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Our take —

The study sought to investigate associations between socioeconomic status (SES) and COVID-19 infection and mortality in Santiago, Chile in 2020. They found that decreased municipality SES was associated with higher test positive rates (an indicator of insufficient testing in addition to infectious disease burden), and higher age-stratified case fatality rates. For instance, in the lowest SES municipality the incidence rate peak was 76.4 cases per 1,000 people, compared to 22.6 cases per 1,000 people in the highest SES municipality. The study also estimated that mobility, based on Facebook data, was reduced by 61% among high-SES municipalities compared to 40% among low-SES municipalities, suggesting different effects lockdown has on various communities. This is one of the largest studies of SES and COVID-19, particularly from South America which is often underrepresented in research.

Study design

Modeling/Simulation

Study population and setting

The study sought to describe the association with socioeconomic status and COVID-19 infection and death in Santiago, Chile. The study used the Social Priority Index from 2019, and defined socioeconomic status based on three components: income and poverty, access and quality of education, and access to healthcare and life expectancy. Beginning in January 2020, the EPIVIGILA platform was developed by the Health Epidemiology Unit of the Ministry of Health. Active testing was done for both suspected asymptomatic and pre-symptomatic cases, particularly those living in vulnerable areas or who live in institutions including jails, nursing homes, and the National Service for Minors. The EVIGILA platform contained population projections, testing rates, test positivity rates, and similar data. The reports track cases for each municipality and are published twice per week. The Vital Statistics System in Chile records deaths and standardized clinical terms from the International Statistical Classification of Diseases (ICD-10) for the cause of death. Additionally, COVID-19-resultant deaths are noted based on WHO coding guidelines. The study also used Facebook’s Data for Good Geoinsights portal which has anonymized location data among Facebook users with a smartphone, binned in 8-hour increments. The study calculated the percentage change in individuals gathered in an area compared to baseline for each 8-hour transition period. The study estimated SARS-CoV-2 infections over time using a Poisson deconvolution model based on observed COVID-19 attributable deaths. The study also calculated excess deaths using Gaussian Processes regression and infection fatality rates using hierarchical Bayesian joint model per age group and municipality.

Summary of Main Findings

Across 34 municipalities and 7 million people living in Santiago during 2020, the maximum SARS-CoV-2 incidence in the highest SES municipality Vitacura was 22.6 weekly cases per 10,000 individuals, while La Pintana (the lowest SES municipality) was 76.4 weekly cases per 10,000 individuals. There were also notable changes in human mobility during the lockdown periods, with the highest SES municipalities reducing mobility by 61% in lockdown, while low SES municipalities had 40% reduced mobility. In the researchers’ models, they estimated that the number of infected individuals was 5 to 10 times larger than the reported values, and that between May and July 2020, there were 1.73 [95% Credible Interval: 1.68, 1.79] times the expected number of deaths, with the peak at 2,110 death counts in the first week in June 2020. The study also found a higher testing capacity in wealthy areas, with a strong negative association between positivity and SES, and an association between increased wait times for receiving test results and lower SES municipalities. Finally, the study also found an increased case fatality rate by age and SES, with the fatality rate 1.7 times higher among 40-60 year olds in low SES areas compared to high SES areas, and 1.4 times higher among 60-80 year olds in low vs. high SES areas.

Study Strengths

The study made use of a wide range of data sources to inform their estimates. Particularly, researchers had access to Facebook data in 8-hour bins in order to get an accurate measurement of mobility using smartphones. Researchers also used a validated SES index that reflects a number of different SES-related factors, as opposed to only using income or education in order to define this complex and multi-faceted positionality. They also were able to examine testing capacity in order to estimate potential underreporting of prior estimates.

Limitations

One limitation is potential selection bias in the mobility data, as it is predicated on Facebook use and smartphone ownership, which may not reflect all sociodemographic groups. It is not clear how this may bias estimates, however. Additionally, their SES index examined 3 factors (healthcare access, education, and income), but there may be additional nuances in socioeconomic position that is not represented in these three categories (such as race/ethnicity, access to healthy foods, etc.). It is also not clear how this potential misidentification of SES may bias results.

Value added

This is one of the largest studies examining associations with socioeconomic status and COVID-19 infection and mortality in South America.

Our take —

This study reports the results of a national rapid antigen testing campaign in Slovakia, which was associated with a greater than 50% reduction in estimated SARS-CoV-2 prevalence over a one week period. Results should be interpreted cautiously, as mass testing occurred in the context of many other SARS-CoV-2 control measures.

