Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate.

Abstract: State and local governments imposed social distancing measures in March and April of 2020 to contain the spread of novel coronavirus disease 2019 (COVID-19). These included large event bans, school closures, closures of entertainment venues, gyms, bars, and restaurant dining areas, and shelter-in-place orders (SIPOs). We evaluated the impact of these measures on the growth rate of confirmed COVID-19 cases across US counties between March 1, 2020 and April 27, 2020. An event-study design allowed each policy's impact on COVID-19 case growth to evolve over time. Adoption of government-imposed social distancing measures reduced the daily growth rate by 5.4 percentage points after 1-5 days, 6.8 after 6-10 days, 8.2 after 11-15 days, and 9.1 after 16-20 days. Holding the amount of voluntary social distancing constant, these results imply 10 times greater spread by April 27 without SIPOs (10 million cases) and more than 35 times greater spread without any of the four measures (35 million). Our paper illustrates the potential danger of exponential spread in the absence of interventions, providing relevant information to strategies for restarting economic activity. [Editor's Note: This Fast Track Ahead Of Print article is the accepted version of the peer-reviewed manuscript. The final edited version will appear in an upcoming issue of Health Affairs.].


Social distancing, population density, and spread of COVID-19 in England: a longitudinal study.

Abstract: BACKGROUND The UK government introduced social distancing measures between 16-22 March 2020, aiming to slow down transmission of COVID-19. AIM To explore the spreading of COVID-19 in relation to population density after the introduction of social distancing measures. DESIGN & SETTING Longitudinal design with 5-weekly COVID-19 incidence rates per 100 000 people for 149 English Upper Tier Local Authorities (UTLAs), between 16 March and 19 April 2020. METHOD Multivariable multilevel model to analyse weekly incidence rates per 100 000 people; time was level-1 unit and UTLA level-2 unit. Population density was divided into quartiles. The model included an interaction between week and population density. Potential confounders were percentage aged ≥65, percentage non-white British, and percentage in two highest classes of the National Statistics Socioeconomic Classification. Co-variates were male life expectancy at birth, and COVID-19 prevalence rate per 100 000 people on March 15. Confounders and co-variates were standardised around the mean. RESULTS Incidence rates per 100 000 people peaked in the week of March 30-April 5, showing higher adjusted incidence rate per 100 000 people (46.2; 95% confidence interval [CI] = 40.6 to 51.8) in most densely populated ULTAs (quartile 4) than in less densely populated ULTAs (quartile 1: 33.3, 95% CI = 27.4 to 37.2; quartile 2: 35.9, 95% CI = 31.6 to 40.1). Thereafter, incidence rate dropped in the most densely populated ULTAs resulting in rate of 22.4 (95% CI = 16.9 to 28.0) in the week of April 13-19; this was lower than in quartiles 1, 2, and 3, respectively 31.4 (95% CI = 26.5 to 36.3), 34.2 (95% CI = 29.9 to 38.5), and 43.2 (95% CI = 39.0 to 47.4). CONCLUSION After the introduction of social distancing measures, the incidence rates per 100 000 people dropped stronger in most densely populated ULTAs.


Evaluating the Effectiveness of Social Distancing Interventions to Delay or Flatten the Epidemic Curve of Coronavirus Disease.

Abstract: By April 2, 2020, >1 million persons worldwide were infected with severe acute respiratory syndrome coronavirus 2. We used a mathematical model to investigate the effectiveness of social distancing interventions in a mid-sized city. Interventions reduced contacts of adults >60 years of age, adults 20-59 years of age, and children <19 years of age for 6 weeks. Our results suggest interventions started earlier in the epidemic delay the epidemic curve and interventions started later flatten the epidemic curve. We noted that, while social distancing interventions were in place, 20% of new cases and most hospitalizations and deaths were averted, even with modest reductions in contact among adults. However, when interventions ended, the epidemic rebounded. Our models suggest that social distancing can provide crucial time to increase healthcare capacity but must occur in conjunction with testing and contact tracing of all suspected cases to mitigate virus transmission.


Effects of Proactive Social Distancing on COVID-19 Outbreaks in 58 Cities, China.

Abstract: Cities across China implemented stringent social distancing measures in early 2020 to curb coronavirus disease outbreaks. We estimated the speed with which these measures contained transmission in cities. A 1-day delay in implementing social distancing resulted in a containment delay of 2.41 (95% CI 0.97-3.86) days.


Vertical social distancing policy is ineffective to contain the COVID-19 pandemic.

Abstract: Considering numerical simulations, this study shows that the so-called vertical social distancing health policy is ineffective to contain the COVID-19 pandemic. We present the SEIR-Net model, for a network of social group interactions, as a development of the classic mathematical model of SEIR epidemics (Susceptible-Exposed-Infected (symptomatic and asymptomatic)-Removed). In the SEIR-Net model, we can simulate social contacts between groups divided by age groups and analyze different strategies of social distancing. In the vertical distancing policy, only older people are distanced, whereas in the horizontal distancing policy all age groups adhere to social distancing. These two scenarios are compared to a control scenario in which no intervention is made to distance people. The vertical distancing scenario is almost as bad as the control, both in terms of people infected and in the acceleration of cases. On the other hand, horizontal distancing, if applied with the same intensity in all age groups, significantly reduces the total infected people "flattening the disease growth curve". Our analysis considers the city of Belo Horizonte, Minas Gerais State, Brazil, but similar conclusions apply to other cities as well. Code implementation of the model in R-language is provided in the supplementary material.


Estimates of regional infectivity of COVID-19 in the United Kingdom following imposition of social distancing measures

Abstract: We describe regional variation in the reproduction number of SARS-CoV-2 infections observed using publicly reported data in the UK, with a view to understanding both if there are clear hot spots in viral spread in the country, or if there are any clear spatial patterns. We estimate that the viral replication number remains slightly above 1 overall but that its trend is to decrease, based on case data up to the 8 April. This suggests the peak of the first wave of COVID-19 patients is imminent. We find that there is significant regional variation in different regions of the UK and that this is changing over time. Within England currently the reproductive ratio is lowest in the Midlands (1.11 95% CI 1.07; 1.14), and highest in the North East of England (1.38 95% CI 1.33-1.42). It remains unclear whether the overall reduction in the reproductive number is a result of social distancing measures, due to the long and variable time delays between infection and positive detection of cases. As we move forwards, if we are to prevent further outbreaks, it is critical that we can both reduce the time taken for detection and improve our ability to predict the regional spread of outbreaks


Sustaining Social Distancing Policies to Prevent a Dangerous Second Peak of COVID-19 Outbreak

Abstract: Governments around the world have enacted strict social distancing policies in order to slow the spread of COVID-19. The next step is figuring out when to relax these restrictions and to what degree. Our results predict potentially disastrous implications of ending these policies too soon, based on projections made from a Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model. Even when infection rates appear to be slowing down or decreasing, prematurely returning to "business as usual" produces a severe second peak far worse than the first. Furthermore, such a second peak is made more likely when very severe restrictions are initially enacted. Only an appropriately measured and committed set of restrictions can appropriately control COVID-19 outbreak levels.


Are we #stayinghome to Flatten the Curve?

Abstract: The recent spread of COVID-19 across the U.S. led to concerted efforts by states to ``flatten the curve" through the adoption of stay-at-home mandates that encourage individuals to reduce travel and maintain social distance and indeed using data on travel activity we find that residents start reducing mobility early in most states. Combining data on changes in travel activity with COVID-19 health outcomes and variation in state policy adoption, we characterize the direct impact of stay-at-home mandates on mobility and social distancing and link these behavioral changes to health benefits. We find evidence of dramatic declines in mobility nationwide prior to the adoption of statewide mandates. Despite these early reductions, we find that statewide stay-at-home policies induced "mandate effects" of between 4.1 and 5.9 percentage point declines relative to pre-COVID-19 levels for the first four states to introduce such policies. These effects persist when considering all states' mandates and alternate estimation strategies that account for states' differences in travel behavior prior to policy adoption. Using previous changes in mobility, we find significant effects on current mortality, with 1% reductions in visits to non-essential businesses weeks prior being associated with 9.2 fewer deaths per 100 million per day, corresponding with over 74,000 lives saved nationwide and resulting economic benefits between $249-$745 billion for observed behavioral changes in March and April. Observed reductions in mobility indeed contribute to flattening the curve and reduce the strain on the medical system during those two months. Our findings provide evidence that statewide stay-at-home ordinances induce additional social distancing, and ultimately attenuate the negative health consequences of COVID-19, revealing themselves as important policy tools in the fight against pandemic. Further, substantial reductions in mobility prior to state-level policies convey important policy implications.


Public health interventions in India slowed the spread of COVID-19 epidemic dynamics

Abstract: Background The government of India implemented social distancing interventions to contain the COVID-19 epidemic. However, effects on epidemic dynamics are yet to be understood. Methods Rates of laboratory-confirmed COVID-19 infections per day and effective reproduction number (Rt) were estimated for 4 periods (Pre-lockdown and Lockdown Phases 1 to 3) according to nationally implemented phased interventions. Adoption of these interventions was estimated using Google mobility data. Estimates at the national level and for 12 Indian states most affected by COVID-19 are presented. Findings Daily case rates ranged from 0.03 to 30.05/10 million people across 4 discrete periods in India. From May 4-17, 2020, the National Capital Territory (NCT) of Delhi had the highest case rate (222/10 million people/day), whereas Kerala had the lowest (2.18/10 million/day). Average Rt was 1.99 (95% CI 1.93-2.06) for India; it ranged from 1.38 to 2.78, decreasing over time. Median mobility in India decreased in all contact domains, with the lowest being 21% in retail/recreation (95% CI 13-46%), except home which increased to 129% (95% CI 117-132%) compared to the 100% baseline value. Interpretation The Indian government imposed strict contact mitigation, followed by a phased relaxation, which slowed the spread of COVID-19 epidemic progression in India. The identified daily COVID-19 case rates and Rt will aid national and state governments in formulating ongoing COVID-19 containment plans. Furthermore, these findings may inform COVID-19 public health policy in developing countries with similar settings to India. Funding Non-funded.


