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The ongoing COVID-19 outbreak has expanded rapidly throughout China. Major behavioral, clinical, and state interventions are underway currently to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, have affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was well explained by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases are still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China have substantially mitigated the spread of COVID-19. One sentence summary: The spread of COVID-19 in China was driven by human mobility early on and mitigated substantially by drastic control measures implemented since the end of January.

As the COVID-19 pandemic continues to unfold, payers across the USA have stepped up to alleviate patients' financial burden by waiving cost-sharing for COVID-19 testing and treatment. However, there has been no substantive discussion of potential long-term effects of COVID-19 on patient health or their financial and policy implications. After recovery, patients remain at risk for lung disease, heart disease, frailty, and mental health disorders. There may also be long-term sequelae of adverse events that develop in the course of COVID-19 and its treatment. These complications are likely to place additional medical, psychological, and economic burdens on all patients, with lower-income individuals, the uninsured and underinsured, and individuals experiencing homelessness being most vulnerable. Thus, there needs to be a comprehensive plan for preventing and managing post-COVID-19 complications to quell their clinical, economic, and public health consequences and to support patients experiencing delayed morbidity and disability as a result.

Public health and social measures have been implemented around the world in a bid to prevent the spread of COVID-19. Public compliance with these measures is key in successfully controlling the pandemic. This online survey assessed the compliance and attitude of adults residing in the southern African Kingdom of Eswatini to government protection, activity and travel measures aimed at controlling the spread of COVID-19. A rapid online survey, comprising of 28 questions, was administered in May 2020. More than 90% of respondents knew the virus could kill anyone and most respondents (70%) reported to be compliant to public health and social measures. Females, those who did not use public transport and those aged 30 years and above were significantly (p<0.01) more compliant, particularly to protective and travel measures. Social media, television and official government websites were the primary source of ongoing COVID-19 information for respondents of this online survey, and these methods should continue to be employed to reach the public who regularly use the internet. More than half of essential workers who responded to the online survey reported to have their own personal protective equipment; however, 32% either did not have any protective equipment or shared their equipment with other staff members. Due to the survey being online, these results should not be generalised to populations of low socioeconomic status.

ObjectivesWe aim to estimate the impact of various mitigation strategies on COVID-19 transmission in a US jail beyond those offered in national guidelines.DesignWe developed a stochastic dynamic transmission model of COVID-19.SettingOne anonymous large urban US jail.ParticipantsSeveral thousand staff and incarcerated individuals.InterventionsThere were four intervention phases during the outbreak: the start of the outbreak, depopulation of the jail, increased proportion of people in single cells and asymptomatic testing. These interventions were implemented incrementally and in concert with one another.Primary and secondary outcome measuresThe basic reproduction ratio, R0, in each phase, as estimated using the next generation method. The fraction of new cases, hospitalisations and deaths averted by these interventions (along with the standard measures of sanitisation, masking and social distancing interventions).ResultsFor the first outbreak phase, the estimated R0 was 8.44 (95% credible interval (CrI): 5.00 to 13.10), and for the subsequent phases, R0,phase 2=3.64 (95% CrI: 2.43 to 5.11), R0,phase 3=1.72 (95% CrI: 1.40 to 2.12) and R0,phase 4=0.58 (95% CrI: 0.43 to 0.75). In total, the jail’s interventions prevented approximately 83% of projected cases, hospitalisations and deaths over 83 days.ConclusionsDepopulation, single celling and asymptomatic testing within jails can be effective strategies to mitigate COVID-19 transmission in addition to standard public health measures. Decision makers should prioritise reductions in the jail population, single celling and testing asymptomatic populations as additional measures to manage COVID-19 within correctional settings.

