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Our take —

This study modeled the impact of several non-pharmaceutical interventions on the number of new cases, hospitalizations, ICU bed needs, and deaths related to COVID-19 in the United Kingdom. Using a stochastic model stratified by five-year age groups, authors determined that although interventions would reduce the number of cases by an estimated 20-30% and delay the peak by 3-8 weeks, these reductions would not be enough to control the epidemic or prevent exceeding ICU bed capacity.

Study design

Modeling/Simulation

Study population and setting

Authors used a stochastic model (i.e., one that takes randomness into account) stratified into 5-year age groups to estimate the effectiveness of non-pharmaceutical interventions (NPIs) in preventing new cases, hospitalizations, ICU bed demand, and deaths from COVID-19 in Wales, Scotland, and Northern Ireland. Over 66 million people aggregated to county-level administrative units were included in the model and a baseline R0 (basic reproductive number) of 2.7 was used. Interventions evaluated through the model were school closures, physical distancing, shielding of older persons, self-isolation of symptomatic persons, a combination of these four, and more locally-focused interventions, including a reduction in leisure events (e.g., spectator sports). Finally, authors estimated the impact increased childcare by grandparents would have on the effectiveness of these interventions, namely school closures.

Summary of Main Findings

In the absence of NPIs, the case-fatality rate was estimated to be 1.5% (95% PI 1.3-1.7), and the infection-fatality ratio was estimated to be 0.63% (95% PI 0.45-0.79); this was estimated to result in a peak number of required ICU beds of 200,000 and 350,000 total deaths. When implemented in the middle of the peak of the unmitigated epidemic, on average, each intervention delayed the peak by 3-8 weeks and decreased the total number of cases by 20-30%. Model results suggest that when implemented locally, the effect of school closures, physical distancing, shielding of older persons, and self-isolation of symptomatic cases only modestly reduced the total number of cases and deaths compared to implementing them nationally. Suspending leisure activities such as spectator sports by 75% averted 1.9 million cases. Model results indicate that over a 3-month time period, the beneficial effects of school closures would be virtually eliminated in terms of the number of deaths and peak ICU bed needs due to the increased contact per weekday between children younger than 15 years and older persons (i.e., grandparents).

Study Strengths

The model estimated both case-fatality and infection-fatality ratios, which allowed it to account for asymptomatic and undiagnosed infections in addition to symptomatic cases. Authors also ran simulations using a distribution of R0 values, SARS-CoV-2 introduction dates, and timing of intervention implementation, which provided robust uncertainty bounds for the model projections. The modeling framework (i.e., location-based contact matrices) makes the scenarios well-oriented for policy making.

Limitations

Projections of the relative effect of the NPIs considered in this study are estimates only and derived from measurements taken in 2006. Per contact matrix (home, work, school, other) and intervention combination (e.g., home contacts assuming school closures), authors assumed decreases in contacts would be uniform between each five year age group pairing. However, the model did not structure individuals by household, which limits the ability to evaluate the impact of these interventions on household contacts. Challenges in comparing the results from this study to empirical UK data exist since implemented interventions differ from the scenarios evaluated here.

Value added

Going beyond the growing body of evidence estimating effectiveness of non-pharmaceutical interventions (NPIs) for individual countries, this study evaluated the impact of NPIs on different spatial scales, and compared their effectiveness when implemented at the local versus national level. This study also considered how the impact of certain NPIs could be negated by resulting changes to contact patterns, such as grandparents being responsible for childcare during school closures.

Our take —

The declaration of a public health emergency on February 23, 2020, and implementation of various physical distancing measures on March 23, 2020 in the absence of a national lockdown, corresponded to substantial reductions in COVID-19 transmission in South Korea, suggesting a combination of non-pharmaceutical interventions without lockdown are effective in mitigating COVID-19 spread.

Study design

Ecological, Modeling/Simulation

Study population and setting

This study included 2,023 confirmed COVID-19 cases in South Korea from January 20 to February 23, 2020 (first time period) and February 24 to April 21, 2020 (second time period). Time periods coincided with the South Korean government’s declaration of public health emergency on February 23, 2020, resulting in subsequent implementation of non-pharmaceutical interventions (enhanced screening and testing in community and clinical settings, quarantining close case contacts, physical distancing measures [implemented beginning March 21, 2020]) designed to mitigate the spread of COVID-19. These strict social distancing measures included cancelling public events, as well as recommendations to avoid social gatherings and leave the household only for essential purposes (i.e., grocery shopping, seeking healthcare). Investigators estimated the effective reproductive number (Rt), a time-varying measure of transmission intensity, using daily case counts to determine the potential impact of combination non-pharmaceutical interventions on the spread of COVID-19.