Study design

Ecological, Modeling/Simulation

Study population and setting

Between October 23 and November 8, 2020, The Slovak Ministry of Health implemented SARS-CoV-2 testing nationally using rapid viral antigen tests in three phases: (1) a pilot phase (October 23-25) in four high-incidence counties; (2) a mass testing campaign implemented nationally in 79 counties (October 31-November 1; round 1); and (3) follow-up testing one week later in 45 counties with the highest SARS-CoV-2 prevalence (November 7-8; round 2). Residents were instructed to present to central testing hubs in their jurisdictions, staffed with over 60,000 trained employees nationally, during each testing campaign phase. Testing was performed using the SD-Biosensor Standard Q rapid antigen test on nasopharyngeal swabs. A national lockdown was imposed in Slovakia at the time of mass testing campaigns, which included business and school closures (for students ages 10 years and above): residents were instructed to stay at home and leave only for essential purposes (i.e., travel to/from work, accompanying children to school, seeking medical care). After estimating changes in SARS-CoV-2 prevalence (defined as the proportion of SARS-CoV-2 tests performed during a campaign phase with positive results) between mass testing campaigns, the authors used an epidemic microsimulation model to evaluate whether observed changes in SARS-CoV-2 prevalence could be attributed to scale-up of rapid antigen testing.

Summary of Main Findings

Nearly 5.3 million rapid antigen tests were conducted across the three testing phases, detecting 50,466 positive cases overall. Population coverage of antigen tests during the pilot, round 1, and round 2 phases was 65%, 66%, and 62%, respectively (84-87% of the census age-eligible population). Test positivity in the pilot phase was 3.91%, 1.01% during round 1, and 0.62% in round 2. Specificity of the rapid test was estimated to be 99.85%. In the 45 counties included in rounds 1 and 2, the estimated SARS-CoV-2 prevalence decreased by 58% (95% CI: 56–63%) between campaigns, with substantial heterogeneity observed between counties. In the four counties included in the pilot phase, infection prevalence decreased by 82% (95%CI: 81-83%) between the pilot and round 2. In microsimulation models, the authors assumed varying levels of effectiveness of other SARS-CoV-2 control measures which were implemented at the same time as testing (e.g., lockdowns, school closures): the only scenario that sufficiently reproduced observed reductions in SARS-CoV-2 prevalence between testing campaigns was a scenario in which confirmed COVID-19 cases isolated from household contacts, suggesting that declines in SARS-CoV-2 prevalence were unlikely to have occurred in the absence of the mass testing campaign.

Study Strengths

This study analyzes an impressive amount of SARS-CoV-2 rapid antigen testing data from three testing campaigns, including one conducted at a national scale in Slovakia.

Limitations

Participation in viral antigen testing was voluntary and required travel to testing sites; the estimated SARS-CoV-2 prevalence, therefore, may not accurately reflect true SARS-CoV-2 transmission dynamics in the population. Despite using an epidemic simulation model to estimate the potential impact of mass testing on SARS-CoV-2 transmission, declines in SARS-CoV-2 prevalence may not be attributable to scale-up of viral antigen tests. Mass testing campaigns were also conducted in the context of a national lockdown, where residents were instructed to only leave their homes for essential purposes. No empirical data were presented on isolation or quarantine of infected cases and their contacts, respectively, which is essential to concluding whether observed reductions in cases were attributable to mass testing or other interventions. While the test used was a rapid antigen test, it was conducted by tens of thousands of trained professionals using nasopharyngeal swabs; thus, results may not be generalizable to most other settings.

Value added

This is among the first studies to assess the impact of mass rapid viral antigen testing on SARS-CoV-2 transmission dynamics.

Our take —

Authors sought to model the impact of the UK’s vaccination program and targeting of priority groups on COVID-19 associated hospitalizations, ICU admissions, and deaths. Authors adjusted models by age group, priority group, and clinical risk and vulnerability. Methods are poorly described, making it difficult to follow the motivation for different assumptions. Overall, these assumptions ignore the dynamical processes that drive disease transmission and outcomes, thus limiting utility and replicability of the analysis.

Study design

Modeling/Simulation

Study population and setting

Authors sought to estimate the effect of the UK’s vaccination plan on the number of COVID-19-associated hospitalizations, ICU admissions, and deaths among ten priority groups. Authors estimated these impacts using data from the UK’s vaccine delivery plan, population size estimates for each priority group, and deaths, hospitalizations, and ICU admissions per priority group and for the total UK population. Authors assumed every individual in priority groups 1 through 4 will have received their first dose by mid-February 2021, uptake within priority groups will be 100%, that the current distribution of clinical outcomes would mirror those in summer 2020, and vaccination is 100% effective in preventing these outcomes (but does not reduce transmission). Various models adjusted for age groups, extremely clinically vulnerable groups, and/or health worker- and social worker-specific admissions. Authors used the above described empirical data to reallocate the age distribution based on these priority groups. Overall, methods were poorly documented and may not be replicable.

