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Our take —

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

Preprint Review

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

Study design

Ecological; Modeling/Simulation

Study population and setting

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

Summary of Main Findings

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

Study Strengths

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

Limitations

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

Value added

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

Our take —

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 —

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.

Our take —

This study used comprehensive data from China on population mobility patterns and SARS-CoV-2 case detection to estimate the individual and combined impact of non-pharmaceutical interventions (NPIs) on SARS-CoV-2 spread. Results showed that rapid detection and isolation of cases through contact tracing had the greatest impact on averting cases, but social distancing and contact restrictions had a similar magnitude of effect. The combined impact of all interventions resulted in the most infections averted; travel restrictions were implemented too late to substantially affect the overall case counts. The study shows that even a week’s difference in the timing of NPI implementation can have a substantial impact on the trajectory of an epidemic, highlighting the need to act early and decisively when epidemics emerge. The NPIs assessed here represent rough aggregates of individual measures, and some of their components may not be replicable outside China.

Study design

Modeling/Simulation

Study population and setting

This study of SARS-CoV-2 spread within mainland China from December 1, 2019 to April 30, 2020 used mobile phone (i.e., smartphone) data on inter-city and inner-city population movement from three sources: aggregated inflow and outflow data from 340 cities in China, a historical city-specific relative movement matrix from 2014-2015, and county-level daily population movement data used to estimate within-city travel. Additionally, the interval between illness onset to case confirmation was estimated at the county level. The authors used these data different data sources to assess impact of three groups of non-pharmaceutical interventions (NPIs): 1) prohibition of travel between cities to prevent intercity viral spread, 2) early identification and isolation of cases through contact tracing, and 3) contact restrictions and social distancing measures. A susceptible-exposed-infection-removed (SEIR) model was used to simulate viral spread between and within cities in China, accounting for observed changes in contact rates due to travel restrictions and social distancing (estimated from mobile phone data) and reductions in the infectious period due to contact tracing (estimated from data on delay from illness onset to case detection). The impacts of individual NPIs and their timing were assessed by simulating different scenarios.

Summary of Main Findings

Joint implementation of all three classes of NPIs had the largest estimated effect in terms of case counts. Of the three types of NPIs assessed, early detection and isolation of cases had the greatest individual impact on the reduction in total number of cases. Inter-city travel bans were implemented after the virus had already spread outside of Hubei; these had the smallest impact on the case count of the three classes of NPIs. Simulations showed substantial impacts on case counts by varying the timing of NPI implementation in either direction; even a single week’s delay in implementation would have increased the number of new infections by threefold.

Study Strengths

This study used comprehensive mobile data on population movements (>7 billion position requests per day) at the city level to assess changes in contact patterns following travel restrictions between provinces and the implementation of social distancing measures.

Limitations

In part because of the near-simultaneous implementation of NPI measures in China, the interventions assessed are broad aggregates of multiple individual interventions: for example, “contact restrictions and social distancing” comprise many distinct measures that include school closures, bans on gatherings, etc. Similarly, case detection and isolation comprise a suite of linked public health measures, some of which may be replicable outside China and some which may not. The impact of travel restrictions and social distancing may be different outside of China. The study assumes once cases are detected, they are effectively isolated with no subsequent onward transmission; thus, the study may overestimate the impact of case detection and isolation. The estimated number of cases implies that China detected over half of all cases of SARS-CoV-2, which may considerably underestimate the size of the epidemic.

Value added

This is one of the first studies to disaggregate the impact of individual NPIs and to compare their individual versus combined effectiveness.

Our take —

Findings indicate that a large proportion of COVID-19 infections were undocumented (meaning not identified or reported) prior to the implementation of travel restrictions in China on January 23, and a large proportion of the total force of infection was mediated through these undocumented infections. These findings explain the rapid geographic spread of SARS-CoV2 and indicate containment of the virus will be particularly challenging. Reporting inaccuracies and changes in care-seeking behavior add uncertainty to these estimates.