Impact of social distancing measures for preventing coronavirus disease 2019 : A systematic review and meta-analysis protocol

Abstract: Abstract Introduction: Social distancing measures (SDMs) protect public health from the outbreak of coronavirus disease 2019 (COVID-19). However, the impact of SDMs has been inconsistent and unclear. This study aims to assess the effects of SDMs (e.g. isolation, quarantine) for reducing the transmission of COVID-19. Methods and analysis: We will conduct a systematic review meta-analysis research of both randomised controlled trials and non-randomised controlled trials. We will search MEDLINE, EMBASE, Allied & Complementary Medicine, COVID-19 Research and WHO database on COVID-19 for primary studies assessing the enablers and barriers associated with SDMs, and will be reported in accordance with PRISMA statement. The PRISMA-P checklist will be used while preparing this protocol. We will use Joanna Briggs Institute guidelines (JBI Critical Appraisal Checklists) to assess the methodological qualities and synthesised performing thematic analysis. Two reviewers will independently screen the papers and extracted data. If sufficient data are available, the random-effects model for meta-analysis will be performed to measure the effect size of SDMs or the strengths of relationships. To assess the heterogeneity of effects, I2 together with the observed effects (Q-value, with degrees of freedom) will be used to provide the true effects in the analysis. Ethics and dissemination: Ethics approval and consent will not be required for this systematic review of the literature as it does not involve human participation. We will be able to disseminate the study findings using the following strategies: we will be publishing at least one paper in peer-reviewed journals, and an abstract will be presented at suitable national/international conferences or workshops. We will also share important information with public health authorities as well as with the World Health Organization.


Estimating the effect of physical distancing on the COVID-19 pandemic using an urban mobility index

Abstract: Governments around the world are implementing population-wide physical distancing measures in an effort to control transmission of COVID-19, but metrics to evaluate their effectiveness are not readily available. We used a publicly available mobility index based on the relative frequency of trips planned in a popular transit application to evaluate the effect of physical distancing on infection growth rates and reproductive number in 34 states and countries. We found that a 10% decrease in relative mobility in the 2nd week of March was associated with a 11.8% relative decrease (exp(β) = 0.882; 95% CI: 0.822, 0.946) in the average daily growth rate in the fourth week of March and a change in the instantaneous reproductive number of -0.054 (95% CI: 0.097, -0.011) in the same period. Our analysis demonstrates that decreases in urban mobility were predictive of declines in epidemic growth at national or sub-national scales. Mobility metrics offer an appealing method to calibrate population-level physical distancing policy and implementation.


Full lockdown policies in Western Europe countries have no evident impacts on the COVID-19 epidemic.

Abstract: This phenomenological study assesses the impacts of full lockdown strategies applied in Italy, France, Spain and United Kingdom, on the slowdown of the 2020 COVID-19 outbreak. Comparing the trajectory of the epidemic before and after the lockdown, we find no evidence of any discontinuity in the growth rate, doubling time, and reproduction number trends. Extrapolating pre-lockdown growth rate trends, we provide estimates of the death toll in the absence of any lockdown policies, and show that these strategies might not have saved any life in western Europe. We also show that neighboring countries applying less restrictive social distancing measures (as opposed to police-enforced home containment) experience a very similar time evolution of the epidemic.


Orthogonal Functions for Evaluating Social Distancing Impact on CoVID-19 Spread

Abstract: Early CoVID-19 growth often obeys: N{t}=N/Iexp[+K/ot], with K/o=[(ln2)/(t/dbl)], where t/dbl is the pandemic doubling time, prior to society-wide Social Distancing. Previously, we modeled Social Distancing with t/dbl as a linear function of time, where N[t]=1exp[+K/A t/(1+ gamma/o t)] is used here. Additional parameters besides {K/o,gamma/o} are needed to better model different rho[t]=dN[t]/dt shapes. Thus, a new Orthogonal Function Model [OFM] is developed here using these orthogonal function series: N(Z) = sum[m=0,M/F] g/m L/m(Z) exp[-Z] , R(Z) = sum[m=0,M/F] c/m L/m(Z) exp[-Z] , where N(Z) and Z[t] form an implicit N[t]=N(Z[t]) function, giving: G/o = [K/A / gamma/o ] , Z[t] = +[ G/o / (1+ gamma/ot) ] , rho[t] = [ gamma/o / G/o ] (Z^2) R(Z) , with L/m(Z) being the Laguerre Polynomials. At large M/F values, nearly arbitrary functions for N[t] and rho[t]=dN[t]/dt can be accommodated. How to determine {K/A, gamma/o} and the {g/m; m=(0,+M/F)} constants from any given N(Z) dataset is derived, with rho[t] set by: c/(M/F - k) = sum[m=0,k] g/m . The bing.com USA CoVID-19 data was analyzed using M/F=(0,1,2) in the OFM. All results agreed to within about 10 percent, showing model robustness. Averaging over all these predictions gives the following overall estimates for the number of USA CoVID-19 cases at the pandemic end: = 5,009,677 (+/-) 269,450 (data to 5/3/20), and = 4,422,803 (+/-) 162,580 (data to 6/7/20), which compares the pre- and post-early May bing.com revisions. The CoVID-19 pandemic in Italy was examined next. The M/F=2 limit was inadequate to model the Italy rho[t] pandemic tail. Thus, regions with a quick CoVID-19 pandemic shutoff may have additional Social Distancing factors operating, beyond what can be easily modeled by just progressively lengthening pandemic doubling times (with 13 Figures).


Enacting national social distancing policies corresponds with dramatic reduction in COVID19 infection rates

Abstract: The outbreak the SARS-CoV-2 (CoV-2) virus has resulted in over 2.5 million cases of COVID19, greatly stressing global healthcare infrastructure. Lacking medical prophylactic measures to combat disease spread, many nations have adopted social distancing policies in order to mitigate transmission of CoV-2. While mathematical models have suggested the efficacy of social distancing to curb the spread of CoV-2, there is a lack of systematic studies to quantify the real-world efficacy of these approaches. Here, we quantify the spread rate of COVID19 before and after national social distancing measures were implemented in 26 nations and compare this to the changes in COVID19 spread rate over equivalent time periods in 27 nations that did not enact social distancing policies. We find that social distancing policies significantly reduced the COVID19 spread rate. Using mixed linear regression models we estimate that social distancing policies reduced the spread of COVID19 by 66%. These data suggest that social distancing policies may be a powerful tool to prevent spread of COVID19 in real-world scenarios.


Estimating the impact of physical distancing measures in containing COVID-19: an empirical analysis

Abstract: Background Epidemic modelling studies predict that physical distancing is critical in containing COVID-19. However, few empirical studies have validated this finding. Our study evaluates the effectiveness of different physical distancing measures in controlling viral transmission. Methods We identified three distinct physical distancing measures with varying intensity and implemented at different times - international travel controls, restrictions on mass gatherings, and lockdown-type measures - based on the Oxford COVID-19 Government Response Tracker. We also estimated the time-varying reproduction number (Rt) for 142 countries and tracked Rt temporally for two weeks following the 100th reported case in each country. We regressed Rt on the physical distancing measures and other control variables (income, population density, age structure, and temperature) and performed several robustness checks to validate our findings. Findings Complete travel bans and all forms of lockdown-type measures have been effective in reducing average Rt over the 14 days following the 100th case. Recommended stay-at-home advisories and partial lockdowns are as effective as complete lockdowns in outbreak control. However, these measures have to be implemented early to be effective. Lockdown-type measures should be instituted two weeks before the 100th case and travel bans about a week before detection of the first case. Interpretation A combination of physical distancing measures, if implemented early, can be effective in containing COVID-19 - tight border controls to limit importation of cases, encouraging physical distancing, moderately stringent measures such as working from home, and a full lockdown in the case of a probable uncontrolled outbreak.


Modelling the impact of control measures against the COVID-19 pandemic in Viet Nam

Abstract: Objectives: Health care system of many countries are facing a surging burden of COVID-19. Although Vietnam has successfully controlled the COVID-19 pandemic to date, there is a sign of initial community transmission. An estimate of possible scenarios to prepare health resources in the future is needed. We used modelling methods to estimate impacts of mitigation measures on the COVID-19 pandemic in Vietnam. Methods: SEIR model built in the COVIDSIM1.1 tool was adopted using available data for estimation. The herd immunization scenario was with no intervention implemented. Other scenarios consisted of isolation and social distancing at different levels (25%, 50%, 75% and 10%, 20%, 30%, respectively). Outcomes include epidemic apex, daily new and cumulative cases, deaths, hospitalized patients and ICU beds needed. Results: By April 8, 2020, there would be 465 infected cases with COVID-19 in Viet Nam, of those 50% were detected. Cumulatively, there would be 1,400 cases and 30 deaths by end of 2020, if 75% of cases was detected and isolated, and 30% of social distancing could be maintained. The most effective intervention scenario is the detection and isolation of 75% infected cases and reduction of 10% social contacts. This will require an expansion of testing capacity at health facilities and in the community, posing a challenge to identify high-risk groups to prioritized testing. Conclusions: In a localized epidemic setting, the expansion of testing should be the key measure to control the epidemic. Social distancing plays a significant role to prevent further transmission to the community.


Instantaneous R calculation for COVID-19 epidemic in Brazil

Abstract: COVID-19 pandemic represents a major challenge to health systems of all countries. Brazilian regions habe been showing marked di[ff]erences in onset and number of cases. Health authorities instituted widespread social distancing and lockdown measures but their implementation has also varied. The authors used data on con[fi]rmed cases of COVID-19 in Brazil and its states to calculate the value of instantaneous reproduction number at these regions. The results show a reduction of instantaneous reproduction number with time, probably due to social distancing measures put in place in the last weeks by brazillian authorities. It seems logical to maintain restrictions to social contact until the epidemic peak has occurred in Brazil.