Summary of Main Findings

In the first time period, the Rt reached a maximum of 2.53 (95% CI: 1.90 – 3.25) secondary transmission events per COVID-19 case. Immediate declines in the Rt were observed 1 week after the emergency declaration on February 23, 2020, reducing to 1.37 (95% CI: 1.27 – 1.47). By February 29, 2020, the Rt fell below 1, indicating falling incidence and declining epidemic spread, and remained <1 for the duration of the observation period. Following the implementation of additional physical distancing measures on March 23, 2020, the Rt declined additionally by 9.75% (95% CI: 7.23 – 12.29%).

Study Strengths

The authors modeled the effective reproductive number before and after implementation of various non-pharmaceutical interventions to strengthen attribution of temporal declines in COVID-19 transmission to these interventions.

Limitations

Given the small number of confirmed cases before February 16, 2020, the authors could not calculate stable Rt estimates prior to this date. Additionally, the authors excluded cases from Daegu-Gyeongsangbuk Province (accounting for approximately 2% of confirmed cases nationally), where superspreading events were documented. The analysis does not support identification of marginal impacts of individual non-pharmaceutical interventions on COVID-19 spread. Though the authors emphasize non-pharmaceutical interventions were implemented without a formal national lockdown, the distancing measures implemented in South Korea were akin to the strictest social distancing measures implemented in other settings, like the United States. Lastly, the mean time of symptom onset between a suspected index case and secondary case was less than zero days in a number of transmission pairs used to estimate the serial interval, which may have impacted downstream model results.

Value added

The study offers insights on sequential reductions in COVID-19 transmission corresponding to: 1) declaration of a public health emergency in South Korea; and 2) implementation of physical distancing measures in the absence of a more strict lockdown.

Our take —

Using daily laboratory-confirmed case counts, this study evaluated the association between control measures implemented in China and the spread of COVID-19 beyond Wuhan. Control measures such as the Wuhan travel ban and implementation of Level 1 Emergency response were associated with a delay in spread to other Chinese provinces and an overall decline in incident cases by mid-February 2020. Restrictions were especially effective when implemented prior to confirmation of COVID-19 cases. This study was unable to disaggregate the impact of travel restrictions from other containment measures. The combination of centrally coordinated measures may not be possible in other locations.

Study design

Modeling/Simulation

Study population and setting

This study used laboratory-confirmed case reports and human mobility data (mobile phone records) from 34 Chinese provinces to estimate effects of transmission control measures during the first 50 days of the COVID-19 epidemic (December 31, 2019 – February 19, 2020). Authors used a linear regression model to evaluate the association between the Wuhan travel ban and spread of COVID-19 to other Chinese provinces. They then used a Susceptible-Exposed-Infected-Recovered (SEIR) model fitted to the number of newly confirmed cases each day from each province to estimate the effect of implemented control measures on the trajectory of the epidemic outside of Wuhan.

Summary of Main Findings

Authors found that the total number of cases detected in each province was strongly associated with the number of travelers estimated from Wuhan. Banning travel in/out of Wuhan slowed COVID-19 transmission to other cities by an estimated 2.91 days (95% CI: 2.54-3.29). Cities that implemented control measures before having any confirmed cases had 33.3% fewer confirmed cases during the first week of their outbreak (13.0 cases, 95% CI 7.1-18.8) compared to cities that implemented controls after detecting cases (20.6 cases, 95% CI 14.5-26.8). Model results estimated that R (the effective reproductive number) decreased from 3.15 to 0.04 after the Level 1 response measures were 95% implemented. Authors estimated that, implemented alone, the Wuhan travel ban or Level 1 response would not have been enough to result in the observed decrease in incident cases by February 19, 2020. However, when combined, these control measures were estimated to have prevented 96% of COVID-19 cases that could have been expected to occur in the absence of all interventions.

