Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020

Temporal variations in international air travel: implications for modelling the spread of infectious diseases

BACKGROUND

The international flight network creates multiple routes by which pathogens can quickly spread across the globe. In the early stages of infectious disease outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common and can help inform disease control efforts. A challenge faced in this modelling is that the latest aviation statistics (referred to as contemporary data) are typically not immediately available. Therefore, flight patterns from a previous year are often used (referred to as historical data). We explored the suitability of historical data for predicting the spatial spread of emerging epidemics.

METHODS

We analysed monthly flight passenger data from the International Air Transport Association to assess how baseline air travel patterns were affected by outbreaks of Middle East respiratory syndrome (MERS), Zika and severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) over the past decade. We then used a stochastic discrete time susceptible-exposed-infected-recovered (SEIR) metapopulation model to simulate the global spread of different pathogens, comparing how epidemic dynamics differed in simulations based on historical and contemporary data.

RESULTS

We observed local, short-term disruptions to air travel from South Korea and Brazil for the MERS and Zika outbreaks we studied, whereas global and longer-term flight disruptions occurred during the SARS-CoV-2 pandemic. For outbreak events that were accompanied by local, small and short-term changes in air travel, epidemic models using historical flight data gave similar projections of the timing and locations of disease spread as when using contemporary flight data. However, historical data were less reliable to model the spread of an atypical outbreak such as SARS-CoV-2, in which there were durable and extensive levels of global travel disruption.

CONCLUSION

The use of historical flight data as a proxy in epidemic models is an acceptable practice, except in rare, large epidemics that lead to substantial disruptions to international travel.

Integrating dynamical modeling and phylogeographic inference to characterize global influenza circulation

Global seasonal influenza circulation involves a complex interplay between local (seasonality, demography, host immunity) and global factors (international mobility) shaping recurrent epidemic patterns. No studies so far have reconciled the two spatial levels, evaluating the coupling between national epidemics, considering heterogeneous coverage of epidemiological and virological data, integrating different data sources. We propose a novel combined approach based on a dynamical model of global influenza spread (GLEAM), integrating high-resolution demographic and mobility data, and a generalized linear model of phylogeographic diffusion that accounts for time-varying migration rates. Seasonal migration fluxes across global macro-regions simulated with GLEAM are tested as phylogeographic predictors to provide model validation and calibration based on genetic data. Seasonal fluxes obtained with a specific transmissibility peak time and recurrent travel outperformed the raw air-transportation predictor, previously considered as optimal indicator of global influenza migration. Influenza A subtypes supported autumn-winter reproductive number as high as 2.25 and an average immunity duration of 2 years. Similar dynamics were preferred by influenza B lineages, with a lower autumn-winter reproductive number. Comparing simulated epidemic profiles against FluNet data offered comparatively limited resolution power. The multiscale approach enables model selection yielding a novel computational framework for describing global influenza dynamics at different scales - local transmission and national epidemics vs. international coupling through mobility and imported cases. Our findings have important implications to improve preparedness against seasonal influenza epidemics. The approach can be generalized to other epidemic contexts, such as emerging disease outbreaks to improve the flexibility and predictive power of modeling.

Heavy-tailed distributions of confirmed COVID-19 cases and deaths in spatiotemporal space

This paper conducts a systematic statistical analysis of the characteristics of the geographical empirical distributions for the numbers of both cumulative and daily confirmed COVID-19 cases and deaths at county, city, and state levels over a time span from January 2020 to June 2022. The mathematical heavy-tailed distributions can be used for fitting the empirical distributions observed in different temporal stages and geographical scales. The estimations of the shape parameter of the tail distributions using the Generalized Pareto Distribution also support the observations of the heavy-tailed distributions. According to the characteristics of the heavy-tailed distributions, the evolution course of the geographical empirical distributions can be divided into three distinct phases, namely the power-law phase, the lognormal phase I, and the lognormal phase II. These three phases could serve as an indicator of the severity degree of the COVID-19 pandemic within an area. The empirical results suggest important intrinsic dynamics of a human infectious virus spread in the human interconnected physical complex network. The findings extend previous empirical studies and could provide more strict constraints for current mathematical and physical modeling studies, such as the SIR model and its variants based on the theory of complex networks.

Collapse transition in epidemic spreading subject to detection with limited resources

Compartmental models are the most widely used framework for modeling infectious diseases. These models have been continuously refined to incorporate all the realistic mechanisms that can shape the course of an epidemic outbreak. Building on a compartmental model that accounts for early detection and isolation of infectious individuals through testing, in this article we focus on the viability of detection processes under limited availability of testing resources, and we study how the latter impacts on the detection rate. Our results show that, in addition to the well-known epidemic transition at R_{0}=1, a second transition occurs at R_{0}^{★}>1 pinpointing the collapse of the detection system and, as a consequence, the switch from a regime of mitigation to a regime in which the pathogen spreads freely. We characterize the epidemic phase diagram of the model as a function of the relevant control parameters: the basic reproduction number, the maximum detection capacity of the system, and the fraction of individuals in shelter. Our analysis thus provides a valuable tool for estimating the detection resources and the level of confinement needed to face epidemic outbreaks.

Vaccine stockpile sharing for selfish objectives

The COVAX program aims to provide global equitable access to life-saving vaccines. Despite calls for increased sharing, vaccine protectionism has limited progress towards vaccine sharing goals. For example, as of April 2022 only ~20% of the population in Africa had received at least one COVID-19 vaccine dose. Here we use a two-nation coupled epidemic model to evaluate optimal vaccine-sharing policies given a selfish objective: in which countries with vaccine stockpiles aim to minimize fatalities in their own population. Computational analysis of a suite of simulated epidemics reveal that it is often optimal for a donor country to share a significant fraction of its vaccine stockpile with a recipient country that has no vaccine stockpile. Sharing a vaccine stockpile reduces the intensity of outbreaks in the recipient, in turn reducing travel-associated incidence in the donor. This effect is intensified as vaccination rates in a donor country decrease and epidemic coupling between countries increases. Critically, vaccine sharing by a donor significantly reduces transmission and fatalities in the recipient. Moreover, the same computational framework reveals the potential use of hybrid sharing policies that have a negligible effect on fatalities in the donor compared to the optimal policy while significantly reducing fatalities in the recipient. Altogether, these findings provide a self-interested rationale for countries to consider sharing part of their vaccine stockpiles.

The impact of air travel on the precocity and severity of COVID-19 deaths in sub-national areas across 45 countries

Human travel fed the worldwide spread of COVID-19, but it remains unclear whether the volume of incoming air passengers and the centrality of airports in the global airline network made some regions more vulnerable to earlier and higher mortality. We assess whether the precocity and severity of COVID-19 deaths were contingent on these measures of air travel intensity, adjusting for differences in local non-pharmaceutical interventions and pre-pandemic structural characteristics of 502 sub-national areas on five continents in April-October 2020. Ordinary least squares (OLS) models of precocity (i.e., the timing of the 1st and 10th death outbreaks) reveal that neither airport centrality nor the volume of incoming passengers are impactful once we consider pre-pandemic demographic characteristics of the areas. We assess severity (i.e., the weekly death incidence of COVID-19) through the estimation of a generalized linear mixed model, employing a negative binomial link function. Results suggest that COVID-19 death incidence was insensitive to airport centrality, with no substantial changes over time. Higher air passenger volume tends to coincide with more COVID-19 deaths, but this relation weakened as the pandemic proceeded. Different models prove that either the lack of airports in a region or total travel bans did reduce mortality significantly. We conclude that COVID-19 importation through air travel followed a 'travel as spark' principle, whereby the absence of air travel reduced epidemic risk drastically. However, once some travel occurred, its impact on the severity of the pandemic was only in part associated with the number of incoming passengers, and not at all with the position of airports in the global network of airline connections.

