Estimating clinical severity of COVID-19 from the transmission dynamics in Wuhan, China

Unreported Cases for Age Dependent COVID-19 Outbreak in Japan

We investigate the age structured data for the COVID-19 outbreak in Japan. We consider a mathematical model for the epidemic with unreported infectious patient with and without age structure. In particular, we build a new mathematical model and a new computational method to fit the data by using age classes dependent exponential growth at the early stage of the epidemic. This allows to take into account differences in the response of patients to the disease according to their age. This model also allows for a heterogeneous response of the population to the social distancing measures taken by the local government. We fit this model to the observed data and obtain a snapshot of the effective transmissions occurring inside the population at different times, which indicates where and among whom the disease propagates after the start of public mitigation measures.

Geospatial Mapping of Orthopaedic Surgeons Age 60 and Over and Confirmed Cases of COVID-19

BACKGROUND

Although elective surgical procedures in the United States have been suspended because of the coronavirus disease 2019 (COVID-19) pandemic, orthopaedic surgeons are being recruited to serve patients with COVID-19 in addition to providing orthopaedic acute care. Older individuals are deemed to be at higher risk for poor outcomes with COVID-19. Although previous studies have shown a high proportion of older providers nationwide across medical specialties, we are not aware of any previous study that has analyzed the age distribution among the orthopaedic workforce. Therefore, the purposes of the present study were (1) to determine the geographic distribution of U.S. orthopaedic surgeons by age, (2) to compare the distribution with other surgical specialties, and (3) to compare this distribution with the spread of COVID-19.

METHODS

Demographic statistics from the most recent State Physician Workforce Data Reports published by the Association of American Medical Colleges were extracted to identify the 2018 statewide proportion of practicing orthopaedic surgeons ≥60 years of age as well as age-related demographic data for all surgical specialties. Geospatial data on the distribution of COVID-19 cases were obtained from the Environmental Systems Research Institute. State boundary files were taken from the U.S. Census Bureau. Orthopaedic workforce age data were utilized to group states into quintiles.

RESULTS

States with the highest quintile of orthopaedic surgeons ≥60 years of age included states most severely affected by COVID-19: New York, New Jersey, California, and Florida. For all states, the median number of providers ≥60 years of age was 105.5 (interquartile range [IQR], 45.5 to 182.5). The median proportion of orthopaedic surgeons ≥60 years of age was higher than that of all other surgical subspecialties, apart from thoracic surgery.

CONCLUSIONS

To our knowledge, the present report provides the first age-focused view of the orthopaedic workforce during the COVID-19 pandemic. States in the highest quintile of orthopaedic surgeons ≥60 years old are also among the most overwhelmed by COVID-19. As important orthopaedic acute care continues in addition to COVID-19 frontline service, special considerations may be needed for at-risk staff. Appropriate health system measures and workforce-management strategies should protect the subset of those who are most potentially vulnerable.

LEVEL OF EVIDENCE

Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

COVID-19 Infection Fatality Rate Associated with Incidence-A Population-Level Analysis of 19 Spanish Autonomous Communities

Previous studies have found large variations in the COVID-19 infection fatality rate (IFR). This study hypothesized that IFR would be influenced by COVID-19 epidemic intensity. We tested the association between epidemic intensity and IFR using serological results from a recent large SARS-CoV-2 serosurvey (N = 60,983) in 19 Spanish regions. The infection fatality rate for Spain as a whole was 1.15% and varied between 0.13% and 3.25% in the regions (median 1.07%, IQR 0.69-1.32%). The IFR by region was positively associated with SARS-CoV-2 seroprevalence (rho = 0.54; p = 0.0162), cases/100,000 (rho = 0.75; p = 0.002), hospitalizations/100,000 (rho = 0.78; p = 0.0001), mortality/100,000 (rho = 0.77; p = 0.0001) and case fatality rate (rho = 0.49; p = 0.0327). These results suggest that the SARS-CoV-2 IFR is not fixed. The Spanish regions with more rapid and extensive spread of SARS-CoV-2 had higher IFRs. These findings are compatible with the theory that slowing the spread of COVID-19 down reduces the IFR and case fatality rate via preventing hospitals from being overrun, and thus allowing better and lifesaving care.

Disease and healthcare burden of COVID-19 in the United States

As of 24 April 2020, the SARS-CoV-2 epidemic has resulted in over 830,000 confirmed infections in the United States¹. The incidence of COVID-19, the disease associated with this new coronavirus, continues to rise. The epidemic threatens to overwhelm healthcare systems, and identifying those regions where the disease burden is likely to be high relative to the rest of the country is critical for enabling prudent and effective distribution of emergency medical care and public health resources. Globally, the risk of severe outcomes associated with COVID-19 has consistently been observed to increase with age^2,3. We used age-specific mortality patterns in tandem with demographic data to map projections of the cumulative case burden of COVID-19 and the subsequent burden on healthcare resources. The analysis was performed at the county level across the United States, assuming a scenario in which 20% of the population of each county acquires infection. We identified counties that will probably be consistently, heavily affected relative to the rest of the country across a range of assumptions about transmission patterns, such as the basic reproductive rate, contact patterns and the efficacy of quarantine. We observed a general pattern that per capita disease burden and relative healthcare system demand may be highest away from major population centers. These findings highlight the importance of ensuring equitable and adequate allocation of medical care and public health resources to communities outside of major urban areas.

COVID-19: the endocrine opportunity in a pandemic

The 2019 Coronavirus disease (COVID-19) pandemic has disrupted the social, economical and medical system worldwide. Although it is strictly an infectious disease, its intricate bidirectional relationship with various non-communicable metabolic diseases and endocrinological factors has been observed. While diabetes, hypertension, obesity have been found to be independent risk factors for COVID-19 disease severity and mortality, more inclination towards sedentary lifestyle, psychosocial stress at this critical time may be the harbingers of metabolic syndrome. Thus, endocrinologists have a great opportunity to play their role to combat this pandemic. This paper examines how various endocrinological disorders influence the dynamics of COVID-19 and vice versa. Moreover, it also intends to review the clinical guidelines to be adopted in practice of endocrinology in this trying time.