Modelling the potential impact of social distancing on the COVID-19 epidemic in South Africa

Abstract: The novel coronavirus (COVID-19) pandemic continues to be a global health problem whose impact has been significantly felt in South Africa. Social distancing has been touted as the best form of response in managing a rapid increase in the number of infected cases. In this paper, we present a deterministic model to model the impact of social distancing on the transmission dynamics of COVID-19 in South Africa. The model is fitted to the currently available data on the cumulative number of infected cases and a scenario analysis on different levels of social distancing are presented. The results show a continued rise in the number of cases in the lock down period with the current levels of social distancing albeit at a lower rate. The model shows that the number of cases will rise to above 4000 cases by the end of the lockdown. The model also looks at the impact of relaxing the social distancing measures after the initial announcement of the lock down measures. A relaxation of the social distancing by 2% can result in a 23% rise in the number of cumulative cases while on the other hand increasing the levels of social distancing by 2% would reduce the number of cumulative cases by about 18%. These results have implications on the management and policy direction in the early phases of the epidemic.


SHELTER IN PLACE ORDER CONTAINED COVID-19 GROWTH RATE IN GREECE

Abstract: Background: The Greek authorities implemented the strong social distancing measures within the first few weeks after the first confirmed case of the virus to curtail the COVID-19 growth rate. Objectives: To estimate the effect of the two-stage strong social distancing measures, the closure of all non-essential shopping centers and businesses on March 16 and the shelter in place orders (SIPOs) on March 23 on the COVID-19 growth rate in Greece Methods: We obtained data on COVID-19 cases in Greece from February 26th through May 4th from publicly available sources. An interrupted time-series regression analysis was used to estimate the effect of the measures on the exponential growth of confirmed COVID-19 cases, controlling for the number of daily testing, and weekly fixed-effects. Results: The growth rate of the COVID-19 cases in the pre-policies implementation period was positive as expected (p=0.003). Based on the estimates of the interrupted time-series, our results indicate that the SIPO on March 23 significantly slowed the growth rate of COVID-19 in Greece (p=0.04). However, we did not find evidence on the effectiveness of standalone and partial measures such as the non-essential business closures implemented on March 16 on the COVID-19 spread reduction. Discussion: The combined social distancing measures implemented by the Greek authorities within the first few weeks after the first confirmed case of the virus reduced the COVID-19 growth rate. These findings provide evidence and highlight the effectiveness of these measures to flatten the curve and to slow the spread of the virus.


Effects of social distancing on the spreading of COVID-19 inferred from mobile phone data

Abstract: A better understanding of how the COVID-19 epidemic responds to social distancing efforts is required for the control of future outbreaks and to calibrate partial lock-downs. We present quantitative relationships between key parameters characterizing the COVID-19 epidemiology and social distancing efforts of nine selected European countries. Epidemiological parameters were extracted from the number of daily deaths data, while mitigation efforts are estimated from mobile phone tracking data. The decrease of the basic reproductive number (R0) as well as the duration of the initial exponential expansion phase of the epidemic strongly correlates with the magnitude of mobility reduction. Utilizing these relationships we decipher the relative impact of the timing and the extent of social distancing on the total death burden of the epidemic.


Social Distancing Has Merely Stabilized COVID-19 in the US

Abstract: Social distancing measures, with varying degrees of restriction, have been imposed around the world in order to stem the spread of COVID-19. In this work we analyze the effect of current social distancing measures in the United States. We quantify the reduction in doubling rate, by state, that is associated with social distancing. We find that social distancing is associated with a statistically-significant reduction in the doubling rate for all but three states. At the same time, we do not find significant evidence that social distancing has resulted in a reduction in the number of daily confirmed cases. Instead, social distancing has merely stabilized the spread of the disease. We provide an illustration of our findings for each state, including point estimates of the effective reproduction number, R, both with and without social distancing. We also discuss the policy implications of our findings.


A single holiday was the turning point of the COVID-19 policy of Israel

Abstract: Background: The impact of COVID-19 has been profound, and the public health challenge seem to be the most serious regarding respiratory viruses since the 1918 H1N1 influenza pandemic. In the absence of effective vaccine or biomedical treatment, the basic rules of public health measures have not changed, namely public distancing. Methods: We analyzed epidemiological investigation reports during the first month of the outbreak in Israel. In addition, we present a deterministic compartment model and simulations of several scenarios emphasizing quarantine and isolation policies given their efficiency. Results: We identify an abrupt change from controlled epidemic regime to an exponential growth (R_0= 2.19) in light of the actual policy-makers decisions and public behavior in Israel. Our analysis show that before the abrupt change, the new cases trend was due to returning citizens infected abroad. The abrupt change followed a holiday in which social distancing was clearly inefficient and many public gatherings were held. We further discuss three different modeled scenarios of quarantine efficiency: high-, medium-, and low-efficiency. Conclusions: Israel early lessons show that there is no allowance to compromise with the directive of social distancing. Even before the onset of the pandemic in Israel, fine-tuned but determined early decisions were taken by policy makers to monitor flight arrivals from Covid-19 affected regions and to limit public gatherings. Our analysis show that one particular holiday has shifted the occurrence curve from controlled regime to exponential growth. Therefore, even a short lapse in public responsiveness can have a dramatic effect.


Flattening the curve and the effect of atypical events on mitigation measures in Mexico: a modeling perspective

Abstract: On 23 and 30 March 2020 the Mexican Federal government implemented social distancing measures to mitigate the COVID-19 epidemic. We use a mathematical model to explore atypical transmission events within the confinement period, triggered by the timing and strength of short time perturbations of social distancing. We show that social distancing measures were successful in achieving a significant reduction of the effective contact rate in the early weeks of the intervention. However, "flattening the curve" had an undesirable effect, since the epidemic peak was delayed too far, almost to the government preset day for lifting restrictions (01 June 2020). If the peak indeed occurs in late May or early June, then the events of children's day and mother's day may either generate a later peak (worst case scenario), a long plateau with relatively constant but high incidence (middle case scenario) or the same peak date as in the original baseline epidemic curve, but with a post-peak interval of slower decay.


Analyzing the World-Wide Impact of Public Health Interventions on the Transmission Dynamics of COVID-19

Abstract: We analyze changes in the reproduction number, R, of COVID-19 in response to public health interventions. Our results indicate that public health measures undertaken in China reduced R from 1.5 in January to 0.4 in mid-March 2020. They also suggest, however, the limitations of isolation, quarantine, and large-scale attempts to limit travel. While the world-wide reproduction number briefly dropped below 1 as China implemented extensive public health measures, the introduction of the virus to other nations swiftly led to an increasing world-wide average value of R. In Italy, the nation hardest-hit following China, social distancing measures brought the local value of R down from 3.71 to 2.51. Nonetheless, the value of R in Italy persisted at levels well above 1, allowing for ongoing transmission. By mid-March 2020, as COVID-19 spread in areas without extensive public health interventions in place, the world-wide value of R increased to a level similar to that of late January.


Estimates of the ongoing need for social distancing and control measures post-"lockdown" from trajectories of COVID-19 cases and mortality.

Abstract: By 21st April 2020 COVID_19 had caused more than 2m cases across more than 200 countries. The majority of countries with significant outbreaks introduced social distancing or "lockdown" measures to reduce viral transmission. The key question now in many countries is when, how, and to what extent these measures can be lifted. By fitting regression models to publically available data on daily numbers of newly-confirmed cases and mortality, trajectories, doubling times and reproduction number (R0) were estimated both before and under the control measures. These data ran up to 21st April 2020, and covered 67 countries that had provided sufficient data for modelling. The estimates of R0, before lockdown, based on these data were broadly consistent with those previously published at between 2.0 and 3.7 in the countries with the largest number of cases available for analysis (USA, Italy, Spain, France and UK). There was little evidence to suggest that the restrictions had reduced R far below 1 in many places, with Spain having the most rapid reductions - R0 0.71 (95%CI 0.65-0.78) based on cases and 0.81 (95%CI 0.77-0.85) based on mortality. Intermittent lockdown has been proposed as a means of controlling the outbreak while allowing periods of increase freedom and economic activity. These data suggest that few countries could have even one week per month unrestricted without seeing resurgence of the epidemic. Similarly, restoring 20% of the activity that has been prevented by the lockdowns looks difficult to reconcile with preventing the resurgence of the disease in most countries.


Act early, save lives: managing COVID-19 in Greece

Abstract: Abstract Objectives: To assess the impact of the implemented social distancing interventions (SD) in Greece. Study Design: A dynamic, discrete time, stochastic individual-based model was developed to simulate COVID-19 transmission. Methods: We fit the transmission model to the observed trends in deaths and ICU beds use. Results: If Greece had not implemented the SD measures, the healthcare system would have been overwhelmed between 30 March and 4 of April. Additionally, the SD interventions averted 4360 deaths and prevent the healthcare system from overwhelmed. Conclusions: The fast reflexes of the Greek government limit the burden of the Covid-19 outbreak.


