ICU bed capacity from 2018, which does not account for field hospitals or other stockpiling of ventilators and other equipment to increase hospital capacity which may occur in response to the pandemic.

Value added

This study estimated the cumulative hospitalization and ICU-related health system burden due to COVID-19 across the United States, and demonstrated that many rural counties will likely be unable to cope with the demand for hospitalizations without efforts to increase capacity, which has important policy implications for resource allocation and planning during the pandemic.

Our take —

Authors estimated the number of individuals with at least one underlying health condition and the percentage at increased risk of severe COVID-19 by age, sex, and country for 188 countries. Overall, authors estimated that 1.7 billion persons worldwide (22% of the global population) have at least one condition that places them at increased risk, and that 349 million persons worldwide (4%) are at high risk of severe disease. Model adjustments for multiple underlying health conditions relied on data from studies conducted in China and Scotland, which may not be appropriate for some locations included in the analysis.

Study design

Modeling/Simulation

Study population and setting

Authors grouped health conditions shown to be associated with increased risk of severe COVID-19 into 11 categories using WHO guidelines and the Global Burden of Diseases, Risk Factors, and Injuries Study (GBD). Authors also included older, healthy (i.e., no underlying condition) adults as a proxy for frailty. Then, using UN mid-year population estimates, authors estimated the number of individuals with underlying conditions by sex, age, and country for 188 countries. Authors estimated the percentage of each country’s population at increased risk with and without age standardization and adjusted for individuals with more than one condition using results from prior multimorbidity studies in China and Scotland. Authors estimated degree of risk among those at increased risk by defining “high risk” as those who would require hospitalization if infected, and assumed males were twice as likely to be at high risk compared to females.

Summary of Main Findings

Overall, authors estimated that 1.7 billion individuals, or 22% of the global population, have at least one underlying condition that places them at increased risk of severe COVID-19. If healthy adults (i.e., with no underlying health conditions) 50 years and older are included as a proxy for frailty, the percentage increases to 34%. Chronic kidney disease, cardiovascular disease, chronic respiratory disease, and diabetes were the most common among individuals 50 years and older. According to results from non-age standardized estimates, the population proportions at risk ranged from 16% of the population in Africa to 31% of the population in Europe. Authors estimated that 349 million, or 4% of the global population, were at high risk of severe COVID-19. This varied considerably by age group: the proportion of those at high risk among individuals 20 years and younger was 1/900 and the proportion among individuals 70 years and older was 1/5.

Study Strengths

Authors generated uncertainty bounds by running sensitivity analyses, varying values for country population size, disease prevalences, and the fraction of each population with more than one condition.

Limitations

Although GBD provides the prevalence of individuals with each underlying condition, it does not provide prevalence for individuals with more than one condition. Authors corrected for this in the estimates, but methods relied on data from Scotland and China, which may not be applicable to all locations included in the analyses. All hypertension conditions were included in the heart disease category, but this may have diluted the association between risk of severe COVID-19 and other diseases caused by hypertension, such as chronic kidney disease. Furthermore, the proportion of individuals with an underlying condition – and therefore at increased risk – may be underestimated if conditions are not diagnosed or unreported. Authors estimated the proportion of individuals at high risk (i.e., would require hospitalization if infected), but were unable to estimate the probability of such individuals ever being infected. Although included in supplementary material and discussed by authors, age-specific risks are largely uncommunicated in results. As authors indicate, this could misconstrue the risk for individuals in certain parts of the world with overall younger populations. In Africa, for example, although the overall proportion of the population at risk is lower than that in Europe, age-specific risks are similar or higher for several conditions.

Value added

To date, studies have largely focused on assessing underlying health factors that put individuals at increased risk of COVID-19 (e.g., diabetes, chronic respiratory conditions). To aid and inform policy makers regarding interventions for vulnerable populations, this study expands beyond these aims by quantifying the number and percentage of individuals at increased risk.

Our take —

Using setting- and disease-specific social contact data from the UK, authors demonstrated that combined test and trace strategies could reduce SARS-CoV-2 transmission more than self-isolation or mass testing alone (60-65% reduction compared to a 2-30% reduction). App-based contact tracing was estimated to be less effective in reducing transmission than manual contact tracing, but these comparative losses may be offset by the redirection of critical human resources to other activities.

Study design

Modeling/Simulation

Study population and setting

Using social contact data from 40,162 UK participants, authors explored the impact of different control measures for SARS-CoV-2 on the reduction in transmission including: self-isolation of cases showing symptoms, household quarantine, manual tracing of all contacts, manual tracing of contacts that have met before (acquaintances), app-based tracing, mass testing regardless of symptoms, limiting the number of daily contacts made outside home, work and school, and having a fraction of the adult population work from home. In addition, authors also estimated the number of primary cases and contacts newly quarantined per day under the different strategies for different levels of COVID-19 incidence.

