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Our take —

This study, available as a preprint and thus not yet peer-reviewed, demonstrated that the effectiveness of local lockdowns on COVID-19 transmission are dependent on the duration of intervention and the spillover effects from neighboring municipalities using municipalities in Chile as a case study. Due to spillover events, local control by itself may not be sufficient to control epidemic growth. Greatest reductions in transmission and case numbers could be achieved by extending the lockdown period of the target and neighboring municipalities.

Study design

Modeling/Simulation

Study population and setting

Authors used the daily time series of COVID-19 cases reported by the Ministry of Health of Chile between March 1 and mid-July, 2020 and adjusted this by the delay from symptom onset to report to reconstruct the epidemic curve by date of onset. Census data and household survey data were also incorporated. Transmission in each municipality was characterized by the instantaneous reproduction number allowing for interactions between neighboring municipalities as different municipalities were subject to different localized lockdown policies. Demographic and economic factors that differed by municipality and could affect transmission such as age, sex, sanitation infrastructure, overcrowding, and monthly income were accounted for. The authors used a causal inference framework to explore the impact of counterfactual lockdown policies of different durations.

Summary of Main Findings

Authors found that the effectiveness of local lockdowns are highly affected by the duration of the local lockdown and the level of spillover from neighboring municipalities that may have different levels of transmission and under different control measures. In three municipalities of Greater Santiago a local lockdown lasting 3-weeks longer would have decreased the reproduction number further, preventing 143 per 100,000; 59 per 100,000; and 267 per 100,000 population in Lo Barnechea, Providencia, and Santiago respectively, representing a 33-62% reduction in reported cases in that time period. Authors estimate that these reductions would have been even greater had the neighboring municipalities also extended their local lockdowns.

Study Strengths

Authors used an integrated dataset of daily reported COVID-19 cases adjusted for reporting delays, household survey, and census data to reconstruct the epidemic course in Chile. The natural variation in local lockdowns implemented in each municipality with varying durations were used to explore their effectiveness in reducing transmission. Authors also accounted for the interconnected nature of neighboring municipalities and explored varying counterfactuals and their effect on the epidemic.

Limitations

As data by date of symptom onset were not available, authors had to reconstruct the epidemic curve accounting for the onset to reporting delay to estimate the reproduction number over time. Information on testing efforts are not provided so it is difficult to determine whether the increase in case numbers, particularly early in the epidemic are a true increase in transmission, or an increase in case finding.

Value added

This study provides insights into the effectiveness of local lockdowns and the importance of considering the spatial interconnectedness of neighbouring municipalities and their local interventions.

Our take —

This perspective piece provides evidence that the true prevalence of antibody response against SARS-CoV-2 in the general population may be significantly underestimated, implying more people have been exposed to SARS-CoV-2 than current testing demonstrates due to sub-optimal serologic assay sensitivity in diagnostic testing. Assays require optimization to detect lower antibody titers from patients with mild infections or to account for significant waning of the immune response over time. Modeling assumptions made in this simulation study may not reflect the true test, transmission and immune response in the population.

Study design

Modeling/Simulation

Study population and setting

FDA-approved serologic assays used to monitor seroprevalence of antibody against SARS-CoV-2 often use samples from patients with severe symptoms and recent infections as positive controls in assay validation and optimization. Samples from these patients have high antibody levels compared to subjects with mild or no symptoms and subjects with less recent infections, where antibody levels are expected to decline over time. The use of these samples in assay validation may result in significant spectrum bias, which overestimates assay sensitivity in the general population leading to underestimates of the actual number of people who have been exposed to SARS-CoV-2. This modeling study aimed to quantify the amount of bias introduced in estimating SARS-CoV-2 seroprevalence in the general population due to potentially inappropriate assay sensitivity validation. The authors modeled the potential impacts of varying assay validation sample donor characteristics (proportion with differing symptom severities and/or sample collection time post-infection) on the sensitivity of measuring SARS-CoV-2 seroprevalence in simulations of the general population. Assay specificity was held constant. Simulated study populations were created by differing in the relative proportions of symptom severities (severe, mild, asymptomatic) as well as different times post infection for assaying seroprevalence.