Laboratory and Radiologic Findings in Pediatrics with COVID-19: A Systematic Review

Context: One of the emerging diseases that has spread in 2019 is COVID-19. Objectives: The present systematic review (SR) was performed to evaluate the status of laboratory and radiological findings in the age group of children and adolescents with COVID-19. Methods: In this SR study, three of the research team members conducted searches in SCOPUS, PUBMED, Science Direct, and ISI international sites. The keywords that were searched included children- pediatric- adolescents- coronavirus- COVID-19- Novel coronavirus SARS-CoV-2-epidemiology- diagnostic findings- laboratory findings- CT SCAN- computed tomography- percentage of lymphocyte- alanine aminotransferase- white blood cell count (WBC). The combination of search keywords with AND as well as OR was performed. For analysis, descriptive data reporting was performed using EndNote software. Results: The analysis of the laboratory and radiology findings of 197 patients of 12 studies included in the SR study showed notable changes in the lung CT scan and laboratory ranges that confirm the COVID-19 infection in these patients. Conclusions: This systematic review provides information on the laboratory and radiology findings of children and adolescents with COVID-19 for the health care team.

A minimal common outcome measure set for COVID-19 clinical research

Clinical research is necessary for an effective response to an emerging infectious disease outbreak. However, research efforts are often hastily organised and done using various research tools, with the result that pooling data across studies is challenging. In response to the needs of the rapidly evolving COVID-19 outbreak, the Clinical Characterisation and Management Working Group of the WHO Research and Development Blueprint programme, the International Forum for Acute Care Trialists, and the International Severe Acute Respiratory and Emerging Infections Consortium have developed a minimum set of common outcome measures for studies of COVID-19. This set includes three elements: a measure of viral burden (quantitative PCR or cycle threshold), a measure of patient survival (mortality at hospital discharge or at 60 days), and a measure of patient progression through the health-care system by use of the WHO Clinical Progression Scale, which reflects patient trajectory and resource use over the course of clinical illness. We urge investigators to include these key data elements in ongoing and future studies to expedite the pooling of data during this immediate threat, and to hone a tool for future needs.

On the evolutionary epidemiology of SARS-CoV-2

There is no doubt that the novel coronavirus SARS-CoV-2 that causes COVID-19 is mutating and thus has the potential to adapt during the current pandemic. Whether this evolution will lead to changes in the transmission, the duration, or the severity of the disease is not clear. This has led to considerable scientific and media debate, from raising alarms about evolutionary change to dismissing it. Here we review what little is currently known about the evolution of SARS-CoV-2 and extend existing evolutionary theory to consider how selection might be acting upon the virus during the COVID-19 pandemic. Although there is currently no definitive evidence that SARS-CoV-2 is undergoing further adaptation, continued evidence-based analysis of evolutionary change is important so that public health measures can be adjusted in response to substantive changes in the infectivity or severity of COVID-19.

Early Detection and Assessment of Covid-19

Background: Since the Covid-19 global pandemic emerged, developing countries have been facing multiple challenges over its diagnosis. We aimed to establish a relationship between the signs and symptoms of COVID-19 for early detection and assessment to reduce the transmission rate of SARS-Cov-2. Methods: We collected published data on the clinical features of Covid-19 retrospectively and categorized them into physical and blood biomarkers. Common features were assigned scores by the Borg scoring method with slight modifications and were incorporated into a newly-developed Hashmi-Asif Covid-19 assessment Chart. Correlations between signs and symptoms with the development of Covid-19 was assessed by Pearson correlation and Spearman Correlation coefficient (rho). Linear regression analysis was employed to assess the highest correlating features. The frequency of signs and symptoms in developing Covid-19 was assessed through Chi-square test two tailed with Cramer's V strength. Changes in signs and symptoms were incorporated into a chart that consisted of four tiers representing disease stages. Results: Data from 10,172 Covid-19 laboratory confirmed cases showed a correlation with Fever in 43.9% (P = 0.000) cases, cough 54.08% and dry mucus 25.68% equally significant (P = 0.000), Hyperemic pharyngeal mucus membrane 17.92% (P = 0.005), leukopenia 28.11% (P = 0.000), lymphopenia 64.35% (P = 0.000), thrombopenia 35.49% (P = 0.000), elevated Alanine aminotransferase 50.02% (P = 0.000), and Aspartate aminotransferase 34.49% (P = 0.000). The chart exhibited a maximum scoring of 39. Normal tier scoring was ≤ 12/39, mild state scoring was 13-22/39, and star values scoring was ≥7/15; this latter category on the chart means Covid-19 is progressing and quarantine should be adopted. Moderate stage scored 23-33 and severe scored 34-39 in the chart. Conclusion: The Hashmi-Asif Covid-19 Chart is significant in assessing subclinical and clinical stages of Covid-19 to reduce the transmission rate.

Multiplexing primer/probe sets for detection of SARS-CoV-2 by qRT-PCR

BACKGROUND

The novel respiratory virus SARS-CoV-2, responsible for over 380,000 COVID-19 related deaths, has caused significant strain on healthcare infrastructure and clinical laboratories globally. The pandemic's initial challenges include broad diagnostic testing, consistent reagent supply lines, and access to laboratory instruments and equipment. In early 2020, primer/probe sets distributed by the CDC utilized the same fluorophore for molecular detection - requiring multiple assays to be run in parallel - consuming valuable and limited resources.

METHODS

Nasopharyngeal swabs submitted to UW Virology for SARS-CoV-2 clinical testing were extracted, amplified by our laboratory developed test (LDT) - a CDC-based quantitative reverse transcriptase PCR reaction - and analyzed for agreement between the multiplexed assay. Laboratory- confirmed respiratory infection samples were included to evaluate assay cross-reaction specificity.