Summary of Main Findings

Authors reported the cumulative impact of vaccination by age group first. If adults >80 years were vaccinated, 62% of deaths, 33% of hospital admissions, and 3% of ICU admissions were prevented; if all adults 18 and older were vaccinated, deaths, hospitalizations, and ICU admissions were all reduced by 100%. Authors then adjusted for priority group. When groups 1 and 2 (care home residents and workers, healthcare workers, social care workers, and adults aged 80+) were vaccinated, 63% of deaths, 37% of hospitalizations, and 8% of ICU admissions were prevented; when all groups (1 through 10) were vaccinated, all measures were reduced by 100%. Finally, when authors reallocated health and social workers (i.e., those extremely clinically vulnerable and at risk) into different priority groups, vaccinating groups 1 and 2 reduced deaths by 63%, hospitalizations by 37%, and ICU admissions by 8%; 100% of these measures were prevented when all 10 groups were vaccinated.

Study Strengths

Authors conducted sensitivity analyses to estimate the impact of vaccines assuming only 90% uptake and/or 90% vaccine effectiveness. Percent reduction of the clinical outcomes was smaller when less-than-perfect uptake and effectiveness was assumed, however, these reductions were relatively minor.

Limitations

Overall, methods were difficult to understand and insufficiently detailed. Data on COVID-19 associated hospital admissions by age group were from February to April 2020; ICU admissions and deaths by age group were from June 2020. At the time of publication, these data were up to one year old and may be considerably outdated. The assumption that infection during the second wave will mirror dynamics seen during the first wave is not likely to be realistic in real-world settings, especially in the context of ongoing mitigation efforts – including vaccination – and the emergence of new viral strains. The modeling does not consider the immunity profile in the population given the history of natural infections that have occurred over the past year. Even at baseline, care and the age burden of infection has changed over time and it seems unlikely that the distribution of outcomes in that first wave would remain the same now. This method also does not seem to consider changes in infection, hospitalization, ICU admission and/or death rates over time (or in changes to relative rates between priority groups over time) as vaccines are rolled out. It is not clear what the “end” time point is for evaluating vaccine impacts. Finally, authors only report cumulative effects of vaccination by age group or priority group, limiting the interpretability of these estimates for specific groups.

Value added

This modeling study attempts to provide potential guidance for monitoring the effectiveness of the UK’s COVID-19 vaccination program and the impact targeting priority groups has on severe clinical outcomes. However, unfounded assumptions about immunity and the age-specific risk of infection and severe outcomes and poorly documented methods limit any potential value added from this study.

Our take —

While the puzzle is far from solved, this study, available as a preprint and thus not yet peer reviewed, provides evidence that the novel SARS-CoV-2 variant of concern 202012/01 is associated with substantial increased transmissibility. No evidence was found to suggest that the new variant was associated with increased hospitalization or death rates. The study also reported that additional stricter mitigation measures along with high rates of vaccination will be necessary to control transmission due to the estimated increased transmissibility of the new variant and its increasing frequency in the population. Testing and sampling biases may partially explain the observed results, and founder and super spreading events cannot be fully ruled out as explanations for increasing frequency and transmissibility of the new variant.

Study design

Ecological, Modeling/Stimulation

Study population and setting

This study reports on the frequency and transmissibility of SARS-CoV-2 variant of concern (VOC) 202012/01 in England between September 28 and December 1, 2020. The frequency and proportion of the VOC in England was estimated from routine diagnostic PCR data. Diagnostic assays fail to detect the spike glycoprotein (S-gene) in the VOC, and so samples exhibiting S-gene target failure (i.e. S gene drop-out) that are positive for other SARS-CoV-2 genes, can be used as a proxy for VOC in the absence of whole-genome sequencing, and was shown to be 97% specific for the VOC in the UK during the analysis period. After estimating the proportion of all positive samples with S gene drop-out, the authors used a two-strain age and regionally structured transmission model fit to COVID-19 hospitalization and death data from seven regions to explain observed increases in COVID-19 diagnoses and frequency of the VOC during the observation period. Four hypotheses to explain the coincident rise in overall cases and the VOC were evaluated: 1) increased infectiousness of the VOC; 2) immune escape (i.e., reinfection of individuals who had been previously infected with a different variant by the VOC); 3) increased susceptibility to the VOC among children as compared to other variants, and 4) a shorter generation time. The model was also used to assess whether there were differences in odds of hospitalization or relative risk of death due to the VOC. The authors also assessed whether changes in contact patterns over time might explain observed changes in transmission using Google mobility data and age-specific contact data from the CoMix social contact survey. Lastly, the authors projected the course of the epidemic under various control strategies, including lockdowns, school closures, and mass vaccination, through the summer.