Abstract Background The outbreak of coronavirus disease 2019 (COVID-19) in December 2019 overlaps with the flu season. Methods We compared clinical and laboratory results from 719 influenza and 973 COVID-19 patients from January to April 2020. We compiled laboratory results from the first 14 days of the hospitalized patients using parameters that are most significantly different between COVID-19 and influenza and hierarchically clustered COVID-19 patients. Findings Compared to influenza, patients with COVID-19 exhibited a continued increase in white blood cell count, rapid decline of hemoglobin, more rapid increase in blood urea nitrogen (BUN) and D-dimer, and higher level of alanine transaminase, C-reactive protein, ferritin, and fibrinogen. COVID-19 patients were sub-classified into 5 clusters through a hierarchical clustering analysis. Medical records were reviewed and patients were risk stratified based on the clinical outcomes. The cluster with the highest risk showed 27·8% fatality, 94% ICU admission, 94% intubation, and 28% discharge rates compared to 0%, 38%, 22%, and 88% in the lowest risk cluster, respectively. Patients in the highest risk cluster had leukocytosis including neutrophilia and monocytosis, severe anemia, increased red blood cell distribution width, higher BUN, creatinine, D-dimer, alkaline phosphatase, bilirubin, and troponin. Interpretation There are significant differences in the clinical and laboratory courses between COVID-19 and influenza. Risk stratification in hospitalized COVID-19 patients using laboratory data could be useful to predict clinical outcomes and pathophysiology of these patients. Funding National Institute of Diabetes and Digestive and Kidney Disease, Department of Defense, and National Heart, Lung, and Blood Institute.

Coronavirus disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has caused more than 6.1 million confirmed cases of COVID-19 in more than 188 countries, and has caused more than 370,000 deaths globally as of June 1, 2020. In addition, thousands of healthcare workers have also got infected with the virus. COVID-19 patients release large amounts of infectious viral particles in form of droplets from cough, sneeze and respiratory secretions. These droplets are the main modes of transmission of COVID-19. This mode of transmission puts the healthcare professionals at an increased risk of infection, especially from asymptomatic patients. As a result, during the current pandemic, most routine surgeries all around the world have been suspended, and only emergency surgeries are being performed. Facial trauma surgery is one such emergency surgery that cannot be delayed or suspended even in this pandemic. This review focuses on precautions surgeons have to take while managing facial trauma patients in the emergency department and while performing emergency surgeries on these patients during the current COVID-19 pandemic.

The objective of this paper is to evaluate a panel of statistical algorithms for temporal outbreak detection. Based on a large dataset of simulated weekly surveillance time series, we performed a systematic assessment of 21 statistical algorithms, 19 implemented in the R package surveillance and two other methods. We estimated false positive rate (FPR), probability of detection (POD), probability of detection during the first week, sensitivity, specificity, negative and positive predictive values and F1-measure for each detection method. Then, to identify the factors associated with these performance measures, we ran multivariate Poisson regression models adjusted for the characteristics of the simulated time series (trend, seasonality, dispersion, outbreak sizes, etc.). The FPR ranged from 0.7% to 59.9% and the POD from 43.3% to 88.7%. Some methods had a very high specificity, up to 99.4%, but a low sensitivity. Methods with a high sensitivity (up to 79.5%) had a low specificity. All methods had a high negative predictive value, over 94%, while positive predictive values ranged from 6.5% to 68.4%. Multivariate Poisson regression models showed that performance measures were strongly influenced by the characteristics of time series. Past or current outbreak size and duration strongly influenced detection performances.

Community mitigation strategies to combat COVID-19, ranging from healthy hygiene to shelter-in-place orders, exact substantial socioeconomic costs. Judicious implementation and relaxation of restrictions amplify their public health benefits while reducing costs. We derive optimal strategies for toggling between mitigation stages using daily COVID-19 hospital admissions. With public compliance, the policy triggers ensure adequate intensive care unit capacity with high probability while minimizing the duration of strict mitigation measures. In comparison, we show that other sensible COVID-19 staging policies, including France’s ICU-based thresholds and a widely adopted indicator for reopening schools and businesses, require overly restrictive measures or trigger strict stages too late to avert catastrophic surges. As proof-of-concept, we describe the optimization and maintenance of the staged alert system that has guided COVID-19 policy in a large US city (Austin, Texas) since May 2020. As cities worldwide face future pandemic waves, our findings provide a robust strategy for tracking COVID-19 hospital admissions as an early indicator of hospital surges and enacting staged measures to ensure integrity of the health system, safety of the health workforce, and public confidence. Selection of COVID-19 mitigation measures requires balancing health outcomes with economic impacts. Here, the authors derive a system to set triggers for increasing mitigation measures to preserve healthcare capacity, and describe how it has been used to support public health decision making in Austin, Texas.