Summary of Main Findings

These modeling analyses imply that current assays lack appropriate sensitivity and thus underestimate the true seroprevalence of antibodies against SARS-CoV-2 in the general population. Using a simulated model set where 95% of all infections were asymptomatic or mild and harbor lower antibody levels than assay validation samples, the authors demonstrated that the true assay sensitivity in the general population of current assays may be as low as 54%. Additionally, most samples used for assay validation are collected within 60 days of infection. Since evidence indicates antibody levels decline over time this also implies that as time since infection increases, the sensitivity of the assays will be reduced, further contributing to an underestimate of the seroprevalence in the general population.

Study Strengths

With respect to sensitivity analysis related to symptom severity, the composition of one simulated data set is 95% asymptomatic carriers and patients with mild symptoms, which aligns closely with the true clinical outcomes of SARS-CoV-2 infection.

Limitations

Several assumptions were made in this analysis, including that assay sensitivity would be greatest for recent severe infections, as well as the transmission model and immune response kinetics, which may not completely represent the true assay, transmission and immune response in the population.

Value added

This modeling simulation provides evidence that serum samples from individuals more representative of the disease spectrum within the general population should be used in SARS-CoV-2 assay validation. The article also highlights the potential benefit of the creation of a reference serum bank for universal SARS-CoV-2 serologic assay validation.

Our take —

This study, available as a preprint and thus not yet peer reviewed, evaluated the impact of 13 different non-pharmaceutical interventions on the time varying reproductive number across 130 countries and territories through June 2020. Impact was assessed using a wide range of models with different time lags (i.e., time frame from implementation of intervention to its impact on transmission) and at varying levels of intensity of the interventions. School closures and internal movement restrictions showed evidence of impact across all models, and thus had strong evidence of effectiveness. However, these results should be interpreted cautiously as multiple interventions were often implemented at the same time, making it difficult to fully disentangle the impact of any single intervention from the others.

Study design

Ecological, Modeling/Simulation

Study population and setting

This study used the implementation dates of a large variety of non-pharmaceutical policy interventions and assed their relationship with COVID-19 time varying reproductive number, Rt, defined as the mean number of secondary cases that one index case will infect at time t) across 130 countries and territories between January 1 and June 22, 2020. Data on non-pharmaceutical COVID-19 policies, categories, implementation dates, and a general index of strength of COVID-19 policy response were obtained from the Oxford COVID-19 Government Response Tracker. Estimates of the Rt across regions and time were from EpiForecasts.

There were four primary analyses. The first main analysis used a statistical regression model to characterize how different types of NPIs were rolled out over time, such as when they occurred and in what order. The second model observed how those NPIs were clustered in time (i.e. what policies were more likely to occur together with what other policies, forming a group or a cluster of policies). A third model attempted to determine the time lags between policy implementation and their effect on Rt by assessing 3 different time lags (1, 5, and 10 days) and estimating goodness-of-fit statistics for each model. Finally, the study used panel regression models to attempt to disentangle the impact of different types of NPIs on Rt accounting for different time lags and the effort at which NPI was implemented (any effort and maximum effort).

Summary of Main Findings

First, the study finds a major increase in NPI intensity across the world in mid-March, followed by a slow reduction in the stringency of interventions. Second, the study finds substantial evidence that NPI policies were more likely to be rolled out in particular orders, typically clustered together in time, depending in part on how policy intensity is defined (any effort or maximum effort). Thirdly, the study found support for lag times between policy implementation and Rt impact between 1-10 days. Finally, the study finds evidence that school closure and internal movement restrictions, and high-intensity public events cancellations and restrictions on gatherings. There was some evidence for impact for workplace closure, income support and debt/contract relief. Evidence for impact was inconclusive for stay-at-home requirements, public information campaigns, public transport closure, international travel controls, testing, and contact tracing.