RESULTS

Triplexing correctly identified SARS-CoV-2 in 98.4% of confirmed positive or inconclusive patient samples by single-plex LDT (n = 183/186). All 170 SARS-CoV-2 negative samples tested by single-plex LDT were negative by triplexing. Other laboratory-confirmed respiratory infections did not amplify for SARS-CoV-2 in the triplex reaction.

CONCLUSIONS

Multiplexing two virus-specific gene targets and an extraction control was found to be comparable to running parallel assays independently, while significantly improving assay throughput.

The effect of large-scale anti-contagion policies on the COVID-19 pandemic

Governments around the world are responding to the coronavirus disease 2019 (COVID-19) pandemic¹, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with unprecedented policies designed to slow the growth rate of infections. Many policies, such as closing schools and restricting populations to their homes, impose large and visible costs on society; however, their benefits cannot be directly observed and are currently understood only through process-based simulations^2-4. Here we compile data on 1,700 local, regional and national non-pharmaceutical interventions that were deployed in the ongoing pandemic across localities in China, South Korea, Italy, Iran, France and the United States. We then apply reduced-form econometric methods, commonly used to measure the effect of policies on economic growth^5,6, to empirically evaluate the effect that these anti-contagion policies have had on the growth rate of infections. In the absence of policy actions, we estimate that early infections of COVID-19 exhibit exponential growth rates of approximately 38% per day. We find that anti-contagion policies have significantly and substantially slowed this growth. Some policies have different effects on different populations, but we obtain consistent evidence that the policy packages that were deployed to reduce the rate of transmission achieved large, beneficial and measurable health outcomes. We estimate that across these 6 countries, interventions prevented or delayed on the order of 61 million confirmed cases, corresponding to averting approximately 495 million total infections. These findings may help to inform decisions regarding whether or when these policies should be deployed, intensified or lifted, and they can support policy-making in the more than 180 other countries in which COVID-19 has been reported⁷.

Association of hypertension and antihypertensive treatment with COVID-19 mortality: a retrospective observational study

AIMS

It remains unknown whether the treatment of hypertension influences the mortality of patients diagnosed with coronavirus disease 2019 (COVID-19).

METHODS AND RESULTS

This is a retrospective observational study of all patients admitted with COVID-19 to Huo Shen Shan Hospital. The hospital was dedicated solely to the treatment of COVID-19 in Wuhan, China. Hypertension and the treatments were stratified according to the medical history or medications administrated prior to the infection. Among 2877 hospitalized patients, 29.5% (850/2877) had a history of hypertension. After adjustment for confounders, patients with hypertension had a two-fold increase in the relative risk of mortality as compared with patients without hypertension [4.0% vs. 1.1%, adjusted hazard ratio (HR) 2.12, 95% confidence interval (CI) 1.17-3.82, P = 0.013]. Patients with a history of hypertension but without antihypertensive treatment (n = 140) were associated with a significantly higher risk of mortality compared with those with antihypertensive treatments (n = 730) (7.9% vs. 3.2%, adjusted HR 2.17, 95% CI 1.03-4.57, P = 0.041). The mortality rates were similar between the renin-angiotensin-aldosterone system (RAAS) inhibitor (4/183) and non-RAAS inhibitor (19/527) cohorts (2.2% vs. 3.6%, adjusted HR 0.85, 95% CI 0.28-2.58, P = 0.774). However, in a study-level meta-analysis of four studies, the result showed that patients with RAAS inhibitor use tend to have a lower risk of mortality (relative risk 0.65, 95% CI 0.45-0.94, P = 0.20).

CONCLUSION

While hypertension and the discontinuation of antihypertensive treatment are suspected to be related to increased risk of mortality, in this retrospective observational analysis, we did not detect any harm of RAAS inhibitors in patients infected with COVID-19. However, the results should be considered as exploratory and interpreted cautiously.

Immunologic aspects of characteristics, diagnosis, and treatment of coronavirus disease 2019 (COVID-19)

On March 11, 2020, the World Health Organization declared the worldwide spread of the infectious disease COVID-19, caused by a new strain of coronavirus, SARS-CoV-2, as a pandemic. Like in all other infectious diseases, the host immune system plays a key role in our defense against SARS-CoV-2 infection. However, viruses are able to evade the immune attack and proliferate and, in susceptible individuals, cause severe inflammatory response known as cytokine storm, particularly in the lungs. The advancement in our understanding of the mechanisms underlying the host immune responses promises to facilitate the development of approaches for prevention or treatment of diseases. Components of immune system, such as antibodies, can also be used to develop sensitive and specific diagnostic methods as well as novel therapeutic agents. In this review, we summarize our knowledge about how the host mounts immune responses to infection by SARS-CoV-2. We also describe the diagnostic methods being used for COVID-19 identification and summarize the current status of various therapeutic strategies, including vaccination, being considered for treatment of the disease.

Meta-analysis of several epidemic characteristics of COVID-19

As the COVID-19 pandemic has strongly disrupted people's daily work and life, a great amount of scientific research has been conducted to understand the key characteristics of this new epidemic. In this manuscript, we focus on four crucial epidemic metrics with regard to the COVID-19, namely the basic reproduction number, the incubation period, the serial interval and the epidemic doubling time. We collect relevant studies based on the COVID-19 data in China and conduct a meta-analysis to obtain pooled estimates on the four metrics. From the summary results, we conclude that the COVID-19 has stronger transmissibility than SARS, implying that stringent public health strategies are necessary.

Distribution of HLA allele frequencies in 82 Chinese individuals with coronavirus disease-2019 (COVID-19)

COVID‐19 is a respiratory disease caused by a novel coronavirus and is currently a global pandemic. HLA variation is associated with COVID‐19 because HLA plays a pivotal role in the immune response to pathogens. Here, 82 individuals with COVID‐19 were genotyped for HLA‐A, ‐B, ‐C, ‐DRB1, ‐DRB3/4/5, ‐DQA1, ‐DQB1, ‐DPA1, and ‐DPB1 loci using next‐generation sequencing (NGS). Frequencies of the HLA‐C*07:29, C*08:01G, B*15:27, B*40:06, DRB1*04:06, and DPB1*36:01 alleles were higher, while the frequencies of the DRB1*12:02 and DPB1*04:01 alleles were lower in COVID‐19 patients than in the control population, with uncorrected statistical significance. Only HLA‐C*07:29 and B*15:27 were significant when the corrected P‐value was considered. These data suggested that some HLA alleles may be associated with the occurrence of COVID‐19.

