Question: how has lack of testing availability led to underreporting of true incidence of Covid-19?

Title: Accounting for underreporting in mathematical modelling of transmission and control of COVID-19 in Iran Content: BACKGROUND: Iran has been the hardest hit country by the outbreak of SARS-CoV-2 in the Middle East with 74,877 confirmed cases and 4,683 deaths as of 15 April 2020. With a relatively high case fatality ratio and limited testing capacity, the number of confirmed cases reported is suspected to suffer from significant under-reporting. Therefore, understanding the transmission dynamics of COVID-19 and assessing the effectiveness of the interventions that have taken place in Iran while accounting for the uncertain level of underreporting is of critical importance. We use a mathematical epidemic model utilizing official confirmed data and estimates of underreporting to understand how transmission in Iran has been changing between February and April 2020. METHODS: We developed a compartmental transmission model to estimate the effective reproduction number and its fluctuations since the beginning of the outbreak in Iran. We associate the variations in the effective reproduction number with a timeline of interventions and national events. The estimation method also accounts for the underreporting due to low case ascertainment by estimating the percentage of symptomatic cases using delay adjusted case fatality ratio based on the distribution of the delay from hospitalization to death. FINDINGS: Our estimates of the effective reproduction number ranged from 0.66 to 1.73 between February and April 2020, with a median of 1.16. We estimate a reduction in the effective reproduction number during this period, from 1.73 (95% CI 1.60-1.87) on 1 March 2020 to 0.69 (95% CI 0.68-0.70) on 15 April 2020, due to various non-pharmaceutical interventions including school closures, a ban on public gatherings including sports and religious events, and full or partial closure of non-essential businesses. Based on these estimates and given that a near complete containment is no longer feasible, it is likely that the outbreak may continue until the end of the 2020 if the current level of physical distancing and interventions continue and no effective vaccination or therapeutic are developed and made widely available. INTERPRETATION: The series of non-pharmaceutical interventions and the public compliance that took place in Iran are found to be effective in slowing down the speed of the spread of COVID-19 within the studied time period. However, we argue that if the impact of underreporting is overlooked, the estimated transmission and control dynamics could mislead the public health decisions, policy makers, and general public especially in the earlier stages of the outbreak. FUNDING: Nil.

Title: New blood tests for antibodies could show true scale of coronavirus pandemic Content: How many COVID-19 cases have gone undetected? And are those who had mild cases of the disease—perhaps so mild they dismissed it as a cold or allergies—immune to new infections? If so, they could slow the spread of the burgeoning pandemic Labs and companies around the world have raced to develop antibody tests, and a few have been used in small studies and received commercial approval, including several from China But so far, large-scale data from such tests—for example showing what fraction of people in the hard-hit city of Wuhan, China, might now be immune—is still lacking or at least not public Scientists hope that will soon change as more tests become available

Title: Evaluating the massive underreporting and undertesting of COVID-19 cases in multiple global epicenters Content: Abstract Background With continuous global COVID-19 outbreak, differing case numbers and mortality rates are observed. While actual case numbers appear vague, mortality numbers related to COVID-19 seem more precise. In this study, we used the mortality rate as the main indicator to evaluate the extent of underreporting and underdetection of COVID-19 cases. Methods We have analyzed all available data provided by the World Health Organization on the development of international COVID-19 cases and mortality numbers on March 17th, 2020. A crude case-fatality risk (cCFR) and adjusted case-fatality risk (aCFR) was calculated for China, South Korea, Japan, Italy, France, Spain, Germany, Iran and the United States. Additionally, a fold-change (FC) was derived for each country. Results The highest aCFR and FC were detected for Spain. Based on their FC values, an extremely high number of undetected COVID-19 cases was displayed in France, the United States, Italy and Spain. For these countries, our findings indicate a detection rate of only 1-2% of total actual COVID-19 cases. Conclusions Due to limited testing capacities, mortality numbers may serve as a better indicator for COVID-19 case spread in many countries. Our data indicate that countries like France, Italy, the United States, Iran and Spain have extremely high numbers of undetected and underreported cases. Differences in testing availability and capacity, containment as well as overall health care and medical infrastructure result in significantly different mortality rates and COVID-19 case numbers for each respective country.

Title: Evaluating the massive underreporting and undertesting of COVID-19 cases in multiple global epicenters Content: BACKGROUND: With continuous global COVID-19 outbreak, differing case numbers and mortality rates are observed. While actual case numbers appear vague, mortality numbers related to COVID-19 seem more precise. In this study, we used the mortality rate as the main indicator to evaluate the extent of underreporting and underdetection of COVID-19 cases. METHODS: We have analyzed all available data provided by the World Health Organization on the development of international COVID-19 cases and mortality numbers on March 17th, 2020. A crude case-fatality risk (cCFR) and adjusted case-fatality risk (aCFR) was calculated for China, South Korea, Japan, Italy, France, Spain, Germany, Iran and the United States. Additionally, a fold-change (FC) was derived for each country. RESULTS: The highest aCFR and FC were detected for Spain. Based on their FC values, an extremely high number of undetected COVID-19 cases was displayed in France, the United States, Italy and Spain. For these countries, our findings indicate a detection rate of only 1-2% of total actual COVID-19 cases. CONCLUSIONS: Due to limited testing capacities, mortality numbers may serve as a better indicator for COVID-19 case spread in many countries. Our data indicate that countries like France, Italy, the United States, Iran and Spain have extremely high numbers of undetected and underreported cases. Differences in testing availability and capacity, containment as well as overall health care and medical infrastructure result in significantly different mortality rates and COVID-19 case numbers for each respective country.

Title: Changes in testing rates could mask the novel coronavirus disease (COVID-19) growth rate Content: Since the novel coronavirus disease (COVID-19) emerged in December 2019 in China, it has rapidly spread around the world, leading to one of the most significant pandemic events of recent history. Deriving reliable estimates of the COVID-19 epidemic growth rate is quite important to guide the timing and intensity of intervention strategies. Indeed, many studies have quantified the epidemic growth rate using time-series of reported cases during the early phase of the outbreak to estimate the basic reproduction number, R0. Using daily time series of COVID-19 incidence, we illustrate how epidemic curves of reported cases may not always reflect the true epidemic growth rate due to changes in testing rates, which could be influenced by limited diagnostic testing capacity during the early epidemic phase.

Title: Changes in testing rates could mask the novel coronavirus disease (COVID-19) growth rate Content: Abstract Since the novel coronavirus disease (COVID-19) emerged in December 2019 in China, it has rapidly propagated to around the world, leading to one of the most significant pandemic events of recent history. Deriving reliable estimates of the COVID-19 epidemic growth rate is quite important to guide the timing and intensity of intervention strategies. Indeed, many studies have quantified the epidemic growth rate using time-series of reported cases during the early phase of the outbreak to estimate the basic reproduction number, R 0. Using daily time series of COVID-19 incidence, we illustrate how epidemic curves of reported cases may not always reflect the true epidemic growth rate due to changes in testing rates, which could be influenced by limited diagnostic testing capacity during the early epidemic phase.

Title: Bounding the Predictive Values of COVID-19 Antibody Tests Content: COVID-19 antibody tests have imperfect accuracy. There has been lack of clarity on the meaning of reported rates of false positives and false negatives. For risk assessment and clinical decision making, the rates of interest are the positive and negative predictive values of a test. Positive predictive value (PPV) is the chance that a person who tests positive has been infected. Negative predictive value (NPV) is the chance that someone who tests negative has not been infected. The medical literature regularly reports different statistics, sensitivity and specificity. Sensitivity is the chance that an infected person receives a positive test result. Specificity is the chance that a non-infected person receives a negative result. Knowledge of sensitivity and specificity permits one to predict the test result given a person's true infection status. These predictions are not directly relevant to risk assessment or clinical decisions, where one knows a test result and wants to predict whether a person has been infected. Given estimates of sensitivity and specificity, PPV and NPV can be derived if one knows the prevalence of the disease, the rate of illness in the population. There is considerable uncertainty about the prevalence of COVID-19. This paper addresses the problem of inference on the PPV and NPV of COVID-19 antibody tests given estimates of sensitivity and specificity and credible bounds on prevalence. I explain the methodological problem, show how to estimate bounds on PPV and NPV, and apply the findings to some tests authorized by the FDA.

Title: Identifying a Kidney Transplant Recipient COVID Phenotype to Aid Test Utilization in the Setting of Limited Testing Availability - Does One Exist? Content: Abstract: The high morbidity and mortality of COVID-19 in immunocompetent patients raises significant concern for immunosuppressed kidney transplant recipients (KTRs). This level of concern, both on the part of the KTRs and transplant professionals, is heightened by a lack of prior knowledge on how SARS-CoV-2 may manifest differently in immunosuppressed patients. Characterizing how KTRs may present differently than the general population would allow for more targeted and timely evaluation and treatment of KTRs with COVID-19 infection. Methods Without prior knowledge of how this virus would affect our transplant center’s delivery of care to KTRs who are SARS-CoV-2 positive or Patients Under Investigation (PUIs), and in the setting of limited testing availability, we initiated a Quality Assurance and Improvement Project (QAPI) to track KTRs followed at our transplant center through the SARS-CoV-2 testing process. Results Of the 53 symptomatic patients, 20 (38%) tested positive for SARS-CoV-2 either on presentation to the emergency department, or referral to a designated outpatient testing center. In addition, 16 (80%) of the 20 patients who tested positive required inpatient treatment. Intriguingly, patients with a history of polyoma BK viremia (BKV) had a higher incidence of testing positive for SARS-CoV-2 compared to patients without history of BKV (80% and 28%, respectively; p= 0.002). The Positive Predictive Value and Likelihood ratio was 80% and 6.6 for this association, respectively. Among our KTRs tested, those receiving belatacept had a lower likelihood of testing positive for SARS-CoV-2. This finding approached, but did not achieve, statistical significance (p=0.06).

Title: Hyperlocal Postcode Based Crowdsourced Surveillance Systems in the COVID-19 Pandemic Response Content: The SARS-CoV-2 pandemic has rapidly saturated healthcare resources across the globe and has led to a restricted screening process, hindering efforts at comprehensive case detection. This has not only facilitated community spread but has also resulted in an underestimation of the true incidence of disease, a statistic which is useful for policy making aimed at controlling the current pandemic and in preparing for future outbreaks. In this perspective, we present a crowdsourced platform developed by us for the true estimation of all SARS-CoV-2 infections in the community, through active self-reporting and layering other authentic datasets. The granularity of data captured by this system could prove to be useful in assisting governments to identify SARS-CoV-2 hotspots in the community facilitating lifting of restrictions in a controlled fashion.

Title: Severe acute respiratory syndrome coronavirus 2, original antigenic sin, and antibody-dependent enhancement: ménage à trois. Content: BACKGROUND Shortly after its emergence in December 2019, the coronavirus disease 2019 (COVID-19) was declared as a pandemic by the World Health Organization. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the seventh member of the Coronaviridae family of viruses that causes disease in humans. THE PROBLEM Despite the established role of molecular diagnostics, COVID-19 serodiagnosis remains a poorly discovered and enigmatic area. Although there are numerous commercial serological products available globally, there is a severe paucity of high-quality peer-reviewed literature on their true performance characteristics. That being said, publications including in-house developed serological tests started to shed light on the kinetics of the humoral response. SUMMARY In spite of intense focus of assessing the performance characteristics of the commercially-available kits, the main issue remains rather invisible, that is, lack of solid science behind COVID-19 serology its clinical usefulness thereof. This short review summarizes the key points as to why COVID-19 is not jest ready to fly. PURPOSE OF REVIEW Despite having been mentioned as a testing option, COVID-19 serology has significant shortcomings that needs discussing. This short review is meant to shed light on one of those aspects.

Title: Group Testing for Sars-Cov-2 to Enable Rapid Scale-Up of Testing and Real-Time Surveillance of Incidence Content: High-throughput molecular testing for SARS-CoV-2 may be enabled by group testing in which pools of specimens are screened, and individual specimens tested only after a pool tests positive. Several labs have recently published examples of pooling strategies applied to SARS-CoV-2 specimens, but overall guidance on efficient pooling strategies is lacking. Therefore we developed a model of the efficiency and accuracy of specimen pooling algorithms based on available data on SAR-CoV-2 viral dynamics. For a fixed number of tests, we estimate that programs using group testing could screen 2 to 20 times as many specimens compared to individual testing; increase the total number of true positive infections identified; and improve the positive predictive value of results. We compare outcomes that may be expected in different testing situations and provide general recommendations for group testing implementation. A free, publicly-available web calculator is provided to help inform laboratory decisions on SARS-CoV-2 pooling algorithms.

Title: Identification of spatial variations in COVID-19 epidemiological data using K-Means clustering algorithm: a global perspective Content: Background: Discerning spatial variations of COVID-19 through quantitative analysis operating on the geographically designated datasets relating to socio-demographics and epidemiological data facilitate strategy planning in curtailing the transmission of the disease and focus on articulation of necessary interventions in an informed manner. Methods: K-means clustering was employed on the available country-specific COVID-19 epidemiological data and the influential background characteristics. Country-specific case fatality rates and the average number of people tested positive for COVID-19 per every 10,000 population in each country were derived from the WHO COVID-19 situation report 107, and were used for clustering along with the background characteristics of proportion of countrys population aged >65 years and percentage GDP spent as public health expenditure. Results: The algorithm grouped the 89 countries into cluster 1 and Cluster 2 of sizes 54 and 35, respectively. It is apparent that Americas, European countries, and Australia formed a major part of cluster 2 with high COVID-19 case fatality rate, higher proportion of countrys population tested COVID-19 positive, higher percentage of GDP spent as public health expenditure, and greater percentage of population being more than 65 years of age. Conclusion: In spite of the positive correlation between high public health expenditure (%GDP) and COVID-19 incidence, case fatality rate, the immediate task ahead of most of the low and middle income countries is to strengthen their public health systems realizing that the correlation found in this study could be spurious in light of the underreported number of cases and poor death registration.

Title: Underestimation of COVID-19 cases in Japan: an analysis of RT-PCR testing for COVID-19 among 47 prefectures in Japan. Content: BACKGROUND Under the unique Japanese policy to restrict reverse transcriptase-polymerase chain reaction (RT-PCR) testing against severe acute respiratory syndrome coronavirus 2, a nationwide number of its confirmed cases and mortality remains to be low. Yet the information is lacking on geographical differences of these measures and their associated factors. AIM Evaluation of prefecture-based geographical differences and associated predictors for the incidence and number of RT-PCR tests for COVID-19. DESIGN Cross-sectional study using regression and correlation analysis. METHODS We retrieved domestic laboratory-confirmed cases, deaths, and the number of RT-PCR testing for COVID-19 from January 15 to April 6, 2020 in 47 prefectures in Japan, using publicly-available data by the Ministry of Health, Labour and Welfare. We did descriptive analyses of these three measures and identified significant predictors for the incidence and RT-PCR testing through multiple regression analyses and correlates with the number of deaths through correlation analysis. RESULTS The median prefectural-level incidence and number of RT-PCR testing per 100,000 population were 1.14 and 38.6, respectively. Multiple regression analyses revealed that significant predictors for the incidence were prefectural-level population (p < 0.001) and the number of RT-PCR testing (p = 0.03); and those for RT-PCR testing were the incidence (p = 0.025), available beds (p = 0.045) and cluster infections (p = 0.034). CONCLUSION Considering bidirectional association between the incidence and RT-PCR testing, there may have been an underdiagnosed population for the infection. The restraint policy for RT-PCR testing should be revisited to meet the increasing demand under the COVID-19 epidemic.