Temporal and spatial evolution of the distribution related to the number of COVID-19 pandemic

This work systematically conducts a data analysis based on the numbers of both cumulative and daily confirmed COVID-19 cases and deaths in a time span through April 2020 to June 2022 for over 200 countries around the world. Such research feature aims to reveal the temporal and spatial evolution of the country-level distribution observed in COVID-19 pandemic, and obtains some interesting results as follows. (1) The distributions of the numbers for cumulative confirmed cases and deaths obey power-law in early stages of COVID-19 and stretched exponential function in subsequent course. (2) The distributions of the numbers for daily confirmed cases and deaths obey power-law in early and late stages of COVID-19 and stretched exponential function in middle stages. The crossover region between power-law and stretched exponential behavior seems to depend on the evolution of "infection" event and "death" event. Such observation implies a kind of important symmetry related to the dynamics process of COVID-19 spreading. (3) The distributions of the normalized numbers for each metric show a temporal scaling behavior in 2-year period, and are well described by stretched exponential function. The observation of power-law and stretched exponential behavior in such country-level distributions suggests underlying intrinsic dynamics of a virus spreading process in human interconnected society. And thus it is important for understanding and mathematically modeling the COVID-19 pandemic.

Structural changes in intercity mobility networks of China during the COVID-19 outbreak: A weighted stochastic block modeling analysis

This study focuses on a mesoscale perspective to examine the structural and spatial changes in the intercity mobility networks of China from three phases of before, during and after the Wuhan lockdown due to the outbreak of COVID-19. Taking advantages of mobility big data from Baidu Maps, we introduce the weighted stochastic block model (WSBM) to measure and compare mesoscale structures in the three mobility networks. The results reveal significant changes to volume and structure of the intercity mobility networks. Particularly, WSBM results show that the intercity network transformed from a typical core-periphery structure in the normal phase, to a hybrid and asymmetric structure with mixing core-peripheries and local communities in the lockdown phase, and to a multi-community structure with nested core-peripheries during the post-lockdown phase. These changes suggest that the outbreak of COVID-19 and the travel restrictions deconstructed the original hierarchy of the intercity mobility network in China, making the network more locally or regionally fragmented, even at the recovery stage. This study provides new empirical and methodological insights into understanding mobility network dynamics under the impact of COVID-19, helping assess the emergency-induced impact as well as the recovery process of the mobility network.

Retrospective Overview of COVID-19 in Europe

A disease of unknown origin connected with severe pneumonia was identified in Wuhan (China) in December 2019. It was named coronavirus disease 2019 (COVID-19). The disease had rapidly spread all over the world, including Europe. The World Health organization (WHO) declared the disease a pandemic. The aim of this study is to summarize and to compare objectively the epidemiological situation of COVID-19 in European countries from 15 February 2020 to 31 December 2021. Due to the significant difference in the population of individual states, all data were calculated per 1 million people (parameter/1M). Cases/1M, number of death/1 M, and % of death (case fatality rate) were compared. The actual situation on 31 December 2021 was quantified by comparing the active cases/1 M in each European country. The situation in Europe has been compared also with those on the other continents of the world, respectively on 31 December 2021. In order to monitor the development of the disease spread on the national level, the European countries were compared after division into six regions: South, West, North, Middle, Balkan and East. These data were recorded daily from 15 February to 31 December 2021.

Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

Computational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation.

The impact of COVID-19 pandemic lockdown on the incidence and outcome of complicated appendicitis

BACKGROUND

Patient attendance at emergency departments (EDs) during the COVID-19 pandemic outbreak has decreased dramatically under the "stay at home" and "lockdown" restrictions. By contrast, a notable rise in severity of various surgical conditions was observed, suggesting that the restrictions coupled with fear from medical facilities might negatively impact non-COVID-19 diseases. This study aims to assess the incidence and outcome of complicated appendicitis (CA) cases during that period.

METHODS

A retrospective study comparing the rate and severity of acute appendicitis (AA) cases during the COVID-19 initial outbreak in Israel during March and April of 2020 (P20) to the corresponding period in 2019 (P19) was conducted. Patient data included demographics, pre-ED status, surgical data, and postoperative outcomes.

RESULTS

Overall, 123 patients were diagnosed with acute appendicitis, 60 patients during P20 were compared to 63 patients in P19. The rate of complicated appendicitis cases was significantly higher during the COVID-19 Lockdown with 43.3% (26 patients) vs. 20.6% (13 patients), respectively (p < 0.01). The average delay in ED presentation between P20 and P19 was 3.4 vs. 2 days (p = 0.03). The length of stay was 2.6 days in P20 vs. 2.3 days in P19 (p = 0.4), and the readmission rate was 12% (7 patients) vs. 4.8% (3 patients), p = 0.17, respectively. Logistic regression demonstrated that a delay in ED presentation was a significant risk factor for complicated appendicitis (OR 1.139, CI 1.011-1.284).

CONCLUSION

The effect of the COVID-19 initial outbreak and Lockdown coupled with hesitation to come to medical facilities appears to have discouraged patients with acute appendicitis from presenting to the ED as complaints began, causing a delay in diagnosis and treatment, which might have led to a higher rate of complicated appendicitis cases and a heavier burden on health care systems.

How to Manage a Strabismus Clinic During the COVID-19 Pandemic; What is Really Urgent, What is Not?: A Single-Center Case Series from Turkey

Objectives:

To evaluate the management of the pediatric ophthalmology and strabismus clinic when strict quarantine conditions were adopted during the coronavirus disease 2019 (COVID-19) pandemic in Turkey.

Materials and Methods:

The study presents a review of the patients examined during the quarantine period. All patients were assessed with the highest possible level of personal protection.

Results:

Ten patients (6 girls, 4 boys) with a mean age of 9 years (range: 2-16) were evaluated. The patients presented 3-20 days after symptom onset. Ocular misalignment and diplopia were the main symptoms. Four of the 10 patients were diagnosed with sixth cranial nerve palsy and three patients were diagnosed with acute-onset comitant esotropia. Six patients had significant cranial magnetic resonance imaging findings.

Conclusion:

Acute-onset neurological conditions are more common during the COVID-19 pandemic. These reports will contribute to global experience and understanding of COVID-19.