Study Strengths

This study critically examines and demonstrates how NPI policies are related to each other, and assesses lags in their impact. These are issues that are critical both to this analysis, and many other policy analyses which often do not acknowledge that NPI policies are highly correlated with respect to timing of implementation, and that interventions have lagging effects, due to changing compliance over time. The study uses well-vetted data appropriate for policy impact evaluation. The discussion section contains a frank and well-written interpretation of the results and the interpretable limitations thereof. We find that the findings that NPIs overall had substantial impact on Rt to be relatively robust.

Limitations

While the discussion section involves cautious interpretation of impact of individual NPI because they were implemented at similar times, the highest impact section (the abstract) does not heed that caution, and strongly implies that they identified which specific types of NPIs were most effective despite temporal correlation. The key difficulty – one examined and partially established in the paper itself – is that these interventions are temporally related to each other, and also have time lagged effects in similar timescales as the policy rollouts. One major issue is that there appears to be substantial limitations in the way in which lagged effects were measured. Lags were assumed to be at a fixed amount of time and the same for all policies, but in reality policy lag effects are spread out over a wide period of time, and will be different for different policies in different situations. This, in addition to the close clustering in time between policies, makes it difficult to conclude which NPIs had the strongest evidence for impact.

Value added

The study shows strong evidence that NPI policies are related to each other over time and how that creates difficulty in examining their impact.

Our take —

Using previously published models, authors quantified the degree to which COVID-19 transmission could interrupt malaria prevention services across sub-Saharan Africa (SSA), and the impact these interruptions could have on malaria deaths. The estimated effects varied according to the duration of interruptions, timing of disruptions and malaria seasonality, and how recently routine vector-control measures were implemented. Across SSA, authors estimated malaria deaths could increase by 696,000. However, due to large uncertainties in the models concerning COVID-19 transmission and how countries will respond, estimates should be considered illustrative rather than true projections.

Study design

Modeling/Simulation

Study population and setting

This study quantified interruptions to malaria preventive services due to COVID-19, and the potential impact of these interruptions on malaria morbidity and mortality in Nigeria and across sub-Saharan Africa (SSA). COVID-19 trajectories were constructed using a previously developed age-structured Susceptible-Exposed-Infectious-Susceptible model. Malaria deaths were estimated using a previously published malaria transmission dynamics model. Assuming an R0 (basic reproductive number) of 3, authors assessed the four COVID-19 transmission scenarios: 1. unmitigated (no direct action, but contact rates reduced by 20%); 2. mitigation (isolation and social distancing reduced contact rates by 45% for 6 months); 3. indefinite suppression (strict interventions reduce contact rates by 75% and are maintained indefinitely); and 4. suppression lift (strict interventions reduce contact rates by 75%, but these are only maintained for 2 months, after which time contact rates return to 80%). Authors included varying combinations of interruptions to the following malaria prevention activities: 1. distribution of long-lasting insecticide-treated nets (LLIN) would be delayed for a year or continue as normal, 2. seasonal malaria chemoprevention (SMC) would be interrupted, reduced, or continue as normal, and 3. clinical treatments would be interrupted, reduced, or continue as normal. Using Nigeria as an example, authors also estimated how changes to R0 (reproductive number) of COVID-19 could impact malaria deaths and the effect of broadening target ages for SMC.

Summary of Main Findings

If SMC coverage and case treatment were reduced by 50%, malaria deaths were estimated to increase by 42,000 (95% Uncertainty Interval: 22,000-62,000) in Nigeria and by 200,000 (95% UI: 115,000-285,000) across SSA, even if LLIN campaigns were not interrupted. However, if LLIN campaigns were also interrupted in addition to SMC and treatment reductions, excess malaria mortality was estimated to be as high as 495,000 (95% UI: 296,000-693,000) across SSA. If LLIN campaigns, SMC coverage, and treatment were all interrupted, malaria deaths were estimated to increase across SSA by as much as 696,000 (95% UI: 413,000-978,000). If R0 was 2.5 compared to 3.0, authors estimated the epidemic in Nigeria would increase from 6 to 9 months, thereby increasing malaria deaths by approximately 17%, even if LLIN campaigns continued and some case treatment was maintained. In scenarios where LLIN campaigns were interrupted, increasing the target age for SMC from <5 years to <15 years was estimated to save 22,500 lives in Nigeria.