Study Strengths

This study leverages mobile phone data for a detailed examination of how travel restrictions affected human mobility. Combining these data with case reports and the timing of control measures allowed multiple approaches to estimating the impact of control measures on transmission. The SEIR model was fitted to empirical case reports, meaning that the model was adapted to be most appropriate for the observed data.

Limitations

Results were generated from statistical and mathematical models, and strong statistical associations between implementation of control measures and observed delays and decreases in incident cases do not indicate a causal relationship. Multiple containment measures were implemented at once, and so this study is unable to disaggregate the impact of some containment measures (e.g., isolation of patients, closure of schools).

Value added

This study adds to the growing evidence regarding the effectiveness of the Wuhan travel ban and other high-level containment and suppression measures implemented in response to the COVID-19 pandemic. As with other studies, this modeling study suggests that these strict mobility interventions delayed the spread of COVID-19 within mainland China, and may have contributed to the observed reduction in cases by mid-February 2020.

Our take —

Results from this study, available as a preprint and thus not yet peer reviewed, suggest that even relatively small changes in the timing of stay-at-home measures at the beginning of the COVID-19 epidemic in the United States could have made a large difference in morbidity and mortality. However, the aggregated, non-specific treatment of non-pharmaceutical interventions in this study limits the utility of its conclusions. Applicability of results are also largely limited to densely populated, metropolitan areas of the country.

Study design

Modeling/Simulation

Study population and setting

Using US county-level reported cases and human mobility data, the authors developed a metapopulation model (which segments populations of interest into sub-populations, usually based on location) to estimate changes in COVID-19 transmission following stay-at-home measures introduced between March 15 and May 3, 2020. Authors used age-stratified infection fatality rates in the model and generated daily confirmed cases and deaths by county. There were 311 counties with over 400 cases by May 3, 2020; these were modeled separately from the remaining counties, which were further segmented into 16 smaller groups. The authors simulated case numbers and fatalities if stay-at-home measures had been implemented one to two weeks earlier.

Summary of Main Findings

If stay-at-home measures had been implemented one week earlier, the authors estimated that 56.5% (95% CI: 48.1% to 65.9%) of cases and 54% (95% CI: 43.6% to 63.8%) of deaths in the US would have been avoided. Under a scenario in which stay-at-home measures were implemented two weeks earlier, the estimate of cases averted rose to 84.0% (95% CI: 78.7% to 88.4%), and the estimate of deaths averted rose to 82.7% (95% CI: 76.1% to 87.6%).

Study Strengths

Authors calibrated the model against known county-level incidence and mortality data from the month prior to and including their timeframe of interest (February 21 to May 3, 2020). In general, metapopulation models allow for the inclusion of more realistic spatial transmission dynamics in epidemic models.

Limitations

The changes in population behavior modeled by the authors are treated as if they are a monolithic response to a broad aggregate of non-pharmaceutical interventions by federal, state, and local governments; they also assume that all behavior changes are a consequence of these NPIs rather than individual decision-making. Assuming that stay-at-home measures could have been implemented one to two weeks earlier with the same effect on infection dynamics is problematic. First, the public may not have complied as readily with stay-at-home measures earlier, as the perceived threat from COVID-19 may have been lower. Second, the timing of control measure implementation was influenced by other considerations, such as economic concerns. Furthermore, assumptions on inter-county movement were based on 2011-2015 data and may not reflect mobility between counties today. Due to the number of parameters that needed to be estimated as part of the model, the authors assumed a fixed value for the latency period, how long an individual was contagious, the infectiousness of unreported infections, and the proportion of movements that were not work-related. Estimated daily death rates incorporated variation in age structure by county but, given the potential impact of sex and race on case-fatality proportions, it is unclear why other demographic factors were not included. Finally, authors focused on metropolitan areas (e.g., New York, Chicago) and study results may not be applicable to less densely populated US counties.

Value added

As our knowledge about the incidence and transmission of COVID-19 evolves, this study contributes to the growing body of literature assessing intervention methods at finer spatial scales. Quantifying the impact of earlier implementation of stay-at-home measures provides a compelling case for early, aggressive intervention.

Our take —

This study, available as a preprint and thus not yet peer-reviewed, highlights the importance of real-world social contact data and networks for evaluating outbreak control measures. Tracing contacts of contacts can reduce outbreak size more than tracing only contacts, but requires high test capacity and results in a large proportion of the population in quarantine. The network model used may not scale to larger cities or for longer outbreaks, however is nevertheless valuable in exploring and optimizing potential control strategies.