Summary of Main Findings

Results showed that the Re (the effective reproductive number, the reproductive number after the implementation of interventions and community transmission has occurred) was most effectively reduced through the implementation of multiple control measures at once. For example, compared to self-isolation within the home, which reduced Re by an average of 29%, a combination of self-isolation, household quarantine, manual contact tracing of acquaintances, app-based tracing, and limiting daily contacts to four per person reduced Re by an average of 66%. The addition of app-based tracing resulted in overall relatively smaller reductions to Re compared to other combinations because both the primary case and contact would need to have and use the app. Mass random testing of 5% of the population every week resulted in the smallest reduction in transmission (2%), because through this method only a small number of infections would be detected, and those detected are likely to have already spread the infection to others. Assuming there were 10,000 new symptomatic cases per day, anywhere from 22,000 to 390,000 contacts would be newly quarantined each day under the different contact tracing strategies.

Study Strengths

Authors used setting-specific social contact data from over 40,000 individuals to estimate how singular and combinations of different control measures impact SARS-CoV-2 transmission. In addition, authors evaluated a wide range of different control strategies and included strategies that have been developed to deal with the magnitude of the pandemic, including app-based tracing methods.

Limitations

Authors did not account for different types of interactions between contacts, including the fact that contacts of primary cases might know one another. This would reduce the number of contacts that would need to be traced. Authors also assumed that contacts made outside the home were different each day. Repeated contacts outside the home would also reduce the number of contacts that need to be traced. Finally, authors only considered four types of settings, work, home, school, so findings may not be applicable to other specific settings, including mass gatherings.

Value added

Using data from over 40,000 individuals in the UK, authors compare different combinations of control measures to reduce transmission including contact tracing, self-isolation, and physical distancing. While previous analyses suggest isolation or tracing alone are not sufficient to control outbreaks, this is one of the first studies using setting- and disease-specific social contact data to assess the potential impact of combined control strategies on SARS-CoV-2 transmission.

Our take —

This study based on seroprevalence data and modeling from Geneva Switzerland, available as a preprint and thus not yet peer-reviewed, confirms previous studies which have shown that the infection fatality ratio increases with age, and increases sharply after 65 years of age.

Study design

Modeling/Simulation

Study population and setting

The authors estimated the overall and age-specific infection fatality risk (IFR) for Geneva Switzerland using daily case and death reports between February 24 and June 2, 2020, combined with five weekly seroprevalence estimates starting April 6, 2020. The authors linked the sources of information to be able to infer IFR, and used Bayesian methods to account for the lag between infection and seroconversion, and between infection and death.

Summary of Main Findings

There were 286 total COVID-related deaths from February 2 to June 2, 2020 in Geneva. The IFR was low among those under 50 years of age at between 0.00032 and 0.0016% but this increased to 0.14% (95% Credible Interval (CrI) 0.096-0.19) among those 50-64 years, and to 5.6% (95% CrI 4.3-7.4) for those 65 years or older. The authors estimated the population-wide IFR to be 0.64% (95% CrI 0.38-0.98). When cases and deaths among care home residents were excluded from the analysis, the estimated IFR was 2.7% (95% CrI: 1.6 – 4.6) for those 65 years or older.

Study Strengths

The study was focused on a specific geographic area (Geneva) which allowed for more complete data of cases and deaths. This was matched with data from sequential seroprevalence surveys from a representative population.

Limitations

The focus on a distinct geographic location may limit the generalizability of the findings outside of Geneva. In the older population (65 years +), half of the deaths were among nursing home residents, but such residents are likely underrepresented in the sero-survey data used to create the estimates, which could bias the results, likely by overestimating the IFR in this group. Finally, if antibody responses differ by disease severity with milder cases having a weaker and shorter response, authors’ estimates of IFR may be biased upwards.

Value added

This study provides additional information on the modeled infection fatality risk (IFR) underpinned by seroprevalence data to add to the small but growing literature on IFR. A diversity of IFR estimates will be needed given that it is likely to differ from one population to another. It also highlights the disproportionate number of deaths in vulnerable settings such as care homes.

Our take —

This study provides credible estimates that by May 4, 2020, the 11 European countries in this study had achieved control of the COVID-19 epidemic by bringing the reproduction number below 1, after 3.2% to 4.0% of the population had become infected (far more than has been reported). The authors further estimate that country-wide non-pharmaceutical interventions (e.g., lockdowns and school closures) prevented over 3 million deaths. Results suggest the need for suppression measures to continue for a long time, since susceptible individuals are still estimated to represent a large fraction of the European population. However, this implication relies on model assumptions that preclude the ability of other factors, including individual behavior changes, to reduce transmission.

Preprint Review

This expert summary is for the peer-reviewed article linked above. We also summarized this paper before it underwent peer-review.  You can find the original review of the preprint by clicking here.

Study design

Ecological; Modeling/Simulation

Study population and setting

A semi-mechanistic Bayesian hierarchical model was fit to observed COVID-19 deaths in 11 European countries, and was used to estimate the number of cases and the reproduction number (Rt) of the infection. The model used partially pooled data across countries, along with the timing of country-specific non-pharmaceutical interventions, to estimate the impact of these interventions on Rt (with particular attention to whether Rt has been driven below 1) and on the number of infections and deaths in these countries up to May 4, 2020.