Mathematical Modeling to Predict COVID-19 Infection and Vaccination Trends

Background: COVID-19 caused by the Severe Acute Respiratory Syndrome Coronavirus 2 placed the health systems around the entire world in a battle against the clock. While most of the existing studies aimed at forecasting the infections trends, our study focuses on vaccination trend(s). Material and methods: Based on these considerations, we used standard analyses and ARIMA modeling to predict possible scenarios in Romania, the second-lowest country regarding vaccinations from the entire European Union. Results: With approximately 16 million doses of vaccine against COVID-19 administered, 7,791,250 individuals had completed the vaccination scheme. From the total, 5,058,908 choose Pfizer−BioNTech, 399,327 Moderna, 419,037 AstraZeneca, and 1,913,978 Johnson & Johnson. With a cumulative 2147 local and 17,542 general adverse reactions, the most numerous were reported in recipients of Pfizer−BioNTech (1581 vs. 8451), followed by AstraZeneca (138 vs. 6033), Moderna (332 vs. 1936), and Johnson & Johnson (96 vs. 1122). On three distinct occasions have been reported >50,000 individuals who received the first or second dose of a vaccine and >30,000 of a booster dose in a single day. Due to high reactogenicity in case of AZD1222, and time of launching between the Pfizer−BioNTech and Moderna vaccine could be explained differences in terms doses administered. Furthermore, ARIMA(1,1,0), ARIMA(1,1,1), ARIMA(0,2,0), ARIMA(2,1,0), ARIMA(1,2,2), ARI-MA(2,2,2), ARIMA(0,2,2), ARIMA(2,2,2), ARIMA(1,1,2), ARIMA(2,2,2), ARIMA(2,1,1), ARIMA(2,2,1), and ARIMA (2,0,2) for all twelve months and in total fitted the best models. These were regarded according to the lowest MAPE, p-value (p < 0.05, p < 0.01, and p < 0.001) and through the Ljung−Box test (p < 0.05, p < 0.01, and p < 0.001) for autocorrelations. Conclusions: Statistical modeling and mathematical analyses are suitable not only for forecasting the infection trends but the course of a vaccination rate as well.

Ameliorating effects of probiotics on alterations in iron homeostasis and inflammation in COVID-19

Introduction

The coronavirus disease (COVID-19) is caused by the severe acute syndrome coronavirus-2 (SARS-COV-2) and still threatens human life. This pandemic is still causing increased mortality throughout the world. Many recent studies have been conducted to discover the pathophysiology of this virus.

Material and methods

However, in this narrative review, we attempted to summarize some of the alterations in physiological pathways that were evident in this viral invasion. Excessive inflammation that progresses to cytokine storm, changes in humoral and cell-mediated immunity, and observed alterations in iron metabolism are included in the pathogenesis of the virus. Iron homeostasis disturbances may persist for more than two months after the onset of COVID-19, which may lead to reduced iron bioavailability, hypoferremia, hyperferritinemia, impaired hemoglobin, and red blood cell synthesis. Furthermore, hypoferriemia may impair immune system function. Until now, the traditional treatments discovered are still being tried.

Results

However, using probiotics as an adjuvant was shown to have beneficial effects on both iron homeostasis and immunity in COVID-19. Herein, we discussed the possible mechanisms achieved by probiotics to ameliorate iron and immunity changes based on the available literature.

Conclusion

We concluded that supplementing probiotics with conventional therapy may improve COVID-19 symptoms and outcomes. Taking into consideration the use of good quality probiotics and appropriate dosage, undesirable effects can be avoided.

COVID-19 and bailout policy: The case of Virgin Australia

The impact of COVID-19 on air transport is unprecedented and some well-known airline brands may disappear as a result. Governments around the world have responded swiftly to cushion the financial impact by offering direct wage subsidies, tax relief, loans, etc. This paper explores the government's appropriate responses to failing airlines' bailout request by examining the case of Virgin Australia. Following the bailout policy principles established in the literature, we suggest that bankruptcy protection should be considered as the first solution to a failing carrier. A bailout decision should be guided by a set of principles and procedures, which should not be taken lightly. Our analysis also shows that the government cannot take a hands-off approach in the absence of private lenders and investors, as the costs to consumers and regional residents would be huge if the carrier could not get through the COVID-19 pandemic. A minimum level of assistance with conditions might be needed to maintain market competition.

The emergence, genomic diversity and global spread of SARS-CoV-2

Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.

Simulation of the impact of people mobility, vaccination rate, and virus variants on the evolution of Covid-19 outbreak in Italy

We have further extended our compartmental model describing the spread of the infection in Italy. As in our previous work, the model assumes that the time evolution of the observable quantities (number of people still positive to the infection, hospitalized and fatalities cases, healed people, and total number of people that has contracted the infection) depends on average parameters, namely people diffusion coefficient, infection cross-section, and population density. The model provides information on the tight relationship between the variation of the reported infection cases and a well-defined observable physical quantity: the average number of people that lie within the daily displacement area of any single person. With respect to our previous paper, we have extended the analyses to several regions in Italy, characterized by different levels of restrictions and we have correlated them to the diffusion coefficient. Furthermore, the model now includes self-consistent evaluation of the reproduction index, effect of immunization due to vaccination, and potential impact of virus variants on the dynamical evolution of the outbreak. The model fits the epidemic data in Italy, and allows us to strictly relate the time evolution of the number of hospitalized cases and fatalities to the change of people mobility, vaccination rate, and appearance of an initial concentration of people positives for new variants of the virus.

Distinct effectiveness in containing COVID-19 epidemic: Comparative analysis of two cities in China by mathematical modeling

For better preparing future epidemic/pandemic, important lessons can be learned from how different parts of China responded to the early COVID-19 epidemic. In this study, we comparatively analyzed the effectiveness and investigated the mechanistic insight of two highly representative cities of China in containing this epidemic by mathematical modeling. Epidemiological data of Wuhan and Wenzhou was collected from local health commission, media reports and scientific literature. We used a deterministic, compartmental SEIR model to simulate the epidemic. Specific control measures were integrated into the model, and the model was calibrated to the recorded number of hospitalized cases. In the epicenter Wuhan, the estimated number of unisolated or unidentified cases approached 5000 before the date of city closure. By implementing quarantine, a 40% reduction of within-population contact was achieved initially, and continuously increased up to 70%. The expansion of emergency units has finally reduced the mean duration from disease onset to hospital admission from 10 to 3.2 days. In contrast, Wenzhou is characterized as an emerging region with large number of primarily imported cases. Quick response effectively reduced the duration from onset to hospital admission from 20 to 6 days. This resulted in reduction of R values from initial 2.3 to 1.6, then to 1.1. A 40% reduction of contact through within-population quarantine further decreased R values until below 1 (0.5; 95% CI: 0.4-0.65). Quarantine contributes to 37% and reduction of duration from onset to hospital admission accounts for 63% to the effectiveness in Wenzhou. In Wuhan, these two strategies contribute to 54% and 46%, respectively. Thus, control measures combining reduction of duration from disease onset to hospital admission and within-population quarantine are effective for both epicenters and settings primarily with imported cases.