Transmission of SARS-CoV-2 in Georgia, USA, 2020

Abstract: Objective: SARS-CoV-2 has significantly impacted Georgia, USA including two major hotspots, Metro Atlanta and Dougherty County in southwestern Georgia. With government deliberations about relaxing social distancing measures, it is important to understand the trajectory of the epidemic in the state of Georgia. Methods: We collected daily cumulative incidence of confirmed COVID-19 cases in Georgia. We estimated the reproductive number (Re) of the COVID-19 epidemic on April 18 and May 2 by characterizing the initial growth phase of the epidemic using the generalized-growth model. Results: The data presents a sub-exponential growth pattern in the cumulative incidence curves. On April 18, 2020, Re was estimated as 1.20 (95% CI: 1.10, 1.20) for the state of Georgia, 1.10 (95% CI: 1.00, 1.20) for Dougherty County, and 1.20 (95% CI: 1.10, 1.20) for Metro Atlanta. Extending our analysis to May 2, 2020, Re estimates decreased to 1.10 (95% CI: 1.10, 1.10) for the state of Georgia, 1.00 (95% CI: 1.00, 1.10) for Dougherty County, and 1.10 (95% CI: 1.10, 1.10) for Metro Atlanta. Conclusions: Transmission appeared to be decreasing after the implementation of social distancing measures. However, these results should be interpreted with caution when considering relaxing control measures due to low testing rates.


Diminishing Marginal Benefit of Social Distancing in Balancing COVID-19 Medical Demand-to-Supply

Abstract: Social distancing has been adopted as a non-pharmaceutical intervention to prevent the COVID-19 pandemic from overwhelming the medical resources across the United States (US). The catastrophic socio-economic impacts of this intervention could outweigh its benefits if the timing and duration of implementation are left uncontrolled and ill-strategized. Here we investigate the dynamics of social distancing on age-stratified US population and benchmark its effectiveness in reducing the burden on hospital and ICU beds. Our findings highlight the diminishing marginal benefit of social distancing, characterized by a linear decrease in medical demands against an exponentially increasing social distancing duration. We determine an optimal intermittent social-to-no-distancing ratio of 5:1 corresponding to ~80% reduction in healthcare demands; beyond this ratio, benefit of social distancing diminishes to a negligible level. COVID-19 Medical Forecast: https://eece.wustl.edu/chakrabarty-group/covid/


COVID-19 outbreak response: a first assessment of mobility changes in Italy following national lockdown

Abstract: Italy is currently experiencing the largest COVID-19 outbreak in Europe so far, with more than 100,000 confirmed cases. Following the identification of the first infections, on February 21, 2020, national authorities have put in place an increasing number of restrictions aimed at containing the outbreak and delaying the epidemic peak. Since March 12, the whole country is under lockdown. Here we provide the first quantitative assessment of the impact of such measures on the mobility and the spatial proximity of Italians, through the analysis of a large-scale dataset on de-identified, geo-located smartphone users. With respect to pre-outbreak averages, we estimate a reduction of 50% of the total trips between Italian provinces, following the lockdown. In the same week, the average users' radius of gyration has declined by about 50% and the average degree of the users' proximity network has dropped by 47% at national level.


Insufficient social distancing may be related to a future COVID-19 outbreak in Ijui-Brazil: Predictions of further social interventions.

Abstract: The coronavirus disease that initiates in 2019 (COVID-19) has proven to be highly contagious since it became pandemic quickly and nowadays presents higher transmission rates worldwide, including small Brazilian cities, as Ijui. Located in the northwestern of the State of Rio Grande do Sul (RS) with 83,475 inhabitants, Ijui was selected to receive a population-based survey divided into four steps separated by 15 days each that involved 1,750 subjects. Subjects were tested for the presence of antibodies against coronavirus (SARS CoV-2) and answered questions about social distance adherence, daily routine, comorbidities, and sociodemographic characteristics. In parallel, the local government registered the official COVID-19 cases in Ijui. In this study, we demonstrated the levels of social distancing adherence and the beginning of COVID-19 community transmission in Ijui and showed some predictions for cases, hospitalization, and deaths. We concluded that the insufficient social distancing registered in the population-based study might be related to the rapid increase of COVID-19 cases in Ijui. Our study predicts a closer outbreak of community infection of COVID-19, which could be avoided or attenuated if the levels of the social distancing in the population increase in the next weeks.


Evaluating the Efficacy of Stay-At-Home Orders: Does Timing Matter?

Abstract: BACKGROUND: The many economic, psychological, and social consequences of pandemics and social distancing measures create an urgent need to determine the efficacy of non-pharmaceutical interventions (NPIs), and especially those considered most stringent, such as stay-at-home and self-isolation mandates. This study focuses specifically on the efficacy of stay-at-home orders, both nationally and internationally, in the control of COVID-19. METHODS: We conducted an observational analysis from April to May 2020 and included countries and US states with known stay-at-home orders. Our primary exposure was the time between the date of the first reported case of COVID-19 to an implemented stay-at-home mandate for each region. Our primary outcomes were the time from the first reported case to the highest number of daily cases and daily deaths. We conducted simple linear regression analyses, controlling for the case rate of the outbreak. RESULTS: For US states and countries, a larger number of days between the first reported case and stay-at-home mandates was associated with a longer time to reach the peak daily case and death counts. The largest effect was among regions classified as the latest 10% to implement a mandate, which in the US, predicted an extra 35.3 days to the peak number of cases (95 % CI: 18.2, 52.5), and 38.3 days to the peak number of deaths (95 % CI: 23.6, 53.0). CONCLUSIONS: Our study supports the potential beneficial effect of earlier stay-at-home mandates, by shortening the time to peak case and death counts for US states and countries. Regions in which mandates were implemented late experienced a prolonged duration to reaching both peak daily case and death counts.


COVID-19 related social distancing measures and reduction in city mobility

Abstract: In the absence of any pharmacological intervention, one approach to slowing the COVID-19 pandemic is reducing the contact rate in the population through social distancing. Governments the world over have instituted different measures to increase social distancing but information on their effectiveness in reducing mobility is lacking. We analyzed the mobility data from 41 cities to look at the effect of these interventions. The median mobility across cities on March 2, 2020 was 100% (IQR: 94%, 107%), which decreased to a median of 10% (IQR: 7%, 17%) on March 26, 2020. We found that the mobility decreased on average by 3.4% (95%CI: 3.3%, 3.6%) per day from March 2 through March 26. Social distancing measures decreased the mobility by an additional 23% (95%CI: 20%, 27%). Our study provides initial evidence for the reduction in mobility in cities instituting social distancing measures.


SOCRATES: An online tool leveraging a social contact data sharing initiative to assess mitigation strategies for COVID-19

Abstract: Objective: Establishing a social contact data sharing initiative and an interactive tool to assess mitigation strategies for COVID-19. Results: We organized data sharing of published social contact surveys via online repositories and formatting guidelines. We analyzed this social contact data in terms of weighted social contact matrices, next generation matrices, relative incidence and R0. We incorporated location-specific isolation measures (e.g. school closure or telework) and capture their effect on transmission dynamics. All methods have been implemented in an online application based on R Shiny and applied to COVID-19 with age-specific susceptibility and infectiousness. Using our online tool with the available social contact data, we illustrate that social distancing could have a considerable impact on reducing transmission for COVID-19. The effect itself depends on assumptions made about disease-specific characteristics and the choice of intervention(s). Keywords: social contact data, user interface, transmission dynamics, infectious diseases, epidemics, social distancing, behavioral changes, data sharing initiative, open-source, COVID-19


Is the impact of social distancing on coronavirus growth rates effective across different settings? A non-parametric and local regression approach to test and compare the growth rate

Abstract: Epidemiologists use mathematical models to predict epidemic trends, and these results are inherently uncertain when parameters are unknown or changing. In other contexts, such as climate, modellers use multi-model ensembles to inform their decision-making: when forecasts align, modellers can be more certain. This paper looks at a sub-set of alternative epidemiological models that focus on the growth rate, and it cautions against relying on the method proposed in (Pike & Saini, 2020): relying on the data for China to calculate future trajectories is likely to be subject to overfitting, a common problem in financial and economic modelling. This paper finds, surprisingly, that the data for China are double-exponential, not exponential; and that different countries are showing a range of different trajectories. The paper proposes using non-parametric and local regression methods to support epidemiologists and policymakers in assessing the relative effectiveness of social distancing policies. All works contained herein are provided free to use worldwide by the author under CC BY 2.0.


The effect of non-pharmaceutical interventions (NPIs) on the spread of COVID-19 pandemic in Japan: A modeling study

Abstract: Non-pharmaceutical interventions (NPIs) are founded to be effective to delay epidemic spread and to reduce the number of patients. Moderate NPIs took in Japan seemed to have reduced the COVID-19 patients and to lower death rates, thus, effects of those NPIs are worthy of investigation. We used open source data and divided the data into three periods: Jan 22 to Feb 25 (Period I), Feb 26 to Apr 6 (Period II), and Apr 7 to May 14 (Period III). We developed the SIRD model and applied the Monte Carlo Simulation to estimate a combination of optimal results, including the peak of infected cases, the peak date, and R0. For Period I, the estimated peak infected cases were smaller than the observed ones, the peak date was earlier than the observed one, and the R0 was about 4.66. For the other two periods, the estimated cases were more, and the peak dates were earlier than the observed ones. The R0 was 2.50 in Period II, and 1.79 in Period III. NPIs took in Japan might have reduced more than 50% of the daily contacts per people compared to that before COVID-19. Owing to the effects of NPIs, the Japanese society had avoided collapse of medical service. Nevertheless, the capacity of daily RT-PCR may have restricted the reported confirmed cases.


Analysis and Prediction of the COVID-19 outbreak in Pakistan

Abstract: In this study, we estimate the severity of the COVID-19 outbreak in Pakistan prior to and after lockdown restrictions were eased. We also project the epidemic curve considering realistic quarantine, social distancing, and possible medication scenarios. We use a deterministic epidemic model that includes asymptomatic, quarantined, isolated, and medicated population compartments for our analysis. We calculate the basic reproduction number R0 for the pre and post lockdown periods, noting that during this time, no medication was available. The pre-lock down the value of R0 is estimated to be 1.07, and the post lockdown value is estimated to be 1.86. We use this analysis to project the epidemic curve for a variety of lockdown, social distancing, and medication scenarios. We note that if no substantial efforts are made to contain the epidemic, it will peak in mid of September, with the maximum projected active cases being close to 700,000. In a realistic, best-case scenario, we project that the epidemic peaks in early to mid-July with the maximum active cases being around 120000.We note that social distancing measures and medication, if available, will help flatten the curve, however without the reintroduction of further lockdown, it would be very difficult to bring R0 below 1. Our study strongly supports the recent WHO recommendation of reintroducing lockdowns to control the epidemic.