Modeling shield immunity to reduce COVID-19 epidemic spread

The COVID-19 pandemic has precipitated a global crisis, with more than 1,430,000 confirmed cases and more than 85,000 confirmed deaths globally as of 9 April 20201-4. Mitigation and suppression of new infections have emerged as the two predominant public health control strategies5. Both strategies focus on reducing new infections by limiting human-to-human interactions, which could be both socially and economically unsustainable in the long term. We have developed and analyzed an epidemiological intervention model that leverages serological tests6,7 to identify and deploy recovered individuals8 as focal points for sustaining safer interactions via interaction substitution, developing what we term 'shield immunity' at the population scale. The objective of a shield immunity strategy is to help to sustain the interactions necessary for the functioning of essential goods and services9 while reducing the probability of transmission. Our shield immunity approach could substantively reduce the length and reduce the overall burden of the current outbreak, and can work synergistically with social distancing. The present model highlights the value of serological testing as part of intervention strategies, in addition to its well-recognized roles in estimating prevalence10,11 and in the potential development of plasma-based therapies12-15.

Epidemiological control measures and predicted number of infections for SARS-CoV-2 pandemic: case study Serbia march-april 2020

BACKGROUND

In this paper, we are studying the response of the Serbian government and health authorities to the SARS-CoV-2 pandemic in the early stage of the local outbreak between Mar. 15th and Apr. 15th, 2020 by predictive numerical models. Such a study should be helpful to access the effectiveness of measures conducted to suppress the pandemic at a local scale.

METHODS

We have performed extrapolation of the number of SARS-CoV-2 infections with the first stable set of data exploiting exponential growth (linear in logarithmic scale). Based on obtained coefficients it is performed prediction of a number of cases until the end of March. After initial exponential growth, we have changed predictive model to the generalized gamma function. Obtained results are compared with the number of infections and the prediction for the remainder of the outbreak is given.

FINDINGS

We have found that the daily growth rate was above 21.5% at the beginning of the period, increased slightly after the introduction of the State of Emergency and the first set of strict epidemical control measures. It took about 13 days after the first set of strict measures to smooth daily growth. It seems that early government measures had an only moderate impact to reduce growth due to the social behavior of citizens and influx of diaspora returning to Serbia from highly affected areas, i.e., the exponential growth of infected persons is kept but with a reduced slope of about 14-15%. Anyway, it is demonstrated that period required that any measure has effect is up to 15 days after introduction, firstly to exponential growth with a smaller rate and after to smooth function representing the number of infected persons below exponential growth rate.

CONCLUSIONS

Obtained results are consistent with findings from other countries, i.e., initial exponential growth slows down within the presumed incubation period of 2 weeks after adopting lockdown and other non-pharmaceutical epidemiological measures. However, it is also shown that the exponential growth can continue after this period with a smaller slope. Therefore, quarantine and other social distancing measures should be adopted as soon as possible in a case of any similar outbreak since alternatives mean prolonged epidemical situation and growing costs in human life, pressure on the health system, economy, etc. For modeling the remainder of the outbreak generalized gamma function is used showing accurate results but requiring more samples and pre-processing (data filtering) concerning exponential part of the outbreak. We have estimated the number of infected persons for the remaining part of the outbreak until the end of June.

Wheat and chaffs in the interpretation of the current COVID19 outbreak in Italy

The COVID19 outbreak in Italy is still a big concern. The Italian Government has recommended citizens to respect faithfully any compulsory legal disposition in order to stay home and so contributing in escaping viral contacts and slowing down epidemic. Emergency has raised a widely animated debate about how to read and comprehend the daily case numbers, the medical and caregivers availability, the needs to swab asymptomatic subjects. In this review the authors discuss about the many wheat and chaffs of how this virus disease is addressed .

Global interim guidance on coronavirus disease 2019 (COVID-19) during pregnancy and puerperium from FIGO and allied partners: Information for healthcare professionals

In response to the World Health Organization (WHO) statements and international concerns regarding the coronavirus disease 2019 (COVID‐19) outbreak, FIGO has issued comprehensive guidance for the management of pregnant women.

Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy

In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.

An evaluation of mathematical models for the outbreak of COVID-19

Mathematical modelling performs a vital part in estimating and controlling the recent outbreak of coronavirus disease 2019 (COVID-19). In this epidemic, most countries impose severe intervention measures to contain the spread of COVID-19. The policymakers are forced to make difficult decisions to leverage between health and economic development. How and when to make clinical and public health decisions in an epidemic situation is a challenging question. The most appropriate solution is based on scientific evidence, which is mainly dependent on data and models. So one of the most critical problems during this crisis is whether we can develop reliable epidemiological models to forecast the evolution of the virus and estimate the effectiveness of various intervention measures and their impacts on the economy. There are numerous types of mathematical model for epidemiological diseases. In this paper, we present some critical reviews on mathematical models for the outbreak of COVID-19. Some elementary models are presented as an initial formulation for an epidemic. We give some basic concepts, notations, and foundation for epidemiological modelling. More related works are also introduced and evaluated by considering epidemiological features such as disease tendency, latent effects, susceptibility, basic reproduction numbers, asymptomatic infections, herd immunity, and impact of the interventions.