Study Strengths

Authors conducted sensitivity analyses to account for uncertainty in the models.

Limitations

Overall, results are subject to several sources of uncertainty, the foremost being that it is unknown how COVID-19 will spread across SSA. Countries will likely experience varying levels of transmission and respond with different combinations of mitigation strategies, both of which will affect the degree to which malaria activities are disrupted. Parameters included in the COVID-19 model are based on data from the United Kingdom and China, which may not be appropriate for SSA. Authors used Nigeria as a case example for some estimates, but even these results should not be generalized to other parts of SSA.The model assumed that the impact of COVID-19 on malaria deaths was determined solely by the duration of malaria service interruptions. However, duration is unlikely to be the only contributing factor to the effects of service interruptions on malaria-associated deaths; other factors, such as to what degree services are interrupted (i.e., intensity of interruptions), also likely play a role. It is not clear to what extent routine malaria services have truly been disrupted.

Value added

Dual epidemics of COVID-19 and malaria could quickly overwhelm already vulnerable health systems in sub-Saharan Africa. This study contributes to the growing body of literature estimating the potential indirect effects of COVID-19 in Africa, and illustrates the need to prioritize malaria prevention methods such as routine distribution of long-lasting insecticide-treated nets.

Our take —

In this study, available as a preprint and thus not yet peer reviewed, authors modeled the propagation of COVID-19 cases and estimated the effectiveness of different contact tracing strategies in identifying secondary cases in the context of overdispersion. Model results suggest that adding backward contact tracing is 2-3 times more efficient at identifying cases than forward tracing alone. Authors demonstrated that backward tracing is a valuable strategy for developing a better epidemiologic understanding of SARS-CoV-2 transmission because it is more likely to capture cases generated from a common source than forward tracing. However, further work is needed to understand the conditions under which additional backward tracing is cost-effective and feasible.

Study design

Modeling/Simulation

Study population and setting

Overdispersion refers to the phenomenon where there is a lot of variability in the number of secondary cases generated from a primary case (i.e., one primary case may infect one other person while another may infect six). “Backward contact tracing” is a disease control strategy where the contact history of multiple cases up to 14 days prior to symptom onset is collected; the goal is to identify one upstream primary case. This contrasts with “forward contact tracing,” which aims to identify all contacts of a confirmed case and whether these contacts become secondary cases. Authors developed a branching process model, which is used to model reproduction of disease in given populations, to estimate the effectiveness of forward contact tracing alone compared to a strategy that uses a combination of both forward and backward tracing in the presence of overdispersed SARS-CoV-2 transmission. Authors assumed the index case that triggers either forward or backward tracing was identified through symptom-based surveillance. Effectiveness was measured by the number of third generation cases averted and the overall relative reduction in third generation cases.

Summary of Main Findings

Results from model simulations suggest that backward tracing is highly effective in identifying clusters of secondary transmission. Forward tracing, on average, is only able to identify the mean number of secondary cases. Adding backward tracing to a forward tracing strategy was more effective than forward tracing alone, increasing the number of identified secondary infections by a factor of 2-3. The higher the degree of overdispersion, the greater the absolute number of cases averted through backwards tracing: the highest degree of overdispersion assumed in the model resulted in 2-3 times more third generation cases detected than the lowest degree assumed.

Study Strengths

The model assumed any primary case is initially unknown in order to simulate how such contact tracing would operationalize in the real world.

Limitations

The model assumed that second generation cases were traced and quarantined before becoming infectious, when in reality many cases may not quarantine until after becoming infectious. Authors also assumed independently identified cases would not have the same primary case. If these cases did actually come from the same primary case, tracing and contact efforts would be unnecessarily duplicated. Authors did not explicitly examine the impact of backward and forward case identification timing, only the probability that they would be. It is unclear how this parameter may impact results. Despite relative benefits of backwards tracing, limited resources may preclude its use in some settings.

Value added

This modeling study demonstrated the benefit of including backwards contact tracing efforts to other contact tracing strategies. As secondary cases are more likely to come from a cluster rather than generate a new cluster, backwards contact tracing has the potential to be extremely valuable in efforts to mitigate SARS-CoV-2 transmission when resources are available.

Our take —

This analysis of smartphone geolocation data associated with 6,644 nursing homes across 23 U.S. states, published as a working paper and not yet peer reviewed, showed that facilities are highly connected, with 7% of phones appearing in a given facility appearing in at least one other facility.  This high connectivity is likely due to shared staff, and persisted even after the implementation of national guidelines designed to limit mobility between nursing homes. While these data highlight vulnerabilities to COVID-19 transmission, rather than recreate actual COVID-19 transmission pathways, the observed associations of nursing home centrality and connectivity with higher COVID-19 cases suggests additional regulations, like single-facility staffing, may be needed to curb COVID-19 outbreaks in nursing homes.

Study design

Ecological, Modeling/Simulation

Study population and setting

Investigators aggregated nursing home resident COVID-19 case data reported to 23 state health departments in the United States through May 31, 2020. Geolocation smartphone data from March 13 to April 23, 2020 were mapped onto COVID-19 case reports to identify mobility patterns among staff, contractors, and residents across 6,644 nursing homes. Adjusting for compositional characteristics of nursing homes (e.g., demographic factors, quality measures), the investigators assessed the relationship between COVID-19 cases at nursing homes and measures of nursing home connectivity (to other homes).

Summary of Main Findings

Even after guidelines restricted social visitors at nursing homes beginning March 13, 2020, approximately 7% of mobile phones that were identified in a given nursing home during the study period were found in at least one other home. Nursing homes were also highly connected (mean number of connections: 15), though these estimates varied widely by state. In regression analysis, nursing home connectivity measures – including the number of other homes to which a nursing home was connected, the number of mobile phones identified in multiple nursing homes, and the number of shared contacts between a nursing home and other homes with high connectivity – were significantly associated with higher cumulative COVID-19 case counts. Higher cumulative COVID-19 cases were also associated with nursing homes in urban areas, more beds, and higher proportions of Black residents (>25%) and Medicaid recipients (>50%).

Study Strengths

Investigators leveraged available geolocation data to identify networks of epidemiologically linked nursing homes vulnerable to COVID-19 transmission. Investigators performed sensitivity analyses to confirm that their findings were robust to COVID-19 prevalence and reporting differences across jurisdictions.

Limitations

The study could not determine whether individuals whose phones appeared in multiple nursing homes were the actual source of SARS-CoV-2 transmission, since the outcome measure was COVID-19 cases aggregated to the nursing home level. Additionally, while geolocation services were useful in constructing nursing home network profiles, the data presented do not offer insights into the duration or frequency of individuals’ exposure to multiple facilities. Lastly, analyses were cross-sectional and did not account for temporal shifts in COVID-19 cases at nursing homes, which could have driven variability in mobility during the observation period that was unexamined in the study.

Value added

This study provides valuable evidence that long-term care facilities are highly connected via shared staff, highlighting potential transmission pathways that threaten vulnerable residents.

Our take —

In this peer-reviewed study, the authors analyzed genomes of SARS-CoV-2 and related viruses (from the Sarbecovirus subgenus) to assess the history of recombination in this group and to estimate the timing of SARS-CoV-2 divergence from its ancestors. The results indicate that while recombination is common in sarbecoviruses, the receptor binding region of SARS-CoV-2 does not appear to be a recent recombination with pangolin coronaviruses and likely derives from ancestral viruses in bats. SARS-CoV-2 was estimated to have diverged from its nearest ancestor in bats between 1948 and 1982, indicating that evolutionary ancestors of the virus have been circulating in bats for many years prior to spillover into humans.

Study design

Ecological, Modeling/Simulation, Other

Study population and setting

The study used 68 full coronavirus genomes from the subgenus Sarbecovirus (containing SARS-CoV and SARS-CoV-2) collected from human cases, bats, and other intermediate hosts in northern, central, and southern China since 2002. The goal of the study was to determine the evolutionary history of SARS-CoV-2, specifically to understand the likely source of SARS-CoV-2 in humans (bats, pangolins, or another species), and to identify how long the virus had been circulating in that animal host.

Summary of Main Findings

The authors found that recombination is common among sarbecoviruses, with 67/68 genomes showing evidence of genomic exchange. They find that SARS-CoV-2 and bat-associated RaTG13 are part of a single lineage separate from SARS-CoV and related sarbevoviruses, suggesting that SARS-CoV-2 is the result of a direct (or nearly-direct) zoonotic transmission from bats. Specifically, SARS-CoV-2 did not acquire its variable loop region of the spike protein (containing the receptor binding domain that interfaces with human ACE2) through a recent recombination event with related sarbecoviruses in pangolins. Rather, RaTG13 is the recombinant virus, having acquired its variable loop domain from an as yet unsampled SARS-related coronavirus. The authors also used three different methods to estimate that SARS-CoV-2 appears to have diverged from a common ancestor in bats between 1948 and 1982. The estimated divergence time between the closest pangolin coronavirus to SARS-CoV-2 and the lineage containing SARS-CoV-2 and RaTG13 was between 1851 and 1877, indicating that pangolins likely acquired coronaviruses independently from bats, and were probably not an intermediate host that facilitated adaptation of SARS-CoV-2 to humans. These results indicate that a direct progenitor for SARS-CoV-2 has been circulating in horseshoe bats for decades before spillover into humans.

Study Strengths

The authors used a robust approach to deal with the issue of recombination in phylogenetic inference, which if unaddressed can lead to longer branch lengths and inflated divergence times. The authors also used a robust approach involving multiple prior distributions to estimate evolutionary divergence times, thereby capturing some of the uncertainty that is inherent in time-measured phylogenetic analysis and improving upon previously published results.

Limitations

As with other phylogenetic analyses of sarbecoviruses, inferences about the evolutionary origin of SARS-CoV-2 and the diversification of sarbecoviruses generally are limited by the current availability of genomes related to SARS-CoV-2 in bats and potential intermediate hosts. Additional sampling of bats and potential intermediate hosts around Wuhan and other areas of central China could reveal sarbecoviruses that represent a closer ancestor of SARS-CoV-2 that would provide more information on when and how the virus spilled over into humans.

Value added

This study provides a detailed explanation of the recombination history among sarbecoviruses and in the lineage containing SARS-CoV-2 and its closest relative in bats, RaTG13. The authors demonstrate that SARS-CoV-2 is not a recent recombinant of pangolin and bat viruses, and instead shares features with bat-associated sarbecoviruses. This suggests that spillover from bats to humans may have been direct or near-direct (i.e., a brief residence in an intermediate host). Pangolins do not appear to have been intermediate hosts based on the currently available data.

Our take —

This phylogenetic study analyzed SARS-CoV-2 sequences isolated from individuals across Brazil. The study investigated how SARS-CoV-2 became established in Brazil, and determined the impact of social and physical distancing measures on the spread of the virus. Specifically, the researchers showed that there were multiple international introductions into Brazil, but that relaxation of distancing measures led to increased spread within the country. Internationally connected cities in Brazil played an important role in the local and inter-state spread of SARS-CoV-2 and its subsequent establishment in the country.

Study design

Modeling/Simulation, Other

Study population and setting

The purpose of the study was to investigate how SARS-CoV-2 became established in Brazil and to quantify the impact of non-pharmaceutical interventions (NPIs) on virus spatiotemporal spread. Authors generated 427 new SARS-CoV-2 genomes from Brazilian samples collected between March 5 and April 30, 2020 from 85 municipalities spanning all regions of Brazil and analyzed these data in conjunction with 63 additional previously published sequences from Brazil. Authors used a continuous phylogeographic model to infer the origins of each phylogenetic node and human mobility data to estimate daily changes in R (effective reproductive number).