'We may be long in the tooth, but it makes us tough': exploring stillness for older adults during the COVID-19 lockdowns

Following the outbreak of the SARS-CoV-2 across the world in 2020, millions of people were reduced in their mobility to hinder the spread of the disease. The lockdown was particularly difficult for older adults, who were deemed 'vulnerable' because many felt unsafe leaving the house and often forced to self-isolate. In this paper, we interpret the lockdowns as a period of prolonged stillness: breaks from everyday practices, including withdrawnness, inefficiency, and retreat. We extend ideas of stillness by integrating the capability approach, which shows how the opportunities and challenges that arise from moments of stillness are dependent on a combination of individual agency and the role of structural or contextual factors. Using the accounts of thirty-eight older adults in the Netherlands and England, we show how the COVID-19 lockdowns established and encouraged different types of stillness which had differing impacts upon the older adults' lives. The effect of the prolonged stillness on these different areas of everyday life is based on individual agency and contextual factors, such as choosing to volunteer or having an adequate internet connection. Thus, our findings contribute to discussions around active ageing and demonstrate that slowing down, and spending more time at home, can provide respite from an otherwise active everyday life.

[Lesions on CT scan in patients hospitalized for coronavirus pneumonia during the first wave of the SARS-CoV-2 pandemic at the University Clinics in Kinshasa (DRC)]

Introduction

l´objectif principal de cette étude, consiste à ressortir les aspects tomodensitométriques thoraciques (TDM) observés lors de la première vague de la pandémie à SARS-CoV-2 chez les patients hospitalisés pour pneumonie à COVID-19 aux Cliniques Universitaires de Kinshasa (CUK).

Méthodes

étude descriptive portant sur les examens TDM thoraciques des 26 patients hospitalisés pour pneumonie à COVID-19 aux CUK sur une période de 9 mois, allant de 17 mars au 17 novembre 2020. Un appareil TDM 16 barrettes de marque Hitachi a été utilisé chez tous nos patients. Après analyse, les lésions trouvées ont été réparties en lésions évocatrices et non évocatrices de l´infection à SARS-CoV-2.

Résultats

l´âge médian des patients était de 53,02 ans. Le sexe masculin était le plus touché avec 76,9%. La détresse respiratoire était le symptôme clinique le plus rencontré avec 61,5%. Comme comorbidité, l´hypertension artérielle (HTA) et l´insuffisance rénale étaient les plus retrouvées avec 30% et 6%. Les opacités en Verre dépoli bilaterales avec prédominance périphérique étaient prédominantes (69,2%), suivies des condensations (57,7%) et des crazy paving (19,2%). Le stade sévère était le plus fréquemment rencontré (34,61%). L´embolie pulmonaire distale et proximale était la complication la plus fréquente (11,5%). Parmi les pathologies associées la pleurésie et l´HTA pulmonaire étaient les plus rencontrées (30%, 8%). La majorité de nos patients, ont présentés des lésions parenchymateuses pulmonaires correspondant au stade TDM précoce de la maladie (50%).

Conclusion

les lésions TDM évocatrices de COVID-19, observées aux CUK lors de la première vague sont dominées par les plages d´opacités en verre dépoli, suivies des condensations parenchymateuses non systématisées et des crazy paving. Les lésions atypiques moins observées, étaient constituées des condensations pseudo-nodulaires, d´atteinte unilatérale, d´atteinte péribronchovasculaire et d´infection sur le poumon remanié. Le stade TDM sévère était la plus fréquente. L´embolie pulmonaire proximale et distale était la complication la plus rencontrée. Les aspects trouvés corroborent majoritairement les observations de la littérature.

Predicting COVID-19 incidence in French hospitals using human contact network analytics

Background  COVID-19 was first detected in Wuhan, China, in 2019 and spread worldwide within a few weeks. The COVID-19 epidemic started to gain traction in France in March 2020. Subnational hospital admissions and deaths were then recorded daily and served as the main policy indicators. Concurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. Contrarily to individual tracking data, these can be a proxy for human contact networks between subnational administrative units. Methods  Motivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between July 2020 and April 2021. We added human contact network analytics, such as clustering coefficients, contact network strength, null links or curvature, as regressors. Findings  We found that predictions can be improved substantially (by more than 50%) at both the national level and the subnational level for up to 2 weeks. Our subnational analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. This original application of network analytics from colocalization data to epidemic spread opens new perspectives for epidemic forecasting and public health.

Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.

Expert Consensus by Qilu Hospital of Shandong University on the diagnosis, management, and treatment of suspected COVID-19 cases (English version)

We devised a protocol to establish a standardized method of screening, diagnosing, and managing suspected cases of coronavirus disease (COVID-19) and to enhance the management of COVID-19 suspected cases. The protocol that included diagnostic criteria, preventive measures, and control measures against COVID-19 was developed based on new evidence regarding the epidemiological and clinical characteristics of COVID-19. A consensus document was subsequently formulated. The consensus focused on the clinical management of patients with suspected fever and reviewed the procedure for undergoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleic acid testing. This consensus will contribute to the ongoing efforts worldwide for the prevention and control of COVID-19.

Comparing the COVID-19 pandemic in space and over time in Europe, using numbers of deaths, crude rates and adjusted mortality trend ratios

Comparison of COVID-19 trends in space and over time is essential to monitor the pandemic and to indirectly evaluate non-pharmacological policies aimed at reducing the burden of disease. Given the specific age- and sex- distribution of COVID-19 mortality, the underlying sex- and age-distribution of populations need to be accounted for. The aim of this paper is to present a method for monitoring trends of COVID-19 using adjusted mortality trend ratios (AMTRs). Age- and sex-mortality distribution of a reference European population (N = 14,086) was used to calculate age- and sex-specific mortality rates. These were applied to each country to calculate the expected deaths. Adjusted Mortality Trend Ratios (AMTRs) with 95% confidence intervals (C.I.) were calculated for selected European countries on a daily basis from 17th March 2020 to 29th April 2021 by dividing observed cumulative mortality, by expected mortality, times the crude mortality of the reference population. These estimated the sex- and age-adjusted mortality for COVID-19 per million population in each country. United Kingdom experienced the highest number of COVID-19 related death in Europe. Crude mortality rates were highest Hungary, Czech Republic, and Luxembourg. Accounting for the age-and sex-distribution of the underlying populations with AMTRs for each European country, four different patterns were identified: countries which experienced a two-wave pandemic, countries with almost undetectable first wave, but with either a fast or a slow increase of mortality during the second wave; countries with consistently low rates throughout the period. AMTRs were highest in Eastern European countries (Hungary, Czech Republic, Slovakia, and Poland). Our methods allow a fair comparison of mortality in space and over time. These might be of use to indirectly estimating the efficacy of non-pharmacological health policies. The authors urge the World Health Organisation, given the absence of age and sex-specific mortality data for direct standardisation, to adopt this method to estimate the comparative mortality from COVID-19 pandemic worldwide.

The Rise of SARS-CoV-2 Variants and the Role of Convalescent Plasma Therapy for Management of Infections

Novel therapies for the treatment of COVID-19 are continuing to emerge as the SARS-Cov-2 pandemic progresses. PCR remains the standard benchmark for initial diagnosis of COVID-19 infection, while advances in immunological profiling are guiding clinical treatment. The SARS-Cov-2 virus has undergone multiple mutations since its emergence in 2019, resulting in changes in virulence that have impacted on disease severity globally. The emergence of more virulent variants of SARS-Cov-2 remains challenging for effective disease control during this pandemic. Major variants identified to date include B.1.1.7, B.1.351; P.1; B.1.617.2; B.1.427; P.2; P.3; B.1.525; and C.37. Globally, large unvaccinated populations increase the risk of more and more variants arising. With successive waves of COVID-19 emerging, strategies that mitigate against community transmission need to be implemented, including increased vaccination coverage. For treatment, convalescent plasma therapy, successfully deployed during recent Ebola outbreaks and for H1N1 influenza, can increase survival rates and improve host responses to viral challenge. Convalescent plasma is rich with cytokines (IL-1β, IL-2, IL-6, IL-17, and IL-8), CCL2, and TNFα, neutralizing antibodies, and clotting factors essential for the management of SARS-CoV-2 infection. Clinical trials can inform and guide treatment policy, leading to mainstream adoption of convalescent therapy. This review examines the limited number of clinical trials published, to date that have deployed this therapy and explores clinical trials in progress for the treatment of COVID-19.

Attitudes and Acceptance of COVID-19 Vaccination Among Nurses and Midwives in Cyprus: A Cross-Sectional Survey

Healthcare workers are at the frontline of the COVID-19 pandemic and have been identified as a priority target group for COVID-19 vaccines. This study aimed to determine the COVID-19 vaccination intention among nurses and midwives in Cyprus and reveal the influential factors that affected their decision. An Internet-based cross-sectional survey was conducted between December 8 and 28, 2020. Data collection was accomplished using a self-administered questionnaire with questions about socio-demographic characteristics, questions assessing general vaccination-related intentions and behaviors, and the intention to accept COVID-19 vaccination. A sample of 437 responders answered the survey, with 93% being nurses and 7% midwives. A small proportion of the participants would accept a vaccine against COVID-19, while 70% could be qualified as "vaccine hesitant." The main reasons for not receiving the COVID-19 vaccine were concerns about the vaccine's expedited development and fear of side effects. More females, individuals with a larger median age, and a higher number of years of working experience, intended to accept the COVID-19 vaccination, compared with those not intended to accept and undecided groups (p < 0.01). Having a seasonal flu vaccination in the last 5 years, receiving the vaccines recommended for health professionals, and working in the private sector were associated with a higher probability of COVID-19 vaccination acceptance. A considerable rate of nurses and midwives in Cyprus reported unwillingness to receive a COVID-19 vaccine due to vaccine-related concerns. Our findings highlight the need for forthcoming vaccination campaigns and programs to tackle coronavirus vaccine hesitancy barriers to achieve the desirable vaccination coverage.

Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach

Background

Despite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally.

Objective

South Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19.

Methods

The country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers.

Results

The risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R₀ was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The R_t was estimated in South Korea and in both Seoul and Gyeonggi.

Conclusions

A statistical model accounting for imported and locally transmitted cases was employed to estimate R₀ and R_t. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea.

Assessing regional risk of COVID-19 infection from Wuhan via high-speed rail

This paper demonstrates that transportation networks may be used to assess and predict the regional risk of COVID-19 infection from the outbreak. We use China's high-speed rail (HSR) network at the scale of prefecture level to assess, based on a probabilistic risk model, the risk of COVID-19 infection from Wuhan to the country's 31 province-level regions at the early stage of domestic spread. We find that the high-risk regions are mainly distributed along the southern half of Beijing-Hong Kong HSR line, where a large number of infection cases have been confirmed at the early stage. Furthermore, the two components of the infection risk, namely, the probability (proxied by the region's correlation with Wuhan through HSR) and the impact (proxied by the region's population with mobility), can play different roles in the risk ranking for different regions. For public health administrators, these findings may be used for better decision making, including the preparation of emergency plans and supplies, and the allocation of limited resources, before the extensive spread of the epidemic. Moreover, the administrators should adopt different intervention measures for different regions, so as to better mitigate the epidemic spread according to their own risk scenarios with respect to the probability of occurring and, once occurred, the impact.

The epidemicity index of recurrent SARS-CoV-2 infections

Several indices can predict the long-term fate of emerging infectious diseases and the effect of their containment measures, including a variety of reproduction numbers (e.g. [Formula: see text]). Other indices evaluate the potential for transient increases of epidemics eventually doomed to disappearance, based on generalized reactivity analysis. They identify conditions for perturbations to a stable disease-free equilibrium ([Formula: see text]) to grow, possibly causing significant damage. Here, we introduce the epidemicity index e0, a threshold-type indicator: if e0 > 0, initial foci may cause infection peaks even if [Formula: see text]. Therefore, effective containment measures should achieve a negative epidemicity index. We use spatially explicit models to rank containment measures for projected evolutions of the ongoing pandemic in Italy. There, we show that, while the effective reproduction number was below one for a sizable timespan, epidemicity remained positive, allowing recurrent infection flare-ups well before the major epidemic rebounding observed in the fall.

Association of Hematologic biomarkers and their combinations with disease severity and mortality in COVID-19- an Indian perspective

BACKGROUND

COVID-19 is a systemic viral infection with a significant impact on the hematopoietic system, hemostasis as well as immune system. It would be of utmost importance to explore if the most routinely used tests could serve as an aid in determining patient's clinical status or predicting severity of the disease.

METHODS

A prospective cross-sectional study was conducted on 506 Covid-19 positive patients and 200 controls over a period of two months (June and July 2020). The cases were sub-classified based on disease severity into mild to moderate (n=337), severe (n=118) and very severe (n=51) and based on survivor status into survivors (n=473) and non-survivors (n=33).

RESULTS

There were statistically significant differences in WBC count, Absolute neutrophil count (ANC), Absolute lymphocyte count (ALC), absolute monocyte count (AMC), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR) Red blood cell distribution width (RDW-SD) and RDW CV between covid cases vs controls; among the clinical subgroups and among the survivors and non-survivors. There was a significant strong positive correlation between various parameters, that is, NLR and MLR (r: 0.852, P=0), MPV and PDW (r: 0.912, P=0), MPV and PLCR (r: 0.956, P=0), PDW and PLCR (r: 0.893, P=0). NLR (AUC: 0.676, P=0) was the best single parameter and NLR+RDW-CV was best combination parameter as per area under curve (0.871) of ROC to distinguish severe from mild to moderate disease.

CONCLUSIONS

Leucocytosis, neutrophilia, lymphopenia and monocytosis were characteristic findings in covid cases while NLR and NLR+RDW-CV emerged as the most effective single and combination CBC parameters in distinguishing mild to moderate and severe cases respectively.

Travel-related control measures to contain the COVID-19 pandemic: an evidence map

OBJECTIVES

To comprehensively map the existing evidence assessing the impact of travel-related control measures for containment of the SARS-CoV-2/COVID-19 pandemic.

DESIGN

Rapid evidence map.

DATA SOURCES

MEDLINE, Embase and Web of Science, and COVID-19 specific databases offered by the US Centers for Disease Control and Prevention and the WHO.

ELIGIBILITY CRITERIA

We included studies in human populations susceptible to SARS-CoV-2/COVID-19, SARS-CoV-1/severe acute respiratory syndrome, Middle East respiratory syndrome coronavirus/Middle East respiratory syndrome or influenza. Interventions of interest were travel-related control measures affecting travel across national or subnational borders. Outcomes of interest included infectious disease, screening, other health, economic and social outcomes. We considered all empirical studies that quantitatively evaluate impact available in Armenian, English, French, German, Italian and Russian based on the team's language capacities.

DATA EXTRACTION AND SYNTHESIS

We extracted data from included studies in a standardised manner and mapped them to a priori and (one) post hoc defined categories.

RESULTS

We included 122 studies assessing travel-related control measures. These studies were undertaken across the globe, most in the Western Pacific region (n=71). A large proportion of studies focused on COVID-19 (n=59), but a number of studies also examined SARS, MERS and influenza. We identified studies on border closures (n=3), entry/exit screening (n=31), travel-related quarantine (n=6), travel bans (n=8) and travel restrictions (n=25). Many addressed a bundle of travel-related control measures (n=49). Most studies assessed infectious disease (n=98) and/or screening-related (n=25) outcomes; we found only limited evidence on economic and social outcomes. Studies applied numerous methods, both inferential and descriptive in nature, ranging from simple observational methods to complex modelling techniques.

CONCLUSIONS

We identified a heterogeneous and complex evidence base on travel-related control measures. While this map is not sufficient to assess the effectiveness of different measures, it outlines aspects regarding interventions and outcomes, as well as study methodology and reporting that could inform future research and evidence synthesis.

Spatiotemporal Analysis of COVID-19 Incidence Data

(1) Background: A better understanding of COVID-19 dynamics in terms of interactions among individuals would be of paramount importance to increase the effectiveness of containment measures. Despite this, the research lacks spatiotemporal statistical and mathematical analysis based on large datasets. We describe a novel methodology to extract useful spatiotemporal information from COVID-19 pandemic data. (2) Methods: We perform specific analyses based on mathematical and statistical tools, like mathematical morphology, hierarchical clustering, parametric data modeling and non-parametric statistics. These analyses are here applied to the large dataset consisting of about 19,000 COVID-19 patients in the Veneto region (Italy) during the entire Italian national lockdown. (3) Results: We estimate the COVID-19 cumulative incidence spatial distribution, significantly reducing image noise. We identify four clusters of connected provinces based on the temporal evolution of the incidence. Surprisingly, while one cluster consists of three neighboring provinces, another one contains two provinces more than 210 km apart by highway. The survival function of the local spatial incidence values is modeled here by a tapered Pareto model, also used in other applied fields like seismology and economy in connection to networks. Model's parameters could be relevant to describe quantitatively the epidemic. (4) Conclusion: The proposed methodology can be applied to a general situation, potentially helping to adopt strategic decisions such as the restriction of mobility and gatherings.

Presentation of a developed sub-epidemic model for estimation of the COVID-19 pandemic and assessment of travel-related risks in Iran

The COVID-19 pandemic is one of the contagious diseases involving all the world in 2019-2020. Also, all people are concerned about the future of this catastrophe and how the continuous outbreak can be prevented. Some countries are not successful in controlling the outbreak; therefore, the incidence is observed in several peaks. In this paper, firstly single-peak SIR models are used for historical data. Regarding the SIR model, the termination time of the outbreak should have been in early June 2020. However, several peaks invalidate the results of single-peak models. Therefore, we should present a model to support pandemics with several extrema. In this paper, we presented the generalized logistic growth model (GLM) to estimate sub-epidemic waves of the COVID-19 outbreak in Iran. Therefore, the presented model simulated scenarios of two, three, and four waves in the observed incidence. In the second part of the paper, we assessed travel-related risk in inter-provincial travels in Iran. Moreover, the results of travel-related risk show that typical travel between Tehran and other sites exposed Isfahan, Gilan, Mazandaran, and West Azerbaijan in the higher risk of infection greater than 100 people per day. Therefore, controlling this movement can prevent great numbers of infection, remarkably.

Covid-19 pandemic in the lens of food safety and security

The recently emerged coronavirus disease (COVID-19), which has been characterised as a pandemic by the World Health Organization (WHO), is impacting all parts of human society including agriculture, manufacturing, and tertiary sectors involving all service provision industries. This paper aims to give an overview of potential host reservoirs that could cause pandemic outbreak caused by zoonotic transmission. Amongst all, continues surveillance in slaughterhouse for possible pathogens transmission is needed to prevent next pandemic outbreak. This paper also summarizes the potential threats of pandemic to agriculture and aquaculture sector that control almost the total food supply chain and market. The history lesson from the past, emerging and reemerging infectious disease including the Severe Acute Respiratory Syndrome (SARS) in 2002, Influenza A H1N1 (swine flu) in 2009, Middle East Respiratory Syndrome (MERS) in 2012 and the recent COVID-19 should give us some clue to improve especially the governance to be more ready for next coming pandemic.

A review: novel coronavirus (COVID-19): an evidence-based approach

The World Health Organization in China was informed about the cases of pneumonia of unknown antecedent ailments. Since then, there have been over 141 million cases globally of 2019 novel coronavirus (Covid-19), 3.01 million deaths, and over 80.4 million recovered. Clinical research of novel agents represent opportunities to inform real-time public health action. In 2018 there was a systematic review to identify priority research questions for Severe Acute Respiratory Syndrome-related coronavirus and Middle East Respiratory Syndrome-related coronavirus. Here, we review information available on COVID-19 and provide evidenced-based approaches in clinical research for the current COVID-19 outbreak.

Effect of internationally imported cases on internal spread of COVID-19: a mathematical modelling study

BACKGROUND

Countries have restricted international arrivals to delay the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These measures carry a high economic and social cost, and might have little effect on COVID-19 epidemics if there are many more cases resulting from local transmission compared with imported cases. Our study aims to investigate the extent to which imported cases contribute to local transmission under different epidemic conditions.

METHODS

To inform decisions about international travel restrictions, we calculated the ratio of expected COVID-19 cases from international travel (assuming no travel restrictions) to expected cases arising from internal spread, expressed as a proportion, on an average day in May and September, 2020, in each country. COVID-19 prevalence and incidence were estimated using a modelling framework that adjusts reported cases for under-ascertainment and asymptomatic infections. We considered different travel scenarios for May and September, 2020: an upper bound with estimated travel volumes at the same levels as May and September, 2019, and a lower bound with estimated travel volumes adjusted downwards according to expected reductions in May and September, 2020. Results were interpreted in the context of local epidemic growth rates.

FINDINGS

In May, 2020, imported cases are likely to have accounted for a high proportion of total incidence in many countries, contributing more than 10% of total incidence in 102 (95% credible interval 63-129) of 136 countries when assuming no reduction in travel volumes (ie, with 2019 travel volumes) and in 74 countries (33-114) when assuming estimated 2020 travel volumes. Imported cases in September, 2020, would have accounted for no more than 10% of total incidence in 106 (50-140) of 162 countries and less than 1% in 21 countries (4-71) when assuming no reductions in travel volumes. With estimated 2020 travel volumes, imported cases in September, 2020, accounted for no more than 10% of total incidence in 125 countries (65-162) and less than 1% in 44 countries (8-97). Of these 44 countries, 22 (2-61) had epidemic growth rates far from the tipping point of exponential growth, making them the least likely to benefit from travel restrictions.