A modelling framework to assess the likely effectiveness of facemasks in combination with 'lock-down' in managing the COVID-19 pandemic

COVID-19 is characterized by an infectious pre-symptomatic period, when newly infected individuals can unwittingly infect others. We are interested in what benefits facemasks could offer as a non-pharmaceutical intervention, especially in the settings where high-technology interventions, such as contact tracing using mobile apps or rapid case detection via molecular tests, are not sustainable. Here, we report the results of two mathematical models and show that facemask use by the public could make a major contribution to reducing the impact of the COVID-19 pandemic. Our intention is to provide a simple modelling framework to examine the dynamics of COVID-19 epidemics when facemasks are worn by the public, with or without imposed 'lock-down' periods. Our results are illustrated for a number of plausible values for parameter ranges describing epidemiological processes and mechanistic properties of facemasks, in the absence of current measurements for these values. We show that, when facemasks are used by the public all the time (not just from when symptoms first appear), the effective reproduction number, Re , can be decreased below 1, leading to the mitigation of epidemic spread. Under certain conditions, when lock-down periods are implemented in combination with 100% facemask use, there is vastly less disease spread, secondary and tertiary waves are flattened and the epidemic is brought under control. The effect occurs even when it is assumed that facemasks are only 50% effective at capturing exhaled virus inoculum with an equal or lower efficiency on inhalation. Facemask use by the public has been suggested to be ineffective because wearers may touch their faces more often, thus increasing the probability of contracting COVID-19. For completeness, our models show that facemask adoption provides population-level benefits, even in circumstances where wearers are placed at increased risk. At the time of writing, facemask use by the public has not been recommended in many countries, but a recommendation for wearing face-coverings has just been announced for Scotland. Even if facemask use began after the start of the first lock-down period, our results show that benefits could still accrue by reducing the risk of the occurrence of further COVID-19 waves. We examine the effects of different rates of facemask adoption without lock-down periods and show that, even at lower levels of adoption, benefits accrue to the facemask wearers. These analyses may explain why some countries, where adoption of facemask use by the public is around 100%, have experienced significantly lower rates of COVID-19 spread and associated deaths. We conclude that facemask use by the public, when used in combination with physical distancing or periods of lock-down, may provide an acceptable way of managing the COVID-19 pandemic and re-opening economic activity. These results are relevant to the developed as well as the developing world, where large numbers of people are resource poor, but fabrication of home-made, effective facemasks is possible. A key message from our analyses to aid the widespread adoption of facemasks would be: 'my mask protects you, your mask protects me'.

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

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.

Estimating the Risk of COVID-19 Death During the Course of the Outbreak in Korea, February-May 2020

BACKGROUND

In Korea, a total of 10,840 confirmed cases of COVID-19 including 256 deaths have been recorded as of May 9, 2020. The time-delay adjusted case fatality risk (CFR) of COVID-19 in Korea is yet to be estimated.

METHODS

We obtained the daily series of confirmed cases and deaths in Korea reported prior to May 9, 2020. Using statistical methods, we estimated the time-delay adjusted risk for death from COVID-19 in Daegu, Gyeongsangbuk-do, other regions in Korea, as well as the entire country.

RESULTS

Our model-based crude CFR fitted the observed data well throughout the course of the epidemic except for the very early stage in Gyeongsangbuk-do; this was partially due to the reporting delay. Our estimates of the risk of death in Gyeongsangbuk-do reached 25.9% (95% Credible Interval (CrI): 19.6%-33.6%), 20.8% (95% CrI: 18.1%-24.0%) in Daegu, and 1.7% (95% CrI: 1.1%-2.5%) in other regions, whereas the national estimate was 10.2% (95% CrI: 9.0%-11.5%).

CONCLUSIONS

The latest estimates of CFR of COVID-19 in Korea are considerably high, even with the early implementation of public health interventions including widespread testing, social distancing, and delayed school openings. Geographic differences in the CFR are likely influenced by clusters tied to hospitals and nursing homes.

Estimating the Risk of COVID-19 Death During the Course of the Outbreak in Korea, February-May 2020

BACKGROUND

In Korea, a total of 10,840 confirmed cases of COVID-19 including 256 deaths have been recorded as of May 9, 2020. The time-delay adjusted case fatality risk (CFR) of COVID-19 in Korea is yet to be estimated.

METHODS

We obtained the daily series of confirmed cases and deaths in Korea reported prior to May 9, 2020. Using statistical methods, we estimated the time-delay adjusted risk for death from COVID-19 in Daegu, Gyeongsangbuk-do, other regions in Korea, as well as the entire country.

RESULTS

Our model-based crude CFR fitted the observed data well throughout the course of the epidemic except for the very early stage in Gyeongsangbuk-do; this was partially due to the reporting delay. Our estimates of the risk of death in Gyeongsangbuk-do reached 25.9% (95% Credible Interval (CrI): 19.6%-33.6%), 20.8% (95% CrI: 18.1%-24.0%) in Daegu, and 1.7% (95% CrI: 1.1%-2.5%) in other regions, whereas the national estimate was 10.2% (95% CrI: 9.0%-11.5%).

CONCLUSIONS

The latest estimates of CFR of COVID-19 in Korea are considerably high, even with the early implementation of public health interventions including widespread testing, social distancing, and delayed school openings. Geographic differences in the CFR are likely influenced by clusters tied to hospitals and nursing homes.

No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic

Background

In the absence of a cure in the time of a pandemic, social distancing measures seem to be the most effective intervention to slow the spread of disease. Various simulation-based studies have been conducted to investigate the effectiveness of these measures. While those studies unanimously confirm the mitigating effect of social distancing on disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. However, real transactional data can reduce uncertainty and provide a less noisy picture of the effectiveness of social distancing.

Objective

The aim of this paper was to integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics from the European Centre for Disease Prevention and Control) to study the role of social distancing policies in 26 countries and analyze the transmission rate of the coronavirus disease (COVID-19) pandemic over the course of 5 weeks.

Methods

Relying on the susceptible-infected-recovered (SIR) model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of COVID-19 in 26 countries for 5 consecutive weeks. Then, we integrated these data with the Google and Apple mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between the mobility factors and β values.

Results

Gradient boosted trees regression analysis showed that changes in mobility patterns resulting from social distancing policies explain approximately 47% of the variation in the disease transmission rates.