Summary of Main Findings

The authors found that the proportion of COVID-19 cases in England due to the VOC has rapidly increased since November. The increasing proportion of VOC cases was also associated with a significant increase in the effective reproductive number, Rt. Of the four hypotheses tested to explain the coincident rise in the VOC and Rt, the observed data were most consistent with the model including increased transmissibility of the VOC relative to pre-existing variants. The authors estimated that the VOC was 56% more transmissible (95% credible interval: 50-74%) than previously observed SARS-CoV-2 variants combined. However, there was no evidence that the VOC led to higher rates of hospitalizations or deaths. Changing contact patterns did not appear to explain the rise in the VOC. Lastly, the authors found that unless schools are closed and vaccinations increased to 2 million per week, the effective reproductive number is unlikely to fall below 1 as a consequence of increased frequency and transmissibility of the VOC.

Study Strengths

This study assessed multiple alternative mechanisms for the rapid rise in COVID-19 cases along with increasing frequency of the VOC in England, including increased transmissibility of the VOC, increased, susceptibility to the VOC among children (which has major implications for school re-openings/closures), and changes in social contact patterns.  Implications of the VOC on vaccination strategies were also explored.

Limitations

Limited information was reported on testing rates or genomic sampling across NHS regions or age-groups over the observation period which may impact interpretation of results. While unlikely, this study does not rule out the possibility that founder and super spreading events with subsequent spread of the VOC to other regions explain the observed data. Of note, there were apparent increases in social contacts among persons <18 years of age immediately prior to the rise in the VOC, which was not addressed in the limitations, despite reported outbreaks among schools over the same time frame. Further, estimates of Rt from contact data appear to fit the data relatively well, except for a brief time period between/during October and November.

Value added

This is the first study to estimate the relative transmissibility of the novel SARS-CoV-2 variant, VOC 202102/01, compared to pre-existing variants and its implications for future transmission dynamics and control.

Our take —

This study used four different analytic approaches to estimate the relative effectiveness of country- and US state-level non-pharmaceutical interventions (NPIs) early in the pandemic. The authors found that social distancing (e.g., cancelling small gatherings), school closures, and travel restrictions (e.g., border closures) were most effective, while environmental measures (e.g., disinfecting surfaces) were least effective. Though each statistical approach was distinct, all relied on a similar set of assumptions, and all were subject to biases. Assessing the effectiveness of NPIs is difficult for many reasons: many were implemented simultaneously, case reporting varies across regions and over time, and the success of a given NPI may heavily depend upon local context. This study is not definitive, but suggestive; it highlights important issues involved with NPI impact evaluation.

Study design

Ecological; Modeling/Simulation

Study population and setting

This study considered 6,068 non-pharmaceutical interventions (NPIs) in 79 territories (countries and US states) implemented in March and April of 2020. The authors ranked the effectiveness of these NPIs in reducing the effective reproduction number of SARS-CoV-2 (Rt) using eight broad themes, and 46 categories of NPIs that were implemented 5 or more times within those themes, established by the Complexity Science Hub COVID-19 Control Strategies List (CCCSL). Four analytic methods were used to rank effectiveness: 1) a case-control study (similar to a difference-in-differences approach), 2) LASSO time-series regression (an approach designed to prevent overfitting of models), 3) random forest regression (a a prediction and classification method) with NPIs ranked by measuring model performance with and without each NPI, and 4) Transformers, a machine learning technique for parallel processing of sequential data that can be applied to time series. All approaches used a range of lag periods (i.e., the timing between implementation of an intervention and its effect on the reproductive number, Rt), and considered possible confounding variables including the duration of the local epidemic, Rt at the time of a given NPI, population size, population density, and the previous number of NPIs implemented (total and within the same category as the given NPI). The results were applied to two external validation data sets.

Summary of Main Findings

There was general agreement among the four methods used to rank NPI effectiveness; of the eight broad NPI themes, social distancing and travel restrictions were the highest ranked, while environmental measures such as disinfecting surfaces were least effective. Six of the 46 categories showed statistically significant reductions in the reproductive number (Rt) with all four methods: canceling small gatherings (change in Rt: -0.22 to -0.35), closing educational institutions (-0.15 to -0.21), border restrictions (-0.06 to -0.23), increasing availability of personal protective equipment (-0.06 to -0.13), individual movement restrictions (-0.08 to -0.13), and national lockdown including U.S. state stay-at-home orders (-0.01 to -0.14). Environmental cleaning, public transport restrictions, and contact tracing measures were among the least effective NPIs across estimating methods. NPI effectiveness was highly heterogeneous across countries. Applying these methods to two external datasets produced broadly similar results. By artificially shifting NPIs to different times in relation to the age of the epidemic, the authors estimated that early adoption was beneficial for lockdown, small gathering cancellations, travel restrictions, and closure of educational institutions.

Study Strengths

This study considered a wide range of disaggregated NPIs, and compared their effectiveness using four distinct methods to examine the sensitivity of NPI ranking to the estimation method. Methods were tested on two external datasets.

Limitations

While the authors employed four different methods for estimating NPI effectiveness, the specification of models in each method was structurally similar, meaning that agreement across methods is less remarkable than it may appear. The estimation of Rt relies on reported cases; case reporting was highly heterogeneous across regions and over time during the study period, and was subject to discrete changes in case definitions and reporting standards. Estimates of changes in Rt may therefore be biased. Also, using this method makes it appear as if measures that increase case ascertainment (e.g., improving testing capacity, contact tracing, etc.) actually increase Rt. The timing of NPI implementation makes identification of impacts difficult, since so many were implemented roughly simultaneously– this issue is exacerbated because the expected lags between implementation and effects are not precisely known. The study is restricted to March and April of 2020, and so territories that were hit earlier by the pandemic are likely overrepresented. Although travel restrictions are ranked highly, they are likely to be effective only during the initial phases of an epidemic when local transmission represents a small share of new cases. In general, the interactions between the age of a local epidemic and NPI effectiveness are not handled well by the approaches here. Finally, although the authors adjusted for several territory-level variables, including the number of previous interventions implemented, residual confounding (e.g., country GDP) and unmeasured interactions (e.g., the success of contact tracing may depend on multiple other factors including public health infrastructure and testing capacity) are likely.

Value added

This is one of the largest attempts to quantify and rank the effectiveness of non-pharmaceutical interventions across multiple regions.  

Our take —

In this study, available as a preprint and thus not yet peer reviewed, authors used a mathematical model to quantify how different testing strategies could be used to reduce the probability of transmitting SARS-CoV-2 to others after leaving quarantine. Any testing during the quarantine period, especially toward the end, contributed to a reduction in the probability of post-quarantine transmission (pPQT). The pPQT was reduced by 98% and 93% for 7-day and 5-day quarantines respectively with PCR testing for SARS-CoV-2 upon entrance and exit into quarantine, as compared to a 3-day quarantine with only entrance testing. Many assumptions in the model, such as an optimistic diagnostic sensitivity and just a 24-hour delay from sampling to returning a test result, may not be realistic or feasible in most settings. However, this work provides a framework to explore how quarantine measures could be adapted to incorporate additional information from test results.

Study design

Modeling/Simulation

Study population and setting

Using a mathematical model, authors explored if different quarantine durations combined with testing at the start and/or end of the quarantine period could have the equivalent reduction in the probability of post-quarantine transmission (pPQT) as the standard 14 day quarantine period with no testing. They explored three scenarios for which quarantine may be needed: ii) for travel regulations; ii) quarantine of contacts identified through contact tracing; and iii) case isolation upon symptom onset. Authors then used a dataset of 4,040 PCR test results from tests administered between April 11 to August 26, 2020 at an offshore oil rig to test their model. They assumed an 8.29 day incubation period and a 24 hour delay between sampling and test results being returned.

Summary of Main Findings

Any testing during the quarantine period contributed to a reduction in the pPQT, with the reduction dependent on the timing of the test and the duration of quarantine. A single test at the end of quarantine of any length consistently resulted in a lower pPQT compared to a single test conducted at the start of quarantine. Optimum time for testing was upon exit from quarantine, day 5, and day 6 for quarantine lasting ≤7 days, 8-13 days, and ≥14 days respectively. In an optimistic scenario with minimal delays to test results, testing at the end (or beginning and end) of quarantine could halve the duration to 7 days. Analysis of the 4,040 PCR test results conducted on an oil rig where workers were tested at the start of a 3-day quarantine, using this framework found that adding a test at the end of a 7 day or 5-day quarantine could reduce the pPQT by 98% and 93% respectively. Authors estimated that 9 offshore transmission events could have resulted in the absence of testing on exit.

Study Strengths

Authors consider several real-world scenarios where quarantine is currently implemented and explore several scenarios for testing and quarantine duration. Their framework is then validated using a large dataset of 4,040 PCR test results conducted amongst employees of offshore oil rigs. Authors also tested if the duration an individual is infected but less likely to infect contacts had an impact on their results.

Limitations

Authors assumed that the incubation period is fixed at 8.29 days which may be unrealistic given the mean incubation period estimated from multiple studies is shorter at 5 – 7 days. The incubation period can also vary between individuals which the authors do not account for in this study. Authors also extrapolated test sensitivity estimates early after contact exposure based on data from hospitalised patients; the diagnostic sensitivity assumptions appear quite optimistic relative to other available data based on more comprehensive data from clinically diverse patient populations. Given their study focuses on the use of testing for release from quarantine we would expect this would also include asymptomatic and/or mild individuals where test sensitivity may differ substantially. Finally, they also made an optimistic assumption that the delay from sampling to receiving test results is 24 hours which may not be realistic or feasible in many settings. The data set used to validate their framework represents a unique closed population which is not representative of the wider population.

Value added

With many countries adopting testing on arrival or as a prerequisite for travel, this study quantifies the value of testing at the end of the quarantine period in addition to, or in place of testing at the beginning of the quarantine period.

Our take —

This study demonstrates that across several plausible assumptions for natural and vaccine-induced immunity to SARS-CoV-2, very different medium- and long-term transmission dynamic scenarios are possible, from elimination to recurring epidemics. The model provides a framework to explore optimal vaccination strategies and highlights key knowledge gaps including the duration and strength of immunity, how this might vary within the population, and the impact of viral evolution and co-infections on infection dynamics and clinical disease presentation.

Study design

Modeling/Simulation

Study population and setting

Authors used a simple (S)usceptible-(I)nfected-(R)ecovered-(S)usceptible compartmental model to explore potential transmission dynamics of SARS-CoV-2 over the first five years of the pandemic under different scenarios. They first explored the impact of non-pharmaceutical interventions (NPIs) leading to a 40% reduction in transmission intensity, assuming climatic conditions similar to New York, over a 5-year period with differing durations of natural immunity. Assuming a weekly vaccination rate of 0-1%, authors then explored the impact of vaccine introduction 18 months into the pandemic assuming different durations of vaccine-induced immunity and reduction in susceptibility to secondary infections. Finally, authors estimated the potential impact of vaccine hesitancy and some sectors of the population having intrinsically higher contact rates.

Summary of Main Findings

Authors found that reduced susceptibility to secondary infections could increase the time to secondary peaks, but that these peaks may be higher due to the accumulation of susceptible individuals. This could be compounded by implementation of NPIs. With increasingly imperfect immunity and especially with short vaccine-induced immunity, high vaccination rates were required to achieve elimination. Thus, both high rates of vaccination and relatively long-lived immunity are required to substantially reduce the burden of secondary infections. These qualitative projections of immunity on medium- and long-term dynamics were not affected by moderate variability in transmission within the population. Vaccine hesitancy increased the vaccination threshold required to achieve herd immunity particularly if these individuals were less adherent to NPIs and therefore had increased contact rates compared to non-refusers. Notably in this scenario, vaccination alone was not enough to prevent outbreaks.

Study Strengths

Authors examined a wide range of plausible scenarios for natural or vaccine-induced immunity based on existing knowledge of SARS-CoV-2 and other seasonal human coronaviruses. They also considered how NPIs, climate, heterogeneity in transmission, and vaccine hesitancy could impact medium- and long-term SARS-CoV-2 transmission dynamics.

Limitations

Authors make a number of simplifying assumptions including that SARS-CoV-2 transmission will become seasonal similarly to Beta-CoV HCoV-HKU1. In all scenarios vaccination is assumed to be transmission blocking rather than reducing disease severity. They also simplify other important factors such as age, clinical severity, and transmissibility which can lead to superspreading events known to be important for SARS-CoV-2 transmission.

Value added

This study provides insights into the potential medium- and long-term dynamics of SARS-CoV-2 transmission beyond the current pandemic and quantifies the potential effect that vaccination could have. Authors also provide an interactive site to explore different strategies required to optimise vaccination and NPI implementation (https://grenfelllab.shinyapps.io/sarscov2/).

Our take —

This preprint, which has not yet been subjected to peer review, used both case counts and deaths in an age-specific model of SARS-CoV-2 transmission in New York City. The authors estimate that the reproduction number of the virus decreased dramatically from the beginning of March to mid-April, 2020 in response to a collection of interventions that reduced mobility (through stay-at-home orders, school closures, business closures, etc.) and mandated mask use. The authors’ attribution of transmission declines to each of these two types of interventions, however, is dependent on many assumptions and subject to much uncertainty.

Study design

Modeling/Simulation, Other

Study population and setting

This study estimated the impacts on SARS-CoV-2 transmission of non-pharmaceutical interventions (NPIs) in New York City (NYC) from March 1 to June 6, 2020. COVID-19 cases included all laboratory-confirmed cases reported to the NYC Department of Health and Mental Hygiene, and deaths combined probable and confirmed deaths associated with COVID-19. Mobility data, used as a proxy for contact rates, were obtained from Safegraph and consisted of anonymized, aggregated counts of visitors (measured by mobile phone location) to locations within each ZIP code. The authors used a neighborhood-specific SEIR network model fit to cases and deaths, stratified by age group, to estimate the effects on transmission of 1) all NPIs, 2) contact-reducing NPIs (such as stay-at-home orders, business closures, etc.), and 3) mask use. Mask use was assumed to explain the reduction in estimated transmission rate that was not accounted for by mobility declines during periods when face coverings were mandated in public places. Model projections beyond the end of the study period were compared to observed cases and deaths.

Summary of Main Findings

Observed, diagnosed COVID-19 cases displayed different age-specific patterns compared to model estimates of underlying infection rates: estimated infection rates were highest for those aged 25-44 years and 45-64 years, and rates for all age groups peaked the week of March 22 or one week later. During the first week of the NYC epidemic (beginning March 1), the estimated time-varying reproduction number (Rt) was 2.99, decreased to 1.37 after the stay-at-home order on March 22, and reached a minimum of 0.56 during the week of April 12. Mobility reductions (a proxy for contact rate reductions arising from stay-at-home mandates, school closures, and other contact-reducing interventions) were estimated to result in a 70.7% (95% CI: 65.0% to 76.4%) decline in Rt by the week of April 12. Assuming that effectiveness of mask use would account for the difference between estimates using a) a linear regression with mobility data alone and b) the SEIR model, the authors estimated that mask use reduced the transmission rate and infectious period by 3.4% (95% CI: -1.9% to 8.6%) over eight weeks, with higher effectiveness during the first month. Estimated mask effectiveness was highest in older age groups and remained stable during the study period (for the first month among those 65-74 years old: 20.8%, 95% CI: -0.1 to 41.6%; 75+ years old: 20.8%, 95% CI: 20.8%, 95% CI: -0.9 to 42.5%). Projections from the week of June 7 through the week of July 26 using parameters based on observed mobility data and estimated mask effectiveness underestimated cumulative cases by 27% and underestimated deaths by 2%.

Study Strengths

The model was fit to both observed cases and deaths, and projections beyond the study period were compared to observed outcomes.

Limitations

This is a preprint, and has not yet been subject to peer review. Aggregated zip-code-level mobility data are an imperfect proxy for actual mobility, which is in turn an imperfect proxy for contact rates. The method used to estimate the effectiveness of mask use relies on strong assumptions and oversimplifications (e.g., all residual reduction in predicted transmission rate after accounting for mobility decline is attributable to mask use; mask use affects both transmission risk per contact and infectious period; dates of mask mandates are a perfect proxy for actual mask use; etc.). Projections did not fit observed data well, which may be an indication that the effect of interventions was overestimated.

Value added

This study provides a useful picture of age-specific patterns of SARS-CoV-2 infection during the spring of 2020 in New York City.

Our take —

Using a simple stochastic transmission model, authors sought to estimate the unobserved SARS-CoV-2 infections in the United States from January 1 through March 12, 2020, as well as projected deaths after March 13 that would have occurred as a result of these infections. The model estimated that fewer than 10% of locally acquired symptomatic infections were detected during this period when surveillance was limited, and that over 100,000 infections actually occurred. The model did not account for geographic differences in cases or deaths, and assumed exponential growth of the epidemic. The estimates were highly uncertain, due to the unpredictable nature of early transmission, which limits the reader’s ability to draw meaningful conclusions.

Study design

Modeling simulation

Study population and setting

Authors sought to estimate the full extent of community-acquired SARS-CoV-2 in the United States from January 1 through March 12, 2020 when testing and surveillance were limited. Using a stochastic simulation model with separate steps for importation of infections and local transmission, authors incorporated data on imported cases and local deaths in the United States, and estimated the probability of detecting daily infections. Authors estimated the total number of infections that occurred through March 12, and then estimated the number of expected deaths after March 13 that would result from those infections.

Summary of Main Findings

There were 1,514 locally transmitted cases and 39 reported deaths in the United States by March 12, 2020. In the baseline analysis, the model estimated that between January 1 and March 12, 45% (95% Posterior Predictive Interval [PPI] 4%, 97%) of imported symptomatic infections were detected; results from the model simulations estimated that 108,689 (95% PPI 1,023, 14,182,310) cumulative infections actually occurred during this time. During the month of February – when authors argue containment measures would have still been possible – the model estimated that fewer than 10% of locally acquired symptomatic infections were detected. The model further estimated that almost 16% of infections occurred on March 12 alone. Among persons estimated infected prior to or on March 12, the model estimated 827 (95% PPI 6, 115,507) additional deaths after March 13 (resulting from the delay from infection to death)

Study Strengths

Overall, results for cumulative infections, local case detection probability, and ratio of deaths after March 12, 2020 were relatively robust to assumptions about serial interval, case fatality risk, timing and probability of early case importations, and delays in reporting. Estimates from the model regarding detection of local symptomatic infections are consistent with previous serological studies.