INTERPRETATION

Countries can expect travellers infected with SARS-CoV-2 to arrive in the absence of travel restrictions. Although such restrictions probably contribute to epidemic control in many countries, in others, imported cases are likely to contribute little to local COVID-19 epidemics. Stringent travel restrictions might have little impact on epidemic dynamics except in countries with low COVID-19 incidence and large numbers of arrivals from other countries, or where epidemics are close to tipping points for exponential growth. Countries should consider local COVID-19 incidence, local epidemic growth, and travel volumes before implementing such restrictions.

FUNDING

Wellcome Trust, UK Foreign, Commonwealth & Development Office, European Commission, National Institute for Health Research, Medical Research Council, and Bill & Melinda Gates Foundation.

Uncovering two phases of early intercontinental COVID-19 transmission dynamics

BACKGROUND

The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies.

METHODS

We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19.

RESULTS

Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission.

CONCLUSIONS

Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic.

Quarantine and the risk of COVID-19 importation

Using a stochastic model, we assess the risk of importation-induced local transmission chains in locations seeing few or no local transmissions and evaluate the role of quarantine in the mitigation of this risk. We find that the rate of importations plays a critical role in determining the risk that case importations lead to local transmission chains, more so than local transmission characteristics, i.e. strength of social distancing measures (NPI). The latter influences the severity of the outbreaks when they do take place. Quarantine after arrival in a location is an efficacious way to reduce the rate of importations. Locations that see no or low-level local transmission should ensure that the rate of importations remains low. A high level of compliance with post-arrival quarantine followed by testing achieves this objective with less of an impact than travel restrictions or bans.

The impact of human mobility networks on the global spread of COVID-19

Human mobility networks are crucial for a better understanding and controlling the spread of epidemics. Here, we study the impact of human mobility networks on the COVID-19 onset in 203 different countries. We use exponential random graph models to perform an analysis of the country-to-country global spread of COVID-19. We find that most countries had similar levels of virus spreading, with only a few acting as the main global transmitters. Our evidence suggests that migration and tourism inflows increase the probability of COVID-19 case importations while controlling for contiguity, continent co-location and sharing a language. Moreover, we find that air flights were the dominant mode of transportation while male and returning travellers were the main carriers. In conclusion, a mix of mobility and geography factors predicts the COVID-19 global transmission from one country to another. These findings have implications for non-pharmaceutical public health interventions and the management of transborder human circulation.

Assessing the relationship between ground levels of ozone (O3) and nitrogen dioxide (NO2) with coronavirus (COVID-19) in Milan, Italy

This paper investigates the correlation between the high level of coronavirus SARS-CoV-2 infection accelerated transmission and lethality, and surface air pollution in Milan metropolitan area, Lombardy region in Italy. For January-April 2020 period, time series of daily average inhalable gaseous pollutants ozone (O3) and nitrogen dioxide (NO2), together climate variables (air temperature, relative humidity, wind speed, precipitation rate, atmospheric pressure field and Planetary Boundary Layer) were analyzed. In spite of being considered primarily transmitted by indoor bioaerosols droplets and infected surfaces or direct human-to-human personal contacts, it seems that high levels of urban air pollution, and climate conditions have a significant impact on SARS-CoV-2 diffusion. Exhibited positive correlations of ambient ozone levels and negative correlations of NO2 with the increased rates of COVID-19 infections (Total number, Daily New positive and Total Deaths cases), can be attributed to airborne bioaerosols distribution. The results show positive correlation of daily averaged O3 with air temperature and inversely correlations with relative humidity and precipitation rates. Viral genome contains distinctive features, including a unique N-terminal fragment within the spike protein, which allows coronavirus attachment on ambient air pollutants. At this moment it is not clear if through airborne diffusion, in the presence of outdoor and indoor aerosols, this protein "spike" of the new COVID-19 is involved in the infectious agent transmission from a reservoir to a susceptible host during the highest nosocomial outbreak in some agglomerated industrialized urban areas like Milan is. Also, in spite of collected data for cold season (winter-early spring) period, when usually ozone levels have lower values than in summer, the findings of this study support possibility as O3 can acts as a COVID-19 virus incubator. Being a novel pandemic coronavirus version, it might be ongoing during summer conditions associated with higher air temperatures, low relative humidity and precipitation levels.

Strategic design of precautionary measures for airport passengers in times of global health crisis Covid 19: Parametric modelling and processing algorithms

Presently, the negative results of a pandemic loom in a threatening manner on an international scale. Facilities such as airports have contributed significantly to the global spread of the COVID-19 virus. Therefore, in order to address this challenge, studies on sanitary risk management and the proper application of countermeasures should be carried out. To measure the consequences over passenger flow, simulation modelling has been set up at Casablanca Mohammed V International Airport. Several scenarios using daily traffic data were run in different circumstances. This allowed the development of some assumptions regarding the overall capacity of the airport. The proposed simulations make it possible to calculate the number of passengers to be processed in accordance with the available check-in counters based on the proposed sanitary measures.

The dynamics of entropy in the COVID-19 outbreaks

With the unfolding of the COVID-19 pandemic, mathematical modelling of epidemics has been perceived and used as a central element in understanding, predicting, and governing the pandemic event. However, soon it became clear that long-term predictions were extremely challenging to address. In addition, it is still unclear which metric shall be used for a global description of the evolution of the outbreaks. Yet a robust modelling of pandemic dynamics and a consistent choice of the transmission metric is crucial for an in-depth understanding of the macroscopic phenomenology and better-informed mitigation strategies. In this study, we propose a Markovian stochastic framework designed for describing the evolution of entropy during the COVID-19 pandemic together with the instantaneous reproductive ratio. Then, we introduce and use entropy-based metrics of global transmission to measure the impact and the temporal evolution of a pandemic event. In the formulation of the model, the temporal evolution of the outbreak is modelled by an equation governing the probability distribution that describes a nonlinear Markov process of a statistically averaged individual, leading to a clear physical interpretation. The time-dependent parameters are formulated by adaptive basis functions, leading to a parsimonious representation. In addition, we provide a full Bayesian inversion scheme for calibration together with a coherent strategy to address data unreliability. The time evolution of the entropy rate, the absolute change in the system entropy, and the instantaneous reproductive ratio are natural and transparent outputs of this framework. The framework has the appealing property of being applicable to any compartmental epidemic model. As an illustration, we apply the proposed approach to a simple modification of the susceptible-exposed-infected-removed model. Applying the model to the Hubei region, South Korean, Italian, Spanish, German, and French COVID-19 datasets, we discover significant difference in the absolute change of entropy but highly regular trends for both the entropy evolution and the instantaneous reproductive ratio.

Power-law distribution in the number of confirmed COVID-19 cases

COVID-19 is an emerging respiratory infectious disease caused by the coronavirus SARS-CoV-2. It was first reported on in early December 2019 in Wuhan, China and within three months spread as a pandemic around the whole globe. Here, we study macro-epidemiological patterns along the time course of the COVID-19 pandemic. We compute the distribution of confirmed COVID-19 cases and deaths for countries worldwide and for counties in the US and show that both distributions follow a truncated power-law over five orders of magnitude. We are able to explain the origin of this scaling behavior as a dual-scale process: the large-scale spread of the virus between countries and the small-scale accumulation of case numbers within each country. Assuming exponential growth on both scales, the critical exponent of the power-law is determined by the ratio of large-scale to small-scale growth rates. We confirm this theory in numerical simulations in a simple meta-population model, describing the epidemic spread in a network of interconnected countries. Our theory gives a mechanistic explanation why most COVID-19 cases occurred within a few epicenters, at least in the initial phase of the outbreak. By combining real world data, modeling, and numerical simulations, we make the case that the distribution of epidemic prevalence might follow universal rules.

Mathematical modelling on diffusion and control of COVID-19

In this paper, we develop a mathematical model for the spread and control of the coronavirus disease. An outbreak of COVID-19 has led to more than one million confirmed cases as of April 3rd, 2020. Understanding the early spread dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe COVID-19 spread with four datasets from within and outside of Wuhan, China; it is estimated how spread in Wuhan varied between January and February 2020. It is used these estimates to assess the potential for sustained human-to-human spread to occur in locations outside Wuhan if disease holders were introduced. It is combined SEIR framework model with data on cases of COVID-19 in China and International cases that originated in Wuhan to estimate how spread had varied over time during January and February 2020. Based on these estimates, it is calculated the probability that freshly introduced cases might produce outbreaks in other regions. Also, it is calculated approximately the median day by day basic reproduction number in Wuhan, refused from 2·45 (95% CI: 1·16-4·87) one week before travel restrictions were introduced on Jan 23rd, 2020, to 1.05 (0·42-2·40) one week after. Based on our estimates of, presumptuous SARS approximating disparity, it is computed that in locations with a similar spread potential to Wuhan in near the beginning of January, some time ago there are at least four independently set up cases, there is a more than fifty percent chance the infection will found within those inhabitants. COVID-19 spreading probably refused in Wuhan during delayed January 2020, corresponding with the prologue of voyage control channels. As more cases arrive in international locations with similar spread potential to Wuhan, before these organize measures, it is likely many chains of spread will fail to create initially but might lead to innovative outbreaks ultimately.

Profiling Early Humoral Response to Diagnose Novel Coronavirus Disease (COVID-19)

BACKGROUND

The emergence of coronavirus disease 2019 (COVID-19) is a major healthcare threat. The current method of detection involves a quantitative polymerase chain reaction (qPCR)-based technique, which identifies the viral nucleic acids when present in sufficient quantity. False-negative results can be achieved and failure to quarantine the infected patient would be a major setback in containing the viral transmission. We aim to describe the time kinetics of various antibodies produced against the 2019 novel coronavirus (SARS-CoV-2) and evaluate the potential of antibody testing to diagnose COVID-19.

METHODS

The host humoral response against SARS-CoV-2, including IgA, IgM, and IgG response, was examined by using an ELISA-based assay on the recombinant viral nucleocapsid protein. 208 plasma samples were collected from 82 confirmed and 58 probable cases (qPCR negative but with typical manifestation). The diagnostic value of IgM was evaluated in this cohort.

RESULTS

The median duration of IgM and IgA antibody detection was 5 (IQR, 3-6) days, while IgG was detected 14 (IQR, 10-18) days after symptom onset, with a positive rate of 85.4%, 92.7%, and 77.9%, respectively. In confirmed and probable cases, the positive rates of IgM antibodies were 75.6% and 93.1%, respectively. The detection efficiency by IgM ELISA is higher than that of qPCR after 5.5 days of symptom onset. The positive detection rate is significantly increased (98.6%) when combining IgM ELISA assay with PCR for each patient compared with a single qPCR test (51.9%).

CONCLUSIONS

The humoral response to SARS-CoV-2 can aid in the diagnosis of COVID-19, including subclinical cases.

Cluster of Coronavirus Disease 2019 (COVID-19) in the French Alps, February 2020

Background

On 07/02/2020, French Health authorities were informed of a confirmed case of SARS-CoV-2 coronavirus in an Englishman infected in Singapore who had recently stayed in a chalet in the French Alps. We conducted an investigation to identify secondary cases and interrupt transmission.

Methods

We defined as a confirmed case a person linked to the chalet with a positive RT-PCR sample for SARS-CoV-2.

Results

The index case stayed 4 days in the chalet with 10 English tourists and a family of 5 French residents; SARS-CoV-2 was detected in 5 individuals in France, 6 in England (including the index case), and 1 in Spain (overall attack rate in the chalet: 75%). One pediatric case, with picornavirus and influenza A coinfection, visited 3 different schools while symptomatic. One case was asymptomatic, with similar viral load as that of a symptomatic case. Seven days after the first cases were diagnosed, one tertiary case was detected in a symptomatic patient with a positive endotracheal aspirate; all previous and concurrent nasopharyngeal specimens were negative. Additionally, 172 contacts were monitored, including 73 tested negative for SARS-CoV-2.

Conclusions

The occurrence in this cluster of one asymptomatic case with similar viral load as a symptomatic patient, suggests transmission potential of asymptomatic individuals. The fact that an infected child did not transmit the disease despite close interactions within schools suggests potential different transmission dynamics in children. Finally, the dissociation between upper and lower respiratory tract results underscores the need for close monitoring of the clinical evolution of suspect Covid-19 cases.

Early Research on COVID-19: A Bibliometric Analysis

In December 2019, an outbreak of pneumonia, which was named COVID-2019, emerged as a global health crisis. Scientists worldwide are engaged in attempts to elucidate the transmission and pathogenic mechanisms of the causative coronavirus. COVID-19 was declared a pandemic by the World Health Organization in March 2020, making it critical to track and review the state of research on COVID-19 to provide guidance for further investigations. Here, bibliometric and knowledge mapping analyses of studies on COVID-19 were performed, including more than 1,500 papers on COVID-19 available in the PubMed and China National Knowledge Infrastructure databases from January 1, 2020 to March 8, 2020. In this review, we found that because of the rapid response of researchers worldwide, the number of COVID-19-related publications showed a high growth trend in the first 10 days of February; among these, the largest number of studies originated in China, the country most affected by pandemic in its early stages. Our findings revealed that the epidemic situation and data accessibility of different research teams have caused obvious difference in emphases of the publications. Besides, there was an unprecedented level of close cooperation and information sharing within the global scientific community relative to previous coronavirus research. We combed and drew the knowledge map of the SARS-CoV-2 literature, explored early status of research on etiology, pathology, epidemiology, treatment, prevention, and control, and discussed knowledge gaps that remain to be urgently addressed. Future perspectives on treatment, prevention, and control are also presented to provide fundamental references for current and future coronavirus research.

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China initiated COVID-19-related research in considerable scope and depth at the early stage of the outbreak

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Researchers all over the world have rapidly launched unprecedented joint research efforts

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The knowledge map of SARS-CoV-2 is becoming increasingly comprehensive, and knowledge gaps to be filled have been identified

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The next step is to consider other factors conducive to research innovation, such as public and private's cooperation, equitable health system