Conclusions

Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing the spread of COVID-19. In addition to providing less noisy and more generalizable support for the idea of social distancing, we provide specific insights for public health policy makers regarding locations that should be given higher priority for enforcing social distancing measures.

The epidemiology and therapeutic options for the COVID-19

An outbreak of coronavirus disease 2019 (COVID-19), a disease caused by a novel pneumonia virus, has affected over 200 countries and regions worldwide. With the increasing number of patients and deaths, WHO have declared it as a global pandemic currently, indicating a third large-scale epidemic coronavirus has appeared since the emergence of severe acute respiratory syndrome coronavirus (SARS) and Middle-East respiratory syndrome (MERS) in the twenty-first century. Considering the great harm it has caused, researchers throughout the world have been chasing to exploit the pathophysiology, characteristics, and potential remedies for COVID-19 to better battle the outbreak. Therefore, the current study revisits advances of the virology, epidemiology, clinical features, therapeutic options, and prevention of COVID-19. The features of asymptomatic carriers are also been explored.

A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model

An epidemic disease caused by a new coronavirus has spread in Northern Italy with a strong contagion rate. We implement an SEIR model to compute the infected population and the number of casualties of this epidemic. The example may ideally regard the situation in the Italian Region of Lombardy, where the epidemic started on February 24, but by no means attempts to perform a rigorous case study in view of the lack of suitable data and the uncertainty of the different parameters, namely, the variation of the degree of home isolation and social distancing as a function of time, the initial number of exposed individuals and infected people, the incubation and infectious periods, and the fatality rate. First, we perform an analysis of the results of the model by varying the parameters and initial conditions (in order for the epidemic to start, there should be at least one exposed or one infectious human). Then, we consider the Lombardy case and calibrate the model with the number of dead individuals to date (May 5, 2020) and constrain the parameters on the basis of values reported in the literature. The peak occurs at day 37 (March 31) approximately, with a reproduction ratio R0 of 3 initially, 1.36 at day 22, and 0.8 after day 35, indicating different degrees of lockdown. The predicted death toll is approximately 15,600 casualties, with 2.7 million infected individuals at the end of the epidemic. The incubation period providing a better fit to the dead individuals is 4.25 days, and the infectious period is 4 days, with a fatality rate of 0.00144/day [values based on the reported (official) number of casualties]. The infection fatality rate (IFR) is 0.57%, and it is 2.37% if twice the reported number of casualties is assumed. However, these rates depend on the initial number of exposed individuals. If approximately nine times more individuals are exposed, there are three times more infected people at the end of the epidemic and IFR = 0.47%. If we relax these constraints and use a wider range of lower and upper bounds for the incubation and infectious periods, we observe that a higher incubation period (13 vs. 4.25 days) gives the same IFR (0.6 vs. 0.57%), but nine times more exposed individuals in the first case. Other choices of the set of parameters also provide a good fit to the data, but some of the results may not be realistic. Therefore, an accurate determination of the fatality rate and characteristics of the epidemic is subject to knowledge of the precise bounds of the parameters. Besides the specific example, the analysis proposed in this work shows how isolation measures, social distancing, and knowledge of the diffusion conditions help us to understand the dynamics of the epidemic. Hence, it is important to quantify the process to verify the effectiveness of the lockdown.

Herd Immunity: Understanding COVID-19

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated disease, COVID-19, has demonstrated the devastating impact of a novel, infectious pathogen on a susceptible population. Here, we explain the basic concepts of herd immunity and discuss its implications in the context of COVID-19.

New Insights of Emerging SARS-CoV-2: Epidemiology, Etiology, Clinical Features, Clinical Treatment, and Prevention

Since the first reports that the novel coronavirus was showing human-to-human transmission characteristics and asymptomatic cases, the number of patients with associated pneumonia has continued to rise and the epidemic has grown. It now threatens the health and lives of people across the world. The governments of many countries have attached great importance to the prevention of SARS-CoV-2, via research into the etiology and epidemiology of this newly emerged disease. Clinical signs, treatment, and prevention characteristics of the novel coronavirus pneumonia have been receiving attention worldwide, especially from medical personnel. However, owing to the different experimental methods, sample sizes, sample sources, and research perspectives of various studies, results have been inconsistent, or relate to an isolated aspect of the virus or the disease it causes. Currently, systematic summary data on the novel coronavirus are limited. This review combines experimental and clinical evidence into a systematic analysis and summary of the current progress of research into SARS-CoV-2, from multiple perspectives, with the aim of gaining a better overall understanding of the disease. Our report provides important information for current clinicians, for the prevention and treatment of COVID-19 pneumonia.

Dynamics of Population Immunity Due to the Herd Effect in the COVID-19 Pandemic

The novel Coronavirus 2 Severe Acute Respiratory Syndrome (SARS-Cov-2) has led to the Coronavirus Disease 2019 (COVID-19) pandemic, which has surprised health authorities around the world, quickly producing a global health crisis. Different actions to cope with this situation are being developed, including confinement, different treatments to improve symptoms, and the creation of the first vaccines. In epidemiology, herd immunity is presented as an area that could also solve this new global threat. In this review, we present the basis of herd immunology, the dynamics of infection transmission that induces specific immunity, and how the application of immunoepidemiology and herd immunology could be used to control the actual COVID-19 pandemic, along with a discussion of its effectiveness, limitations, and applications.

Differentially Private Mobile Crowd Sensing Considering Sensing Errors

An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people’s surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants’ surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered.

Distribution of COVID-19 Morbidity Rate in Association with Social and Economic Factors in Wuhan, China: Implications for Urban Development

Social and economic factors relate to the prevention and control of infectious diseases. The purpose of this paper was to assess the distribution of COVID-19 morbidity rate in association with social and economic factors and discuss the implications for urban development that help to control infectious diseases. This study was a cross-sectional study. In this study, social and economic factors were classified into three dimensions: built environment, economic activities, and public service status. The method applied in this study was the spatial regression analysis. In the 13 districts in Wuhan, the spatial regression analysis was applied. The results showed that: 1) increasing population density, construction land area proportion, value-added of tertiary industry per unit of land area, total retail sales of consumer goods per unit of land area, public green space density, aged population density were associated with an increased COVID-19 morbidity rate due to the positive characteristics of estimated coefficients of these variables. 2) increasing average building scale, GDP per unit of land area, and hospital density were associated with a decreased COVID-19 morbidity rate due to the negative characteristics of estimated coefficients of these variables. It was concluded that it is possible to control infectious diseases, such as COVID-19, by adjusting social and economic factors. We should guide urban development to improve human health.