Evaluation of Turkish social distancing measures on the spread of COVID-19

Abstract: The coronavirus disease (COVID-19) affecting across the globe. The government of different countries has adopted various policies to contain this epidemic and the most common were social distancing and lockdown. We use a simple log-linear model with intercept and trend break to evaluate whether the measures are effective preventing/slowing down the spread of the disease in Turkey. We estimate the model parameters from the Johns Hopkins University (2020) epidemic data between 15th March and 16th April 2020. Our analysis revealed that the measures can slow down the outbreak. We can reduce the epidemic size and prolong the time to arrive at the epidemic peak by seriously following the measures suggested by the authorities.


The nexus of travel restriction, air pollution and COVID-19 infection: Investigation from a megacity of the southern China

Abstract: To control and prevent the spread of COVID-19, generalized social distancing measures, such as traffic control and travel restriction acted in China. Previous studies indicated that the traffic conditions had significant influence on the air quality, and which was related to the respiratory diseases. This study aimed to reveal the nexus of travel restriction, air pollution and COVID-19. Shenzhen, one of the top 4 megacities in China was considered as the study area, statistical analysis methods, including linear/nonlinear regression and bivariate correlation was conducted to evaluate the relationship of the traffic and passenger population, travel intensity, NO2, PM10, PM2.5 and the number of COVID-19 confirmed cases. The results suggested that traffic control and travel restriction had a significant correlation with the number of COVID-19 confirmed cases, which shown negative correlation with the traffic intensity of the city, NO2, PM10 and PM2.5 show significant positive correlation with the traffic intensity, traffic control and travel restriction would slow down and prevent the spread of the viruses at the outbreak period. Different study scale might results in different results, thus the research focused on the nexus of traffic control and travel restriction, air pollution and COVID-19 should been enhanced in future, and differentiated epidemic control and prevention measures should be considered according to the different situation of cities as well as countries.


COVID-19 Control Strategies and Intervention Effects in Resource Limited Settings: A Modeling Study

Abstract: Background Many countries with weaker health systems are struggling to put together a coherent strategy against the COVID-19 epidemic. We explored COVID-19 control strategies that could offer the greatest benefit in resource limited settings. Methods Using an age-structured SEIR model, we explored the effects of COVID-19 control interventions--a lockdown, physical distancing measures, and active case finding (testing and isolation, contact tracing and quarantine)-- implemented individually and in combination to control a hypothetical COVID-19 epidemic in Kathmandu (population 2.6 million), Nepal. Results A month-long lockdown that is currently in place in Nepal will delay peak demand for hospital beds by 36 days, as compared to a base scenario of no interventions (peak demand at 108 days (IQR 97-119); a 2 month long lockdown will delay it by 74 days, without any difference in annual mortality, or healthcare demand volume. Year-long physical distancing measures will reduce peak demand to 36% (IQR 23%-46%) and annual morality to 67% (IQR 48%-77%) of base scenario. Following a month long lockdown with ongoing physical distancing measures and an active case finding intervention that detects 5% of the daily infection burden could reduce projected morality and peak demand by more than 99%. Interpretation Limited resources settings are best served by a combination of early and aggressive case finding with ongoing physical distancing measures to control the COVID-19 epidemic. A lockdown may be helpful until combination interventions can be put in place but is unlikely to reduce annual mortality or healthcare demand.


A sub-national analysis of the rate of transmission of COVID-19 in Italy

Abstract: Italy was the first European country to experience sustained local transmission of COVID-19. As of 1st May 2020, the Italian health authorities reported 28,238 deaths nationally. To control the epidemic, the Italian government implemented a suite of non-pharmaceutical interventions (NPIs), including school and university closures, social distancing and full lockdown involving banning of public gatherings and non essential movement. In this report, we model the effect of NPIs on transmission using data on average mobility. We estimate that the average reproduction number (a measure of transmission intensity) is currently below one for all Italian regions, and significantly so for the majority of the regions. Despite the large number of deaths, the proportion of population that has been infected by SARS-CoV-2 (the attack rate) is far from the herd immunity threshold in all Italian regions, with the highest attack rate observed in Lombardy (13.18% [10.66%-16.70%]). Italy is set to relax the currently implemented NPIs from 4th May 2020. Given the control achieved by NPIs, we consider three scenarios for the next 8 weeks: a scenario in which mobility remains the same as during the lockdown, a scenario in which mobility returns to pre-lockdown levels by 20%, and a scenario in which mobility returns to pre-lockdown levels by 40%. The scenarios explored assume that mobility is scaled evenly across all dimensions, that behaviour stays the same as before NPIs were implemented, that no pharmaceutical interventions are introduced, and it does not include transmission reduction from contact tracing, testing and the isolation of confirmed or suspected cases. New interventions, such as enhanced testing and contact tracing are going to be introduced and will likely contribute to reductions in transmission; therefore our estimates should be viewed as pessimistic projections. We find that, in the absence of additional interventions, even a 20% return to pre-lockdown mobility could lead to a resurgence in the number of deaths far greater than experienced in the current wave in several regions. Future increases in the number of deaths will lag behind the increase in transmission intensity and so a second wave will not be immediately apparent from just monitoring of the daily number of deaths. Our results suggest that SARS-CoV-2 transmission as well as mobility should be closely monitored in the next weeks and months. To compensate for the increase in mobility that will occur due to the relaxation of the currently implemented NPIs, adherence to the recommended social distancing measures alongside enhanced community surveillance including swab testing, contact tracing and the early isolation of infections are of paramount importance to reduce the risk of resurgence in transmission.


Impact of control strategies on COVID-19 pandemic and the SIR model based forecasting in Bangladesh.

Abstract: COVID-19 is transmitting worldwide drastically and infected nearly two and half million of people sofar. Till date 2144 cases of COVID-19 is confirmed in Bangladesh till 18th April though the stage-3/4 transmission is not validated yet. To project the final infection numbers in Bangladesh we used the SIR mathematical model. We also tried to demonstrate the impact of control strategies like social distancing on the COVID-19 transmission. Due to large population and socio-economic characteristics, we assumed 60% social distancing and lockdown can be possible. Assuming that, the predicated final size of infections will be 3782558 on the 92th day from the first infections. To estimate the impact of social distancing we assumed eight different scenarios, the predicted results confirmed the positive impact of this type of control strategies suggesting that by strict social distancing and lockdown, COVID-19 infection can be under control and then the infection cases will steadily decrease down to zero.


Modeling the Transmission of Respiratory Infectious Diseases in Mass Transportation Systems

Abstract: Mass transportation is one of the areas that are badly hit by respiratory infectious disease outbreaks due to moderate to high exposure risk to pathogens brought about by the interaction among commuters. Here, we formulate agent-based models that simulate the spread of a respiratory infectious disease in a train wagon in the Manila Light Rail Transit System, and in a 49-seater public utility bus. We consider preventive measures such as implementation of social distancing, and limitation of interaction or movement among the commuters to investigate how these measures will inhibit disease transmission. We also consider the effect of protective gears and practices, crowd density, and prevalence of disease in the community on the possible number of newly-infected individuals. Our simulations show that (i) individuals must have protection with more than 90% effectiveness to inhibit transmission of the disease; (ii) social or physical distancing by more than 1m distance reduces the risk of being infected; (iii) minimizing movement or interaction with other passengers reduces the risk of transmission by 50%; (iv) passenger capacity should be less than 10-50% of the maximum seating capacity to reduce the number of infections depending on the level of imposed social distancing and passenger interaction; (v) vehicles with greater number of occupied seating capacity generate higher number of infections but vehicles with smaller dimensions have faster disease transmissions; and (vi) ideal set-up for a 24-seater train wagon (49-seater bus) is to allow a maximum of 12 (24) passengers, with little to no interaction among passengers, with social distancing of more than 1m distance apart, and each person has a protection with 90% effectiveness as much as possible. These results can aid policy makers in determining optimal strategies to minimize infections while maintaining transportation services during pandemics or disease outbreaks.


Reacting to outbreaks at neighboring localities

Abstract: We study the dynamics of epidemics in a networked metapopulation model. In each subpopulation, representing a locality, disease propagates according to a modified susceptible-exposed-infected-recovered (SEIR) dynamics. We assume that individuals reduce their number of contacts as a function of the weighted sum of cumulative number of cases within the locality and in neighboring localities. The susceptible and exposed (pre-symptomatic and infectious) individuals are allowed to travel between localities undetected. To investigate the combined effects of mobility and contact reduction on disease progression within interconnected localities, we consider a scenario with two localities where disease originates in one and is exported to the neighboring locality via travel of undetected pre-symptomatic individuals. We associate the behavior change at the disease-importing locality due to the outbreak size at the origin with the level of preparedness of the locality. Our results show that restricting mobility is valuable if the importing locality is increasing its level of preparedness with respect to the outbreak size at the origin. Moreover, increased levels of preparedness can yield lower total outbreak size by further reducing the outbreak size at the importing locality, even when the response at the origin is weak. Our results highlight that public health decisions on social distancing at localities with less severe outbreaks should strongly account for potential impact of neighbouring localities with a poor response to the outbreak rather than localities with successful responses.