Title: Underestimation of COVID-19 cases in Japan: an analysis of RT-PCR testing for COVID-19 among 47 prefectures in Japan Content: BACKGROUND: Under the unique Japanese policy to restrict reverse transcriptase-polymerase chain reaction (RT-PCR) testing against severe acute respiratory syndrome coronavirus 2, a nationwide number of its confirmed cases and mortality remains to be low. Yet the information is lacking on geographical differences of these measures and their associated factors. AIM: Evaluation of prefecture-based geographical differences and associated predictors for the incidence and number of RT-PCR tests for COVID-19. DESIGN: Cross-sectional study using regression and correlation analysis. METHODS: We retrieved domestic laboratory-confirmed cases, deaths, and the number of RT-PCR testing for COVID-19 from January 15 to April 6, 2020 in 47 prefectures in Japan, using publicly-available data by the Ministry of Health, Labour and Welfare. We did descriptive analyses of these three measures and identified significant predictors for the incidence and RT-PCR testing through multiple regression analyses and correlates with the number of deaths through correlation analysis. RESULTS: The median prefectural-level incidence and number of RT-PCR testing per 100,000 population were 1.14 and 38.6, respectively. Multiple regression analyses revealed that significant predictors for the incidence were prefectural-level population (p < 0.001) and the number of RT-PCR testing (p = 0.03); and those for RT-PCR testing were the incidence (p = 0.025), available beds (p = 0.045) and cluster infections (p = 0.034). CONCLUSION: Considering bidirectional association between the incidence and RT-PCR testing, there may have been an underdiagnosed population for the infection. The restraint policy for RT-PCR testing should be revisited to meet the increasing demand under the COVID-19 epidemic.

Title: Excess registered deaths in England and Wales during the COVID-19 pandemic, March 2020 to May 2020 Content: Official counts of COVID-19 deaths have been criticized for potentially including people who did not die of COVID-19 but merely died with COVID-19. I address that critique by fitting a generalized additive model to weekly counts of all deaths registered in England and Wales during the 2010s. The model produces baseline rates of death registrations expected without the COVID-19 pandemic, and comparing those baselines to recent counts of registered deaths exposes the emergence of excess deaths late in March 2020. By April's end, England and Wales registered 45,300 $\pm$ 3200 excess deaths of adults aged 45+. Through 22 May, the last day of available all-deaths data, 56,600 $\pm$ 4400 were registered (about 53% of which were of men). Both the ONS's corresponding count of 43,205 death certificates which mention COVID-19, and the Department of Health and Social Care's count of 33,671 deaths, are appreciably less, implying that their counting methods have underestimated, not overestimated, the pandemic's true death toll. If underreporting rates have held steady during May, about 59,000 direct and indirect COVID-19 deaths might have been registered through the end of May but not yet publicly reported in full.

Title: COVID-19: Are Africa’s diagnostic challenges blunting response effectiveness? Content: Since its emergence in Wuhan, China in December 2019, novel Coronavirus disease - 2019 (COVID-19) has rapidly spread worldwide, achieving pandemic status on 11 (th) March, 2020. As of 1 (st) April 2020, COVID-19, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), had infected over 800,000 people and caused over 40,000 deaths in 205 countries and territories. COVID-19 has had its heaviest toll on Europe, United States and China. As of 1 (st) of April 2020, the number of confirmed COVID-19 cases in Africa was relatively low, with the highest number registered by South Africa, which had reported 1,380 confirmed cases. On the same date (also the date of this review), Africa had reported 5,999 confirmed cases, of which 3,838 (almost 65%) occurred in South Africa, Algeria, Egypt, Morocco and Tunisia, with the remaining 2,071 cases distributed unevenly across the other African countries. We speculate that while African nations are currently experiencing much lower rates of COVID-19 relative to other continents, their significantly lower testing rates may grossly underestimate incidence rates. Failure to grasp the true picture may mean crucial windows of opportunity shut unutilized, while limited resources are not deployed to maximum effect. In the absence of extensive testing data, an overestimation of spread may lead to disproportionate measures being taken, causing avoidable strain on livelihoods and economies. Here, based on the African situation, we discuss COVID-19 diagnostic challenges and how they may blunt responses.

Title: Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset Content: Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. A pretrained deep convolutional neural network has been tuned on 102 COVID-19 cases and 102 other pneumonia cases in a 10-fold cross validation. The results were all 102 COVID-19 cases were correctly classified and there were 8 false positives resulting in an AUC of 0.997. On a test set of 20 unseen COVID-19 cases all were correctly classified and more than 95% of 4171 other pneumonia examples were correctly classified. This study has flaws, most critically a lack of information about where in the disease process the COVID-19 cases were and the small data set size. More COVID-19 case images will enable a better answer to the question of how useful chest X-rays can be for diagnosing COVID-19 (so please send them).

Title: Barriers to conducting deprescribing in the elderly population amid the COVID-19 pandemic Content: Deprescribing aims to reduce polypharmacy, especially in the elderly population, in order to maintain or improve quality of life, reduce harm from medications, and limit healthcare expenditure. Coronavirus disease (COVID-19) is an infectious disease that has led to a pandemic and has changed the lives many throughout the world. The mode of transmission of this virus is from person to person through the transfer of respiratory droplets. Therefore, non-essential healthcare services involving direct patient interactions, including deprescribing, has been on hiatus to reduce spread. Barriers to deprescribing before the pandemic include patient and system related factors, such as resistance to change, patient's knowledge deficit about deprescribing, lack of alternatives for treatment of disease, uncoordinated delivery of health services, prescriber's attitudes and/or experience, limited availability of guidelines for deprescribing, and lack of evidence on preventative therapy. Some of these barriers can be mitigated by using the following interventions:patient education, prioritization of non-pharmacological therapy, incorporation of electronic health record (EHR), continuous prescriber education, and development of research studies on deprescribing. Currently, deprescribing cannot be delivered through in person interactions, so virtual care is a reasonable alternative format. The full incorporation of EHR throughout Canada can add to the success of this strategy. However, there are several challenges of conducting deprescribing virtually in the elderly population. These challenges include, but are not limited, to their inability to use technology, lack of literacy, lack of assistance from others, greater propensity for withdrawal effects, and increased risk of severe consequences, if hospitalized. Virtual care is the future of healthcare and in order to retain the benefits of deprescribing, additional initiatives should be in place to address the challenges that elderly patients may experience in accessing deprescribing virtually. These initiatives should involve teaching elderly patients how to use technology to access health services and with technical support in place to address any concerns.

Title: Estimating the true (population) infection rate for COVID-19: A Backcasting Approach with Monte Carlo Methods Content: Differences in COVID-19 testing and tracing across countries, as well as changes in testing within each country over time, make it difficult to estimate the true (population) infection rate based on the confirmed number of cases obtained through RNA viral testing. We applied a backcasting approach, coupled with Monte Carlo methods, to estimate a distribution for the true (population) cumulative number of infections (infected and recovered) for 15 countries where reliable data are available. We find a positive relationship between the testing rate per 1,000 people and the implied true detection rate of COVID-19, and a negative relationship between the proportion who test positive and the implied true detection rate. Our estimates suggest that the true number of people infected across our sample of 15 developed countries is 18.2 (5-95% CI: 11.9-39.0) times greater than the reported number of cases. In individual countries, the true number of cases exceeds the reported figure by factors that range from 1.7 (5-95% CI: 1.1-3.6) for Australia to 35.6 (5-95% CI: 23.2-76.3) for Belgium.

Title: COVID 19 in Bangladesh: Assumption of possible infection and death Content: Abstract: It is a painful job to predict the death of people. But sometimes it is important to predict the future and concern the government. A furious future is waiting for Bangladesh. Objective: Objective of the study is to assume the number of positive case and death till 30th December, 2020 in Bangladesh. Study design: This study was designed with systematic review and data analysis. Method: The study was completed by analyzing data available on website. First COVID 19 case in Bangladesh was identified on 8th March. Analyzing the data increasing rate/common ratio of infection and death has been identified. Then this common ratio has been used in the formula of multiplication series. Data of China, Iran, Italy and the USA was also analyzed to assume how the death and case number increased. Social issues of Bangladesh were also analyzed. Considering all these the assumption was made. Result: It has been assumed that by the 43rd week (on 30th December, 2020) of first identification the total case can be 17024322 and total death can be 86265 by 30 December, 2020. As this is an assumption this can be true, partially true or false. But the base of assumption is strong enough so the possibility of being true or nearly true is higher. Policy Suggestion: Government should choose properly one between two options. Either government should declare curfew or let people lead normal life. A very weak lock down for a long time does not make any sense.

Title: Urologic Surgery and COVID-19: How the Pandemic Is Changing the Way We Operate Content: The coronavirus disease 2019 (COVID-19) pandemic has had a global impact on all aspects of health care, including surgical procedures. For urologists, it has affected and will continue to influence how we approach the care of patients preoperatively, intraoperatively, and postoperatively. A risk-benefit assessment of each patient undergoing surgery should be performed during the COVID-19 pandemic based on the urgency of the surgery and the risk of viral illness and transmission. Patients with advanced age and comorbidities have a higher incidence of mortality. Routine preoperative testing and symptom screening is recommended to identify those with COVID-19. Adequate personal protective equipment (PPE) for the surgical team is essential to protect health care workers and ensure an adequate workforce. For COVID-19 positive or suspected patients, the use of N95 respirators is recommended if available. The anesthesia method chosen should attempt to minimize aerosolization of the virus. Negative pressure rooms are strongly preferred for intubation/extubation and other aerosolizing procedures for COVID-19 positive patients or when COVID status is unknown. Although transmission has not yet been shown during laparoscopic and robotic procedures, efforts should be made to minimize the risk of aerosolization. Ultra-low particulate air filters are recommended for use during minimally invasive procedures to decrease the risk of viral transmission. Thorough cleaning and sterilization should be performed postoperatively with adequate time allowed for the operating room air to be cycled after procedures. COVID-19 patients should be separated from noninfected patients at all levels of care, including recovery, to decrease the risk of infection. Future directions will be guided by outcomes and infection rates as social distancing guidelines are relaxed and more surgical procedures are reintroduced. Recommendations should be adapted to the local environment and will continue to evolve as more data become available, the shortage of testing and PPE is resolved, and a vaccine and therapeutics for COVID-19 are developed.

Title: A novel IDEA: The impact of serial interval on a modified-Incidence Decay and Exponential Adjustment (m-IDEA) model for projections of daily COVID-19 cases Content: The SARS-CoV-2 virus causes the disease COVID-19, and has caused high morbidity and mortality worldwide. Empirical models are useful tools to predict future trends of disease progression such as COVID-19 over the near-term. A modified Incidence Decay and Exponential Adjustment (m-IDEA) model was developed to predict the progression of infectious disease outbreaks. The modification allows for the production of precise daily estimates, which are critical during a pandemic of this scale for planning purposes. The m-IDEA model was employed using a range of serial intervals given the lack of knowledge on the true serial interval of COVID-19. Both deterministic and stochastic approaches were applied. Model fitting was accomplished through minimizing the sum-of-square differences between predicted and observed daily incidence case counts, and performance was retrospectively assessed. The performance of the m-IDEA for projection cases in the near-term was improved using shorter serial intervals (1–4 days) at early stages of the pandemic, and longer serial intervals at mid-to late-stages (5–9 days) thus far. This, coupled with epidemiological reports, suggests that the serial interval of COVID-19 might increase as the pandemic progresses, which is rather intuitive: Increasing serial intervals can be attributed to gradual increases in public health interventions such as facility closures, public caution and social distancing, thus increasing the time between transmission events. In most cases, the stochastic approach captured the majority of future reported incidence data, because it accounts for the uncertainty around the serial interval of COVID-19. As such, it is the preferred approach for using the m-IDEA during dynamic situation such as in the midst of a major pandemic.

Title: Urologic surgery and COVID-19: How the pandemic is changing the way we operate. Content: The coronavirus disease 2019 (COVID-19) pandemic has had a global impact on all aspects of healthcare, including surgical procedures. For urologists, it has affected and will continue to influence how we approach the care of patients pre-operatively, intra-operatively, and post-operatively. A risk-benefit assessment of each patient undergoing surgery should be performed during the COVID-19 pandemic based on the urgency of the surgery and the risk of viral illness and transmission. Patients with advanced age and comorbidities have a higher incidence of mortality. Routine preoperative testing and symptom screening is recommended to identify those with COVID-19. Adequate personal protective equipment (PPE) for the surgical team is essential to protect healthcare workers and ensure an adequate workforce. For COVID-19 positive or suspected patients, the use of N95 respirators is recommended if available. The anesthesia method chosen should attempt to minimize aerosolization of the virus. Negative pressure rooms are strongly preferred for intubation/extubation and other aerosolizing procedures. Although transmission has not yet been shown during laparoscopic and robotic procedures, efforts should be made to minimize the risk of aerosolization. Ultra low particulate air filters are recommended for use during minimally invasive procedures to decrease the risk of viral transmission. Thorough cleaning and sterilization should be performed post-operatively with adequate time allowed for the operating room air to be cycled after procedures. COVID-19 patients should be separated from non-infected patients at all levels of care including recovery to decrease the risk of infection. Future directions will be guided by outcomes and infection rates as social distancing guidelines are relaxed and more surgical procedures are reintroduced. Recommendations should be adapted to the local environment and will continue to evolve as more data becomes available, the shortage of testing and PPE is resolved, and a vaccine and therapeutics for COVID-19 are developed.

Title: Estimation of Undetected Covid-19 Infections in India Content: Background and Objectives: While the number of detected COVID-19 infections are widely available, an understanding of the extent of undetected COVID- 19 cases is urgently needed for an effective tackling of the pandemic and as a guide to lifting the lockdown. The aim of this work is to estimate and predict the true number of COVID-19 (detected and undetected) infections in India for short to medium forecast horizons. In particular, using publicly available COVID-19 infection data upto 16th April 2020, we predict the true number of infections in India during and upto the end of the formal lockdown period (21st April 2020). Methods: The high death rate observed in most COVID-19 hit countries is suspected to be a function of the undetected infections existing in the population. An estimate of the age weighted infection fatality rate (IFR) of the disease of 0.41%, specifically calculated by taking into account the age structure of Indian population, is already available in the literature. In addition, the recorded case fatality rate (CFR= 0.70%) of Kerala, the only state in India to report single digit new infections over the second week of April, is used as a second estimate of the IFR. These estimates are used to formulate a relationship between deaths recorded and the true number of infections. The estimated undetected and detected cases time series based on these two IFR estimates are then used to fit a discrete time multivariate infection model to predict the total infections at the end of the formal lockdown period. Results: In two consecutive fortnights during the lockdown, it was noted that the rise in detected infections has decreased by 2.7 times. For an IFR of 0.41%, the rise in undetected infections decreased by 3.2 times and the predicted number of total infections in India is 3.14 lakhs. While for an IFR of 0.70%, the rise in undetected cases decreased by 3.3 times and the total number of infections predicted on 21st April is 1.75 lakhs. Interpretation and Conclusions: The behaviour of the undetected cases over time effectively illustrates the effects of lockdown and increased testing. From our estimates, it is found that the lockdown has brought down the undetected to detected cases ratio, and has consequently dampened the increase in the number of total cases. However, even though the rate of rise in total infections has fallen, the lifting of the lockdown should be done keeping in mind that 1.75 to 3 lakhs undetected cases will already exist in the population on 21st April.