Study design

Modeling/Simulation

Study population and setting

Authors used detailed contact data collected from 468 individuals during the 2017/2018 citizen-science BBC Pandemic Project in Haslemere, Surrey, UK. These high-resolution GPS data were collected to be specifically relevant for infectious disease modelling, and were used to construct a “social network” that represented real-world close contacts that could result in SARS-CoV-2 transmission such as “face-to-face” contacts. COVID-19 outbreaks were simulated using a mathematical model across this social network testing different network structures on how individuals within the network may be connected to each other. Authors allowed factors such as the proportion of asymptomatic individuals and their infectiousness to vary. Four control scenarios and their impact on the epidemic were assessed: 1) no control, where no individuals are isolated or quarantined; 2) case isolation, where individuals isolate when they start having symptoms; 3) primary contact tracing with quarantine, where individuals isolate once they have symptoms and traced contacts are quarantined upon their infector’s symptom onset (after a delay); and 4) secondary contact tracing which is the same as (3) but includes tracing of the contacts of contacts. The model assumed that isolated and quarantined individuals were all isolated for 14 days and explored how well contact tracing was conducted (% of contacts traced) impacted the effect of control. Finally authors explored a range of “test and release” strategies by varying the time from quarantine to being tested, and also looked at two social distancing scenarios whereby individuals either reduce or completely stop contacts with people they spend the least time with (e.g. outside the household) and instead spend this time with frequent contacts (e.g. within the household).

Summary of Main Findings

Compared to an estimated 12% of the Haslemere network being infected after 70 days in an uncontrolled epidemic scenario, case isolation of symptomatic individuals (control scenario 2); primary contact tracing (control scenario 3); and contact tracing contact of contacts (control scenario 4) resulted in a median 9.3%, 9%, and 7.3% of the population being infected respectively. Tracing contacts of contacts led to the greatest reduction in epidemic size, but also the highest proportion (29%) of individuals under quarantine at any one time. All control scenarios reduced the overall size and growth rate of the simulated outbreaks, with more efficient contact tracing (higher % of contacts traced) also leading to smaller outbreaks. Increasing test capacity, allowing more isolated cases and non-infectious contacts to be released from quarantine, led to substantial reductions in the number of quarantined cases but only a very small increase in outbreak size in both contact tracing scenarios. However this test and release strategy required high test capacity. Social distancing measures combined with contact tracing substantially reduced the number of tests required and the number of individuals in quarantine at any one time.

Study Strengths

This study uses very detailed app-based social contact data which were not limited by recall bias (often the case for other self-reported contact surveys), or restricted to a certain setting (such as schools or workplaces). By examining transmission dynamics in a social network that better captures real world interactions, authors were able to examine how different intervention methods may impact local epidemics. The study highlights how specific social structures should be considered in the design of control measures.

Limitations

The social contact data did not include children <13 years and were collected in a small geographic area over a short period of time. Therefore the social network explored here may not extrapolate to larger populations or over longer time periods. A short delay between isolation/quarantine and testing was assumed (48 hours) and so these testing strategies may only apply when there is sufficient testing capacity. Although social distancing was considered this was not in the context of specific strategies such as workplace closures. Therefore conclusions on what would be an effective social distancing intervention cannot be drawn.

Value added

This study highlights the value of real-world social contact data in epidemic modelling and the optimization of different control measures. It also shows the potential trade-offs between level of control (tracing contacts of contacts leading to smallest outbreaks), resources required (high testing capacity required), and the feasibility of intervention strategies (results in almost 30% of the population in quarantine).

Our take —

An epidemic model of COVID-19 spread in Germany revealed noteworthy reductions in COVID-19 epidemic growth following the implementation of sequential non-pharmaceutical interventions, including suspension of large public gatherings, business and school closures, and a nationwide contact ban.

Study design

Modeling/Simulation

Study population and setting

The objective of the study was to model COVID-19 growth trajectories in Germany, and use that to determine the impact of sequentially implemented physical distancing interventions on the spread of COVID-19. The authors specified an epidemic model using confirmed COVID-19 cases in Germany through April 21, 2020, to predict the rate of new infections in correspondence with three time points when the following physical distancing interventions were implemented: 1) suspension of large (1,000+) public gatherings (March 9), 2) business and school closures (March 16), and 3) a nationwide contact ban, including closure of all non-essential businesses (March 23). The authors also simulated different implementation scenarios, including the magnitude and timing of interventions, to assess their relative impact on the spread of COVID-19.

Summary of Main Findings

The growth rate of COVID-19 case rate decreased from 0.43 to 0.25 following the suspension of large public gatherings. The growth rate subsequently decreased again from 0.25 to 0.15 following widespread business and school closures on March 16. The implementation of the contact ban ultimately resulted in a negative growth [-3% (95%CI: -5%, -2%)]; resulting in declining case counts. Scenario-based models, which varied the timing of implementation of physical interventions to five days before and after March 16, revealed strong temporal effects of these interventions, with earlier and later implementation resulting in major differences in cumulative COVID-19 cases.

Study Strengths

The primary epidemic model in the study contained a limited number of parameters, which facilitated epidemic growth estimations using a limited number of data points (i.e., COVID-19 cases). The authors also uniquely specified change points into their model parameters to flexibly account for differential rates of exponential growth/decline in COVID-19 cases after implementation of various physical distancing interventions at different points in time. Models fit observed case count data well, and numerous sensitivity analyses were performed.

Limitations

Due to the limited number of parameters specified in the study’s final model, other relevant epidemic parameters (e.g., testing capacity, geographic differences in epidemic trajectories) were not included, which may be essential for more robust, accurate forecasting of COVID-19 spread and impact of physical distancing measures in other settings. Data was from just one country and the models required a number of assumptions, making the broader implications unclear.

Value added

This is among the first studies to specify change points aligned with the implementation of various non-pharmaceutical interventions in an epidemic model for estimating their relative impact in curtailing COVID-19 transmission.

Our take —

This study estimated the extent of SARS-CoV-2 infection and rates of hospitalization, ICU admission, and deaths in the French population. The study estimated that the lockdown reduced the reproductive number from 2.90 (95% CrI: 2.80–2.99) to 0.67 (95% CrI: 0.65–0.68). It estimated 2.8 million (4.4%) people in France were infected up to May 11, 2020, but was unable to account for closed communities such as retirement homes, and may have underestimated the number of people infected.

Study design

Modeling/Simulation

Study population and setting

Authors used passive hospital surveillance data from the French national public health agency and Poisson Likelihood models to estimate the number of hospitalizations, ICU admissions, and deaths from COVID-19 in 8 age groups per sex. Authors used active surveillance data from the Diamond Princess cruise ship and Poisson Likelihood models to estimate the number of deaths aboard the ship. Both estimates were extrapolated to describe the dynamics of SARS-CoV-2 infection and COVID-19 disease burden in France through May 11, 2020. Finally, authors assessed how SARS-CoV-2 transmission was impacted by country-wide lockdowns.

Summary of Main Findings

Overall, results estimated 2.8 million people were infected with SARS-CoV-2 in France, approximately 4.4% of the population. An estimated 3.6% (95% Credible Interval: 2.1–5.6%) of infected persons were eventually hospitalized for COVID-19, and of those hospitalized, 19.0% (95% CrI: 18.7–19.4%) were admitted to the ICU. Median time from hospitalization to ICU admission was 1.5 days and increased with age. Regardless of ICU admission, 18.1% (95% CrI: 17.8–18.4%) of those hospitalized died . Among those infected (regardless of hospitalization), 0.001% of people <20 years old died, compared to 10.1% (95% CrI: 6.0–15.6%) of those >80 years old. The R0 (basic reproductive number) prior to lockdown was estimated at 2.90 (95% CrI: 2.80–2.99). After the lockdown, R (effective reproductive number) dropped to 0.67 (95% CrI: 0.65 – 0.68), representing a 77% (95% CI: 76–78%) reduction in transmission.

Study Strengths

The study integrated both passive and active surveillance data, which may make estimates more robust as compared to hospital-only based reporting. Authors used the date of disease occurrence, rather than date of report, which corrected for delays in reporting. Authors validated the modeling framework using known data.

Limitations

The study did not include non-hospitalized COVID-19-related deaths and excluded institutionalized populations (i.e., persons living in retirement communities), which may have resulted in the number of infections being underestimated within the population and limits generalizability.

Value added

This study estimated country-wide infection and case fatality rates, as well as the effect lockdown efforts had on the reproductive rate in France. These findings have important policy implications and results may be applicable to other high-income countries with similar timelines regarding implementation and easing of mobility restrictions.

Our take —

This study used the Lives Saved Tool to estimate the number of excess deaths from COVID-19 due to indirect effects of interruption to reproductive, maternal, neonatal, and child health services in 118 low- and middle-income countries. Indirect effects of COVID-19 may be more burdensome in the long-term than direct and immediate health effects, and should be considered by decision-makers. This study was not meant to provide formal projections, but could be useful for future response and recovery planning.

Study design

Modeling/Simulation

Study population and setting

Authors estimated potential excess deaths in 118 low- and middle-income countries caused by reduced coverage of reproductive, maternal, newborn, and child health (RMNCH) services, and increased prevalence of wasting (low weight for height). Authors assumed no (0%), small (5%), moderate (10%), and large (25%) reductions in RMNCH service and relative increases in prevalence of wasting of 10%, 20%, and 50% among children. Using the Lives Saved Tool (LiST), which estimates changes in mortality due to changes in intervention coverage, authors projected excess deaths under three scenarios and for three time periods. These scenarios were based on a health systems framework developed by authors that included availability of health workers, availability of supplies, demand for services, and access to health. Excess deaths represent the increase in deaths compared to the counterfactual of no changes to intervention coverage or prevalence of risk factors (e.g., wasting).

Summary of Main Findings

The scenario representing the smallest reduction in coverage (5%) resulted in an estimated 2,030 excess maternal and 42,240 child deaths per month. The scenario representing the most severe reductions in coverage (25%) resulted in an estimated excess 9,450 maternal and 192,830 child deaths per month. Assuming increases in wasting and reductions in coverage of 5%, 10%, and 25%, this represents relative increases in monthly child deaths of 9.8%, 17.3%, and 44.7%, respectively.

Study Strengths

Authors considered 19 maternal and 29 child health services; authors included contributions of individual RMNCH interventions and assumed varying levels of reduction to individual services would result in greater overall reduced coverage. LiST is a standardized tool used for estimating mortality under different intervention coverage scenarios.

Limitations

Increases in childhood wasting accounted for a significant proportion of the excess deaths, which may or may not have been accurately represented in these simulations. This study presents only hypothetical estimates on the utilization of maternal and child health services under different assumed scenarios of COVID-19-related disruption. Furthermore, assumptions were held constant for all 118 low- and middle-income countries, and country-specific responses were not accounted for. Although the authors present country-specific results in supplemental materials, assumptions are not likely to hold across such a diverse group of countries, and results may be under or overestimated for some countries.

Value added

This study adds to the growing body of work regarding the burden of COVID-19 in low- and middle-income countries, and for the potential impact of interruptions to primary care and other essential health services. This study estimates the indirect effects of COVID-19 on maternal and child health, which builds on previous estimates and may be useful for planners and decision-makers.

Our take —

This study, available as a preprint and thus not yet peer reviewed, shows that targeted measures to reduce transmission in high-risk populations can substantially reduce deaths and hospital burden, but only when combined with measures to reduce transmission in the general population. As many sources of uncertainty were not taken into account, this result may be more useful qualitatively than quantitatively. A better understanding of risk factors will enable other studies to build on the methods developed here.

Study design

Modeling/Simulation

Study population and setting

This study modeled the relaxing of physical distancing measures in Austin, Texas, USA. They compared strategies which reduced community transmission only, strategies which reduced transmission to high-risk individuals only, and a combination of both. These comparisons were done for a range of assumed effectiveness of transmission reduction.

Summary of Main Findings

The authors found that by April 23, 2020, physical distancing measures had reduced transmission in Austin, Texas by 95% (95% CI: 70%-100%). If physical distancing measures were completely relaxed on May 1, 2020, hospital capacity would be exceeded in 29 days (95% CI: 19-44). If, instead, partial relaxation of physical distancing measures was able to maintain a 75% reduction in transmission, hospital surge capacity would be reached after 109 days (95% CI: 72-184). The authors then examined the effect of additional “cocooning measures” to protect those at high risk, including incentives for these individuals to stay home, increasing resources and staffing at care homes, and enabling homeless individuals to physically distance. They found that if these measures could reduce transmission to high-risk individuals by 95%, and they were combined with the previous measures reducing transmission by 75% for the general population, then hospital capacity would not be exceeded, and 64.6% of hospitalizations and 74.7% of deaths would be avoided.