Study design

Modeling/Simulation

Study population and setting

Authors developed a mathematical model that simulates the spatiotemporal dynamics of infections among 375 Chinese cities during the early stage of the epidemic (January 10–23, 2020) and after travel restrictions were implemented (January 24–February 8, 2020). The model divides infections into two classes: 1) documented infected individuals with symptoms severe enough to be confirmed (i.e., observed), and 2) undocumented infected individuals (i.e., not observed).

Summary of Main Findings

The best-fitting model estimated 13,118 total new COVID-19 infections (both observed and unobserved) occurred during January 10–23, 2020 in Wuhan City. An estimated eighty-six percent of people infected were infected by undocumented cases. Nationwide, 16,829 total new COVID-19 infections occurred during January 10–23, 86% of which were infected by undocumented cases. After travel restrictions were put into place, the model estimated that 65% of all infections were documented, which is up from 14% prior to travel restrictions.

Study Strengths

In a method similar to performing a “positive control” in a laboratory experiment, the authors verified the model’s ability to recuperate parameters related to unidentified infections using synthetic data prior to applying their techniques to real data.

Limitations

Uncertainty around the estimates exist due to changes in travel restrictions, control measures, reporting inaccuracies, and changes in care-seeking. The ability of a system to identify all infections are likely impacted by differences in control measures, viral surveillance and testing, and case definition and reporting, and these findings may not be applicable to countries with different control, surveillance, and reporting practices.

Value added

Study estimates prevalence and contagiousness of unidentified infections before and after implementation of travel restrictions in China.

Our take —

As a result of social distancing measures, total interpersonal contacts dropped significantly in China, and mixing among school-aged children was almost entirely eliminated. The study estimates that school closures by themselves would reduce peak incidence by a considerable amount, though they would not be sufficient to completely halt transmission. In contrast to a previous study, significant differences in susceptibility to infection by age were observed (younger individuals appeared to have lower susceptibility), though these results come from a sample of index cases that may not represent the age structure and symptom profile of all SARS-CoV-2 infections.

Preprint Review

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

Study design

Cross-sectional; Ecological; Modeling/Simulation

Study population and setting

This study examined contacts reported by 636 participants in Wuhan, China, and 557 participants in Shanghai, China. The surveys were conducted from February 1 to 10, 2020, and participants were asked to report contacts on two days: a typical weekday in the pre-epidemic period of December 24 to 30, 2019, and then on the day before the survey was conducted. A contact was defined as either direct physical contact or a two-way, in-person conversation of three or more words. The authors used the survey data to estimate changes in age-mixing contact patterns. In a separate analysis of age-specific infection susceptibility, the authors used data from the Hunan CDC field contact tracing and testing of 136 index cases in Hunan Province, China and 7,375 of their contacts, identified between January 15 and March 1, 2020. Close contacts of confirmed cases were involuntarily isolated and tested for SARS-CoV-2; authors estimated odds ratios of infection for different age strata. Finally, the authors used estimated contact patterns and age-specific susceptibility in an SIR model to 1) estimate how differences in age-mixing patterns have affected SARS-CoV-2 transmission, and 2) how school closures and social distancing would be likely to affect future transmission.

Summary of Main Findings

Daily contacts dropped from 14.6 to 2.0 in Wuhan and from 18.8 to 2.3 in Shanghai. The highest density of contacts in the baseline period was observed among school-aged children; in the outbreak period, this mixing almost entirely disappeared, leaving only within-household mixing. Estimated susceptibility to SARS-CoV-2 infection increased with age: relative to the reference category of ages 15-64, the odds ratio (OR) of infection for children under 15 was 0.34 (95% CI: 0.24, 0.49); the OR for adults 65 and older was 1.47 (95% CI: 1.12, 1.92). Alterations in mixing patterns were estimated to decrease the reproduction number (R0) considerably. Social distancing measures (the broad aggregate of all measures implemented in China) were sufficient by themselves to extinguish the epidemic. Simulating pre-emptive school closures without any other intervention reduced peak incidence by 42-64%, depending on assumptions about the degree to which school-aged contacts were diminished. School closures were not sufficient to extinguish the epidemic by themselves, however.