Title: Number of International Arrivals Predicts Severity of the first Global Wave of the COVID-19 Pandemic Content: Background: Reported death rates from different countries during the COVID-19 pandemic vary. Lack of universal testing and death underreporting make between-country comparisons difficult. The country-level determinants of COVID-19 mortality are unknown. Objective: Derive a measure of COVID-related death rates that is comparable across countries and identify its country-level predictors. Methods: An ecological study design of publicly available data was employed. Countries reporting >25 COVID-related deaths until May 1, 2020 were included. The outcome was the mean mortality rate from COVID-19, an estimate of the country-level daily increase in reported deaths during the ascending phase of the epidemic curve. Potential predictors assessed were most recently published Demographic parameters (population and population density, percentage population living in urban areas, median age, average body mass index, smoking prevalence), Economic parameters (Gross Domestic Product per capita; environmental parameters: pollution levels, mean temperature (January-April)), co-morbidities (prevalence of diabetes, hypertension and cancer), health systems parameters (WHO Health Index and hospital beds per 10,000 population and international arrivals). Multivariable linear regression was used to analyse the data. Results: Thirty-one countries were included. Of all country-level predictors included in the multivariable model, only total number of international arrivals was significantly associated with the mean death rate: Beta 0.3798 (95% Confidence Interval 0.2414, 0.5182), P <0.001. Conclusion: International travel was directly associated with the mortality slope and thus potentially the spread of COVID-19. Stopping international travel, particularly from affected areas, may be the most effective strategy to control COVID outbreak and prevent related deaths.

Title: High-sensitivity COVID-19 group testing by digital PCR Content: Background: Worldwide demand for SARS-CoV-2 RT-PCR testing is increasing as more countries are impacted by COVID-19 and as testing remains central to contain the spread of the disease, both in countries where the disease is emerging and in countries that are past the first wave but exposed to re-emergence. Group testing has been proposed as a solution to expand testing capabilities but sensitivity concerns have limited its impact on the management of the pandemic. Digital PCR (RT-dPCR) has been shown to be more sensitive than RT-PCR and could help in this context. Methods: We implemented RT-dPCR based COVID-19 group testing on commercially available system and assay (Naica System from Stilla Technologies) and investigated the sensitivity of the method in real life conditions of a university hospital in Paris, France, in May 2020. We tested the protocol in a direct comparison with reference RT-PCR testing on 448 samples split into groups of 3 sizes for RT-dPCR analysis: 56 groups of 8 samples, 28 groups of 16 samples and 14 groups of 32 samples. Results: Individual RT-PCR testing identified 25 positive samples. Using groups of 8, testing by RT-dPCR identified 23 groups as positive, corresponding to 26 true positive samples including 2 samples not initially detected by individual RT-PCR but confirmed positive by further RT-PCR and RT-dPCR investigation. For groups of 16, 15 groups tested positive, corresponding to 25 true positive samples identified. 100% concordance is found for groups of 32 but with limited data points.

Title: It can be dangerous to take epidemic curves of COVID-19 at face value Content: During an epidemic with a new virus, we depend on modelling to plan the response: but how good are the data? The aim of our work was to better understand the impact of misclassification errors in identification of true cases of COVID-19 on epidemic curves. Data originated from Alberta, Canada (available on 28 May 2020). There is presently no information of sensitivity (Sn) and specificity (Sp) of laboratory tests used in Canada for the causal agent for COVID-19. Therefore, we examined best attainable performance in other jurisdictions and similar viruses. This suggested perfect Sp and Sn 60–95%. We used these values to re-calculate epidemic curves to visualize the potential bias due to imperfect testing. If the sensitivity improved, the observed and adjusted epidemic curves likely fall within 95% confidence intervals of the observed counts. However, bias in shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60–70% range. These issues are minor early in the epidemic, but hundreds of undiagnosed cases are likely later on. It is therefore hazardous to judge progress of the epidemic based on observed epidemic curves unless quality of testing is better understood. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.17269/s41997-020-00367-6) contains supplementary material, which is available to authorized users.

Title: Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazi Content: The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Currently, several Brazilian states are in a state of lock-down. However, there is political pressure for this type of measures to be lifted. This work considers the impact that such a termination would have on how the virus evolves locally. This was done by extending the SEIR model with an on / off strategy. Given the simplicity of SEIR we also attempted to gain more insight by developing a neural regressor. We chose to employ features that current clinical studies have pinpointed has having a connection to the lethality of COVID-19. We discuss how this data can be processed in order to obtain a robust assessment.

Title: Underreporting of death by COVID-19 in Brazil's second most populous state Content: The COVID-19 pandemic brings to light the reality of the Brazilian health system. The underreporting of COVID-19 deaths in the state of Minas Gerais (MG), where is concentrated the second largest population of the country, reveals government unpreparedness, as there is a low capacity of testing in the population, which prevents the real understanding of the general panorama of Sars-Cov-2 dissemination. The goals of this research are to analyze the causes of deaths in the different Brazilian government databases (ARPEN and SINAN) and to assess whether there are sub-records shown by the unexpected increase in the frequency of deaths from causes clinically similar to COVID-19. A descriptive and quantitative analysis of the number of COVID-19 deaths and similar causes was made in different databases. Ours results demonstrate that the different official sources had a discrepancy of 209.23% between these data referring to the same period. There was also a 648.61% increase in SARS deaths in 2020, when compared to the average of previous years. Finally, it was shown that there was an increase in the rate of pneumonia and respiratory insufficiency (RI) by 5.36% and 5.72%, respectively. In conclusion, there is an underreporting of COVID-19 deaths in MG due to the unexplained excess of SARS deaths, Respiratory insufficiency and pneumonia compared to previous years.

Title: COVID-19 in England: spatial patterns and regional outbreaks Content: Aims: to investigate the spatiotemporal distribution of COVID-19 cases in England; to provide spatial quantification of risk at a high resolution; to provide information for prospective antigen and serological testing. Approach: We fit a spatiotemporal Negative Binomial generalised linear model to Public Health England SARS-CoV-2 testing data at the Lower Tier Local Authority region level. We assume an order-1 autoregressive model for case progression within regions, coupling discrete spatial units via observed commuting data and time-varying measures of traffic flow. We fit the model via maximum likelihood estimation in order to calculate region-specific risk of ongoing transmission, as well as measuring regional uncertainty in incidence. Results: We detect marked heterogeneity across England in COVID-19 incidence, not only in raw estimated incidence, but in the characteristics of within-region and between-region dynamics of PHE testing data. There is evidence for a spatially diverse set of regions having a higher daily increase of cases than others, having accounted for current case numbers, population size, and human mobility. Uncertainty in model estimates is generally greater in rural regions. Conclusions: A wide range of spatial heterogeneity in COVID-19 epidemic distribution and infection rate exists in England currently. Future work should incorporate fine-scaled demographic and health covariates, with continued improvement in spatially-detailed case reporting data. The method described here may be used to measure heterogeneity in real-time as behavioural and social interventions are relaxed, serving to identify "hotspots" of resurgent cases occurring in diverse areas of the country, and triggering locally-intensive surveillance and interventions as needed. Caveats: There is general concern over the ability of PHE testing data to capture the true prevalence of infection within the population, though this approach is designed to provide measures of spatial prevalence based on testing that can be used to guide further future testing effort. Now-casts of epidemic characteristics are presented based on testing data alone (as opposed to "true" prevalence in any one area). The model used in this analysis is phenomenological for ease and speed of principled parameter inference; we choose the model which best fits the current spatial case timeseries, without attempting to enforce "SIR"-type epidemic dynamics.

Title: Translating COVID-19 Pandemic Surge Theory to Practice in the Emergency Department: How to Expand Structure Content: Multiple professional societies, nongovernment and government agencies have studied the science of sudden onset disaster mass casualty incidents to create and promote surge response guidelines. The COVID-19 pandemic has presented the health-care system with challenges that have limited science to guide the staff, stuff, and structure surge response.This study reviewed the available surge science literature specifically to guide an emergency department's surge structural response using a translational science approach to answer the question: How does the concept of sudden onset mass casualty incident surge capability apply to the process to expand COVID-19 pandemic surge structure response?The available surge structural science literature was reviewed to determine the application to a pandemic response. The on-line ahead of print and print COVID-19 scientific publications, as well as gray literature were studied to learn the best available COVID-19 surge structural response science. A checklist was created to guide the emergency department team's COVID-19 surge structural response.

Title: Translating COVID-19 Pandemic Surge Theory to Practice in the Emergency Department: How to Expand Structure Content: Multiple professional societies, nongovernment and government agencies have studied the science of sudden onset disaster mass casualty incidents to create and promote surge response guidelines. The COVID-19 pandemic has presented the health-care system with challenges that have limited science to guide the staff, stuff, and structure surge response. This study reviewed the available surge science literature specifically to guide an emergency department’s surge structural response using a translational science approach to answer the question: How does the concept of sudden onset mass casualty incident surge capability apply to the process to expand COVID-19 pandemic surge structure response? The available surge structural science literature was reviewed to determine the application to a pandemic response. The on-line ahead of print and print COVID-19 scientific publications, as well as gray literature were studied to learn the best available COVID-19 surge structural response science. A checklist was created to guide the emergency department team’s COVID-19 surge structural response.

Title: Host response-based screening to identify undiagnosed cases of COVID-19and expand testing capacity Content: The COVID-19 pandemic has created unprecedented challenges in diagnostic testing. At the beginning of the epidemic, a confluence of factors resulted in delayed deployment of PCR-based diagnostic tests, resulting in lack of testing of individuals with symptoms of the disease. Although these tests are now more widely available, it is estimated that a three- to ten-fold increase in testing capacity will be required to ensure adequate surveillance as communities reopen(1). In response to these challenges, we evaluated potential roles of host-response based screening in the diagnosis of COVID-19. Previous work from our group showed that the nasopharyngeal (NP) level of CXCL10, a protein produced as part of the host response to viral infection, is a sensitive predictor of respiratory virus infection across a wide spectrum of viruses(2). Here, we show that NP CXCL10 is elevated during SARS-CoV-2 infection and use a CXCL10-based screening strategy to identify four undiagnosed cases of COVID-19 in Connecticut in early March. In a second set of samples tested at the Yale New Haven Hospital, we show that NP CXCL10 had excellent performance as a rule-out test (NPV 0.99, 95% C.I. 0.985-0.997). Our results demonstrate how biomarker-based screening could be used to leverage existing PCR testing capacity to rapidly enable widespread testing for COVID-19.

Title: Modelling the Effect of Area-deprivation on COVID-19 Incidences: A Study of Chennai Megacity, India Content: Abstract Objectives Socioeconomic inequalities may affect COVID-19 incidence. The goal of the research was to explore the association between deprivation of socioeconomic status (SES) and spatial patterns of COVID-19 incidence in Chennai megacity for unfolding the disease epidemiology. Study design Ecological (or contextual) study for electoral wards (sub-cities) of Chennai megacity. Methods Using data of confirmed COVID-19 cases from May 15, 2020, to May 21, 2020, for 155 electoral wards obtained from the official website of the Chennai municipal corporation, we examined the incidence of COVID-19 diseases using two count regression models namely, Poisson Regression (PR) and Negative Binomial Regression (NBR). As explanatory factors, we considered area-deprivation that represented the deprivation of socioeconomic status (SES). An index of multiple deprivations (IMD) developed to measure the area-deprivation using an advanced local statistic, Geographically Weighted Principal Component Analysis (GWPCA). Based on the availability of appropriately scaled data, five domains (i.e. poor housing condition, low asset possession, poor availability of WaSH services, lack of household amenities and services, and gender disparity) were selected as components of the IMD in this study. Results The Hot-spot analysis revealed that area-deprivation was significantly associated with higher incidences of COVID-19 in Chennai megacity. The high variations (adj. R2: 72.2%) with the lower BIC (124.34) and AIC (112.12) for the NBR compared to PR suggests that the NBR model better explains the relationship between area-deprivation and COVID-19 incidences in Chennai megacity. NBR with two-sided tests, and p<0.05 was considered statistically significant. The outcome of the PR and NBR suggests that when all other variables were constant, according to NBR, the relative risk (RR) of COVID-19 incidences was 2.19 for the wards with high housing deprivation or in other words, the wards with high housing deprivation having 119% higher probability (RR= e0.786=2.19, 95% CI=1.98 to 2.40) compared to areas with low deprivation. Similarly, in the wards with poor availability of WaSH services, chances of having COVID-19 incidence was 90% higher compared to the wards with good WaSH services (RR= e0.642=1.90, 95% CI=1.79 to 2.00). Spatial risks of COVID-19 infections were predominantly concentrated in the wards with higher levels of area-deprivation which were mostly located in the north-eastern parts of Chennai megacity. Conclusions We formulated an area-based IMD, which was substantially related to COVID-19 incidences in the Chennai megacity. This study highlights that the risks of COVID-19 infections tend to be higher in more deprived areas of SES and the north-eastern part of Chennai megacity was predominantly high-risk areas. Our results can guide measures of COVID-19 control and prevention by considering spatial risks and area-deprivation.

Title: Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios Content: Background: COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose: This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods: A Naive-Bayes model for machine learning is proposed for handling different scarcity scenarios, including managing symptomatic essential workforce and absence of diagnostic tests. Hemogram result data was used to predict qRT-PCR results in situations where the latter was not performed, or results are not yet available. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results: Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity. Data assessment can be performed in an individual or simultaneous basis, according to desired outcome. Based on hemogram data and background scarcity context, resource distribution is significantly optimized when model-based patient selection is observed, compared to random choice. The model can help manage testing deficiency and other critical circumstances. Conclusions: Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

Title: Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios Content: COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods: A Naive-Bayes model for machine learning is proposed for handling different scarcity scenarios, including managing symptomatic essential workforce and absence of diagnostic tests. Hemogram result data was used to predict qRT-PCR results in situations where the latter was not performed, or results are not yet available. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity. Data assessment can be performed in an individual or simultaneous basis, according to desired outcome. Based on hemogram data and background scarcity context, resource distribution is significantly optimized when model-based patient selection is observed, compared to random choice. The model can help manage testing deficiency and other critical circumstances. Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

Title: Spatiotemporal Analysis of the 2014 Ebola Epidemic in West Africa Content: In 2014–2016, Guinea, Sierra Leone and Liberia in West Africa experienced the largest and longest Ebola epidemic since the discovery of the virus in 1976. During the epidemic, incidence data were collected and published at increasing resolution. To monitor the epidemic as it spread within and between districts, we develop an analysis method that exploits the full spatiotemporal resolution of the data by combining a local model for time-varying effective reproduction numbers with a gravity-type model for spatial dispersion of the infection. We test this method in simulations and apply it to the weekly incidences of confirmed and probable cases per district up to June 2015, as reported by the World Health Organization. Our results indicate that, of the newly infected cases, only a small percentage, between 4% and 10%, migrates to another district, and a minority of these migrants, between 0% and 23%, leave their country. The epidemics in the three countries are found to be similar in estimated effective reproduction numbers, and in the probability of importing infection into a district. The countries might have played different roles in cross-border transmissions, although a sensitivity analysis suggests that this could also be related to underreporting. The spatiotemporal analysis method can exploit available longitudinal incidence data at different geographical locations to monitor local epidemics, determine the extent of spatial spread, reveal the contribution of local and imported cases, and identify sources of introductions in uninfected areas. With good quality data on incidence, this data-driven method can help to effectively control emerging infections.