What does the COVID-19 pandemic mean for HIV, tuberculosis, and malaria control?

Despite its current relatively low global share of cases and deaths in Africa compared to other regions, coronavirus disease 2019 (COVID-19) has the potential to trigger other larger crises in the region. This is due to the vulnerability of health and economic systems, coupled with the high burden of human immunodeficiency virus (HIV), tuberculosis (TB), and malaria. Here we examine the potential implications of COVID-19 on the control of these major epidemic diseases in Africa. We use current evidence on disease burden of HIV, TB, and malaria, and epidemic dynamics of COVID-19 in Africa, retrieved from the literature. Our analysis shows that the current measures to control COVID-19 neglect important and complex context-specific epidemiological, social, and economic realities in Africa. There is a similarity of clinical features of TB and malaria, with those used to track COVID-19 cases. This coupled with institutional mistrust and misinformation might result in many patients with clinical features similar to those of COVID-19 being hesitant to voluntarily seek care in a formal health facility. Furthermore, most people in productive age in Africa work in the informal sector, and most of those in the formal sector are underemployed. With the current measures to control COVID-19, these populations might face unprecedented difficulties to access essential services, mainly due to reduced ability of patients to support direct and indirect medical costs, and unavailability of transportation means to reach health facilities. Therefore, if not accompanied with appropriate economic and epidemiological considerations, we anticipate that these measures might result in unprecedented difficulties among vulnerable segments of society to access essential services, including antiretroviral and prophylactic drugs among people living with HIV and Acquired Immune Deficiency Syndrome, anti-tuberculosis drugs, and curative and preventive treatments for malaria among pregnant women and children. This might increase the propensity of patients taking substandard doses and/or medicines, which has the potential to compromise drug efficacy, and worsen health inequalities in the region. COVID-19 responses at country level should include measures to protect vulnerable and under-served segments of society.

Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada

BACKGROUND

Physical-distancing interventions are being used in Canada to slow the spread of severe acute respiratory syndrome coronavirus 2, but it is not clear how effective they will be. We evaluated how different nonpharmaceutical interventions could be used to control the coronavirus disease 2019 (COVID-19) pandemic and reduce the burden on the health care system.

METHODS

We used an age-structured compartmental model of COVID-19 transmission in the population of Ontario, Canada. We compared a base case with limited testing, isolation and quarantine to scenarios with the following: enhanced case finding, restrictive physical-distancing measures, or a combination of enhanced case finding and less restrictive physical distancing. Interventions were either implemented for fixed durations or dynamically cycled on and off, based on projected occupancy of intensive care unit (ICU) beds. We present medians and credible intervals from 100 replicates per scenario using a 2-year time horizon.

RESULTS

We estimated that 56% (95% credible interval 42%-63%) of the Ontario population would be infected over the course of the epidemic in the base case. At the epidemic peak, we projected 107 000 (95% credible interval 60 760-149 000) cases in hospital (non-ICU) and 55 500 (95% credible interval 32 700-75 200) cases in ICU. For fixed-duration scenarios, all interventions were projected to delay and reduce the height of the epidemic peak relative to the base case, with restrictive physical distancing estimated to have the greatest effect. Longer duration interventions were more effective. Dynamic interventions were projected to reduce the proportion of the population infected at the end of the 2-year period and could reduce the median number of cases in ICU below current estimates of Ontario's ICU capacity.

INTERPRETATION

Without substantial physical distancing or a combination of moderate physical distancing with enhanced case finding, we project that ICU resources would be overwhelmed. Dynamic physical distancing could maintain health-system capacity and also allow periodic psychological and economic respite for populations.

Hypertension, Thrombosis, Kidney Failure, and Diabetes: Is COVID-19 an Endothelial Disease? A Comprehensive Evaluation of Clinical and Basic Evidence

The symptoms most commonly reported by patients affected by coronavirus disease (COVID-19) include cough, fever, and shortness of breath. However, other major events usually observed in COVID-19 patients (e.g., high blood pressure, arterial and venous thromboembolism, kidney disease, neurologic disorders, and diabetes mellitus) indicate that the virus is targeting the endothelium, one of the largest organs in the human body. Herein, we report a systematic and comprehensive evaluation of both clinical and preclinical evidence supporting the hypothesis that the endothelium is a key target organ in COVID-19, providing a mechanistic rationale behind its systemic manifestations.

Review of Evidence Available on Hesperidin-Rich Products as Potential Tools against COVID-19 and Hydrodynamic Cavitation-Based Extraction as a Method of Increasing Their Production

Based on recent computational and experimental studies, hesperidin, a bioactive flavonoid abundant in citrus peel, stands out for its high binding affinity to the main cellular receptors of SARS-CoV-2, outperforming drugs already recommended for clinical trials. Thus, it is very promising for prophylaxis and treatment of COVID-19, along with other coexistent flavonoids such as naringin, which could help restraining the proinflammatory overreaction of the immune system. Controlled hydrodynamic cavitation processes showed the highest speed, effectiveness and efficiency in the integral and green aqueous extraction of flavonoids, essential oils and pectin from citrus peel waste. After freeze-drying, the extracted pectin showed high quality and excellent antioxidant and antibacterial activities, attributed to flavonoids and essential oils adsorbed and concentrated on its surface. This study reviews the recent evidence about hesperidin as a promising molecule, and proposes a feasible and affordable process based on hydrodynamic cavitation for the integral aqueous extraction of citrus peel waste resulting in hesperidin-rich products, either aqueous extracts or pectin tablets. The uptake of this process on a relevant scale is urged, in order to achieve large-scale production and distribution of hesperidin-rich products. Meanwhile, experimental and clinical studies could determine the effective doses either for therapeutic and preventive purposes.