Study Strengths

Age-specific contact matrices were estimated from surveys, and age-specific susceptibility to infection was estimated from detailed contact tracing data.

Limitations

Responses to contact surveys may be limited by recall bias, particularly for the pre-epidemic period. Respondents may have under-reported contacts in the epidemic period due to social pressure. If the age profile of the index cases and their transmission chains is not representative of the population of all individuals infected with SARS-CoV-2 (e.g., because younger individuals may experience less severe symptoms or are more commonly asymptomatic), age-specific susceptibility estimates may be biased. Estimates are derived from a small number (136) of index cases and their contacts in one geographic area. As with most studies of NPIs in China, it is difficult to isolate the effects of individual interventions since multiple measures were implemented nearly simultaneously.

Value added

This is one of the few studies to offer estimates of age-specific susceptibility to SARS-CoV-2 infection, and to estimate effects of school closures on the reproductive number.

Our take —

This study evaluated the effect of the Wuhan travel ban on the spread of COVID-19 within Mainland China and internationally. Using airport closures and airline suspensions, results from this model suggest that reducing long-range travel from Wuhan by up to 90% only delayed the epidemic in Mainland China by less than two weeks. Similarly, reductions in international cases imported from Mainland China were only maintained for a few weeks before picking back up. Overall, this study found that without other interventions aimed at reducing transmissibility (such as early case detection and isolation) implemented in concert with travel restrictions, domestic and international case reductions are modest and unsustainable.

Study design

Modeling/Simulation

Study population and setting

Using publicly available, de-identified population mobility data and data on disease importation, study authors modeled the domestic (within-China) and international spread of COVID-19. Authors also estimated the effects of the travel ban implemented in Wuhan (airport shutdowns beginning on January 23, 2020), mobility restrictions in Mainland China, and other travel restrictions adopted by other countries in early February 2020.

Summary of Main Findings

The model showed a reduction of 10% in cumulative cases within Mainland China by January 31, 2020 as a result of the Wuhan travel ban, relative to the counterfactual scenario of no restrictions. Authors assumed a doubling time (the period required for the number of cases in the epidemic to double) of 4-5 days, so this reduction corresponds to a delay in the epidemic trajectory of only 1-6 days. Furthermore, using WHO situational reports, authors estimated that only 1 in 4 infections are detected and confirmed in Mainland China. Authors estimated that 86% of international cases originated from Wuhan before the ban, and immediately after the ban, cases imported from Mainland China to other countries were reduced by 77%. However, this reduction was not sustained, and internationally imported cases rose again in the following weeks (mainly from Shanghai, Beijing, and Shenzhen). Finally, authors estimated case reduction through various combinations of travel restrictions and other interventions aimed at reducing transmissibility. Results indicate that even 90% travel reductions could at best delay the epidemic by no more than two weeks, without significant reductions in transmissibility from other measures.

Study Strengths

This study estimated spread of COVID-19 using real-world mobility data. Authors independently validated the model by comparing model projections of cumulative number of cases across Mainland China with WHO situational reports; model projections were highly correlated with observed data.

Limitations

Information used by the model regarding transmission (e.g., generation time– the time between source and recipient infections) are based on early results of the COVID-19 outbreak and epidemiology of SARS-CoV-1 and MERS; these parameters have changed as knowledge of SARS-CoV-2 has evolved. The models assumed homogeneous susceptibility and contact patterns by age, which may in fact vary considerably. Containment measures implemented prior to travel restrictions (e.g., body temperature screenings for departure in Wuhan International Airport) were not included in the model. Authors assumed long-term enforcement of travel restrictions in Wuhan through June 2020, which as of April 2020, is no longer applicable.