Title: Simulation-based Estimation of the Spread of COVID-19 in Iran Content: Background: The 2019 Coronavirus (COVID-19) has turned into a global pandemic with unprecedented challenges for the global community. Understanding the state of the disease and planning for future trajectories relies heavily on data on the spread and mortality. Yet official data coming from various countries are highly unreliable: symptoms similar to common cold in majority of cases and limited screening resources and delayed testing procedures may contribute to under-estimation of the burden of disease. Anecdotal and more limited data are available, but few have systematically combined those with official statistics into a coherent view of the epidemic. This study is a modeling-in-real-time of the emerging outbreak for understanding the state of the disease. Our focus is on the case of the spread of disease in Iran, as one of the epicenters of the disease in the first months of 2020. Method: We develop a simple dynamic model of the epidemic to provide a more reliable picture of the state of the disease based on existing data. Building on the generic SEIR (Susceptible, Exposed, Infected, and Recovered) framework we incorporate two behavioral and logistical considerations. First we capture the endogenous changes in contact rate (average contact per person) as more death are reported. As a result the reproduction number changes endogenously in the model. Second we differentiate reported and true cases by including simple formulations for how only a fraction of cases might be diagnosed, and how that fraction changes in response to epidemic's progression. In estimating the model we use both the official data as well as the discovered infected travelers and unofficial medical community estimates and triangulate these sources to build a more complete picture. Calibration is completed by forming a likelihood function for observing the actual time series data conditional on model parameters, and conducting a Markov Chain Monte Carlo simulations. The model is used to estimate current "true" cases of infection and death. We analyze the future trajectory of the disease under six conditions related to the seasonal effects and policy measures targeting social distancing. Findings: The model closely replicates the past data but also shows the true number of cases is likely far larger. We estimate about 493,000 current infected cases (90% CI: 271K-810K) as of March 20th, 2020. Our estimate for cumulative cases of infection until that date is 916,000 (90% CI: 508K, 1.5M), and for total death is 15,485 (90% CI: 8.4K, 25.8K). These numbers are significantly (more than one order of magnitude) higher than official statistics. The trajectory of the epidemic until the end of June could take various paths depending on the impact of seasonality and policies targeting social distancing. In the most optimistic scenario for seasonal effects, depending on policy measures, 1.6 million Iranians (90% CI: 0.9M-2.6M) are likely to get infected, and death toll will reach about 58,000 cases (90% CI: 32K-97K), while in the more pessimistic scenarios, death toll may exceed 103,000 cases (90% CI: 56K-172K). Implication: Our results suggest that the number of cases and deaths may be over an order of magnitude larger than official statistics in Iran. Absent extended testing capacity other countries may face a significant under-count of existing cases and thus be caught off guard about the actual toll of the epidemic.

Title: COVID-19: The unreasonable effectiveness of simple models Content: When the novel coronavirus disease SARS-CoV2 (COVID-19) was officially declared a pandemic by the WHO in March 2020, the scientific community had already braced up in the effort of making sense of the fast-growing wealth of data gathered by national authorities all over the world. However, despite the diversity of novel theoretical approaches and the comprehensiveness of many widely established models, the official figures that recount the course of the outbreak still sketch a largely elusive and intimidating picture. Here we show unambiguously that the dynamics of the COVID-19 outbreak belongs to the simple universality class of the SIR model and extensions thereof. Our analysis naturally leads us to establish that there exists a fundamental limitation to any theoretical approach, namely the unpredictable non-stationarity of the testing frames behind the reported figures. However, we show how such bias can be quantified self-consistently and employed to mine useful and accurate information from the data. In particular, we describe how the time evolution of the reporting rates controls the occurrence of the apparent epidemic peak, which typically follows the true one in countries that were not vigorous enough in their testing at the onset of the outbreak. The importance of testing early and resolutely appears as a natural corollary of our analysis, as countries that tested massively at the start clearly had their true peak earlier and less deaths overall.

Title: COVID-19: The unreasonable effectiveness of simple models Content: Abstract When the novel coronavirus disease SARS-CoV2 (COVID-19) was officially declared a pandemic by the WHO in March 2020, the scientific community had already braced up in the effort of making sense of the fast-growing wealth of data gathered by national authorities all over the world. However, despite the diversity of novel theoretical approaches and the comprehensiveness of many widely established models, the official figures that recount the course of the outbreak still sketch a largely elusive and intimidating picture. Here we show unambiguously that the dynamics of the COVID-19 outbreak belongs to the simple universality class of the SIR model and extensions thereof. Our analysis naturally leads us to establish that there exists a fundamental limitation to any theoretical approach, namely the unpredictable non-stationarity of the testing frames behind the reported figures. However, we show how such bias can be quantified self-consistently and employed to mine useful and accurate information from the data. In particular, we describe how the time evolution of the reporting rates controls the occurrence of the apparent epidemic peak, which typically follows the true one in countries that were not vigorous enough in their testing at the onset of the outbreak. The importance of testing early and resolutely appears as a natural corollary of our analysis, as countries that tested massively at the start clearly had their true peak earlier and less deaths overall.

Title: Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States Content: Detection of SARS-CoV-2 infections to date has relied heavily on RT-PCR testing. However, limited test availability, high false-negative rates, and the existence of asymptomatic or sub-clinical infections have resulted in an under-counting of the true prevalence of SARS-CoV-2. Here, we show how influenza-like illness (ILI) outpatient surveillance data can be used to estimate the prevalence of SARS-CoV-2. We found a surge of non-influenza ILI above the seasonal average in March 2020 and showed that this surge correlated with COVID-19 case counts across states. If 1/3 of patients infected with SARS-CoV-2 in the US sought care, this ILI surge would have corresponded to more than 8.7 million new SARS-CoV-2 infections across the US during the three-week period from March 8 to March 28, 2020. Combining excess ILI counts with the date of onset of community transmission in the US, we also show that the early epidemic in the US was unlikely to have been doubling slower than every 4 days. Together these results suggest a conceptual model for the COVID-19 epidemic in the US characterized by rapid spread across the US with over 80% infected patients remaining undetected. We emphasize the importance of testing these findings with seroprevalence data and discuss the broader potential to use syndromic surveillance for early detection and understanding of emerging infectious diseases.

Title: It can be dangerous to take epidemic curves of COVID-19 at face value Content: During an epidemic with a new virus, we depend on modelling to plan the response: but how good are the data? The aim of our work was to better understand the impact of misclassification errors in identification of true cases of COVID-19 on epidemic curves. Data originated from Alberta, Canada (available on 28 May 2020). There is presently no information of sensitivity (Sn) and specificity (Sp) of laboratory tests used in Canada for the causal agent for COVID-19. Therefore, we examined best attainable performance in other jurisdictions and similar viruses. This suggested perfect Sp and Sn 60-95%. We used these values to re-calculate epidemic curves to visualize the potential bias due to imperfect testing. If the sensitivity improved, the observed and adjusted epidemic curves likely fall within 95% confidence intervals of the observed counts. However, bias in shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. These issues are minor early in the epidemic, but hundreds of undiagnosed cases are likely later on. It is therefore hazardous to judge progress of the epidemic based on observed epidemic curves unless quality of testing is better understood.

Title: BCG vaccination policy and preventive chloroquine usage: do they have an impact on COVID-19 pandemic? Content: Coronavirus disease 2019 (COVID-19) is a severe acute respiratory syndrome caused by Coronavirus 2 (SARS-CoV-2). In the light of its rapid global spreading, on 11 March 2020, the World Health Organization has declared it a pandemic. Interestingly, the global spreading of the disease is not uniform, but has so far left some countries relatively less affected. The reason(s) for this anomalous behavior are not fully understood, but distinct hypotheses have been proposed. Here we discuss the plausibility of two of them: the universal vaccination with Bacillus Calmette-Guerin (BCG) and the widespread use of the antimalarial drug chloroquine (CQ). Both have been amply discussed in the recent literature with positive and negative conclusions: we felt that a comprehensive presentation of the data available on them would be useful. The analysis of data for countries with over 1000 reported COVID-19 cases has shown that the incidence and mortality were higher in countries in which BCG vaccination is either absent or has been discontinued, as compared with the countries with universal vaccination. We have performed a similar analysis of the data available for CQ, a widely used drug in the African continent and in other countries in which malaria is endemic; we discuss it here because CQ has been used as the drug to treat COVID-19 patients. Several African countries no longer recommend it officially for the fight against malaria, due to the development of resistance to Plasmodium, but its use across the continent is still diffuse. Taken together, the data in the literature have led to the suggestion of a possible inverse correlation between BCG immunization and COVID-19 disease incidence and severity.

Title: BCG vaccination policy and preventive chloroquine usage: do they have an impact on COVID-19 pandemic? Content: Coronavirus disease 2019 (COVID-19) is a severe acute respiratory syndrome caused by Coronavirus 2 (SARS-CoV-2). In the light of its rapid global spreading, on 11 March 2020, the World Health Organization has declared it a pandemic. Interestingly, the global spreading of the disease is not uniform, but has so far left some countries relatively less affected. The reason(s) for this anomalous behavior are not fully understood, but distinct hypotheses have been proposed. Here we discuss the plausibility of two of them: the universal vaccination with Bacillus Calmette–Guerin (BCG) and the widespread use of the antimalarial drug chloroquine (CQ). Both have been amply discussed in the recent literature with positive and negative conclusions: we felt that a comprehensive presentation of the data available on them would be useful. The analysis of data for countries with over 1000 reported COVID-19 cases has shown that the incidence and mortality were higher in countries in which BCG vaccination is either absent or has been discontinued, as compared with the countries with universal vaccination. We have performed a similar analysis of the data available for CQ, a widely used drug in the African continent and in other countries in which malaria is endemic; we discuss it here because CQ has been used as the drug to treat COVID-19 patients. Several African countries no longer recommend it officially for the fight against malaria, due to the development of resistance to Plasmodium, but its use across the continent is still diffuse. Taken together, the data in the literature have led to the suggestion of a possible inverse correlation between BCG immunization and COVID-19 disease incidence and severity.

Title: Deep learning COVID-19 detection bias: accuracy through artificial intelligence Content: BACKGROUND: Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests. METHODS: We developed a deep learning model to improve accuracy of reported cases and to precisely predict the disease from chest X-ray scans. Our model relied on convolutional neural networks (CNNs) to detect structural abnormalities and disease categorization that were keys to uncovering hidden patterns. To do so, a transfer learning approach was deployed to perform detections from the chest anterior-posterior radiographs of patients. We used publicly available datasets to achieve this. RESULTS: Our results offer very high accuracy (96.3%) and loss (0.151 binary cross-entropy) using the public dataset consisting of patients from different countries worldwide. As the confusion matrix indicates, our model is able to accurately identify true negatives (74) and true positives (32); this deep learning model identified three cases of false-positive and one false-negative finding from the healthy patient scans. CONCLUSIONS: Our COVID-19 detection model minimizes manual interaction dependent on radiologists as it automates identification of structural abnormalities in patient's CXRs, and our deep learning model is likely to detect true positives and true negatives and weed out false positive and false negatives with > 96.3% accuracy.

Title: Deep learning COVID-19 detection bias: accuracy through artificial intelligence Content: BACKGROUND: Detection of COVID-19 cases’ accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests. METHODS: We developed a deep learning model to improve accuracy of reported cases and to precisely predict the disease from chest X-ray scans. Our model relied on convolutional neural networks (CNNs) to detect structural abnormalities and disease categorization that were keys to uncovering hidden patterns. To do so, a transfer learning approach was deployed to perform detections from the chest anterior-posterior radiographs of patients. We used publicly available datasets to achieve this. RESULTS: Our results offer very high accuracy (96.3%) and loss (0.151 binary cross-entropy) using the public dataset consisting of patients from different countries worldwide. As the confusion matrix indicates, our model is able to accurately identify true negatives (74) and true positives (32); this deep learning model identified three cases of false-positive and one false-negative finding from the healthy patient scans. CONCLUSIONS: Our COVID-19 detection model minimizes manual interaction dependent on radiologists as it automates identification of structural abnormalities in patient’s CXRs, and our deep learning model is likely to detect true positives and true negatives and weed out false positive and false negatives with > 96.3% accuracy.

Title: Cancer therapy and treatments during COVID-19 era Content: The COVID-19 pandemic has put a serious strain on health treatments as well at the economies of many nations. Unfortunately, there is not currently available vaccine for SARS-Cov-2/COVID-19. Various types of patients have delayed treatment or even routine check-ups and we are adapting to a virtual world. In many cases, surgeries are delayed unless they are essential. This is also true with regards to cancer treatments and screening. Interestingly, some existing drugs and nutraceuticals have been screened for their effects on COVID-19. Certain FDA approved drugs, vitamin, natural products and trace minerals may be repurposed to treat or improve the prevention of COVID-19 infections and disease progression. This review article will summarize how the treatments of various cancer patients has changed during the COVID-19 era as well as discuss the promise of some existing drugs and other agents to be repurposed to treat this disease.

Title: Analysis of the sex ratio of reported gonorrhoea incidence in Shenzhen, China Content: OBJECTIVE: To assess the clinical process of gonorrhoea diagnosis and report in China, and to determine the difference of sex ratio between reported incidence based on reporting data and true diagnosis rate based on reference tests of gonorrhoea. SETTING: A total of 26 dermatology and sexually transmitted disease (STD) departments, 34 obstetrics-gynaecology clinics and 28 urology outpatient clinics selected from 34 hospitals of Shenzhen regarded as our study sites. PARTICIPANTS: A total of 2754 participants were recruited in this study, and 2534 participants completed the questionnaire survey and provided genital tract secretion specimens. There were 1106 male and 1428 female participants. Eligible participants were patients who presented for outpatient STD care at the selected clinics for the first time in October 2012 were at least 18 years old, and were able to give informed consent. OUTCOME MEASURES: Untested rate, true-positive rate, false-negative rate and unreported rate of gonorrhoea, as well as reported gonorrhoea incidence sex ratio and true diagnosis sex ratio were calculated and used to describe the results. RESULTS: 2534 participants were enrolled in the study. The untested rate of gonorrhoea among females was significantly higher than that among males (female 88.1%, male 68.3%, p=0.001). The male-to-female sex ratios of untested rate, true-positive rate, false-negative rate and unreported rate were 1:1.3, 1.2:1, 1:1.6 and 1:1.4, respectively. The reported gonorrhoea incidence sex ratio of new diagnosed gonorrhoea was 19.8:1 (male vs female: 87/1106 vs 5/1420), while the true diagnosis sex ratio was 2.5:1 (male vs female: 161/1106 vs 84/1420). These data indicate that the sex ratio of reported gonorrhoea incidence has been overestimated by a factor of 7.9 (19.8/2.5). CONCLUSIONS: We found the current reported gonorrhoea incidence and sex ratios to be inaccurate due to underestimations of gonorrhoea incidence, especially among women.

Title: The "true" incidence of surgically treated deep prosthetic joint infection after 32,896 primary total hip arthroplasties: a prospective cohort study. Content: BACKGROUND AND PURPOSE It has been suggested that the risk of prosthetic joint infection (PJI) in patients with total hip arthroplasty (THA) may be underestimated if based only on arthroplasty registry data. We therefore wanted to estimate the "true" incidence of PJI in THA using several data sources. PATIENTS AND METHODS We searched the Danish Hip Arthroplasty Register (DHR) for primary THAs performed between 2005 and 2011. Using the DHR and the Danish National Register of Patients (NRP), we identified first revisions for any reason and those that were due to PJI. PJIs were also identified using an algorithm incorporating data from microbiological, prescription, and clinical biochemistry databases and clinical findings from the medical records. We calculated cumulative incidence with 95% confidence interval. RESULTS 32,896 primary THAs were identified. Of these, 1,546 had first-time revisions reported to the DHR and/or the NRP. For the DHR only, the 1- and 5-year cumulative incidences of PJI were 0.51% (0.44-0.59) and 0.64% (0.51-0.79). For the NRP only, the 1- and 5-year cumulative incidences of PJI were 0.48% (0.41-0.56) and 0.57% (0.45-0.71). The corresponding 1- and 5-year cumulative incidences estimated with the algorithm were 0.86% (0.77-0.97) and 1.03% (0.87-1.22). The incidences of PJI based on the DHR and the NRP were consistently 40% lower than those estimated using the algorithm covering several data sources. INTERPRETATION Using several available data sources, the "true" incidence of PJI following primary THA was estimated to be approximately 40% higher than previously reported by national registries alone.