Evaluating the effectiveness of social distancing interventions against COVID-19

Abstract: SARS-CoV-2 has infected over 140,000 people as of March 14, 2020. We use a mathematical model to investigate the effectiveness of social distancing interventions lasting six weeks in a middle-sized city in the US. We explore four social distancing strategies by reducing the contacts of adults over 60 years old, adults over 60 years old and children, all adults (25, 75 or 95% compliance), and everyone in the population. Our results suggest that social distancing interventions can avert cases by 20% and hospitalizations and deaths by 90% even with modest compliance within adults as long as the intervention is kept in place, but the epidemic is set to rebound once the intervention is lifted. Our models suggest that social distancing interventions will buy crucial time but need to occur in conjunction with testing and contact tracing of all suspected cases to mitigate transmission of SARS-CoV-2.


Optimal Control applied to a SEIR model of 2019-nCoV with social distancing

Abstract: Does the implementation of social distancing measures have merit in controlling the spread of the novel coronavirus? In this study, we develop a mathematical model to explore the effect of social distancing on new disease infections. Mathematical analyses of our model indicate that successful eradication of the disease is strongly dependent on the chosen preventive measure. Numerical computations of the model solution demonstrate that the ability to flatten the curve becomes easier as social distancing is strictly enforced. Based on our model, we also formulate an optimal control problem and solve it using Pontryagin's Maximum Principle and an efficient numerical iterative method. Our numerical results of an optimal 2019-nCoV treatment protocol that yields a minimum disease burden from this disease indicates that social distancing is vitally important.


Assessing the effect of global travel and contact reductions to mitigate the COVID-19 pandemic and resurgence

Abstract: Travel and physical distancing interventions have been implemented across the World to mitigate the COVID-19 pandemic, but studies are needed to quantify the effectiveness of these measures across regions and time. Timely population mobility data were obtained to measure travel and contact reductions in 135 countries or territories. During the 10 weeks of March 22 - May 30, 2020, domestic travel in study regions has dramatically reduced to a median of 59% (interquartile range [IQR] 43% - 73%) of normal levels seen before the outbreak, with international travel down to 26% (IQR 12% - 35%). If these travel and physical distancing interventions had not been deployed across the World, the cumulative number of cases might have shown a 97-fold (IQR 79 - 116) increase, as of May 31, 2020. However, effectiveness differed by the duration and intensity of interventions and relaxation scenarios, with variations in case severity seen across populations, regions, and seasons.


Clinical Ethical Challenges in the Covid-19 Crisis in South Africa


Organising a COVID-19 Triage Unit: a Swiss Perspective.

Abstract: BACKGROUND With the rapid global spread of the acute respiratory syndrome coronavirus 2, urgent health-care measures have been implemented. We describe the organizational process in setting up a coronavirus disease 2019 triage unit in a Swiss tertiary care hospital. METHODS Our triage unit was set-up outside of the main hospital building and consists of three areas: 1. Pre-triage, 2. Triage, and 3. Triage plus. The Pre-triage check-points identify any potential COVID-19-infected patients and re-direct them to the main Triage area where trained medical staff screen which patients undergo diagnostic testing. If testing is indicated, nasopharyngeal swabs are performed. If patients require further investigations, they are referred to Triage plus. At this stage, patients are then discharged home after additional testing or admitted to the hospital for management. OBSERVATIONS A total of 1,265 patients were screened between March 10th 2020 and April 12th 2020 at our Triage unit. Of these, 112 (8.9%) tested positive. 73 (65%) of the positively-tested patients were female and 39 (35%) were male. The mean age for all patients was 43.8 years (SD 16.3 years). Distinguishing between genders, mean age for females was 41.1 (SD 16.5) and mean age for males was 48.6 (SD 14.9), with females being significantly younger than males (p<0.001). CONCLUSION Our triage unit was set-up as part of a large-scale restructuring process. Current challenges include low sensitivity for test results as well as limited staff and resources. We hope that our experience will help other health care institutions develop similar triage systems.


COVID-19: Management.

Abstract: Coronavirus disease (COVID-19) has grasped the world including Pakistan. Clinical features of this disease are variable, ranging from asymptomatic to critical disease. In this unprecedented global war, the Pakistan Chest Society has written a guideline for quick review for the specialists providing care to suspected or confirmed patients. This review highlights the approach to a patient with COVID-19, including definition of the various syndromes of the disease, the abnormal laboratory parameters and outlines the therapeutic measures which are currently under investigation.


Use of Radiographic Features in COVID-19 Diagnosis: Challenges and Perspectives.

Abstract: The rapid surge and wide-spread of the coronavirus disease-2019 (COVID-19) overshadows the entire medical industries worldwide. The stringent medical resources hinder the diagnostic capacity globally, while 84 thousands of new cases confirmed within a single day of 14 April 2020. Real-time reverse-transcriptase polymerase chain reaction (RT-PCR) with is the current first-line diagnosis, but the false-negative rate remains concerned. Radiographic technologies and tools, including Computed tomography (CT) and Chest X-ray (CXR), were applied for initial screening and follow-up, from which provides detail diagnosis with specific pathologic features for staging and treatment arrangement. Although the radiographic imaging is found less sensitive, numerous CT-positive patients were not screened out by RT-PCR initially and later confirmed as COVID-19 positive. Besides, the shortage of sampling kits and the longer turn-over time of PCR examinations in some areas were noticed due to logistic issues and healthcare burden. In this review, we will discuss the challenges and the future perspectives of using radiographic modalities for COVID-19 diagnosis in view of securing human lives amid the crisis.


Reorganising the pandemic triage processes to ethically maximise individuals' best interests.

Abstract: PURPOSE To provide a revised definition, process and purpose of triage to maximise the number of patients receiving intensive care during a crisis. METHODS Based on the ethical principle of virtue ethics and the underlying goal of providing individual patients with treatment according to their best interests, the methodology of triage is reassessed and revised. RESULTS The decision making processes regarding treatment decisions during a pandemic are redefined and new methods of intensive care provision recommended as well as recommending the use of a 'ranking' system for patients excluded from intensive care, defining the role of non-intensive care specialists, and applying two types of triage as 'organisational triage' and 'treatment triage' based on the demand for intensive care. CONCLUSION Using a different underlying ethical basis upon which to plan for a pandemic crisis could maximise the number of patients receiving intensive care based on individual patients' best interests.


Management Considerations for the Surgical Treatment of Colorectal Cancer During the Global Covid-19 Pandemic.

Abstract: OBJECTIVE The COVID-19 pandemic requires to conscientiously weigh "timely surgical intervention" for colorectal cancer against efforts to conserve hospital resources and protect patients and health care providers. SUMMARY BACKGROUND DATA Professional societies provided ad-hoc guidance at the outset of the COVID-19 pandemic on deferral of surgical and perioperative interventions, but these lack specific parameters to determine the optimal timing of surgery. METHODS Using the GRADE system, published evidence was analyzed to generate weighted statements for stage, site, acuity of presentation and hospital setting to specify when surgery should be pursued, the time and duration of oncologically acceptable delays, and when to utilize non-surgical modalities to bridge the waiting period. RESULTS Colorectal cancer surgeries - prioritized as emergency, urgent with (a) imminent emergency or (b) oncologically urgent, or elective - were matched against the phases of the pandemic. Surgery in COVID-19 positive patients must be avoided. Emergent and imminent emergent cases should mostly proceed unless resources are exhausted. Standard practices allow for postponement of elective cases and deferral to nonsurgical modalities of stage II/III rectal and metastatic colorectal cancer. Oncologically urgent cases may be delayed for 6(-12) weeks without jeopardizing oncological outcomes. Outside established principles, administration of nonsurgical modalities is not justified and increases the vulnerability of patients. CONCLUSION The COVID-19 pandemic has stressed already limited health care resources and forced rationing, triage and prioritization of care in general, specifically of surgical interventions. Established guidelines allow for modifications of optimal timing and type of surgery for colorectal cancer during an unrelated pandemic.


[COVID-19 Triage: Who is an inpatient? The Essen triage model].

Abstract: INTRODUCTION Data about optimal initial assessment in patients with suspicion for COVID19-infection or already confirmed infection are sparse. Especially, in preparation for expected mass casualty incident it is necessary to distinguish early and efficiently between outpatient and inpatient treatment including the need for intensive care therapy. METHODS We present a model for a safe and efficient triage, which is established and used in the university hospital of Essen, Germany. It is intended for a non-disaster situation. This model is a combination of clinical assessment by using vital parameters and Manchester triage scale (MTS). Possible additional parameters are POCT (point-of-care-testing) values, electrocardiogram, CT pulmonary angiography, SARS-Cov2-PCR as well as detailed diagnostic of laboratory values. The model was validated by 100 consecutive patients. We demonstrate three patients to illustrate this model. RESULTS During the first two weeks after implementing this model in our normal operation at the emergency department, we had an efficient selectivity between need for inpatient and outpatient treatment. 16 patients were classified as "inpatients" according to initial assessment. Among 84 patients who were initially classified as "outpatients", 7 patients returned to our emergency department within 14 days. Three of these patients returned due to complaints other than COVID19. One female patient had to be admitted due to progressive dyspnea. CONCLUSIONS This introduced triage-model seems to be an efficient concept. Adjustment might be necessary after further experience and after a growing number of patients.


The formation and design of the TRIAGE study--baseline data on 6005 consecutive patients admitted to hospital from the emergency department.

Abstract: BACKGROUND Patient crowding in emergency departments (ED) is a common challenge and associated with worsened outcome for the patients. Previous studies on biomarkers in the ED setting has focused on identification of high risk patients, and and the ability to use biomarkers to identify low-risk patients has only been sparsely examined. The broader aims of the TRIAGE study are to develop methods to identify low-risk patients appropriate for early ED discharge by combining information from a wide range of new inflammatory biomarkers and vital signs, the present baseline article aims to describe the formation of the TRIAGE database and characteristize the included patients. METHODS We included consecutive patients ≥ 17 years admitted to hospital after triage staging in the ED. Blood samples for a biobank were collected and plasma stored in a freezer (-80 °C). Triage was done by a trained nurse using the Danish Emergency Proces Triage (DEPT) which categorizes patients as green (not urgent), yellow (urgent), orange (emergent) or red (rescusitation). Presenting complaints, admission diagnoses, comorbidities, length of stay, and 'events' during admission (any of 20 predefined definitive treatments that necessitates in-hospital care), vital signs and routine laboratory tests taken in the ED were aslo included in the database. RESULTS Between September 5(th) 2013 and December 6(th) 2013, 6005 patients were included in the database and the biobank (94.1 % of all admissions). Of these, 1978 (32.9 %) were categorized as green, 2386 (39.7 %) yellow, 1616 (26.9 %) orange and 25 (0.4 %) red. Median age was 62 years (IQR 46-76), 49.8 % were male and median length of stay was 1 day (IQR 0-4). No events were found in 2658 (44.2 %) and 158 (2.6 %) were admitted to intensive or intermediate-intensive care unit and 219 (3.6 %) died within 30 days. A higher triage acuity level was associated with numerous events, including acute surgery, endovascular intervention, i.v. treatment, cardiac arrest, stroke, admission to intensive care, hospital transfer, and mortality within 30 days (p < 0.001). CONCLUSION The TRIAGE database has been completed and includes data and blood samples from 6005 unselected consecutive hospitalized patients. More than 40 % experienced no events and were therefore potentially unnecessary hospital admissions.


Extracorporeal Membrane Oxygenation for Pediatric Patients With Coronavirus Disease 2019-Related Illness.

Abstract: OBJECTIVE To describe current hospital guidelines and the opinions of extracorporeal membrane oxygenation leaders at U.S. children's hospitals concerning the use of extracorporeal membrane oxygenation for coronavirus disease 2019-positive pediatric patients. DESIGN Confidential, self-administered questionnaire. SETTING One hundred twenty-seven U.S. pediatric extracorporeal membrane oxygenation centers. SUBJECTS Extracorporeal membrane oxygenation center program directors and coordinators. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS In March 2020, a survey was sent to 127 pediatric extracorporeal membrane oxygenation centers asking them to report their current hospital extracorporeal membrane oxygenation guidelines for coronavirus disease 2019-positive patients. Respondents were also asked their opinion on three ethical dilemmas including: prioritization of children over adults for extracorporeal membrane oxygenation use, institution of do-not-resuscitate orders, and the use of extracorporeal cardiopulmonary resuscitation for coronavirus disease 2019-positive patients. Forty-seven extracorporeal membrane oxygenation centers had enacted guidelines including 46 (100%) that offer venovenous-extracorporeal membrane oxygenation and 42 (89%) that offer venoarterial-extracorporeal membrane oxygenation for coronavirus disease 2019-positive pediatric patients. Forty-four centers (94%) stated that the indications for extracorporeal membrane oxygenation candidacy in coronavirus disease 2019 disease were similar to those used in other viral illnesses, such as respiratory syncytial virus or influenza. Most program directors (98%) did not endorse that children hospitalized with coronavirus disease 2019 should be made do-not-resuscitate and had variable opinions on whether children should be given higher priority over adults when rationing extracorporeal membrane oxygenation. Over half of program directors (60%) did not support the use of extracorporeal cardiopulmonary resuscitation for coronavirus disease 2019. CONCLUSIONS The majority of pediatric extracorporeal membrane oxygenation centers have proactively established guidelines for the use of extracorporeal membrane oxygenation for coronavirus disease 2019-related illnesses. Further work is needed to help guide the fair allocation of extracorporeal membrane oxygenation resources and to determine the appropriateness of extracorporeal cardiopulmonary resuscitation.


Immersion in an emergency department triage center during the Covid-19 outbreak: first report of the Liège University hospital experience.

Abstract: OBJECTIVES Since the beginning of the novel coronavirus outbreak, different strategies have been explored to stem the spread of the disease and appropriately manage patient flow. Triage, an effective solution proposed in disaster medicine, also works well to manage Emergency Department (ED) flow. The aim of this study was to describe the role of an ED Triage Center for patients with suspected novel coronavirus disease (Covid-19) and characterize the patient flow. Methods. In March 2020, we established a Covid-19 triage center close to the Liège University EDs. From March 2 to March 23, we planned to analyze the specific flow of patients admitted to this triage zone and their characteristics in terms of inner specificities, work-up and management. During this period, all patients presented to the ED with symptoms suggestive of Covid-19 were included in the study. RESULTS A total amount of 1071 patients presented to the triage center during the study period. 41.50% of the patients presented with flu-like symptoms. In 82.00% of the cases, no risk factor of virus transmission was found. The SARS-Cov2 positive patients represented 29.26% of the screened patients. 83.00% of patients were discharged home while 17.00% were admitted to the hospital. CONCLUSION Our experience suggests that triage centers for the assessment and management of Covid-19 suspected patients is an essential key strategy to prevent the spread of the disease among non-symptomatic patients who present to the EDs for care. This allows for a disease-centered work-up and safer diversion of Covid-19 patients to specific hospital units.


Response of a comprehensive cancer center to the COVID-19 pandemic: the experience of the Fondazione IRCCS-Istituto Nazionale dei Tumori di Milano.

Abstract: BACKGROUND The rapid spread of coronavirus disease (COVID-19) is affecting many countries. While healthcare systems need to cope with the need to treat a large number of people with different degrees of respiratory failure, actions to preserve aliquots of the healthcare system to guarantee treatment to patients are mandatory. METHODS In order to protect the Fondazione IRCCS-Istituto Nazionale dei Tumori di Milano from the spread of COVID-19, a number of to-hospital and within-hospital filters were applied. Among others, a triage process to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positivity in patients with cancer was developed consisting of high-resolution low-dose computed tomography (CT) scan followed by reverse transcription polymerase chain reaction (RT-PCR) detection of SARS-CoV-2 in nose-throat swabs whenever CT was suggestive of lung infection. To serve symptomatic patients who were already admitted to the hospital or in need of hospitalization while waiting for RT-PCR laboratory confirmation of infection, a COVID-19 surveillance zone was set up. RESULTS A total of 301 patients were screened between March 6 and April 3, 2020. Of these, 47 were hospitalized, 53 needed a differential diagnosis to continue with their cancer treatment, and 201 were about to undergo surgery. RT-PCR was positive in 13 of 40 hospitalized patients (32%), 14 of 52 day hospital patients (27%), and 6 of 201 surgical patients (3%). CONCLUSION Applying filters to protect our comprehensive cancer center from COVID-19 spread contributed to guaranteeing cancer care during the COVID-19 crisis in Milan. A surveillance area and surgical triage allowed us to protect the hospital from as many as 33 patients infected with SARS-CoV-2.


Simple, fast and affordable triaging pathway for COVID-19.

Abstract: Coronavirus disease 2019 has caused a global pandemic. The majority of patients will experience mild disease, but others will develop a severe respiratory infection that requires hospitalisation. This is causing a significant strain on health services. Patients are presenting at emergency departments with symptoms of dyspnoea, dry cough and fever with varying severity. The appropriate triaging of patients will assist in preventing health services becoming overwhelmed during the pandemic. This is assisted through clinical assessment and various imaging and laboratory investigations, including chest X-ray, blood analysis and identification of viral infection with SARS-CoV-2. Here, a succinct triaging pathway that aims to be fast, reliable and affordable is presented. The hope is that such a pathway will assist health services in appropriately combating the pandemic.


Analysis and suggestions for the preview and triage screening of children with suspected COVID-19 outside the epidemic area of Hubei Province

Abstract: Background: Since December 2019, a number of patients infected with COVID-19 (SARS-CoV-2) have been identified in Wuhan, Hubei, China. As the epidemic has spread, similar cases have also been found in other parts of mainland China and abroad. The main reason for this spread is the highly contagious nature of the virus and the fact that children can also become infected during its incubation period. This has made the virus a substantial challenge for the outpatient triage staff of children's hospitals outside the epidemic area of the Hubei Province. It is very important for the preview and triage personnel to accurately grasp the epidemiology of the virus and identify children's symptoms in the fever clinic. Methods: We performed an analysis of our early preview and triage of suspected COVID-19 in 36 children presenting at fever clinics. Two specialists either excluded suspected cases or referred cases to the isolation ward for new nucleic acid testing. Results: All 14 children who were transferred to the isolation ward had a fever, and 71.43% of them had a cough. Their nucleic acid testing results were negative. The suspected cases and excluded suspected cases had similar epidemiology history as well as complete blood count results. With reference to the diagnostic criteria in existing pediatric guidelines, we have further improved the triage screening questionnaire for children with fever in our hospital. Conclusions: According to the situation in our city and hospital, an evaluation questionnaire that is suitable for use with children in our hospital has been formulated to achieve the goals of early detection, isolation, diagnosis, and treatment. We provided an important basis for the next step in developing accurate preview and triage screening standards and appropriate guidelines for pediatric patients.


“Daily Work in the Fever Clinics of Wuhan Union Hospital During the Novel Coronavirus Pneumonia Epidemic: A Special Spring Festival in Wuhan, China”Extracorporeal Membrane Oxygenation for Pediatric Patients With Coronavirus Disease 2019–Related Illness

Abstract: Objective: To describe current hospital guidelines and the opinions of extracorporeal membrane oxygenation leaders at U S children’s hospitals concerning the use of extracorporeal membrane oxygenation for coronavirus disease 2019–positive pediatric patients Design: Confidential, self-administered questionnaire Setting: One hundred twenty-seven U S pediatric extracorporeal membrane oxygenation centers Subjects: Extracorporeal membrane oxygenation center program directors and coordinators Interventions: None Measurements and Main Results: In March 2020, a survey was sent to 127 pediatric extracorporeal membrane oxygenation centers asking them to report their current hospital extracorporeal membrane oxygenation guidelines for coronavirus disease 2019–positive patients Respondents were also asked their opinion on three ethical dilemmas including: prioritization of children over adults for extracorporeal membrane oxygenation use, institution of do-not-resuscitate orders, and the use of extracorporeal cardiopulmonary resuscitation for coronavirus disease 2019–positive patients Forty-seven extracorporeal membrane oxygenation centers had enacted guidelines including 46 (100%) that offer venovenous-extracorporeal membrane oxygenation and 42 (89%) that offer venoarterial-extracorporeal membrane oxygenation for coronavirus disease 2019–positive pediatric patients Forty-four centers (94%) stated that the indications for extracorporeal membrane oxygenation candidacy in coronavirus disease 2019 disease were similar to those used in other viral illnesses, such as respiratory syncytial virus or influenza Most program directors (98%) did not endorse that children hospitalized with coronavirus disease 2019 should be made do-not-resuscitate and had variable opinions on whether children should be given higher priority over adults when rationing extracorporeal membrane oxygenation Over half of program directors (60%) did not support the use of extracorporeal cardiopulmonary resuscitation for coronavirus disease 2019 Conclusions: The majority of pediatric extracorporeal membrane oxygenation centers have proactively established guidelines for the use of extracorporeal membrane oxygenation for coronavirus disease 2019–related illnesses Further work is needed to help guide the fair allocation of extracorporeal membrane oxygenation resources and to determine the appropriateness of extracorporeal cardiopulmonary resuscitation Drs MacGregor and Antiel are co-first authors Supplemental digital content is available for this article Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http://journals lww com/pccmjournal) The authors have disclosed that they do not have any potential conflicts of interest For information regarding this article, E-mail: baddr@wustl edu ©2020The Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies


Predicting individual risk for COVID19 complications using EMR data

Abstract: Background: The global pandemic of COVID-19 has challenged healthcare organizations and caused numerous deaths and hospitalizations worldwide. The need for data-based decision support tools for many aspects of controlling and treating the disease is evident but has been hampered by the scarcity of real-world reliable data. Here we describe two approaches: a. the use of an existing EMR-based model for predicting complications due to influenza combined with available epidemiological data to create a model that identifies individuals at high risk to develop complications due to COVID-19 and b. a preliminary model that is trained using existing real world COVID-19 data. Methods: We have utilized the computerized data of Maccabi Healthcare Services a 2.3 million member state-mandated health organization in Israel. The age and sex matched matrix used for training the XGBoost ILI-based model included, circa 690,000 rows and 900 features. The available dataset for COVID-based model included a total 2137 SARS-CoV-2 positive individuals who were either not hospitalized (n=1658), or hospitalized and marked as mild (n=332), or as having moderate (n=83) or severe (n=64) complications. Findings: The AUC of our models and the priors on the 2137 COVID-19 patients for predicting moderate and severe complications as cases and all other as controls, the AUC for the ILI-based model was 0.852[0.824-0.879] for the COVID19-based model - 0.872[0.847-0.879].. Interpretation: These models can effectively identify patients at high-risk for complication, thus allowing optimization of resources and more focused follow up and early triage these patients if once symptoms worsen.


DERIVATION OF A SCORE TO PREDICT ADMISSION TO INTENSIVE CARE UNIT IN PATIENTS WITH COVID-19: THE ABC-GOALS SCORE

Abstract: Background. COVID-19 pandemic poses a burden on hospital resources and intensive care unit (ICU) occupation. The study aimed to derive a scoring system that, assessed upon patient first-contact evaluation, predicts the need for admission to ICU. Methods. All consecutive patients admitted to a COVID-19 reference center were prospectively assessed. Patients were segregated into a group that required admission to ICU during their hospitalization and a group that never required ICU admission and was already discharged from hospitalization. Three models including clinical, laboratory and imaging findings were derived by logistic regression analysis and internally validated. A score, defined as the ABC-GOALS score was created by assigning values based on the variables weighted odd ratios. Results. The study comprised 329 patients, 115 (35%) required ICU admission and 214 (65%) were hospitalized and discharged from general wards. The clinical prediction model (ABC-GOALSc) included sex, obesity, the Charlson comorbidity index, dyspnea, arterial pressure and respiratory rate at triage evaluation. The clinical plus laboratory model (ABC-GOALScl) added serum albumin, glucose, lactate dehydrogenase and S/F ratio to the clinical model. The model that included imaging (ABC-GOALSclx) added the CT scan finding of >50% lung involvement. All three models outperformed other pneumonia-specific scores with area under the curve of 0.79 (0.74-0.83), 0.86 (0.82-0.90) and 0.88 (0.84-0.92) for the clinical, laboratory and imaging model, respectively. Conclusion. The ABC-GOALS score is a tool to evaluate patients with COVID-19 at admission to the emergency department that allows to timely predict their risk of admission to an ICU and may help optimize healthcare capacities.


Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients

Abstract: BACKGROUND: Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODS: We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTS: A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONS: This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.


Derivation and validation of a prognostic model for predicting in-hospital mortality in patients admitted with COVID-19 in Wuhan, China: the PLANS (Platelet Lymphocyte Age Neutrophil Sex) model

Abstract: OBJECTIVE To develop and validate a prognostic model for in-hospital mortality in COVID-19 patients using routinely collected demographic and clinical characteristics. DESIGN Multicenter, retrospective cohort study. SETTING Jinyintan Hospital, Union Hospital, and Tongji Hosptial in Wuhan, China. PARTICIPANTS A pooled derivation cohort of 1008 COVID-19 patients from Jinyintan Hospital, Union Hospital in Wuhan and an external validation cohort of 1031 patients from Tongji Hospital in Wuhan, China. MAIN OUTCOME MEASURES Outcome of interest was in-hospital mortality, treating discharged alive from hospital as the competing event. Fine-Gray models, using backward elimination for inclusion of predictor variables and allowing non-linear effects of continuous variables, were used to derive a prognostic model for predicting in-hospital mortality among COVID-19 patients. Internal validation was implemented to check model overfitting using bootstrap approach. External validation to a separate hospital was implemented to evaluate the generalizability of the model. RESULTS The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (n=1008, 43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (n=1031, 47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted survival curves were close to the observed survival curves across patients with different risk profiles. CONCLUSIONS The PLANS model based on the five routinely collected demographic and clinical characteristics (platelet count, lymphocyte count, age, neutrophil count, and sex) showed excellent discriminative and calibration accuracy in predicting in-hospital mortality in COVID-19 patients. This prognostic model would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.


Triage of Acute Ischemic Stroke in Confirmed COVID-19: Large Vessel Occlusion Associated With Coronavirus Infection

Abstract: The outbreak of COVID-19 has posed a significant challenge to global healthcare. Acute stroke care requires rapid bedside attendance, imaging, and intervention. However, for acute stroke patients who have a diagnosis of or are under investigation for COVID-19, the concern for nosocomial transmission moderates operational procedures for acute stroke care. We present our experience with an in-hospital stroke code called on a COVID-19-positive patient with a left middle cerebral artery syndrome and the challenges faced for timely examination, imaging, and decision to intervene. The outlook for the ongoing COVID-19 pandemic necessitates the development of protocols to sustain timely and effective acute stroke care while mitigating healthcare-associated transmission.


Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study

Abstract: IMPORTANCE The outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality for severely and critically ill patients. However, the availability of validated nomograms and the machine-learning model to predict severity risk and triage of affected patients is limited. OBJECTIVE To develop and validate nomograms and machine-learning models for severity risk assessment and triage for COVID-19 patients at hospital admission. DESIGN, SETTING, AND PARTICIPANTS A retrospective cohort of 299 consecutively hospitalized COVID-19 patients at The Central Hospital of Wuhan, China, from December 23, 2019, to February 13, 2020, was used to train and validate the models. Six cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020, were used to prospectively validate the models. MAIN OUTCOME AND MEASURES The main outcome was the onset of severe or critical illness during hospitalization. Model performances were quantified using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTS Of the 299 hospitalized COVID-19 patients in the retrospective cohort, the median age was 50 years ((interquartile range, 35.5-63.0; range, 20-94 years) and 137 (45.8%) were men. Of the 426 hospitalized COVID-19 patients in the prospective cohorts, the median age was 62.0 years ((interquartile range, 50.0-72.0; range, 19-94 years) and 236 (55.4%) were men. The model was prospectively validated on six cohorts yielding AUCs ranging from 0.816 to 0.976, with accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off values of the low, medium, and high-risk probabilities were 0.072 and 0.244. The developed online calculators can be found at www.predict19risk.ai. CONCLUSION AND RELEVANCE The machine learning models, nomograms, and online calculators might be useful for the prediction of onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Further prospective research and clinical feedback are necessary to evaluate the clinical usefulness of this model and to determine whether these models can help optimize medical resources and reduce mortality rates compared with current clinical practices.


Triage tool for suspected COVID-19 patients in the emergency room: AIFELL score

Abstract: Clinical prediction scores support the assessment of patients in the emergency setting to determine the need for further diagnostic and therapeutic steps. During the current COVID-19 pandemic, physicians in emergency rooms (ER) of many hospitals have a considerably higher patient load and need to decide within a short time frame whom to hospitalize. Based on our clinical experiences in dealing with COVID-19 patients at the University Hospital Zurich, we created a triage score with the acronym AIFELL consisting of clinical, radiological and laboratory findings. The score was then evaluated in a retrospective analysis of 122 consecutive patients with suspected COVID-19 from March until mid-April 2020. Descriptive statistics, Student's t-test, ANOVA and Scheffe's post hoc analysis confirmed the diagnostic power of the score. The results suggest that the AIFELL score has potential as a triage tool in the ER setting intended to select probable COVID-19 cases for hospitalization in spontaneously presenting or referred patients with acute respiratory symptoms.