Summary of Main Findings

The reproduction number of the virus is estimated to have been reduced from 3.8 (95% credible interval: 2.4 to 5.6) to below 1 in all countries. Many times more people are estimated to have been infected by SARS-CoV-2 than have been confirmed: across all 11 countries, estimated cases totaled 12 to 15 million for an overall attack rate of 3.2% to 4.0%. Estimates of country-specific attack rates ranged from 0.46% (0.34% to 0.61%) in Norway to 8.0% (6.1% to 11.0%) in Belgium. 3.1 million deaths (2.8 million to 3.5 million) are estimated to have been averted by all non-pharmaceutical interventions by May 4. Of the categories of intervention, only lockdown had an identifiable impact.

Study Strengths

The model is fit to observed deaths, which are likely to be more reliable than case counts or hospitalizations. Estimates of case counts are supported by evidence from serologic surveys. The model reproduces observed data up to May 4th, 2020 very well. Uncertainty from various sources is appropriately handled by the model explicitly and by the discussion implicitly. Prior distributions and parameter values are chosen based on current, best available data.

Limitations

The model did not allow for other factors, such as changes in individual risk-avoiding behavior, to affect Rt. The infection fatality ratio, which is key to estimation of the number of infections, is treated as a fixed value by the model; there remains considerable uncertainty about the true value of this parameter. The timing of country-specific interventions makes it difficult to distinguish between the effects of specific interventions. Interventions are assumed to have the same impact across countries and time. Effect estimates in the model are heavily influenced by countries with a high number of deaths that implemented interventions earlier.

Value added

This study is the most thorough model-based estimate of the impact of European country-wide non-pharmaceutical interventions so far.

Our take —

In this study, available as a preprint and thus not yet peer reviewed, authors projected the total number of severe COVID-19 cases and estimated national capacity to meet hospital bed-, ICU bed-, and ventilator-needs assuming different levels of care seeking in 52 African countries. Overall, countries were ill-equipped to handle the number of projected hospitalizations at the peak of the outbreak, even assuming care-seeking as low as 30% among severe cases. Model assumptions likely resulted in the overestimation of average national capacity, but this study provides useful estimates for surge capacity preparation in countries in which the outbreak has not yet peaked.

Study design

Modeling/Simulation

Study population and setting

Using data on current case numbers and interventions, authors modified a Susceptible-Exposed-Contagious-Infected-Recovered (SECIR) compartmental model to predict the total number of severe COVID-19 cases for 52 African countries. Then, using these projections, authors estimated the number of hospital beds, ICU beds, and ventilators needed at the peak of the outbreak per country assuming four levels of care-seeking behavior (if 30, 50, 70, or 100% of patients with severe COVID-19 symptoms sought care). Authors assumed that interventions would decrease transmission by 25% while implemented, but would return to 90% of the pre-intervention value after being lifted. Although they did not explicitly account for age and comorbidities in the model, authors did include these parameters as a function of the projected number of people who would develop severe disease.

Summary of Main Findings

The average number of total projected severe cases per country was 138 per 100,000 (this ranged from 102 per 100,000 to 145 per 100,000). Assuming the most burdensome care volume (i.e., 100% of persons with severe infection sought care), an average of 131.7 per 100,000 hospital beds, 6.5 per 100,000 ICU beds, and 3.18 per 100,000 ventilators would be needed. Authors estimated that 62% of countries do not have enough hospital beds to meet this need and that some countries would require up to 10,000 additional beds. Authors estimated that over 87% of countries do not have sufficient ICU beds to handle the projected number of severe cases and that countries would require 5,000 to 12,000 additional ICU beds. Authors estimated that over 91% of countries do not have enough ventilators to meet national needs. Assuming the least burdensome care volume (i.e., 30% seek care), authors still estimated that 20%, 71%, and 76% of countries could not meet hospital bed, ICU bed, and ventilator needs.

Study Strengths

Authors used current case numbers and specific interventions per country, so estimates are country-specific.

Limitations

Authors assumed that all resources (hospital beds, ICU beds, and ventilators) would be functional and immediately available for first use by COVID-19 patients. This discounts the consumption of these resources by other patients hospitalized for non-COVID-19 related reasons and likely overestimates each country’s true capacity. Authors also assumed hospital beds, ICU beds, and ventilators were distributed evenly throughout each country’s population; these resources are often clustered in urban areas and this overestimates the availability of these resources to populations in more rural areas, which also likely overestimates national capacity. Authors used case severity data from China, Europe, and the US; studies that have since been released suggest that case severity may differ considerably in African countries, which have younger age distributions and different contact patterns and potential exposures.

Value added

As confirmed COVID-19 cases and deaths continue to increase across the African continent, critical care capacity, gaps, and needs should be assessed to inform pandemic planning and preparation. This study is among the first to provide a comprehensive assessment of these parameters across Africa.