Title: Cardiovascular Risks in Patients with COVID-19: Potential Mechanisms and Areas of Uncertainty Content: PURPOSE OF REVIEW: COronaVirus Disease 2019 (COVID-19) has spread at unprecedented speed and scale into a global pandemic with cardiovascular risk factors and complications emerging as important disease modifiers. We aim to review available clinical and biomedical literature on cardiovascular risks of COVID-19. RECENT FINDINGS: SARS-CoV2, the virus responsible for COVID-19, enters the cell via ACE2 expressed in select organs. Emerging epidemiological evidence suggest cardiovascular risk factors are associated with increased disease severity and mortality in COVID-19 patients. Patients with a more severe form of COVID-19 are also more likely to develop cardiac complications such as myocardial injury and arrhythmia. The true incidence of and mechanism underlying these events remain elusive. Cardiovascular diseases appear intricately linked with COVID-19, with cardiac complications contributing to the elevated morbidity/mortality of COVID-19. Robust epidemiologic and biologic studies are urgently needed to better understand the mechanism underlying these associations to develop better therapies.

Title: Cardiovascular Risks in Patients with COVID-19: Potential Mechanisms and Areas of Uncertainty Content: PURPOSE OF REVIEW: COronaVirus Disease 2019 (COVID-19) has spread at unprecedented speed and scale into a global pandemic with cardiovascular risk factors and complications emerging as important disease modifiers. We aim to review available clinical and biomedical literature on cardiovascular risks of COVID-19. RECENT FINDINGS: SARS-CoV2, the virus responsible for COVID-19, enters the cell via ACE2 expressed in select organs. Emerging epidemiological evidence suggest cardiovascular risk factors are associated with increased disease severity and mortality in COVID-19 patients. Patients with a more severe form of COVID-19 are also more likely to develop cardiac complications such as myocardial injury and arrhythmia. The true incidence of and mechanism underlying these events remain elusive. SUMMARY: Cardiovascular diseases appear intricately linked with COVID-19, with cardiac complications contributing to the elevated morbidity/mortality of COVID-19. Robust epidemiologic and biologic studies are urgently needed to better understand the mechanism underlying these associations to develop better therapies.

Title: Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County Content: To reliably estimate the demand on regional health systems and perform public health planning, it is necessary to have a good estimate of the prevalence of infection with SARS-CoV-2 (the virus that causes COVID-19) in the population. In the absence of wide-spread testing, we provide one approach to infer prevalence based on the assumption that the fraction of true infections needing hospitalization is fixed and that all hospitalized cases of COVID-19 in Santa Clara are identified. Our goal is to estimate the prevalence of SARS-CoV-2 infections, i.e. the true number of people currently infected with the virus, divided by the total population size. Our analysis suggests that as of March 17, 2020, there are 6,500 infections (0.34% of the population) of SARS-CoV-2 in Santa Clara County. Based on adjusting the parameters of our model to be optimistic (respectively pessimistic), the number of infections would be 1,400 (resp. 26,000), corresponding to a prevalence of 0.08% (resp. 1.36%). If the shelter-in-place led to R0 < 1, we would expect the number of infections to remain about constant for the next few weeks. However, even if this were true, we expect to continue to see an increase in hospitalized cases of COVID-19 in the short term due to the fact that infection of SARS-CoV-2 on March 17th can lead to hospitalizations up to 14 days later.

Title: Is there evidence that BCG vaccination has non-specific protective effects for COVID 19 infections or is it an illusion created by lack of testing? Content: The goal of this paper is to showcase that the COVID-19 disease pattern is evolving and to study the relationship between mandatory BCG policy and caseload/million or death/per million. We analyze seven recent publications on the impact of BCG vaccinations on the development of COVID19 illness and extend presented findings using the latest data from April 10, 2020. We analyze data from 98 countries and we extend existing models by adding the dimension of COVID-19-related testing conducted by the analyzed countries. Similarly to prior studies, we find that COVID-19 attributable case and death incidences across countries share a relationship with the BCG vaccination inclusion in the national immunization program of a country when testing is not taken into consideration. However, this relationship vanishes when we add the dimension of testing. We observe that case and death incidences conditional on testing do not get affected by the BCG vaccination inclusion in the national immunization program of a country. Therefore, we show that there is no statistical evidence to support the assertion that inclusion of BCG vaccination in national immunization program (NIP) has any impact of COVID 19 infections (cases) or mortality.

Title: Nonsurgical Rhinoplasty: A Systematic Review of Technique, Outcomes, and Complications. Content: BACKGROUND Nonsurgical rhinoplasty using filler injections has become a common procedure in cosmetic practices. This is offered to patients that prefer a temporary outcome or would like to avoid general anesthesia. In addition, it can be used in postrhinoplasty patients to correct nasal deformities or irregularities. This systematic review highlights common filler types and injection techniques, and associated patient satisfaction and complications to further guide practitioners. METHODS A systematic review was performed using keywords and Medical Subject Headings search terms. PubMed, EmBase, the Cochrane Library, and Scopus were searched using the appropriate search terms. Data collected from each study included patient satisfaction and complications, in addition to injection material, location, and technique. RESULTS Four thousand six hundred thirty-two studies were found based on search criteria. After full-text screening for inclusion and exclusion criteria, 23 studies were included. A total of 1600 patients underwent nonsurgical rhinoplasty, most commonly with hyaluronic acid (73.38 percent), followed by calcium hydroxyapatite (12.44 percent). Nearly 95 percent of patients were satisfied with results, and there were only 26 relatively minor complications reported. There were no reports of vascular complications such as skin necrosis or visual compromise. CONCLUSIONS Based on the authors' review of the literature, nonsurgical rhinoplasty is an effective temporary alternative to traditional augmentation rhinoplasty for corrections of nasal shape with a high degree of patient satisfaction. Complications may be underreported, and thus further investigation is needed to better understand the true incidence of major complications related to vascular compromise.

Title: The COVID-19 (SARS-CoV-2) Uncertainty Tripod in Brazil: Assessments on model-based predictions with large under-reporting Content: The COVID-19 pandemic (SARS-CoV-2 virus) is the defying global health crisis of our time. The absence of mass testing and the relevant presence of asymptomatic individuals causes the available data of the COVID-19 pandemic in Brazil to be largely under-reported regarding the number of infected individuals and deaths. We propose an adapted Susceptible-Infected-Recovered (SIR) model which explicitly incorporates the under-reporting and the response of the population to public policies (such as confinement measures, widespread use of masks, etc) to cast short-term and long-term predictions. Large amounts of uncertainty could provide misleading models and predictions. In this paper, we discuss the role of uncertainty in these prediction, which is illustrated regarding three key aspects. First, assuming that the number of infected individuals is under-reported, we demonstrate an anticipation regarding the peak of infection. Furthermore, while a model with a single class of infected individuals yields forecasts with increased peaks, a model that considers both symptomatic and asymptomatic infected individuals suggests a decrease of the peak of symptomatic. Second, considering that the actual amount of deaths is larger than what is being register, then demonstrate the increase of the mortality rates. Third, when consider generally under-reported data, we demonstrate how the transmission and recovery rate model parameters change qualitatively and quantitatively. We also investigate the effect of the"COVID-19 under-reporting tripod", i.e. the under-reporting in terms of infected individuals, of deaths and the true mortality rate. If two of these factors are known, the remainder can be inferred, as long as proportions are kept constant. The proposed approach allows one to determine the margins of uncertainty by assessments on the observed and true mortality rates.

Title: Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios Content: BACKGROUND: COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. PURPOSE: This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. METHODS: Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. RESULTS: Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. CONCLUSIONS: Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

Title: Elective, Non-urgent Procedures and Aesthetic Surgery in the Wake of SARS-COVID-19: Considerations Regarding Safety, Feasibility and Impact on Clinical Management Content: BACKGROUND: The worldwide spread of a novel coronavirus disease (COVID-19) has led to a near total stop of non-urgent, elective surgeries across all specialties in most affected countries. In the field of aesthetic surgery, the self-imposed moratorium for all aesthetic surgery procedures recommended by most international scientific societies has been adopted by many surgeons worldwide and resulted in a huge socioeconomic impact for most private practices and clinics. An important question still unanswered in most countries is when and how should elective/aesthetic procedures be scheduled again and what kind of organizational changes are necessary to protect patients and healthcare workers when clinics and practices reopen. Defining manageable, evidence-based protocols for testing, surgical/procedural risk mitigation and clinical flow management/contamination management will be paramount for the safety of non-urgent surgical procedures. METHODS: We conducted a MEDLINE/PubMed research for all available publications on COVID-19 and surgery and COVID-19 and anesthesia. Articles and referenced literature describing possible procedural impact factors leading to exacerbation of the clinical evolution of COVID-19-positive patients were identified to perform risk stratification for elective surgery. Based on these impact factors, considerations for patient selection, choice of procedural complexity, duration of procedure, type of anesthesia, etc., are discussed in this article and translated into algorithms for surgical/anesthesia risk management and clinical management. Current recommendations and published protocols on contamination control, avoidance of cross-contamination and procedural patient flow are reviewed. A COVID-19 testing guideline protocol for patients planning to undergo elective aesthetic surgery is presented and recommendations are made regarding adaptation of current patient information/informed consent forms and patient health questionnaires. CONCLUSION: The COVID-19 crisis has led to unprecedented challenges in the acute management of the crisis, and the wave only recently seems to flatten out in some countries. The adaptation of surgical and procedural steps for a risk-minimizing management of potential COVID-19-positive patients seeking to undergo elective aesthetic procedures in the wake of that wave will present the next big challenge for the aesthetic surgery community. We propose a clinical algorithm to enhance patient safety in elective surgery in the context of COVID-19 and to minimize cross-contamination between healthcare workers and patients. New evidence-based guidelines regarding surgical risk stratification, testing, and clinical flow management/contamination management are proposed. We believe that only the continuous development and broad implementation of guidelines like the ones proposed in this paper will allow an early reintegration of all aesthetic procedures into the scope of surgical care currently performed and to prepare the elective surgical specialties better for a possible second wave of the pandemic. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Title: Elective, Non-urgent Procedures and Aesthetic Surgery in the Wake of SARS–COVID-19: Considerations Regarding Safety, Feasibility and Impact on Clinical Management Content: BACKGROUND: The worldwide spread of a novel coronavirus disease (COVID-19) has led to a near total stop of non-urgent, elective surgeries across all specialties in most affected countries. In the field of aesthetic surgery, the self-imposed moratorium for all aesthetic surgery procedures recommended by most international scientific societies has been adopted by many surgeons worldwide and resulted in a huge socioeconomic impact for most private practices and clinics. An important question still unanswered in most countries is when and how should elective/aesthetic procedures be scheduled again and what kind of organizational changes are necessary to protect patients and healthcare workers when clinics and practices reopen. Defining manageable, evidence-based protocols for testing, surgical/procedural risk mitigation and clinical flow management/contamination management will be paramount for the safety of non-urgent surgical procedures. METHODS: We conducted a MEDLINE/PubMed research for all available publications on COVID-19 and surgery and COVID-19 and anesthesia. Articles and referenced literature describing possible procedural impact factors leading to exacerbation of the clinical evolution of COVID-19-positive patients were identified to perform risk stratification for elective surgery. Based on these impact factors, considerations for patient selection, choice of procedural complexity, duration of procedure, type of anesthesia, etc., are discussed in this article and translated into algorithms for surgical/anesthesia risk management and clinical management. Current recommendations and published protocols on contamination control, avoidance of cross-contamination and procedural patient flow are reviewed. A COVID-19 testing guideline protocol for patients planning to undergo elective aesthetic surgery is presented and recommendations are made regarding adaptation of current patient information/informed consent forms and patient health questionnaires. CONCLUSION: The COVID-19 crisis has led to unprecedented challenges in the acute management of the crisis, and the wave only recently seems to flatten out in some countries. The adaptation of surgical and procedural steps for a risk-minimizing management of potential COVID-19-positive patients seeking to undergo elective aesthetic procedures in the wake of that wave will present the next big challenge for the aesthetic surgery community. We propose a clinical algorithm to enhance patient safety in elective surgery in the context of COVID-19 and to minimize cross-contamination between healthcare workers and patients. New evidence-based guidelines regarding surgical risk stratification, testing, and clinical flow management/contamination management are proposed. We believe that only the continuous development and broad implementation of guidelines like the ones proposed in this paper will allow an early reintegration of all aesthetic procedures into the scope of surgical care currently performed and to prepare the elective surgical specialties better for a possible second wave of the pandemic. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Title: Quantifying spatiotemporal heterogeneity of MERS-CoV transmission in the Middle East region: A combined modelling approach Content: Abstract MERS coronavirus cases notified in the Middle East region since the identification of the virus in 2012 have displayed variations in time and across geography. Through a combined modelling approach, we estimate the rates of generation of cases along the zoonotic and human-to-human transmission routes and assess their spatiotemporal heterogeneity. We consider all cases notified to WHO from March 2012 to mid-September 2014. We use a stochastic modelling of the time series of case incidence in the Middle East region to estimate time- and space-dependent zoonotic and human-to-human transmission parameters. The model also accounts for possible lack of identification of secondary transmissions among notified cases. This approach is combined with the analysis of imported cases out of the region to assess the rate of underreporting of cases. Out of a total of 32 possible models, based on different parameterisation and scenario considered, the best-fit model is characterised by a large heterogeneity in time and across space for both zoonotic and human-to-human transmission. The variation in time that occurred during Spring 2014 led to a 17-fold and 3-fold increase in the two transmissions, respectively, bringing the reproductive rate to values above 1 during that period for all regions under study. The model suggests that 75% of MERS-CoV cases are secondary cases (human-to-human transmission), which is substantially higher than the 34% of reported cases with an epidemiological link to another case. Overall, estimated reporting rate is 0.26. Our findings show a higher level of spatial heterogeneity in zoonotic transmission compared to human-to-human, highlighting the strong environmental component of the epidemic. Since sporadic introductions are predicted to be a small proportion of notified cases and are responsible for triggering secondary transmissions, a more comprehensive understanding of zoonotic source and path of transmission could be critical to limit the epidemic spread.

Title: Covid-19: Pandemonium in our time. Content: While pandemonium has come to mean wild and noisy disorder, the reference here is to John Milton's epic poem Paradise Lost and the upheaval following Lucifer's banishment from Heaven and his construction of Pandæmonium as his hub. Today's avalanche of conflicting news on how to deal with the coronavirus disease 2019 (Covid-19) brings to mind the Trinity nuclear bomb test with Enrico Fermi estimating its strength by releasing small pieces of paper into the air and measuring their displacement by the shock wave. Fermi's result, in fact not far from the true value, emphasised his ability to make good approximations with few or no actual data. The current wave of Covid-19 presents just this kind of situation as it engulfs the world from ground zero in Wuhan, China. Much information is indeed missing, but datasets that might lead to useful ideas on how to handle this pandemic are steadily accumulating.

Title: Covid-19: Pandemonium in our time Content: While pandemonium has come to mean wild and noisy disorder, the reference here is to John Milton's epic poem Paradise Lost and the upheaval following Lucifer's banishment from Heaven and his construction of Pandæmonium as his hub. Today's avalanche of conflicting news on how to deal with the coronavirus disease 2019 (Covid-19) brings to mind the Trinity nuclear bomb test with Enrico Fermi estimating its strength by releasing small pieces of paper into the air and measuring their displacement by the shock wave. Fermi's result, in fact not far from the true value, emphasised his ability to make good approximations with few or no actual data. The current wave of Covid-19 presents just this kind of situation as it engulfs the world from ground zero in Wuhan, China. Much information is indeed missing, but datasets that might lead to useful ideas on how to handle this pandemic are steadily accumulating.