Cooperative virus propagation in COVID-19 transmission.. [PMID: 32511475 PMCID: PMC7273243 DOI: 10.1101/2020.05.05.20092361]

The global pandemic due to the emergence of a novel coronavirus (COVID-19) is a threat to humanity. There remains an urgent need to understand its transmission characteristics and design effective interventions to mitigate its spread. In this study, we define a non-linear (known in biochemistry models as allosteric or cooperative) relationship between viral shedding, viral dose and COVID-19 infection propagation. We develop a mathematical model of the dynamics of COVID-19 to link quantitative features of viral shedding, human exposure and transmission in nine countries impacted by the ongoing COVID-19 pandemic, and state-wide transmission in the United States of America (USA). The model was then used to evaluate the efficacy of interventions against virus transmission. We found that cooperativity was important to capture country-specific transmission dynamics and leads to resistance to mitigating transmission in mild or moderate interventions. The behaviors of the model emphasize that strict interventions greatly limiting both virus shedding and human exposure are indispensable to achieving effective containment of COVID-19. Finally, in the USA we find that by the summer of 2021, a difference of about 1.5 million deaths may be observed depending on whether the interventions are to be maintained strictly or lifted in entirety.

SARS-CoV-2/COVID-19: Evidence-Based Recommendations on Diagnosis and Therapy

COVID-19, a new viral disease affecting primarily the respiratory system and the lung, has caused a pandemic posing serious challenges to healthcare systems around the world. In about 20% of patients, severe symptoms occur after a mean incubation period of 5 - 6 days; 5% of patients need intensive care therapy. Mortality is about 1 - 2%. Protecting healthcare workers is of paramount importance in order to prevent hospital-acquired infections. Therefore, during all procedures associated with aerosol production, personal protective equipment consisting of a FFP2/FFP3 (N95) respiratory mask, gloves, safety glasses and a waterproof overall should be used. Therapy is based on established recommendations issued for patients with acute lung injury (ARDS). Lung protective ventilation, prone position, restrictive fluid management and adequate management of organ failure are the mainstays of therapy. In case of fulminant lung failure, veno-venous extracorporeal membrane oxygenation may be used as a rescue in experienced centres. New, experimental therapies are evolving with ever increasing frequency; currently, however, no evidence-based recommendation is possible. If off-label and compassionate use of these drugs is considered, an individual benefit-risk assessment is necessary, since serious side effects have been reported.

Using the COVID-19 to influenza ratio to estimate the numbers of symptomatic COVID-19 cases in Wuhan prior to the lockdown

A recent study tested 45 throat swabs taken from adults over age 30 who sought outpatient care at one of two central Wuhan hospitals for influenza-like-illness between December 30, 2019 and January 19, 2020. Although none were confirmed COVID-19 cases, nine retrospectively tested positive for the virus. Using the fact that Wuhan has 393 other hospitals, we extrapolate the total number of undetected cases of symptomatic COVID-19 in adults during this period. we estimate that there were 5,558 [95% CI: 2,761-9,864] adults with symptomatic COVID-19 infections in Wuhan between December 30th and January 19th, 2020.

Rethinking the role of hydroxychloroquine in the treatment of COVID-19

There are currently no proven or approved treatments for coronavirus disease 2019 (COVID-19). Early anecdotal reports and limited in vitro data led to the significant uptake of hydroxychloroquine (HCQ), and to lesser extent chloroquine (CQ), for many patients with this disease. As an increasing number of patients with COVID-19 are treated with these agents and more evidence accumulates, there continues to be no high-quality clinical data showing a clear benefit of these agents for this disease. Moreover, these agents have the potential to cause harm, including a broad range of adverse events including serious cardiac side effects when combined with other agents. In addition, the known and potent immunomodulatory effects of these agents which support their use in the treatment of auto-immune conditions, and provided a component in the original rationale for their use in patients with COVID-19, may, in fact, undermine their utility in the context of the treatment of this respiratory viral infection. Specifically, the impact of HCQ on cytokine production and suppression of antigen presentation may have immunologic consequences that hamper innate and adaptive antiviral immune responses for patients with COVID-19. Similarly, the reported in vitro inhibition of viral proliferation is largely derived from the blockade of viral fusion that initiates infection rather than the direct inhibition of viral replication as seen with nucleoside/tide analogs in other viral infections. Given these facts and the growing uncertainty about these agents for the treatment of COVID-19, it is clear that at the very least thoughtful planning and data collection from randomized clinical trials are needed to understand what if any role these agents may have in this disease. In this article, we review the datasets that support or detract from the use of these agents for the treatment of COVID-19 and render a data informed opinion that they should only be used with caution and in the context of carefully thought out clinical trials, or on a case-by-case basis after rigorous consideration of the risks and benefits of this therapeutic approach.

Japanese strategy to COVID-19: How does it work?

Despite substantial inflow of infected cases at the early stage of the pandemic, as of the end of April, Japan manages the outbreak of COVID-19 without systematic breakdown of health care. This Japanese paradox - limited fatality despite loose restriction - may have multiple contributing factors, including general hygiene practice of the population, customs such as not shaking hands or hugging, lower prevalence of obesity and other risk factors. Along with these societal and epidemiological conditions, health policy options, which are characteristic to Japan, would be considered as one of the contribution factors. Some health policy factors relatively unique to Japan are described in this article.

Population flow drives spatio-temporal distribution of COVID-19 in China

Sudden, large-scale and diffuse human migration can amplify localized outbreaks of disease into widespread epidemics^1-4. Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here we use 11,478,484 counts of mobile phone data from individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until 19 February 2020, across mainland China. Third, we develop a spatio-temporal 'risk source' model that leverages population flow data (which operationalize the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, we use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan; the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks.