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Our take —

The city of Jena was the first in Germany to mandate mask use in all public spaces, and after 20 days, the number of new cases of COVID-19 per day had declined considerably. This analysis models a counterfactual scenario (using data from other German regions) in which Jena did not mandate masks, concluding that the mandate was effective in reducing new cases. The authors estimated similar but smaller effects in other German regions with mask mandates. This study did not directly measure the effectiveness of wearing a mask in preventing SARS-CoV-2 transmission, and unmeasured factors may have contributed to the decline in new cases, but the study adds to the evidence base suggesting that policies requiring mask use may be an important part of controlling transmission.

Study design

Ecological, Other

Study population and setting

This study was carried out in Germany and examined the effectiveness of compulsory public face mask requirements to reduce SARS-CoV-2 transmission. The authors first compared the experience of the city of Jena, which mandated mask use by the public on April 6, 2020, to a counterfactual scenario in which Jena did not implement a mask mandate. To construct the counterfactual, the authors employed a synthetic control approach, in which data from 401 regions in Germany were used to construct a “donor pool” of regions that were weighted to most closely estimate the pre-mandate cumulative case count in Jena and other regional characteristics. Observed cumulative cases after the mask mandate were compared to the modeled counterfactual. Second, the authors used the same approach for all regions in Germany that mandated mask use by April 22, 2020 (n=32). The authors also performed various sensitivity analyses and checks on how well the model assumptions were met.

Summary of Main Findings

There were 16 newly reported cases of COVID-19 in Jena between April 6 and April 26, 2020; in the counterfactual scenario of no mandate in Jena (the synthetic control), there were 62 new cases; this represents an estimated 74% reduction in new cases over the 20-day span. Placebo-in-space tests (estimating “effects” in other locations that did not actually have a mask mandate, and comparing the estimated effects in Jena to the distribution of these “placebo effects”) indicated statistically significant differences in Jena (p<0.10) beginning 13 days after the mask mandate. This indicates the results from Jena were unlikely to be due solely to chance. The lag was argued to be concordant with the incubation period of COVID-19, plus delays in testing and reporting. When considering all regions with mask mandates, the authors estimated that an average of 28 cases per region were prevented over a 20-day period after the intervention, corresponding to a 51% reduction in new cases during the post-intervention period.

Study Strengths

The authors performed several analyses to check the robustness of their findings to alternative assumptions, considered alternative explanations for the observed reduction in new cases during the study period (including anticipation of the mandate), and used an SIR model to estimate the lag period after which any effects of a mask mandate might be observed.

Limitations

Dates of mask mandates may be poor proxies for individual mask-wearing behavior. The study estimates the effectiveness of a mask mandate, not of mask wearing, and this difference was not always emphasized. There may be geographic heterogeneity in personal behavior that was associated with the timing of mandates. The authors considered cumulative case counts, demographic characteristics, and health care system characteristics as covariates, but did not consider any other non-pharmaceutical interventions that may have varied by region. The authors ruled out other interventions as an explanation for the results because other interventions in Jena were at least 10 days away from the mask mandate, but there may have been unmeasured confounding by other factors, including individual behavior change and/or epidemiologic characteristics of SARS-CoV-2 spread in Jena. There was little discussion of the makeup of the donor pool for the primary analysis, though the authors did perform some sensitivity analyses. Finally, results may not be generalizable to mask mandates during other time periods or locations, which may have different public responses to a mask mandate and different levels of general community transmission.

Value added

This study provides evidence of the effectiveness of mask mandates in reducing transmission of SARS-CoV-2 at the regional level in Germany.

Our take —

In this report, the authors describe a sarbecovirus related to SARS-CoV-2 in Rhinolophus cornutus bats from Japan. The virus is more distantly related to SARS-CoV-2 than other viruses from bats and pangolins within the same phylogenetic group. Experiments suggest that the virus is only able to infect cells of R. cornutus and not human cells. These findings expand the geographic and host range of SARS-CoV-2-related viruses, but should be regarded as preliminary due to the very limited sample size.

Study design

Other

Study population and setting

The paper describes the characterization of a novel coronavirus detected in feces from captured Rhinolophus cornutus bats in Iwate prefecture, Japan. The samples were originally collected in 2013 and partial genetic fragments were sequenced from two samples. The new analysis of RNA sequences captured a full genome from one fecal sample. The authors also assess the ability of the novel coronavirus they characterized to enter human or bat cells.

Summary of Main Findings

Phylogenetic analysis placed the novel coronavirus (Rc-o319) from R. cornutus into the subgenus Sarbecovirus and within the clade that includes SARS-CoV-2. Nucleic acid similarity between Rc-o319 and SARS-Cov-2 was 81.5% and there was no evidence of genetic exchange (recombination) between Rc-o319 and other coronaviruses. Analysis of the receptor binding motif of the spike protein of Rc-o319 shows that the virus has a unique deletion in one region of the protein important to binding the human angieotensin-converting enzyme receptor 2 (ACE2) prior to cell entry. Since this deletion was unique compared to other bat coronaviruses with and without the ability to enter human cells, the authors could not infer the ability of Rc-o319 to enter human cells from genetic data alone. By using a combination of pseudotyped vesicular stomatitis virus (VSV) containing spike protein from Rc-o319 and other sarbecoviruses, the authors showed that Rc-o319 pseudotyped VSV could enter cells expressing ACE2 from R. cornutus but not from another Rhinolophus species (R. ferrumequinum) or from humans.

Study Strengths

No specific strengths were noted other than the use of full genome sequencing and the experiments assessing binding ability of Rc-o319 to human ACE2.

Limitations

The very small number of samples prevents assessment of the prevalence and phylogenetic diversity of SARS-CoV-2-related viruses in R. cornutus in Japan. There are two other Rhinolophus species in Japan, so the possibility of virus sharing among species cannot be fully evaluated from these results.

Value added

This study increases the number of sarbecoviruses identified within the SARS-CoV-2 clade, as well as the host and geographic range of the SARS-CoV-2 clade. The results also provide information on additional deletions in the spike receptor binding domain that may predict the potential of viruses to infect human cells.

Our take —

This peer-reviewed paper reports the presence and transmission of SARS-CoV-2 in farmed mink populations and humans linked with mink farms in Denmark. Epizootological data and virus sequences suggest that the virus was introduced to farms from a human index case in mid-May or early June 2020 and then spread rapidly to three farms. Continued monitoring of SARS-CoV-2 infection in mink farming operations in Europe, Asia, and North America and in wild mink populations (both native and introduced) will be necessary to prevent large outbreaks and the establishment of SARS-CoV-2 in a new animal reservoir.

Study design

Other

Study population and setting

The study focused on three mink farms in the Northern Jutland region of Denmark where animals (Neovison vison) were infected with SARS-CoV-2-. The farms were chosen because there were PCR-confirmed human COVID-19 cases linked to them. Farms were visited at least twice, and mink were tested for evidence of SARS-CoV-2 infection either through an enzyme-linked immunosorbent assay for antibodies in serum samples from live animals or through PCR on swab samples from live or dead animals. Additionally, samples of exhaled air from mink or from within 1 m of mink enclosures were collected and tested for SARS-CoV-2 RNA. All sampling occurred between June 14 and July 2, 2020. Full SARS-CoV-2 genomes were sequenced from PCR-positive samples.

Summary of Main Findings

The pattern of SARS-CoV-2 infection differed across the three farms. Live animals from farms 1 and 3 had high seroprevalence (>66%) for SARS-Cov-2 antibodies, suggesting that the virus had been actively spreading in the mink population for some time before sampling. Seroprevalence at farm 2 was low at first sampling (3%) but increased to 97% more than a week later. SARS-CoV-2 RNA was detected in indoor air samples from farms 2 and 3 but not from farm 1; samples of outside air and mink feed tested negative for all farms. SARS-CoV-2 genome sequences from farmed mink and associated human cases were very similar and all clustered within the European clade 20B of the global SARS-CoV-2 phylogeny. Sequencing information suggests that a human index case from mid-May introduced SARS-CoV-2 to mink at farm 1 and to other individuals linked to farms 2 and 3 that spread the virus to the other farms.

Study Strengths

Repeated visits to farms and testing for both SARS-CoV-2 RNA and antibodies provided information about the timing of virus introduction and transmission.

Limitations

The study only investigated farms with human COVID-19 cases linked to them, so we do not know how many mink farms in Denmark have been infected. The virus from the human index case linked to mink farm 1 was not genotyped at one key nucleotide position, so its relationship relative to the viruses detected on mink farms is likely close but uncertain. The authors also note that some of the virus mutants detected on mink farms were absent or rare in human cases in Jutland and globally prior to 10 June 2020, but were seen in viruses from mink on a farm in the Netherlands. Whether this mutant is an adaptation to mink (e.g., with higher transmission efficiency) that emerged simultaneously in multiple locations is not clear from this work. Additionally, the increased frequency of the mutant in human cases after June 2020 cannot be definitively linked with the mink farm outbreaks and may have other plausible explanations. Finally, the paper described the human cases as being linked to mink farms but did not provide details on the occupation of these individuals and their direct exposure to infected mink, or whether these individuals were family members of mink farm workers.

Value added

This is the first report of SARS-CoV-2 infection on mink farms in Denmark and the first outside of the Netherlands. Since mink farms are present in other countries in Europe and North America, these results provide valuable information on the two-way transmission of SARS-CoV-2 between humans and mink on these farms and the connections with ongoing community transmission of virus variants.

Our take —

This study, available as a preprint and thus not yet peer reviewed, leveraged seropositivity data from a large family-based cohort study in Geneva, Switzerland during the early phase of the SARS-CoV-2 pandemic and supports previous findings that working age, male sex, and symptomatic infection play important roles in increased transmission and infection risk. Individuals were estimated to have a 20% risk of being infected from a single SARS-CoV-2 positive household member compared to a 5% risk of being infected due to community transmission, the authors note these risks may not be widely generalizable. The authors also estimated asymptomatically infected individuals account for roughly 20% of all household infections, and thus play a non-insignificant role in household transmission risk.

Study design

Other

Study population and setting

This retrospective population-based serological analysis leveraged serological data from 4,534 individuals older than five from 2,267 households in Geneva, Switzerland between April and June 2020. Serology-based analyses were conducted to confirm previous SARS-CoV-2 infections of household members, which were combined with demographic data, reported contacts, reported symptoms, and behavior to estimate the risk of SARS-CoV-2 infection from household members and the risk of infection from extra-household exposures.

Summary of Main Findings

Overall, 6.6% of individuals included within the study has evidence of prior SARS-CoV-2 infection, of whom 70.6% were symptomatic. While 9.8% of households had at least one seropositive household member, this proportion increased with household size (4.8% of single-person households to 17% of three-person households). Overall, the probability of being infected from a single infected household member was 17.2 % (95% Credible Interval [CrI]: 13.3%-21.5%), with the risk of infection increasing with age (Aged 5-9 years: 7.5% (95% CrI: 1.3%-20.3%), Aged >65 years: 30.2% (95% CrI: 14.3%-48.2%)). Those without any defined symptoms (cough, fever, shortness of breath, loss of smell/taste) had 0.25 times the odds (95% CrI: 0.10-0.56) of infecting other household members, reflecting an estimated 19.6% (95% CrI: 12.9%-24.5%) of all within-household infections, compared to symptomatic individuals. The cumulative risk of infection from extra-household sources was 5.1% (95% CrI: 4.5%-5.8%), with men being more likely to be infected outside the household (Odds Ratio 1.4, 95% CrI: 1.1-1.8). While those 20-49 years old had the highest risk (7.4%, 95% CrI: 5.9%-9.0%) of extra-household infection, those who reduced extra-household contacts in this age-group had a reduced risk of extra-household infection (OR 0.66 , 95% CrI: 0.39-1.2). The authors estimate that 18.8% (95% CrI: 16.7%-20.4%) of all infections were due to household transmission, with the attributable proportion of intra-household infection increasing with household size (16.1% of two-person households (95% CrI: 13.7%-20.4%) to 41.2% (95% CrI: 35.3%-46.5%) of five-person households).

Study Strengths

This study builds off a well-described population-based survey study, SEROCoV-POP, which is well suited to explore household-associated transmission risks. In addition to using appropriate transmission models , all code and related notes are provided.

Limitations

As noted by the authors, while symptomatic infection was defined using multiple COVID-19-assocated symptoms, additional known symptoms such as muscle aches or pain, chills, tiredness, and gastro-intestinal symptoms were not used to identify symptomatic infections and thus individuals with symptomatic disease could have been misclassified as asymptomatic. It is unclear how the final analytical sample was selected (2,627 households had available data and 2,267 households were included), which potentially introduces selection biases if the included households had more (or less) household transmission compared to excluded households. It is also unclear how extra-household transmission results from younger individuals were obtained (extra-household contact data was not collected for those <14, yet extra-household transmission results for these children are provided). The authors point out Geneva is an urban high-income area with a small average household size (37.9% of households with available data were single-person households), which affects the ability to generalize these results outside high-income urban settings. Additionally, as the initial surge of the SARS-CoV-2 pandemic in Geneva may not reflect the burden of disease elsewhere, extrapolating any estimated extra-household infection risks to other regions should be made with caution.

Value added

This study provides important findings related to the risks of household and extra-household SARS-CoV-2 infection, and confirms the important role asymptomatic infection plays in transmission. In addition to reaffirming the importance of age and sex in the risk of infection, this study quantified the risk attributable to asymptomatic infections within the household. Additional study in non-wealthy or urban areas with small household sizes, and in cohorts with a greater number of young children, is needed.

Our take —

This study uses 247 viral sequences from SARS-CoV-2 patients in two different counties (Dane and Milwaukee) in Wisconsin to understand epidemic dynamics on a sub-state level. They found evidence for distinct epidemics with limited mixing between the two counties, and showed that introductions were more common in Dane county, though sampling bias may have affected this conclusion. They also found that the “Safer at Home” statewide order enacted on March 25, 2020, likely reduced case counts in both counties in April 2020.

Study design

Other

Study population and setting

This study includes data from 247 SARS-CoV-2 genomes from two counties in the US state of Wisconsin, collected between January 30 and April 26, 2020. The two counties—Dane and Milwaukee—are both in Southern Wisconsin (127 km apart) and are the most populous counties in the state, despite having notably different demographics. Dane county was of particular interest because it was home to the 12th reported COVID-19 case in the United States. Additionally, a statewide “Safer at Home” order was enacted on March 25, 2020, and lasted throughout the duration of the study period, providing an opportunity for the authors to examine the effects of these social distancing requirements on the number of reported cases.

Summary of Main Findings

The authors found that the viruses in Dane and Milwaukee counties were genetically distinct, and that there was evidence of multiple introductions of SARS-CoV-2 into each county. However, they also found evidence for different epidemic dynamics in the two counties, with more introductions and viral diversity in Dane county. There was limited evidence for viral spread between the two counties, suggesting largely separate epidemics within the same state. They also claim that there was no onward transmission of the first SARS-CoV-2 case in Dane county (the 12th reported case in the United States), citing successful control practices. Finally, the study presents evidence for reduced transmission (due to a lower estimated reproductive number, R0) after the “Safer at Home” order enacted on March 25, 2020, suggesting this order was at least partially responsible for the decline in cases in April compared to earlier months.

Study Strengths

Data from two densely populated counties from within the same state allowed the authors to explore epidemic dynamics at a sub-state level. Genomic data as well as demographic data from both regions allowed the authors to hypothesize explanations for observed differences in transmission patterns.

Limitations

This study has two problematic limitations: first, the conclusion of no onward transmission from the first case in Dane county relies heavily on the finding of an in-frame deletion in the SARS-CoV-2 genome isolated from this sample that has not been observed in any other samples. However, this deletion occurs in a poly-A homopolymer, and the sequencing technology used has known issues in homopolymer/low-complexity regions. The authors did not provide any details (e.g., read depth, validation on another platform) in support of this finding, and in general, provide minimal details on laboratory controls and sequence validation. Second, the authors only minimally addressed that biased sampling could artificially reduce the viral variation observed in Milwaukee county, did not provide details of their sampling strategy (which cases were selected for genomic sequencing) and did not control for population size/density in any way.

Value added

This study demonstrates that geographically close counties in the United States could have separate epidemics with limited mixing, and that non-pharmaceutical interventions such as the Wisconsin “Safer at Home” order likely played a role in reducing cases in April 2020.

Our take —

The degree of immunity acquired by individuals after SARS-CoV-2 infection, and how it changes over time, is a matter of considerable public health concern. In contrast to a previous study showing rapidly waning levels of antibodies that target a different SARS-CoV-2 antigen among patients with mild symptoms, this study from New York City shows that neutralizing antibodies to SARS-CoV-2 spike protein diminished over time but remained at high levels up to 3 months after symptom onset in a group of patients with mostly mild illness. The patient population was small and not well described, so it is unclear if these results apply to the broader population of those infected with SARS-CoV-2. Important questions remain about how varying levels of SARS-CoV-2 antibody translate to protection from infection.

Study design

Case series; prospective cohort; other

Study population and setting

This paper reports on three related studies: 1) Enzyme linked immunoassay (ELISA) testing for IgG antibodies to the SARS-CoV-2 spike protein among 72,401 individuals with laboratory-confirmed or suspected infection approximately 30 days after symptom onset from the Mount Sinai Health System in New York City, from March to October 6, 2020; 2) testing of 120 samples that had a known ELISA titer for neutralization of SARS-CoV-2 using a quantitative neutralization assay; and 3) longitudinal screening of 121 patients for IgG antibodies to the SARS-CoV-2 spike protein at two additional time points (approximately 82 and 148 days after symptom onset) after the initial screening (approximately 30 days after symptom onset).

Summary of Main Findings

Less than 5% of all individuals screened required hospitalization or emergency room evaluation. Of those screened, 30,082 (42%) tested positive for detectable antibodies to the SARS-CoV-2 spike protein at a titer of 1:80 or higher. Most of those testing positive had moderate-to-high titers (defined as 1:320 or higher): 2.3% had a titer of 1:80, 4.8% of 1:160, 22.5% of 1:320, 31.8% of 1:960, and 38.6% of 1:2880. Neutralizing titers significantly correlated with ELISA titers (Spearman’s r=0.87). Half of sera with spike-binding titers between 1:80 and 1:160 had neutralizing activity, while 90% of sera with 1:320 titers and all those with 1:960 titers or above had neutralizing activity. Among the 121 individuals sampled over a total of three time points (at an average of 30, 82, and 148 days post-symptom onset), the geometric mean titers (GMT) declined from 764 to 690 to 404. Among those with a 1:320 titer or lower, antibody titers increased on average at the second time point, followed by a decrease at the third time point. Three individuals with initially low titers (1:80) lost reactivity, one at the second time point and two at the third. The correlation between neutralizing and ELISA titers remained high at the third time point (r=0.79).

Study Strengths

This study examined antibodies to the spike protein of SARS-CoV-2, which are likely to be more relevant to immunity than antibodies to nucleoprotein. Antibody titers were measured in a large number of individuals using an assay with high accuracy in a validation panel. Longitudinal analyses of antibody titers considered two time points after the initial assay, allowing for discernment of nonlinearities in trajectory.

Limitations

Patient demographic and clinical characteristics were not reported, which makes it difficult to interpret how these data apply to any particular group of individuals. Of particular concern is the lack of data on COVID-19 clinical severity in either the larger study population or in the two substudies. Thus, while it appears that these studies were conducted on primarily mild cases of COVID-19, this study did not assess any relationship between disease severity and antibody response. Also, the timing of initial antibody screening relative to symptom onset or date of potential exposure was not characterized in the larger study population and there was variation in the timing of antibody assays in the longitudinal study. Not all individuals screened for antibodies were tested for SARS-CoV-2 infection, which means an unknown number of people who had been infected with SARS-CoV-2 may have tested negative for antibodies. The size of the population in the longitudinal study (n=121) and the lack of reported demographic or clinical characteristics makes it difficult to generalize the results.

Value added

This study provides some of the strongest evidence to date regarding the persistence of neutralizing antibodies to SARS-CoV-2 over time, particularly among those without severe disease. This has implications for both pandemic planning and vaccine development.

Our take —

This study utilizing surveillance data from across the US, predominantly from Utah, compared testing positivity rates and inpatient trends by age in March/April 2020 versus June/July 2020. Over time testing positivity rates increased among those under 50 years and decreased in those over 50 years; the lack of a corresponding decrease in the median age of inpatients suggests that changes in testing are partly driving the observed case increases among young people. However, the increased positivity rates over time among young people suggests that there may be more than surveillance changes occurring and that the number of undiagnosed younger infections remains high despite expanded testing. Further, these inferences rely on a limited amount of inpatient data and depend largely on demographic data from individuals tested for SARS-CoV-2 in the state of Utah. Thus, caution is warranted for any generalizations made to describe incidence trends outside of Utah, and data presented do not strongly support the claim that surveillance and not true epidemiological shifts explain changing trends over time.

Study design

Other

Study population and setting

This retrospective analysis of surveillance data explored the age distributions and positivity rates of 227,601 SARS-CoV-2 molecular testing results from a Utah-based national reference laboratory between March 10 and July 8, 2020. Conducted using three assays of similar sensitivity and detection limits, the national reference laboratory processed SARS-Cov-2 tests from over 1000 hospitals across the US, with nearly half (48.4%) from Utah. Test positivity and age distributions of cases were compared across two time periods, defined as early (March 10 to April 30,2020) and late (June 1 to July 8,2020). The age of COVID-19 inpatients and outpatients were also compared using data available from individuals tested through the University of Utah Healthcare system during these time periods.

Summary of Main Findings

A total of 19,320 out of 277,601 SARS-CoV-2 tests (7%) were positive. In Utah and other represented states, the median age of positive cases decreased from early (March-April) to late (June-July) period, with the overall median age of those testing positive dropping from 40.8 to 35.8. While the positivity rate increased for each age group younger than 50 years between the early and late time periods, with the greatest increase among individuals under 18 years (rising from 3.3% to 10%), positivity rates decreased for each age group over 50 years (e.g., decreasing from 6.1% to 3.6% in those over 70 years). Using a limited number of Utah individuals with known inpatient status, the authors described an increase in the median age of inpatients (increase of 5.8 years) and a decrease in the median age of outpatients (decrease of 3.9 years) from the early to late period. Finally, while no detailed information on changes in testing strategies were provided aside from increased test availability in the late period, the authors state that the observed changes in the median age of inpatients vs. outpatients over time suggest changes in testing rather than changes in underlying COVID-19 epidemiology explain the decreased age among those testing positive for COVID-19 later in the pandemic.

Study Strengths

This study utilizes data from a large national reference laboratory that provides clinical testing capacity for over 1000 US hospitals. With roughly half of the data analyzed derived from Utah, the study was well powered to detect clinically meaningful differences in age among those testing positive for SARS-CoV-2 in Utah.

Limitations

As noted by the authors, much of the testing data and all the results with inpatient-related information were from Utah. The assumption is made that the national reference laboratory testing data is representative of the true burden of disease in Utah, which may be more likely for the early period considering half of all confirmed Utah cases were captured within this early period. This assumption is problematic for the late period as a smaller proportion (20%) of confirmed Utah cases were captured during the late period. We agree with the authors that it would be inappropriate to make generalizations outside of Utah if Utah’s age distribution and testing strategies were not representative of other states or the whole US. If age or inpatient status were associated with missing inpatient-status data, it is possible that the inferences based on the limited number of inpatient cases from the early (N=39) and late periods (N=47) are biased, as inpatient status was missing for 38.6% of the Utah early period cases compared to 22.4% of the Utah late period cases. Therefore, larger studies with complete inpatient data are needed to confirm inpatient status-related findings. While the three assays utilized to identify positive cases are stated to have similar limits of detection and sensitivity, no data or literature is provided to support these claims. Aside from the increased availability of tests, no descriptions of the various testing strategies employed across the US or in Utah were included for either time period. This is problematic considering such changes in testing strategy were suggested by the authors to explain the decreased age among those testing positive for SARS-CoV-2. Finally, while the median age does decrease across time periods, the increased positivity rates over time among younger individuals and decreased positivity rates among older people suggests that it may be more than expanded testing that is changing case counts among younger populations, calling the conclusions into question.

Value added

This study replicates previous observations of increased diagnoses of COVID-19 among younger individuals, showing that over time test positivity rates for SARS-CoV-2 infection are increasing among younger people. This overall increase in cases is likely due to increased diagnosis of asymptomatic infections and cases with mild infections, given that the inpatient ages did not decrease over time, but also in part may be due to epidemiological changes in infections given the increasing positivity rate amongst younger populations.

Our take —

New Zealand is one of the only countries in the world to achieve rapid, sustained success in containing its COVID-19 epidemic. This paper broadly describes how the extinguishing of the epidemic coincided with a rapidly amplified series of nationwide non-pharmaceutical interventions including border closures, bans on gatherings, stay-at-home orders, and a progressively expanded test/trace/isolate system. Disentangling the success of these measures from the unique geographic and socioeconomic circumstances in New Zealand, however, was beyond the scope of this paper.

Study design

Other

Study population and setting

This study considered all probable and laboratory-confirmed cases of SARS-CoV-2 infection in New Zealand from February 2 (the date of a travel ban from mainland China) to May 13, 2020 (by which time, community transmission had stopped). Probable cases were defined as close contacts of confirmed cases with compatible symptoms. Imported cases were defined as those with international travel within 14 days before symptom onset, import-related cases were defined as those with an epidemiologic link to imported cases, and all others were defined as locally acquired. Five phases of national response were defined: Phase 1 (February 2 to March 15) comprised initial travel restrictions; Phase 2 (March 16 to 25) included rapid scale-up of non-pharmaceutical interventions including bans on public gatherings and closed borders except for returning residents; Phase 3 (March 26 to April 10) was the first half of lockdown and included widespread stay-at-home orders and expansion of test/trace/isolate protocols; Phase 4 (April 11 to 27) was the second half of lockdown and included testing expansion; and Phase 5 (April 28 to May 13) saw an easing of movement restrictions and permission for small gatherings. The authors related these response phases to case counts, disease outcomes, time-to-event durations, and transmission patterns. To assess outcomes related to transmission, cases were assigned to intervention phases on the basis of estimated infection date (one incubation period before case identification). To assess public health response, cases were assigned to intervention phases based on date of case notification. Logistic regression was used to identify risk factors for severe outcomes, and parametric distributions were fit to time-to-event durations.

Summary of Main Findings

There were 1,503 COVID-19 cases in New Zealand during the study period, of which 1,153 (77%) were confirmed, with 95 hospital admissions (6.3%) and 22 deaths (1.5%). Thirty-eight percent of cases were classified as imported, 31% were import-related, and 31% were locally acquired. The cumulative incidence nationwide was 303 per million, and the estimated infection rate per million peaked during Phase 2 (March 16 to 25) at 8.5. Females represented 56% of cases, and the highest cumulative incidence was seen in the age group of 20-34 years (464 per million). The share of cases identified via contact tracing more than doubled from Phase 1 (30%) to Phase 3 (66%). Testing incidence increased 220-fold from Phase 1 to Phase 5, and test positivity declined from a peak above 5% in late March to below 1% in the second week of April. Median time from symptom onset to case notification declined from 9.7 days in Phase 1 to 1.7 days in Phase 4. Median time from symptom onset to isolation declined from 7.2 days in Phase 1 to -2.7 days in Phase 4 (negative time implies cases were quarantined before symptom onset).

Study Strengths

All COVID-19 cases in New Zealand during its epidemic were included, and authors described a broad range of epidemiologic parameters. The authors were able to draw from multiple comprehensive data sources for cases, testing, mobility, policy changes, demographic data, and other variables.

Limitations

Though the aims of this study were more broad and the descriptive epidemiology presented is sound, few conclusions can be drawn from this study about the relative effectiveness of different NPIs implemented in New Zealand: each response phase encompassed multiple changes in NPIs, and the temporal relation of response phase to outcomes was imprecise. Moreover, the experience of New Zealand may not be particularly generalizable, given its status as a relatively small, economically developed island nation.

Value added

This paper provides a broad description of a rare success story during the COVID-19 pandemic: New Zealand’s rapid and efficient containment of its epidemic.

Our take —

This study, available as a preprint and thus not yet peer reviewed, of SARS-CoV-2 sequences collected at hospitals, ports of entry, and community testing events between March and June 2020 in coastal Kenya provides evidence for multiple introductions of the virus into Kenya, but finds that only a single lineage is present in community-transmitted cases. The study is limited by sample size and potential sequencing errors, but overall offers evidence that the interventions imposed in Kenya early in the outbreak, including testing and quarantining at ports of entry, limited new introductions of the virus from spreading throughout the local community. The interventions implemented in Kenya could be used to inform containment measures in other parts of the world.

Study design

Other

Study population and setting

This study involves generation and analysis of 274 SARS-CoV-2 sequences collected in coastal Kenya between March 17 and June 24, 2020. These genomes come from four of six coastal communities, and were sampled from individuals arriving at ports of entry into Kenya, individuals presenting at major hospitals in this region, contacts of confirmed cases, and as part of a mass testing effort in the city of Mombasa. The 274 genomes were analyzed alongside 983 publicly-available genomes (collected from December 2019 to June 2020) from around the globe. The purpose of this study was to assess the effectiveness of early public health interventions in Kenya and to help design future COVID-19 control measures in Kenya and the surrounding region.

Summary of Main Findings

The authors generated 274 SARS-CoV-2 sequences collected in coastal Kenya. Phylogenetic analysis showed that these sequences belong to many different lineages, suggesting multiple introductions of the virus into the local population. The authors found that a majority of the sequences belong to the B.1 lineage, which is widespread in Europe and the United States. This lineage accounted for 82% of the sequenced samples and was found in individuals with no known travel or contacts, suggesting community transmission. While several other lineages were identified, these lineages were found in smaller numbers and in patients with known travel. This suggests there was limited community spread of these additional lineages. The limited detection of non-B.1 lineages outside of ports of entry suggests that the containment efforts and travel restrictions imposed in Kenya as early as March 2020 were effective.