Summary of Main Findings

The study showed that NPIs reduced R from >3 to close to 1 in São Paulo and Rio de Janeiro during the start of the epidemic, but the increased mobility after this initial decline led to a rising R in Sao Paulo. Genome analysis showed that 99% of the Brazilian sequences belonged to SARS-CoV-2 lineage B, with only 5 strains belonging to lineage A. Within the dominant B lineage, the authors found that sequences from Brazil fell into three primary clusters. Analysis of these three clades indicated that community-driven transmission was already established in Brazil by early March, 2020, and international travel restrictions initiated after this period may have had limited impact. Further phylogeographic analysis revealed there were at least 102 international introductions of SARS-CoV-2 into Brazil, primarily into internationally well-connected states such as São Paulo (36% of all imports), Minas Gerais (24%), Ceará (10%) and Rio de Janeiro (8%). Overall, the authors found that SARS-CoV-2 spread was initially driven by local and within-state movement, but was later characterized by long-distance movement, though within-state virus movement was always more frequent than between-state movement.

Study Strengths

A key strength of this study is that the genomic data is representative of the distribution of cases in Brazil. Furthermore, the methodology is clear and well presented, and the results of this study revealed the importance of customizing diagnostic assays with specificity for locally circulating strains.

Limitations

There is limited description of the 24% Brazilian sequences that did not fall in the three main clades.

Value added

The COVID-19 epidemic in Brazil is currently among the largest in the world, and this paper explains how it grew to that level. The paper demonstrates the role that introductions from other countries had in fueling the spread of the virus and examines the relative contributions of within- and between-state travel. The authors also describe how social and physical distancing measures initially curbed spread of the virus before their relaxation led to increased transmission and details the different SARS-CoV-2 lineages and clades that are spreading in the region, which may have an impact on the diagnostic tests used locally.

Our take —

Authors show that rapid case isolation can shorten the duration of onward transmission and thus be an effective non-pharmaceutical intervention (NPI). Furthermore, the serial interval, the time between symptom onset in the infector and infectee should not be considered as a single fixed distribution that is generalizable across different settings. Authors demonstrate that non-pharmaceutical interventions, and specifically case isolation, shortened the serial interval over the course of the epidemic in China, which can substantially affect the estimates of the reproduction number. An “effective” serial interval distribution may therefore help to refine estimates of transmissibility and offer insights into the impact of public health interventions.

Study design

Modeling/Simulation

Study population and setting

Authors compiled a database of 1,407 Covid-19 “transmission pairs” where information on who infected whom (infector to infectee) could be constructed from publicly available data on 9,120 confirmed cases in 27 provinces and 264 health commissions in China. Cases from Hubei province were excluded as chains of transmission were harder to reconstruct. Analysis was restricted to the 677 pairs for which dates of symptom onset and their social relationships were available. Infectors reported symptom onset between January 9 to February 13, 2020 during which key interventions were implemented. The authors estimated the time between symptom onset of the infector and symptom onset of the infectee (the serial interval) over three time periods: i) January 9 to 22 or “pre-peak”; ii) January 23 to 29 or “peak-week”; and iii) January 30 to February 13 or “post-peak”. They also estimated the serial interval over time using a rolling average. Authors then explored whether changes in the serial interval could be explained by factors including age, sex, social relationships, or non-pharmaceutical interventions (NPIs) and the potential impact of changing serial intervals over the course of the epidemic on estimates of transmissibility (Rt or the reproduction number).