Title: Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase Content: Abstract The recently emerged SARS-CoV-2 caused a major outbreak of coronavirus disease 2019 (COVID-19) and instigated a widespread fear, threatening global health security. To date, no licensed antiviral drugs or vaccines are available against COVID-19 although several clinical trials are underway to test possible therapies. During this urgency situation, computational drug discovery methods provide an alternative to tiresome high-throughput screening, particularly in the hit-to-lead-optimization stage. Identification of small molecules that specifically target viral replication apparatus has indicated highest potential towards antiviral drug discovery. In this work, we present potential compounds that specifically target SARS-CoV-2 vital proteins, including the main protease, Nsp12 RNA polymerase and Nsp13 helicase. An integrative virtual screening and molecular dynamics simulations approach led to the identification of potential binding modes and favourable molecular interaction profile of corresponding compounds. Moreover, the identification of structurally important binding site residues in conserved motifs located inside the active site highlights relative importance of ligand binding based on residual energy decomposition analysis. Although the current study lacks experimental validation, the structural information obtained from this computational study has paved way for the design of targeted inhibitors to combat COVID-19 outbreak.

Title: Structural elucidation of SARS-CoV-2 vital proteins: Computational methods reveal potential drug candidates against main protease, Nsp12 polymerase and Nsp13 helicase Content: The recently emerged SARS-CoV-2 caused a major outbreak of coronavirus disease 2019 (COVID-19) and instigated a widespread fear, threatening global health security. To date, no licensed antiviral drugs or vaccines are available against COVID-19 although several clinical trials are underway to test possible therapies. During this urgency situation, computational drug discovery methods provide an alternative to tiresome high-throughput screening, particularly in the hit-to-lead-optimization stage. Identification of small molecules that specifically target viral replication apparatus has indicated highest potential towards antiviral drug discovery. In this work, we present potential compounds that specifically target SARS-CoV-2 vital proteins, including the main protease, Nsp12 RNA polymerase and Nsp13 helicase. An integrative virtual screening and molecular dynamics simulations approach led to the identification of potential binding modes and favourable molecular interaction profile of corresponding compounds. Moreover, the identification of structurally important binding site residues in conserved motifs located inside the active site highlights relative importance of ligand binding based on residual energy decomposition analysis. Although the current study lacks experimental validation, the structural information obtained from this computational study has paved way for the design of targeted inhibitors to combat COVID-19 outbreak.

Title: Estimating the Early Outbreak Cumulative Incidence of COVID-19 in the United States: Three Complementary Approaches Content: Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the weekly incidence of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the reported positive COVID-19 case counts. We introduce three complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 during the early outbreak in each state in the US as well as in New York City, using a combination of excess influenza-like illness reports, COVID-19 test statistics, and COVID-19 mortality reports. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 symptomatic case counts from March 1 to April 4, 2020 varied from 5 to 50 times greater than the official positive test counts. Nationally, our estimates of COVID-19 symptomatic cases in the US as of April 4 have a likely range of 2.2 to 5.1 million cases, with possibly as high as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 12.2 million, which compares with 1.5 million positive test counts. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems during the flu season for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 during upcoming flu seasons in the US and other countries with comparable influenza surveillance systems.

Title: COVID-19, Australia: Epidemiology Report 16 (Reporting week to 23:59 AEST 17 May 2020). Content: Confirmed cases in Australia notified up to 17 May 2020: notifications = 7,075; deaths = 100. The incidence of new cases of COVID-19 has reduced dramatically since a peak in mid-March. Social distancing measures, public health action and the reduction in international travel have likely been effective in slowing the spread of the disease, in the Australian community. Testing rates over the past week have increased markedly, with a continued very low proportion of people testing positive. These low rates of detection are indicative of low levels of COVID-19 transmission. It is important that testing rates and community adherence to public health measures remain high to support the continued suppression of the virus, particularly in vulnerable high-risk groups and settings. New cases of COVID-19 are currently being reported by by only some jurisdictions, albeit at relatively low rates. Although case numbers are low, new cases tend to still be a mix of overseas-acquired and locally-acquired infections. Most locally-acquired cases can be linked back to a known case or cluster. Although the proportion of locally-acquired cases has increased, the overall rate of new cases, regardless of place of acquisition, continues to decrease. The crude case fatality rate in Australia remains low (1.4%), compared with the WHO reported global rate (6.9%). The low case fatality rate is likely reflective of high case detection and high quality of health care services in Australia. Deaths from COVID-19 in Australia have occurred predominantly among the elderly and those with comorbidities, with no deaths occurring in those under 40 years. The highest rate of COVID-19 continues to be among people aged 60-79 years. One third of all cases in this age group have been associated with several outbreaks linked to cruise ships. The lowest rate of disease is in young children, a pattern reflected in international reports. Internationally, while the number of new cases each day remains relatively stable at the global level, some areas such as Brazil and India are showing a dramatic rise in reported cases. Although some low-income countries have so far reported few cases, it is possible that this is due to limited diagnostic and public health capacity, and may not be reflective of true disease incidence.

Title: COVID-19, Australia: Epidemiology Report 16 (Reporting week to 23:59 AEST 17 May 2020) Content: Confirmed cases in Australia notified up to 17 May 2020: notifications = 7,075; deaths = 100. The incidence of new cases of COVID-19 has reduced dramatically since a peak in mid-March. Social distancing measures, public health action and the reduction in international travel have likely been effective in slowing the spread of the disease, in the Australian community. Testing rates over the past week have increased markedly, with a continued very low proportion of people testing positive. These low rates of detection are indicative of low levels of COVID-19 transmission. It is important that testing rates and community adherence to public health measures remain high to support the continued suppression of the virus, particularly in vulnerable high-risk groups and settings. New cases of COVID-19 are currently being reported by by only some jurisdictions, albeit at relatively low rates. Although case numbers are low, new cases tend to still be a mix of overseas-acquired and locally-acquired infections. Most locally-acquired cases can be linked back to a known case or cluster. Although the proportion of locally-acquired cases has increased, the overall rate of new cases, regardless of place of acquisition, continues to decrease. The crude case fatality rate in Australia remains low (1.4%), compared with the WHO reported global rate (6.9%). The low case fatality rate is likely reflective of high case detection and high quality of health care services in Australia. Deaths from COVID-19 in Australia have occurred predominantly among the elderly and those with comorbidities, with no deaths occurring in those under 40 years. The highest rate of COVID-19 continues to be among people aged 60-79 years. One third of all cases in this age group have been associated with several outbreaks linked to cruise ships. The lowest rate of disease is in young children, a pattern reflected in international reports. Internationally, while the number of new cases each day remains relatively stable at the global level, some areas such as Brazil and India are showing a dramatic rise in reported cases. Although some low-income countries have so far reported few cases, it is possible that this is due to limited diagnostic and public health capacity, and may not be reflective of true disease incidence.

Title: Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States Content: In March of 2020, many U.S. state governments encouraged or mandated restrictions on social interactions to slow the spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2 that has spread to nearly 180 countries. Estimating the effectiveness of these social-distancing strategies is challenging because surveillance of COVID-19 has been limited, with tests generally being prioritized for high-risk or hospitalized cases according to temporally and regionally varying criteria. Here we show that reductions in mobility across U.S. counties with at least 100 confirmed cases of COVID-19 led to reductions in fever incidences, as captured by smart thermometers, after a mean lag of 6.5 days ($90\%$ within 3--10 days) that is consistent with the incubation period of COVID-19. Furthermore, counties with larger decreases in mobility subsequently achieved greater reductions in fevers ($p<0.01$), with the notable exception of New York City and its immediate vicinity. These results indicate that social distancing has reduced the transmission of influenza like illnesses, including COVID 19, and support social distancing as an effective strategy for slowing the spread of COVID-19.

Title: COVID-19 Clinical Trial Oversight at a Major Academic Medical Center: Approach of the Michigan Medicine COVID-19 Clinical Trial Committees Content: Clinicians - eager to offer the best care in the absence of guiding data - have provided patients with COVID-19 diverse clinical interventions. This usage has led to perceptions of efficacy of some interventions that, while receiving media coverage, lack robust evidence. Moving forward, randomized controlled clinical trials (RCTs) are necessary to ensure that clinicians can treat patients effectively during this outbreak and the next. To do so, academic medical centers must address two key research issues: (1) how to effectively and efficiently determine which trials have the best chance of benefiting current and future patients, and (2) how to establish a transparent and ethical process for subject recruitment while maintaining research integrity and without overburdening patients or staff. We share here the current methods used by the University of Michigan to address these issues.

Title: COVID-19 Clinical Trial Oversight at a Major Academic Medical Center: Approach of the Michigan Medicine COVID-19 Clinical Trial Committees Content: Clinicians – eager to offer the best care in the absence of guiding data – have provided patients with COVID-19 diverse clinical interventions. This usage has led to perceptions of efficacy of some interventions that, while receiving media coverage, lack robust evidence. Moving forward, randomized controlled clinical trials (RCTs) are necessary to ensure that clinicians can treat patients effectively during this outbreak and the next. To do so, academic medical centers must address two key research issues: (1) how to effectively and efficiently determine which trials have the best chance of benefiting current and future patients, and (2) how to establish a transparent and ethical process for subject recruitment while maintaining research integrity and without overburdening patients or staff. We share here the current methods used by the University of Michigan to address these issues.

Title: COVID-19 (SARS-CoV-2) Ventilator Resource Management Using a Network Optimization Model and Predictive System Demand Content: The COVID-19 (SARS-CoV-2) pandemic is overwhelming global healthcare delivery systems due to the exponential spike in cases requiring specialty tests, facilities and equipment, including complex, precision devices like ventilators. In particular, the surge in critically ill patients has revealed a significant deficiency in regional availability of respiratory care ventilators. The authors offer a mathematical framework for ventilator distribution under scarcity conditions using an optimized network model and solver. The framework is interoperable with existing COVID-19 healthcare demand models and scales for different user-defined system sizes, including hospital networks, city, state, regional and national-scale prioritization. The authors' approach improves current capabilities for medical device resource management within the existing incident command system while accounting for availability of devices, ventilation treatment time periods, disinfection and cleaning between patients, as well as shipping logistics time. The authors present a proof of concept using a high fidelity COVID-19 data set from Colorado, discusses how to scale nationally, and emphasizes the importance of applying ethical human-in-the-loop decision making when using this or similar approaches to managing medical device resources during epidemic emergencies.

Title: ‘These are answers we need.’ WHO plans global study to discover true extent of coronavirus infections Content: In an effort to understand how many people have been infected with the new coronavirus, the World Health Organization (WHO) is planning a coordinated study to test blood samples for the presence of antibodies to the virus Called Solidarity II, the program, which will involve more than half a dozen countries around the globe, will launch in the coming days, says Maria Van Kerkhove, who is helping coordinate WHO’s COVID-19 response Knowing the true number of cases—including mild ones—will help pin down the prevalence and mortality rate of COVID-19 in different age groups It will also help policymakers decide how long shutdowns and quarantines should last “These are answers we need, and we need the right answers to drive policy,” WHO’s executive director for health emergencies, Michael Ryan, told a press briefing on 27 March

Title: The need for COVID-19 research in low- and middle-income countries Content: In the early months of the pandemic, most reported cases and deaths due to COVID-19 occurred in high-income countries. However, insufficient testing could have led to an underestimation of true infections in many low- and middle-income countries. As confirmed cases increase, the ultimate impact of the pandemic on individuals and communities in low- and middle-income countries is uncertain. We therefore propose research in three broad areas as urgently needed to inform responses in low- and middle-income countries: transmission patterns of SARS-CoV-2, the clinical characteristics of the disease, and the impact of pandemic prevention and response measures. Answering these questions will require a multidisciplinary approach led by local investigators and in some cases additional resources. Targeted research activities should be done to help mitigate the potential burden of COVID-19 in low- and middle-income countries without diverting the limited human resources, funding, or medical supplies from response activities.

Title: Predicting Whom to Test is More Important Than More Tests - Modeling the Impact of Testing on the Spread of COVID-19 Virus By True Positive Rate Estimation Content: I estimate plausible true positive (TP) rates for the number of COVID-19 tests per day, most relevant when the number of test is on the same order of magnitude as number of infected persons. I then modify a standard SEIR model to model current growth patterns and detection rates in South Korea and New York state. Although reducing transmission rates have the largest impact, increasing TP rates by ~10% in New York can have an impact equal to adding tens of thousands of new tests per day. Increasing both TP rates and tests per day together can have significant impacts and likely be more easily sustained than social distancing restrictions. Systematic and standardized data collection, even beyond contact tracking, should be ongoing and quickly made available for research teams to maximize the efficacy of testing.

Title: Trends in excess cancer and cardiovascular deaths in Scotland during the COVID-19 pandemic 30 December 2019 to 20 April 2020 Content: Understanding the trends in causes of death for different diseases during the current COVID-19 pandemic is important to determine whether there are excess deaths beyond what is normally expected. Using the most recent report from National Records Scotland (NRS) on 29 April 2020, we examined the percentage difference in crude numbers of deaths in 2020 compared to the average for 2015-2019 by week of death within calendar year. To determine if trends were similar, suggesting underreporting/underdiagnosed COVID-19 related deaths, we also looked at the trends in % differences for cardiovascular disease deaths. From the first 17 weeks' of data, we found a peak in excess deaths between weeks 14 of 2020, about four weeks after the first case in Scotland was detected on 1 March 2020-- but by week 17 these excesses had diminished around the time lockdown in the UK began. Similar observations were seen for cardiovascular disease-related deaths. These observations suggest that the short-term increase in excess cancer and cardiovascular deaths might be associated with undetected/unconfirmed deaths related to COVID-19. Both of these conditions make patients more susceptible to infection and lack of widespread access to testing for COVID-19 are likely to have resulted in under-estimation of COVID-19 mortality. These data further suggest that the cumulative toll of COVID-19 on mortality is likely undercounted. More detailed analysis is needed to determine if these excesses were directly or indirectly related to COVID-19. Disease specific mortality will need constant monitoring for the foreseeable future as changes occur in increasing capacity and access to testing, reporting criteria, changes to health services and different measures are implemented to control the spread of the COVID-19. Multidisciplinary, multi-institutional, national and international collaborations for complementary and population specific data analysis is required to respond and mitigate adverse effects of the COVID-19 pandemic and to inform planning for future pandemics.

Title: COVID- 19 Infection in Children: Estimating Pediatric Morbidity and Mortality Content: BACKGROUND: Estimates of pediatric morbidity and mortality from COVID-19 are vital for planning optimal use of human and material resources throughout this pandemic. METHODS: Government websites from countries with minimum 1000 cases in adults and children on April 13, 2020 were searched to find the number of cases confirmed in children, the age range, and the number leading to hospitalization, intensive care unit (ICU) admission or death. A systematic literature search was performed April 13, 2020 to find additional data from cases series. RESULTS: Data on pediatric cases were available from government websites for 23 of the 70 countries with minimum 1000 cases by April 13, 2020. Of 424 978 cases in these 23 countries, 8113 (1.9%) occurred in children. Nine publications provided data from 4251 cases in 4 additional countries. Combining data from the websites and the publications, 330 of 2361 cases required admission (14%). The ICU admission rate was 2.2 % of confirmed cases (44 of 2031) and 7.2% of admitted children (23 of 318). Death was reported for 15 cases. CONCLUSION: Children accounted for 1.9% of confirmed cases. The true incidence of pediatric infection and disease will only be known once testing is expanded to individuals with less severe or no symptoms. Admission rates vary from 0.3 to 10% of confirmed cases (presumably varying with the threshold for testing) with about 7% of admitted children requiring ICU care. Death is rare in middle and high income countries.

Title: New statistical model for misreported data with application to current public health challenges Content: The main goal of this work is to present a new model able to deal with potentially misreported continuous time series. The proposed model is able to handle the autocorrelation structure in continuous time series data, which might be partially or totally underreported or overreported. Its performance is illustrated through a comprehensive simulation study considering several autocorrelation structures and two real data applications on human papillomavirus incidence in Girona (Catalunya, Spain) and COVID-19 incidence in the Chinese region of Heilongjiang.

Title: The Potential Impacts of Pandemic Policing on Police Legitimacy: Planning Past the COVID-19 Crisis Content: One of the biggest challenges facing modern policing in recent years has been the lack of police legitimacy. The tipping point of this phenomenon is often attributed to the Rodney King incident in Los Angeles in 1991, where Los Angeles Police Department (LAPD) officers were videoed assaulting a lone black male. They were arrested and charged but eventually all were acquitted, thereby etching deep distrust between communities and police. Now the Rodney King example is an extreme and criminal act by police but it was the beginning of communities and media focusing on what the police were doing and how they were doing it. This lack of legitimacy coupled with what is referred to as the militarization of policing have lasting consequences and impacts on police–community relations and how interactions between police and community shape society today. In the wake of pandemic policing due to COVID-19, there are tales of two eventualities for police legitimacy that will be explored in this article: (1) The police response to the pandemic results in further militarization and draws deeper divides between police and communities or (2) the police response is compassionate and build on procedurally just operations resulting in the rebuilding of police legitimacy post-pandemic.

Title: Risk of secondary infection waves of COVID-19 in an insular region: the case of the Balearic Islands, Spain Content: The Spanish government declared the lockdown on March 14th, 2020 to tackle the fast-spreading of COVID-19. As a consequence the Balearic Islands remained almost fully isolated due to the closing of airports and ports, These isolation measures and the home-based confinement have led to a low incidence of COVID-19 in this region. We propose a compartmental model for the spread of COVID-19 including five compartments (Susceptible, Latent, Infected, Diseased, and Recovered), and the mobility between municipalities. The model parameters are calibrated with the temporal series of confirmed cases provided by the Spanish Ministry of Health. After calibration, the proposed model captures the trend of the official confirmed cases before and after the lockdown. We show that the estimated number of cases depends strongly on the initial dates of the local outbreak onset and the number of imported cases before the lockdown. Our estimations indicate that the population has not reached the level of herd immunization necessary to prevent future outbreaks. While the low incidence, in comparison to mainland Spain, has prevented the saturation of the health system, this low incidence translates into low immunization rates, therefore facilitating the propagation of new outbreaks that could lead to secondary waves of COVID-19 in the region. These findings warn about scenarios regarding after-lockdown-policies and the risk of second outbreaks, emphasize the need for widespread testing, and could potentially be extrapolated to other insular and continental regions.

Title: Are coinfections with COVID-19 and influenza low or underreported? An observational study examining current published literature including three new unpublished cases Content: As the coronavirus disease-2019 (COVID-19) pandemic continues, one major point of uncertainty is the impact this novel pathogen will have during the upcoming 2020 to 2021 flu season. While the influenza virus is a known contributor to human morbidity and mortality, the question of how a coinfection between COVID-19 and influenza might manifest is of utmost concern. The aim of this study was to review the limited cases of COVID-19/influenza coinfection currently available in the literature, along with cases in the community of El Paso, TX, to determine whether any patterns of clinical presentation and morbidity emerged. An international review of the literature was conducted. Six published articles describing COVID-19/influenza coinfection were identified, with a total of 13 patients described therein. Three additional patients were identified from the El Paso, TX data. The most common presenting symptoms were fever and cough. The most common laboratory findings were elevated C-reactive protein and lymphocytopenia. Thirteen patients presented with viral pneumonia findings on CT, and nine had findings of ground-glass opacity. Finally, complications were reported in six patients, with most common complication being acute respiratory distress syndrome. The results of the review indicate that, due to the similarity in presentation between COVID-19 and influenza, further analysis will be required to understand the effects of coinfection on morbidity and mortality. However, the limited number of coinfection cases in the literature indicates that the implementation of COVID-19 control measures may continue to play a role in limiting the spread of these human respiratory pathogens.

Title: Prevalence of acute liver injury and hypertransaminemia in patients with COVID-19: a protocol for a systematic review Content: INTRODUCTION: COVID-19 has spread rapidly in China and around the world. Published studies have revealed that some patients with COVID-19 had abnormal liver function in laboratory tests. However, the results were inconsistent and the analysis of epidemiological data stratified by the severity of COVID-19 was not available in previous meta-analyses. Furthermore, these meta-analyses were suspected of overestimating the incidence of liver injury in patients with COVID-19 because some studies considered transaminase elevation as liver injury, which might partially result from cardiac and muscle injury. This systematic review aims to enrol published literatures related to COVID-19 without language restriction, analyse the data based on the severity of the COVID-19 and explore the impact of varied definitions of liver injury on the incidence of liver injury. METHODS AND ANALYSIS: We have conducted a preliminary search on PubMed and Excerpta Medica Database on 13 April 2020, for the studies published after December 2019 on the prevalence of acute liver injury and hypertransaminemia in patients with COVID-19. Two reviewers will independently screen studies, extract data and assess the risk of bias. We will estimate the pooled incidence of hypertransaminemia and acute liver injury in patients with COVID-19 by using the random-effects model. The I(²) test will be used to identify the extent of heterogeneity. Publication bias will be assessed by funnel plot and performing the Begg’s and Egger’s test if adequate studies are available. We will perform a risk of bias assessment using the Joanna Briggs Institute’s critical appraisal checklist. ETHICS AND DISSEMINATION: Since this study will be based on the published data, it does not require ethical approval. The final results of this study will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42020179462.

Title: Prevalence of acute liver injury and hypertransaminemia in patients with COVID-19: a protocol for a systematic review Content: INTRODUCTION: COVID-19 has spread rapidly in China and around the world. Published studies have revealed that some patients with COVID-19 had abnormal liver function in laboratory tests. However, the results were inconsistent and the analysis of epidemiological data stratified by the severity of COVID-19 was not available in previous meta-analyses. Furthermore, these meta-analyses were suspected of overestimating the incidence of liver injury in patients with COVID-19 because some studies considered transaminase elevation as liver injury, which might partially result from cardiac and muscle injury. This systematic review aims to enrol published literatures related to COVID-19 without language restriction, analyse the data based on the severity of the COVID-19 and explore the impact of varied definitions of liver injury on the incidence of liver injury. METHODS AND ANALYSIS: We have conducted a preliminary search on PubMed and Excerpta Medica Database on 13 April 2020, for the studies published after December 2019 on the prevalence of acute liver injury and hypertransaminemia in patients with COVID-19. Two reviewers will independently screen studies, extract data and assess the risk of bias. We will estimate the pooled incidence of hypertransaminemia and acute liver injury in patients with COVID-19 by using the random-effects model. The I² test will be used to identify the extent of heterogeneity. Publication bias will be assessed by funnel plot and performing the Begg's and Egger's test if adequate studies are available. We will perform a risk of bias assessment using the Joanna Briggs Institute's critical appraisal checklist. ETHICS AND DISSEMINATION: Since this study will be based on the published data, it does not require ethical approval. The final results of this study will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42020179462.

Title: Discrepancies between guidelines and clinical practice regarding prostate-specific antigen testing. Content: BACKGROUND Most guidelines recommend a judicious use of prostate-specific antigen (PSA) testing, whereas in daily practice, an increase of the incidence of PSA testing has been shown. Accurate up-to-date PSA test incidence rates are, however, lacking. OBJECTIVE To investigate the PSA test incidence rates in general practices over the past 10 years and to study which factors are associated with more frequent test use. METHODS We performed a retrospective cohort study using the routine health care database of the Julius General Practitioners Network from 2002 to 2011, of which data were available from more than 65 000 male patients. We calculated the annual incidence of PSA testing rates per 1000 person-years. Co-morbidities were analysed by means of International Classification of Primary Care codes. Relative risks (RRs) of having a PSA test were estimated as the ratio of observed rates of co-morbidities in men who underwent the test compared with a control group, and 95% confidence intervals (CI) were calculated. RESULTS From 2002 to 2011, the overall incidence rate of PSA testing in men ≥45 years increased almost 4-fold, from 15.5 to 54.3 per 1000 person-years. As from 2005, the incidence rates appear to increase more than those before 2005. Men with cardiovascular diseases, joint disorders, psychiatric diseases, respiratory diseases, overweight and diabetes mellitus were predisposed to undergo a PSA test, but men with urinary problems had the highest relative risk (RR 1.77, 95% CI 1.72-1.82). CONCLUSIONS From 2002 to 2011, PSA incidence testing rates increased, particularly in men with urinary symptoms and cardiovascular disease, despite several international guidelines that suggest a judicious use of PSA tests.

Title: Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images Content: The current pandemic, caused by the outbreak of a novel coronavirus (COVID-19) in December 2019, has led to a global emergency that has significantly impacted economies, healthcare systems and personal wellbeing all around the world. Controlling the rapidly evolving disease requires highly sensitive and specific diagnostics. While real-time RT-PCR is the most commonly used, these can take up to 8 hours, and require significant effort from healthcare professionals. As such, there is a critical need for a quick and automatic diagnostic system. Diagnosis from chest CT images is a promising direction. However, current studies are limited by the lack of sufficient training samples, as acquiring annotated CT images is time-consuming. To this end, we propose a new deep learning algorithm for the automated diagnosis of COVID-19, which only requires a few samples for training. Specifically, we use contrastive learning to train an encoder which can capture expressive feature representations on large and publicly available lung datasets and adopt the prototypical network for classification. We validate the efficacy of the proposed model in comparison with other competing methods on two publicly available and annotated COVID-19 CT datasets. Our results demonstrate the superior performance of our model for the accurate diagnosis of COVID-19 based on chest CT images.

Title: Building a Multi-Institutional and Interdisciplinary Team to Develop a Zoonotic Tuberculosis Roadmap Content: Tuberculosis (TB), as the major infectious disease in the world, has devastating consequences for not only humans, but also cattle and several wildlife species. This disease presents additional challenges to human and veterinary health authorities given the zoonotic nature of the pathogens responsible for the disease across species. One of the main public health challenges regarding zoonotic TB (ZTB) caused by Mycobacterium bovis is that the true incidence of this type of TB in humans is not known and is likely to be underestimated. To effectively address challenges posed by ZTB, an integrated One Health approach is needed. In this manuscript, we describe the rationale, major steps, timeline, stakeholders, and important events that led to the assembling of a true integrated multi-institutional and interdisciplinary team that accomplished the ambitious goal of developing a ZTB roadmap, published in October, 2017. It outlines key activities to address the global challenges regarding the prevention, surveillance, diagnosis, and treatment of ZTB. We discuss and emphasize the importance of integrated approaches to be able to accomplish the short (year 2020) and medium term (year 2025) goals outlined in the ZTB roadmap.

Title: An Evolving Approach to the Laboratory Assessment of COVID-19 Content: As the COVID-19 outbreak has evolved in each country, the approach to the laboratory assessment of SARS-CoV-2 infection has had to evolve as well. This review addresses the evolving approach to the laboratory assessment of COVID-19 and discusses how algorithms for testing have been driven, in part, by the demand for testing overwhelming the capacity to accomplish such testing. This review focused on testing in the United States as this testing is evolving whereas in China and other countries such as South Korea testing is widely available and includes both molecular testing for SARS-CoV-2 as well as serological testing using both ELISA methodology and lateral flow immunoassay methodology. Although commercial testing systems are becoming available, there will likely be insufficient numbers of such tests due to high demand. Serological testing will be the next testing issue as the COVID-19 begins to subside. This will allow immunity testing as well as will allow the parameters of the COVID-19 outbreak to be defined. This article is protected by copyright. All rights reserved.

Title: An Evolving Approach to the Laboratory Assessment of COVID‐19 Content: As the COVID‐19 outbreak has evolved in each country, the approach to the laboratory assessment of SARS‐CoV‐2 infection has had to evolve as well. This review addresses the evolving approach to the laboratory assessment of COVID‐19 and discusses how algorithms for testing have been driven, in part, by the demand for testing overwhelming the capacity to accomplish such testing. This review focused on testing in the United States as this testing is evolving whereas in China and other countries such as South Korea testing is widely available and includes both molecular testing for SARS‐CoV‐2 as well as serological testing using both ELISA methodology and lateral flow immunoassay methodology. Although commercial testing systems are becoming available, there will likely be insufficient numbers of such tests due to high demand. Serological testing will be the next testing issue as the COVID‐19 begins to subside. This will allow immunity testing as well as will allow the parameters of the COVID‐19 outbreak to be defined. This article is protected by copyright. All rights reserved.

Title: Reading First Coordinates from the Nephrogenic Zone in Human Fetal Kidney. Content: While substantial information is available on organ anlage and the primary formation of nephrons, molecular mechanisms acting during the late development of the human kidney have received an astonishing lack of attention. In healthy newborn babies, nephrogenesis takes place unnoticed until birth. Upon delivery, morphogenetic activity in the nephrogenic zone decreases, and the stem cell niches aligned beyond the organ capsule vanish by an unknown signal. However, this signal also plays a key role in preterm and low birth weight babies. Although they are born in a phase of active nephrogenesis, pathological findings illustrate that they evolve to a high incidence oligonephropathy and prematurity of renal parenchyma. Different extra- and intrauterine influences seem to be responsible, but independent from chemical nature, all of them culminate in the nephrogenic zone. One assumes that the marred development is caused either by an overshoot of metabolites, misleading signaling of morphogens, unbalanced synthesis of extracellular matrix or restricted contact between mesenchymal and epithelial stem cells. Even more surprising is that there is only a few vague morphological information of the nephrogenic zone in the human fetal kidney available and ultrastructural data is severely lacking. On this account, the first coordinates were determined by optical microscopy and morphometry. Without claiming to be complete, generated results made it possible to create schematic illustrations true to scale for orientation. It will help graduating students, young pediatricians, pathologists, and scientists working in the field of biomedicine to interpret professionally the nephrogenic zone and contained niches.

Title: Advanced Digital Health Technologies for COVID-19 and Future Emergencies Content: Background: Coronavirus disease 2019 (COVID-19) has led to a national health care emergency in the United States and exposed resource shortages, particularly of health care providers trained to provide critical or intensive care. This article describes how digital health technologies are being or could be used for COVID-19 mitigation. It then proposes the National Emergency Tele-Critical Care Network (NETCCN), which would combine digital health technologies to address this and future crises. Methods: Subject matter experts from the Society of Critical Care Medicine and the Telemedicine and Advanced Technology Research Center examined the peer-reviewed literature and science/technology news to see what digital health technologies have already been or could be implemented to (1) support patients while limiting COVID-19 transmission, (2) increase health care providers' capability and capacity, and (3) predict/prevent future outbreaks. Results: Major technologies identified included telemedicine and mobile care (for COVID-19 as well as routine care), tiered telementoring, telecritical care, robotics, and artificial intelligence for monitoring. Several of these could be assimilated to form an interoperable scalable NETCCN. NETCCN would assist health care providers, wherever they are located, by obtaining real-time patient and supplies data and disseminating critical care expertise. NETCCN capabilities should be maintained between disasters and regularly tested to ensure continual readiness. Conclusions: COVID-19 has demonstrated the impact of a large-scale health emergency on the existing infrastructures. Short term, an approach to meeting this challenge is to adopt existing digital health technologies. Long term, developing a NETCCN may ensure that the necessary ecosystem is available to respond to future emergencies.

Title: Advanced Digital Health Technologies for COVID-19 and Future Emergencies. Content: Background: Coronavirus disease 2019 (COVID-19) has led to a national health care emergency in the United States and exposed resource shortages, particularly of health care providers trained to provide critical or intensive care. This article describes how digital health technologies are being or could be used for COVID-19 mitigation. It then proposes the National Emergency Tele-Critical Care Network (NETCCN), which would combine digital health technologies to address this and future crises. Methods: Subject matter experts from the Society of Critical Care Medicine and the Telemedicine and Advanced Technology Research Center examined the peer-reviewed literature and science/technology news to see what digital health technologies have already been or could be implemented to (1) support patients while limiting COVID-19 transmission, (2) increase health care providers' capability and capacity, and (3) predict/prevent future outbreaks. Results: Major technologies identified included telemedicine and mobile care (for COVID-19 as well as routine care), tiered telementoring, telecritical care, robotics, and artificial intelligence for monitoring. Several of these could be assimilated to form an interoperable scalable NETCCN. NETCCN would assist health care providers, wherever they are located, by obtaining real-time patient and supplies data and disseminating critical care expertise. NETCCN capabilities should be maintained between disasters and regularly tested to ensure continual readiness. Conclusions: COVID-19 has demonstrated the impact of a large-scale health emergency on the existing infrastructures. Short term, an approach to meeting this challenge is to adopt existing digital health technologies. Long term, developing a NETCCN may ensure that the necessary ecosystem is available to respond to future emergencies.

Title: Characterization of the COVID-19 pandemic and the impact of uncertainties, mitigation strategies, and underreporting of cases in South Korea, Italy, and Brazil Content: By April 7th, 2020, the Coronavirus disease 2019 (COVID-19) has infected one and a half million people worldwide, accounting for over 80 thousand of deaths in 209 countries and territories around the world. The new and fast dynamics of the pandemic are challenging the health systems of different countries. In the absence of vaccines or effective treatments, mitigation policies, such as social isolation and lock-down of cities, have been adopted, but the results vary among different countries. Some countries were able to control the disease at the moment, as is the case of South Korea. Others, like Italy, are now experiencing the peak of the pandemic. Finally, countries with emerging economies and social issues, like Brazil, are in the initial phase of the pandemic. In this work, we use mathematical models with time-dependent coefficients, techniques of inverse and forward uncertainty quantification, and sensitivity analysis to characterize essential aspects of the COVID-19 in the three countries mentioned above. The model parameters estimated for South Korea revealed effective social distancing and isolation policies, border control, and a high number in the percentage of reported cases. In contrast, underreporting of cases was estimated to be very high in Brazil and Italy. In addition, the model estimated a poor isolation policy at the moment in Brazil, with a reduction of contact around 40%, whereas Italy and South Korea estimated numbers for contact reduction are at 75% and 90%, respectively. This characterization of the COVID-19, in these different countries under different scenarios and phases of the pandemic, supports the importance of mitigation policies, such as social distancing. In addition, it raises serious concerns for socially and economically fragile countries, where underreporting poses additional challenges to the management of the COVID-19 pandemic by significantly increasing the uncertainties regarding its dynamics.

Title: Scalable and Resilient SARS-CoV2 testing in an Academic Centre Content: The emergence of the novel coronavirus SARS-CoV-2 has led to a pandemic infecting more than two million people worldwide in less than four months, posing a major threat to healthcare systems. This is compounded by the shortage of available tests causing numerous healthcare workers to unnecessarily self-isolate. We provide a roadmap instructing how a research institute can be repurposed in the midst of this crisis, in collaboration with partner hospitals and an established diagnostic laboratory, harnessing existing expertise in virus handling, robotics, PCR, and data science to derive a rapid, high throughput diagnostic testing pipeline for detecting SARS-CoV-2 in patients with suspected COVID-19. The pipeline is used to detect SARS-CoV-2 from combined nose-throat swabs and endotracheal secretions/ bronchoalveolar lavage fluid. Notably, it relies on a series of in-house buffers for virus inactivation and the extraction of viral RNA, thereby reducing the dependency on commercial suppliers at times of global shortage. We use a commercial RT-PCR assay, from BGI, and results are reported with a bespoke online web application that integrates with the healthcare digital system. This strategy facilitates the remote reporting of thousands of samples a day with a turnaround time of under 24 hours, universally applicable to laboratories worldwide.

Title: Are co-infections with COVID-19 and Influenza low or underreported? An observational study examining current published literature including three new unpublished cases Content: As the COVID-19 pandemic continues, one major point of uncertainty is the impact this novel pathogen will have during the upcoming 2020-2021 flu season. While the influenza virus is a known contributor to human morbidity and mortality, the question of how a coinfection between COVID-19 and influenza might manifest is of utmost concern. The aim of this study was to review the limited cases of COVID-19/influenza coinfection currently available in the literature, along with cases in the community of El Paso, Texas, to determine whether any patterns of clinical presentation and morbidity emerged. An international review of the literature was conducted. Six published articles describing COVID-19/influenza coinfection were identified, with a total of thirteen patients described therein. Three additional patients were identified from the El Paso, Texas data. The most common presenting symptoms were fever and cough. The most common laboratory findings were elevated C-reactive protein and lymphocytopenia. Thirteen patients presented with viral pneumonia findings on CT, and nine had findings of ground-glass opacity. Finally, complications were reported in six patients, with most common complication being acute respiratory distress syndrome (ARDS). The results of the review indicate that, due to the similarity in presentation between COVID-19 and influenza, further analysis will be required to understand the effects of coinfection on morbidity and mortality. However, the limited number of coinfection cases in the literature indicates that the implementation of COVID-19 control measures may continue to play a role in limiting the spread of these human respiratory pathogens. This article is protected by copyright. All rights reserved.

Title: Are co-infections with COVID-19 and Influenza low or underreported? An observational study examining current published literature including three new unpublished cases. Content: As the COVID-19 pandemic continues, one major point of uncertainty is the impact this novel pathogen will have during the upcoming 2020-2021 flu season. While the influenza virus is a known contributor to human morbidity and mortality, the question of how a coinfection between COVID-19 and influenza might manifest is of utmost concern. The aim of this study was to review the limited cases of COVID-19/influenza coinfection currently available in the literature, along with cases in the community of El Paso, Texas, to determine whether any patterns of clinical presentation and morbidity emerged. An international review of the literature was conducted. Six published articles describing COVID-19/influenza coinfection were identified, with a total of thirteen patients described therein. Three additional patients were identified from the El Paso, Texas data. The most common presenting symptoms were fever and cough. The most common laboratory findings were elevated C-reactive protein and lymphocytopenia. Thirteen patients presented with viral pneumonia findings on CT, and nine had findings of ground-glass opacity. Finally, complications were reported in six patients, with most common complication being acute respiratory distress syndrome (ARDS). The results of the review indicate that, due to the similarity in presentation between COVID-19 and influenza, further analysis will be required to understand the effects of coinfection on morbidity and mortality. However, the limited number of coinfection cases in the literature indicates that the implementation of COVID-19 control measures may continue to play a role in limiting the spread of these human respiratory pathogens. This article is protected by copyright. All rights reserved.

Title: Coronavirus 2019 and health systems affected by protracted conflict: The case of Syria Content: INTRODUCTION: Two thirds of countries globally are unprepared to respond to a health emergency as per the International Health Regulations (2005), with conflict-affected countries like Syria being particularly vulnerable. Political influences on outbreak preparedness, response and reporting may also adversely affect control of SARS-CoV-2 in Syria. Syria reported its first case on 22 March 2020; however, concerns were raised that this was delayed and that underreporting continues. DISCUSSION: Syria's conflict has displaced more than half of its pre-war population, leaving 6.7 million people internally displaced. The consequent overcrowding - with insufficient water, sanitation and healthcare (including laboratory capacity) - could lead to conditions that are ideal for spread of SARS-CoV-2 in Syria. Political changes have led to the formation of at least three health systems within Syria's borders, each with its own governance, capacity and planning. This fragmentation, with little interaction between them, could lead to poor resource allocation and adversely affect control. As such, COVID-19 could overwhelm the health systems (particularly intensive care capacity), leading to high deaths across the population, particularly for the most vulnerable such as detainees. CONCLUSIONS: Locally implementable interventions that rapidly build WASH and health system capacity are required across Syria to ensure early detection and management of COVID-19 cases.

Title: Defining the true impact of coronavirus disease 2019 in the at-risk population of patients with cancer Content: BACKGROUND: In light of the coronavirus disease 2019 (COVID-19) pandemic, cancer centres in the United Kingdom and Europe re-organised their services at an unprecedented pace, and many patients with cancer have had their treatments severely disrupted. Patients with cancer were considered at high risk on sparse evidence, and despite a small number of emerging observational studies, the true incidence and impact of COVID-19 in the ‘at-risk’ population of patients with cancer is yet to be defined. METHODS: Epidemiological and clinical data were collected prospectively for patients attending the Royal Marsden Hospital and three network hospitals between March 1st and April 30th 2020 that were confirmed to have SARS-CoV2 infection. Significance of clinical and pathological characteristics was assessed using the Fisher's exact test and Wilcoxon rank sum test, whilst univariate and multivariate logistic regression models were used to further assess risk. The number of patients attending in March/April 2020 for face-to-face attendances was also extracted. FINDINGS: During the 2-month study period, 867 of 13,489 (6.4%) patients met the criteria leading to swab testing. Of the total at-risk population, only 113 of 13,489 (0.84%) were swab positive, 101 of 13,489 (0.75%) required hospital admission and 29 of 13,489 (0.21%) died of COVID-19. Of the patients that attended the hospital to receive cytotoxic chemotherapy alone or in combination with other therapy, 59 of 2001 (2.9%) were admitted to the hospital for COVID-19–related issues and 20 of 2001 (1%) died. Of the patients that collected targeted treatments, 16 of 1126 (1.4%) were admitted and 1 of 1126 (0.1%) died. Of the 11 patients that had received radiotherapy, 6 of 1042 (0.6%) required inpatient admission and 2 of 1042 (0.2%) died. INTERPRETATIONS: Administration of systemic anticancer therapy appears to be associated with a modest risk of severe COVID-19 infection. Based on this snapshot taken as the first wave of COVID-19 hit our practice, we conclude that continuation of active cancer treatment, even in the palliative setting, is appropriate.

Title: Back-projection of COVID-19 diagnosis counts to assess infection incidence and control measures: analysis of Australian data Content: Back-projection is an epidemiological analysis method that was developed to estimate HIV incidence using surveillance data on AIDS diagnoses. It was used extensively during the 1990s for this purpose as well as in other epidemiological contexts. Surveillance data on COVID-19 diagnoses can be analysed by the method of back-projection using information about the probability distribution of the time between infection and diagnosis, which is primarily determined by the incubation period. This paper demonstrates the value of such analyses using daily diagnoses from Australia. It is shown how back-projection can be used to assess the pattern of COVID-19 infection incidence over time and to assess the impact of control measures by investigating their temporal association with changes in incidence patterns. For Australia, these analyses reveal that peak infection incidence coincided with the introduction of border closures and social distancing restrictions, while the introduction of subsequent social distancing measures coincided with a continuing decline in incidence to very low levels. These associations were not directly discernible from the daily diagnosis counts, which continued to increase after the first stage of control measures. It is estimated that a one week delay in peak incidence would have led to a fivefold increase in total infections. Furthermore, at the height of the outbreak, half to three-quarters of all infections remained undiagnosed. Automated data analytics of routinely collected surveillance data are a valuable monitoring tool for the COVID-19 pandemic and may be useful for calibrating transmission dynamics models.

Title: How many lives can be saved? A global view on the impact of testing, herd immunity and demographics on COVID-19 fatality rates Content: In this work, we assess the global impact of COVID-19 showing how demographic factors, testing policies and herd immunity are key for saving lives. We extend a standard epidemiological SEIR model in order to: (a) identify the role of demographics (population size and population age distribution) on COVID-19 fatality rates; (b) quantify the maximum number of lives that can be saved according to different testing strategies, different levels of herd immunity, and specific population characteristics; and (d) infer from the observed case fatality rates (CFR) what the true fatality rate might be. Different from previous SEIR model extensions, we implement a Bayesian Melding method in our calibration strategy which enables us to account for data limitation on the total number of deaths. We derive a distribution of the set of parameters that best replicate the observed evolution of deaths by using information from both the model and the data.

Title: Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results Content: Since the sudden outbreak of coronavirus disease 2019 (COVID-19), it has rapidly evolved into a momentous global health concern. Due to the lack of constructive information on the pathogenesis of COVID-19 and specific treatment, it highlights the importance of early diagnosis and timely treatment. In this study, 11 key blood indices were extracted through random forest algorithm to build the final assistant discrimination tool from 49 clinical available blood test data which were derived by commercial blood test equipments. The method presented robust outcome to accurately identify COVID-19 from a variety of suspected patients with similar CT information or similar symptoms, with accuracy of 0.9795 and 0.9697 for the cross-validation set and test set, respectively. The tool also demonstrated its outstanding performance on an external validation set that was completely independent of the modeling process, with sensitivity, specificity, and overall accuracy of 0.9512, 0.9697, and 0.9595, respectively. Besides, 24 samples from overseas infected patients with COVID-19 were used to make an in-depth clinical assessment with accuracy of 0.9167. After multiple verification, the reliability and repeatability of the tool has been fully evaluated, and it has the potential to develop into an emerging technology to identify COVID-19 and lower the burden of global public health. The proposed tool is well-suited to carry out preliminary assessment of suspected patients and help them to get timely treatment and quarantine suggestion. The assistant tool is now available online at http://lishuyan.lzu.edu.cn/COVID2019_2/.

Title: Clinical and transmission dynamics characteristics of 406 children with coronavirus disease 2019 in China: A review Content: OBJECTIVE: Chinese pediatricians are working on the front line to fight COVID-19. They have published a great amount of first-hand clinical data. Collecting their data and forming a large sample for analysis is more conducive to the recognition, prevention and treatment of coronavirus disease 2019 in children. The epidemic prevention and control experience of Chinese pediatricians should be shared with the world. METHODS: By searching Chinese and English literature, the data of 406 children with COVID-19 in China were analyzed. RESULTS: It was found that the clustered incidence of children's families is a dynamic transmission feature; the incidence is low; asymptomatic infections and mild cases account for 44.8%, with only 7 cases of critical illness; laboratory examination of lymphocyte counts is not reduced, as it is for adults; chest CT findings are less severe than those for adults. These presentations are the clinical features of COVID-19 in children. Only 55 of the 406 cases were tested by anal swab for virus nucleic acid, 45 of which were positive, accounting for 81.8% of stool samples. CONCLUSION: There are more children than adults with asymptomatic infections, milder conditions, faster recovery, and a better prognosis. Some concealed morbidity characteristics also bring difficulties to the early identification, prevention and control of COVID-19. COVID-19 screening is needed in the pediatric fever clinic, and respiratory and digestive tract nucleic acid tests should be performed. Efforts should be made to prevent children from becoming a hidden source of transmission in kindergartens, schools or families. Furthermore, China's experience in treating COVID-19 in children has led to faster recovery of sick children.

Title: Clinical and Transmission Dynamics Characteristics of 406 Children with Coronavirus Disease 2019 in China: A Review Content: Abstract Objective Chinese pediatricians are working on the front line to fight COVID-19. They have published a great amount of first-hand clinical data. Collecting their data and forming a large sample for analysis is more conducive to the recognition, prevention and treatment of coronavirus disease 2019 in children. The epidemic prevention and control experience of Chinese pediatricians should be shared with the world. Methods By searching Chinese and English literature, the data of 406 children with COVID-19 in China were analyzed. Results It was found that the clustered incidence of children's families is a dynamic transmission feature; the incidence is low; asymptomatic infections and mild cases account for 44.8%, with only 7 cases of critical illness; laboratory examination of lymphocyte counts is not reduced, as it is for adults; chest CT findings are less severe than those for adults. These presentations are the clinical features of COVID-19 in children. Only 55 of the 406 cases were tested by anal swab for virus nucleic acid, 45 of which were positive, accounting for 81.8% of stool samples. Conclusion There are more children than adults with asymptomatic infections, milder conditions, faster recovery, and a better prognosis. Some concealed morbidity characteristics also bring difficulties to the early identification, prevention and control of COVID-19. COVID-19 screening is needed in the pediatric fever clinic, and respiratory and digestive tract nucleic acid tests should be performed. Efforts should be made to prevent children from becoming a hidden source of transmission in kindergartens, schools or families. Furthermore, China's experience in treating COVID-19 in children has led to faster recovery of sick children.