Study Strengths

By focusing on samples collected at ports of entry, the authors of this study ensured that they could capture introductions that did not lead to sustained transmission within the surrounding communities. This sampling strategy allowed them to answer specific questions about the effectiveness of quarantine and containment measures.

Limitations

The authors acknowledged that the sample size was small and that a limited number of samples came from counties outside Mombasa, Kenya. This could mean that they were simply not capturing community transmission of additional lineages that exist. Additionally, they performed a brief analysis of 19 patients who were placed in quarantine facilities and were tested more than once. In two cases, they found that samples from the same patient belonged to different viral clades – while the authors claim this is likely due to co-infections, this is not consistent with other literature. This observation calls into question the accuracy of their sequences. While the overall findings of the study likely still hold—that they found multiple lineages at ports of entry, but the B.1 lineage uniquely caused community transmission—any more detailed analyses of specific clades should be taken with some caution due to potential sequencing error. Finally, because of the low diversity of this virus, it is impossible to determine if the B.1 lineage circulating in the community is the result of a single or many introductions.

Value added

This paper finds evidence for multiple introductions of SARS-CoV-2 into coastal Kenya yet limited sustained transmission. This points to the effectiveness of the interventions imposed in this region, which focused on catching cases at ports of entry and avoiding community transmission. This strategy could be a model for other countries struggling to contain the virus.

Our take —

This study provides evidence that rapid antigen tests may have a better probability of detecting the most infectious or contagious individuals in the population as compared to RT-PCR tests. However, the rapid test missed almost one-quarter of the samples that were positive by RT-PCR, and the predictive value depends on prevalence and will decrease with decreasing prevalence. Given that antigen tests can provide a result within minutes, they could be a useful public health tool for improving timeliness of prevention measures particularly among those who might be most likely to transmit to others. More research is needed to understand how infectivity in the lab correlates to infectivity in the real world, as well as to better understand how well antigen tests may be able to detect infectious samples from pre- or asymptomatic individuals.

Study design

Other

Study population and setting

38 samples with evidence of SARS-CoV-2 by RT-PCR (Lyra) were collected prospectively from individuals symptomatic for COVID-19 with onset of symptoms occurring in previous 0-7 days. Samples were tested by rapid antigen test (BD Veritor) and in laboratory-based cell culture to assess infectivity.

Summary of Main Findings

Rapid antigen test correctly identified 29/38 (76%) RT-PCR positive samples as antigen positive. 28/38 (73.7%) RT-PCR positive samples were able to infect cells in laboratory-based cell culture, and samples that were able to infect cells had on average more quantifiable RNA in them than samples that were not able to infect cells (p < 0.001). The rapid test was able to detect antigen in 27/28 (96%) of the samples that were able to infect cells. The positive predictive value, or the probability that a sample was infectious to cells given a positive test result, was 90% for the antigen test and 73.7% for the RT-PCR test.

Study Strengths

Samples were collected prospectively and were well characterized with known duration of time since symptom onset. Paired samples collected from the same individuals at the same time were used for antigen, RT-PCR, and infectivity studies allowing for direct comparison.

Limitations

Small sample size limits the precision of the estimates provided. The limitation of study participants with symptoms for less than 8 days limits our understanding of the correlation between test results and infectiousness in pre- or asymptomatic individuals. The authors used infectivity in cells cultured in a lab, which is the best lab method to measure infectious virus, but is not necessarily the same as infectious or transmissible in the real world. The study only compares a single rapid test (BD Veritor) to a single RT-PCR test (Quidel Lyra), so further evaluation using other tests is necessary before these results can be extrapolated more generally to other antigen tests. Additionally, predictive value depends on disease prevalence, and as disease prevalence declines so will the predictive value of both tests.

Value added

This study integrated RT-PCR test results and quantification, with rapid antigen test results and cell culture infectivity in a single study with samples collected from symptomatic SARS-CoV-2 infected patients which allowed them to compare the results of the two types of tests to each other in the context of viral load and infectivity which hasn’t been done previously. Through this they were able to show that, at least in this study, among patients with symptom onset of one week or less, the rapid antigen test could be used to identify patients with infectious virus, which has the potential to be a useful public health tool.