Limitations

Posterior predictive intervals were quite wide for several estimates, indicating large uncertainty in the model. Although authors intentionally incorporated uncertainty in eight of the parameters used by the model, the width of the intervals makes many of the estimates challenging to interpret. The branching process in the model also assumed exponential growth of infections and did not take into account the effect that social distancing or other factors would have on transmission.

Value added

This modeling study sought to understand the true extent of SARS-CoV-2 community transmission in the United States compared to the number of cases and deaths reported, particularly during early 2020 when testing and surveillance were limited. Projections indicated that surveillance was able to detect fewer than 10% of locally acquired symptomatic cases.

Our take —

This study, available as a preprint and thus not yet peer reviewed, suggests that daily incident PCR positive detections and deaths peaked in Kenya in August 2020, and that cumulative infections are nearing population herd immunity, the theoretical threshold value where there is enough immunity in the population to provide indirect protection to susceptible individuals. The authors developed an innovative model of SARS-CoV-2 transmission that used available seroprevalence, mobility, and PCR testing data to estimate the course of the pandemic. The limitations of the approach, specifically relying on mobility trends and limited seroprevalence estimates, may be overestimating the amount of transmission that has occurred. If this is the case, the stated finding that these locations in Kenya have peaked and reached herd immunity, along with reporting such a notably low infection fatality ratio, should be taken with caution.

Study design

Modeling Simulation

Study population and setting

Authors constructed a data-driven dynamic transmission model to estimate the transmission and seroprevalence of SARS-CoV-2 virus infection in Kenya. The authors developed a simple but effective mathematical model to use the relatively sparse data available to model transmission, using seroprevalence estimates, mobility trends data, and national PCR testing data. In addition to estimating cumulative infections, the authors estimated an effective size parameter, which represents a percentage reduction in the size of the population at risk of infection due to population heterogeneity in connectivity and risk. A smaller effective population size lowers the expected herd immunity threshold, the theoretical threshold value where there is enough immunity in the population to provide indirect protection to susceptible individuals.

Summary of Main Findings

Forty-point-nine percent of Nairobi (95% CI, 24.3-54.7%), 33.8% (95% CI, 23.7-46.5%) of Mombasa are estimated to have been infected with SARS-CoV-2 by August 1, 2020. Authors inferred Nairobi’s effective population size to be 77% of the total population, while Mombasa’s was inferred to be 56% of the total population. These estimates suggest that the epidemic has largely peaked across Kenya, with Nairobi peaking in late July/early August 2020, Mombasa peaking in mid-June, and the rest of the country peaking between August and September. The authors also assume, based on the estimated effective population sizes, that Kenya is nearing herd immunity. The infection fatality ratio in Nairobi was estimated to be 0.014% (95% CI, 0.010-0.023%) and 0.02% (95% CI, 0.014-0.028%) in Mombasa, contrasting with the age-adjusted expected estimate of 0.26%.

Study Strengths

This study developed an innovative yet relatively simple data-driven model for using multiple sources of available data to characterize the pandemic in Kenya. The authors validated the transmission estimates against reported deaths, demonstrating good fit.

Limitations

The model relied on a strong assumption that infectious contact rates were fully correlated with mobility as estimated by Google mobility trends. Thus, their estimates of the time varying reproductive number (Rt) correlated fully with these mobility estimates. This is particularly problematic because transmission has been demonstrated to not fully correlate with increasing mobility trends, as people have improved their ability to limit transmission through social distancing, masks, and other means. As a result, the authors may be overestimating transmission following lockdown in Kenya, resulting in an overestimate of infections and seroprevalence. Additionally, this model is highly reliant on estimates of seroprevalence at two time points from a survey that took place in May and early June. Because of this time gap and limited population representation, it is very possible these two estimates are not generalizable and too long ago to truly inform infection later in the summer and across these populations. Finally, the authors do not account for COVID-19 death underreporting. While the authors state this as a limitation, their estimate of IFR is grounded in this assumption, thus the IFR is very likely underestimated.

Value added

This study provides the first complete assessment of SARS-CoV transmission for Kenya and one of the first for a country in Africa, where substantial questions remain as to why the pandemic has progressed differently than originally expected. This study cleverly combines mobility, seroprevalence, and PCR testing data to estimate transmission and seroprevalence, and provides an important and useful blueprint for doing this in other countries where sparse data have limited the understanding of the pandemic.