Value added

Effective national and international public health response planning depends on robust assessments of the effects of travel restrictions and other interventions. Results from this study indicate that the Wuhan travel ban delayed the spread of SARS-CoV-2, but was of modest utility as the epidemic continued, suggesting ongoing responses should focus on mitigation rather than containment.

Our take —

Authors estimated that disruptions to HIV services from the COVID-19 epidemic could result in 38,000 excess HIV-related deaths over a five-year period in South Africa. In some scenarios, excess HIV-related deaths exceeded those saved from social distancing measures. Authors assumed no increased risk of COVID-19 among persons living with HIV, and did not include increased transmission risk due to service interruptions or behavior change due to social distancing in the models.

Study design

Modeling/Simulation

Study population and setting

Using three potential scenarios of disruption to HIV services in South Africa due to the COVID-19 pandemic, authors modeled the number of excess HIV-attributable deaths that could occur between 2020 and 2024 as a result of such interruptions. These scenarios were “managed pause” (least severe; expansion of services paused, but current services maintained), “managed disruption” (disruptions, such as reductions to viral load testing, occur but are managed to mitigate worst impacts), and “interruption of supply” (most severe; supply of key medicines interrupted and a proportion of persons living with HIV [PLWH] are forced off antiretroviral treatment [ART]). Scenarios were cumulative, and as they increased in severity, also incorporated all components from less severe scenarios. Authors compared modeled excess deaths to a baseline model in which no interruptions occurred and coverage would have been maintained as otherwise anticipated. In the first simulation, authors assumed disruptions would begin in mid-April 2020 and last for three months. Authors also compared the effect of interruptions to HIV programs to the estimated direct effects of COVID-19 epidemic in South Africa. Different mortality risks for PLWH who are forced off ART, the proportion of PLWH forced off ART, and duration of interruptions (i.e., months) were used for sensitivity analyses and estimate excess mortality in differing scenarios.

Summary of Main Findings

Model results suggest that excess HIV-related deaths in the “managed pause” and “managed disruption” scenarios are less than 1% in the first year. Conversely, in the “interruption of supply” scenario, the model estimates an excess of 23,000 HIV-related deaths in the first year, which represents more than a 30% increase compared to the 71,000 estimated HIV-related deaths in 2018. Over the five-year period, assuming a three-month interruption, results estimated that total HIV-related deaths will be 38,000, which is less than the number of estimated COVID-19 deaths. Assuming the lowest (0.26%) mortality risk per month for PLWH who are forced to drop off ART, in some situations, the excess number of deaths was estimated to exceed the number of COVID-19 deaths prevented through mitigation efforts such as social distancing. Compared to direct effects of COVID-19, excess deaths from HIV are distributed throughout the 2020-2024 time period (whereas COVID-19 deaths all occur in 2020-2021).

Study Strengths

Authors repeated multiple model iterations using different combinations of scenarios and parameters in order to minimize uncertainty. Given the high HIV burden in South Africa, baseline estimates used for HIV-related deaths are likely robust.

Limitations

Authors did not model interactions between COVID-19 and HIV, and assume that PLWH have the same risk of acquiring or dying from COVID-19 than persons without HIV. Risks for PLWH is currently unknown, and this may have important implications for the model results. Interruptions to services and epidemic mitigation strategies may have important implications for transmission risk. Potential increases in mother-to-child transmission, new partner acquisition, and drug resistance to ART regimens may contribute to excess HIV deaths in the long-term, but were not included in the model. Finally, authors note that the risk of death is likely to increase with accrued time off of ART, but do not include this increase in the model. Limitations in the results also arise from uncertainties regarding the scale of the COVID-19 epidemic in South Africa and the extent to which HIV programs will actually be interrupted.

Value added

The maintenance of primary healthcare programs during an acute healthcare crisis is critical to mitigating overall burden. This study estimates, under varying levels of intensity, how interruptions to primary healthcare services (HIV) can worsen the overall impact of the COVID-19 pandemic, and may be useful for planners and decision-makers regarding mitigation efforts and strategies to minimize interruptions to non-COVID-19 healthcare.