Summary of Main Findings

Authors estimated that the serial interval shortened over the course of the epidemic from 7.8 days (95% credible interval: 7.0 – 8.6) pre-peak to 5.1 days (95% CrI: 4.6 – 5.7) during the peak, and 2.6 days (95% CrI: 1.9 – 3.2) post-peak. This decline was also observed when the serial interval was estimated over a rolling time window and when stratified by age, sex, and social relationship. Authors found that the delay from SARS-COV-2 confirmation to isolation was the primary driver and could explain 51.5% of the variability in the observed serial interval duration. For each day of early isolation, the serial interval decreased on average by 0.7 days. NPI strategies and population immunity explained an additional 15-20% of variability. Authors found that using a fixed serial interval distribution in analyses led to mis-estimation of Rt and potentially underestimation early in the pandemic.

Study Strengths

Authors used a large number of transmission pairs (677 pairs) for which detailed epidemiological information was available to estimate the serial interval over multiple time points and explicitly excluded data from Hubei province which was deemed less reliable. Authors estimated the serial interval using non-overlapping and rolling average time-windows and found the same declining trend in the serial interval over time.

Limitations

While authors consider a large number of transmission pairs, some epidemiological data may be limited by recall bias e.g. date of symptom onset. Additional unknown factors aside from time to isolation, NPIs or population immunity could have resulted in the reduction of the serial interval. Additionally, the effect of other NPIs and population immunity could not be disentangled.

Value added

Authors demonstrate that rapid case isolation can be an effective NPI. They additionally show that the serial interval is not a fixed interval as is often assumed and can be impacted significantly by non-pharmaceutical interventions. The use of “effective” serial intervals in analyses may provide better estimates of the reproduction number.

Our take —

Using a stochastic transmission model, authors compared the impact of different contact tracing strategies in reducing Re (effective reproductive number) from 1.2, an assumed value that represents SARS-CoV-2 transmission in settings with physical distancing. A traditional contact tracing scenario was generally unable to bring R to below 1 if there were testing delays, whereas decreases in R achieved through contact tracing via mobile apps was more robust to these delays due to their high contact tracing coverage.

Study design

Modeling/Simulation

Study population and setting

Using a stochastic compartmental model originally developed for smallpox vaccination, authors analyzed how delays in testing and contact tracing at the population level impact Re (effective reproductive number) and the proportion of transmissions that could be prevented per index case. To explore trade-offs within different components of a contact tracing program on Re, authors used their model to explore many sets of parameters. Among these, the authors defined a single conventional contact tracing scenario (80% testing coverage, 80% and 50% tracing coverage of close and casual contacts respectively, 4 day testing delay from symptom onset to receiving a test result, 3 day tracing delay from returning a case test result to contact quarantine) in relation to multiple mobile app contact tracing scenarios that had wider ranges in their assumed testing and tracing coverages and zero-day testing and tracing delays.

Summary of Main Findings

Assuming an app-based scenario where 80% of symptomatic persons were tested, 80% of their contacts traced, and testing and tracing delays were zero days, R was reduced from 1.2 to 0.8 (0.7-1.0) and 79.9% of transmission events were prevented. When the testing delay was three days or more, it was not possible to reduce R from 1.2 to below 1 (even when there was no tracing delay) . Mobile app-based tracing scenarios were more effective due to a combination of zero testing and tracing delays and greater tracing coverage for close and casual contacts. Sensitivity analysis showed, however, that high testing and tracing coverage were more important in reducing transmission than time delays. Even assuming a two-day testing delay, contact tracing via a mobile app could keep R below 1 as long as 80% symptomatic persons are tested and 80% of their contacts traced.

Study Strengths

This study accounts for all individual components of contact tracing (e.g., timing of index case infection through quarantining of contacts). Model assumptions were also based on real-world experience from public health professionals working in contact tracing.

Limitations

Considering the high testing coverage in the modeled scenarios, the reductions to R described in this study may be unrealistic in settings like the United States. Further, the authors use the terms “conventional” and “mobile app-based” contact tracing to refer to model assumptions. Thus, we urge caution in assuming these results would apply generically to a conventional or app-based contact tracing program, as their real-world performances could vary widely and even overlap.

Value added

Previous work indicates that contact tracing and isolation alone may not be sufficient to control outbreaks and that other interventions may be needed. This study assessed the degree to which each component of a contact tracing program contributes to its effectiveness.