Study Strengths

The authors estimated the proportion of high-risk individuals by age, and used age-stratified contact data to model the reduction in transmission. Combining these data enables more accurate estimates of the impact of cocooning, compared to models which treat all high-risk individuals as having the same risk regardless of age.

Limitations

The methods are not very well documented, making reproducibility and validation more difficult. Sources for some model parameters, such as age-specific contact rates, are not clearly given. The confidence intervals for the number of hospitalizations and deaths under each scenario only take into account two sources of uncertainty: uncertainty in the impact of physical distancing measures so far, and inherent randomness in the transmission and disease progression processes. They do not account for uncertainty in other model parameters, such as the relative risk of hospitalization, and death for high-risk versus low-risk individuals, which are not well-known. The study also assumed that high-risk conditions for COVID-19 are the same as those for influenza, and that all high-risk individuals in a given age group have the same risk regardless of underlying condition; as risk factors for COVID-19 become better understood, these results may have to be revised. Finally, the study looks at combinations of different percentage reductions in transmission for the general population and in high-risk populations, but does not comment on the relative feasibility and ethics of these combinations — for example, whether a 75% reduction for the general population and a 95% reduction in high-risk groups is more or less feasible than an 85% reduction for both, and what practical measures are needed to reach these reductions.

Value added

This study highlights the value of targeted measures to reduce transmission in high-risk populations, and thus reduce the number of hospitalized cases and deaths. It also shows that cocooning high-risk individuals alone will not avoid exceeding hospital capacity, and will lead to substantial hospitalizations and deaths.

Our take —

Preliminary results from this study of real-time COVID-19 symptom data collected through an app suggests that app-based symptom tracking may be useful for predicting spikes and drops in new cases several days in advance of other more traditional measures (e.g. confirmed positive tests).

Study design

Cross-Sectional; Modeling/Simulation; Other

Study population and setting

The authors developed an app-based symptom tracker, “COVID Symptom Study” (referred to previously as COVID Symptom Tracker), and demonstrated proof-of-concept for real-time tracking of symptoms through mobile phones. App data can be used to immediately inform public health responses to COVID-19 by detecting outbreaks, disparities in testing, and emerging symptoms. The app was tested in the United Kingdom and United States of America. Recruitment for the app relied on downloads, but also recruitment through existing large cohort studies that often have higher under-represented populations.

Summary of Main Findings

Initial findings were reported based on 1.6 million users in the UK, including 265,851 who experienced COVID-19 symptoms, of whom 0.4% received testing. Those that reported fatigue and/or cough along with another symptoms were more likely to test positive, but 20% of individuals that reported this combination of symptoms did not receive testing. Among those who tested positive, loss of smell was more common than fever, suggesting that anosmia may be a good predictor for testing positive for COVID-19. Based on modeling using existing data, the authors were able to predict increases and decreases in COVID-19 cases in geographic areas across the UK several days in advance of confirmed case data.

Study Strengths

The app provides a useful tool to collect longitudinal data on COVID-19 symptoms and testing on the scale needed for meaningful analysis. Recruitment for app use included leveraging existing large cohort studies, thereby improving diversity and the potential to link existing cohort data with COVID Symptom Study data. Software updates allow questions to be modified as knowledge of the outbreak develops and new hypotheses emerge. As the app collected data on symptoms and testing over time, authors were able to assess which symptoms were more likely to result in a positive test and predict where hotspots may emerge.

Limitations

As the authors acknowledge, the population contributing data to the COVID Symptom Study may not be representative of the broader population. App users must be over the age of 18 years, and app use requires daily access to a smartphone as well as English language skills. The vast majority (75%) of the first 1.6 million users in the UK were female which may bias study findings, especially as male sex is associated with COVID-19 disease severity. The app does not currently meet accessibility standards for those with limited sight.

Value added

This app demonstrates proof-of-concept for large scale real-time mobile data collection on symptoms, testing, and mobility data of potential cases that can be used to predict potential outbreaks.