Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'

Using an expert survey and user feedback to construct PRECHECK: A checklist to evaluate preprints on COVID-19 and beyond

Background

The quality of COVID-19 preprints should be considered with great care, as their contents can influence public policy. Surprisingly little has been done to calibrate the public's evaluation of preprints and their contents. The PRECHECK project aimed to generate a tool to teach and guide scientifically literate non-experts to critically evaluate preprints, on COVID-19 and beyond.

Methods

To create a checklist, we applied a four-step procedure consisting of an initial internal review, an external review by a pool of experts (methodologists, meta-researchers/experts on preprints, journal editors, and science journalists), a final internal review, and a Preliminary implementation stage. For the external review step, experts rated the relevance of each element of the checklist on five-point Likert scales, and provided written feedback. After each internal review round, we applied the checklist on a small set of high-quality preprints from an online list of milestone research works on COVID-19 and low-quality preprints, which were eventually retracted, to verify whether the checklist can discriminate between the two categories.

Results

At the external review step, 26 of the 54 contacted experts responded. The final checklist contained four elements (Research question, study type, transparency and integrity, and limitations), with 'superficial' and 'deep' evaluation levels. When using both levels, the checklist was effective at discriminating a small set of high- and low-quality preprints. Its usability for assessment and discussion of preprints was confirmed in workshops with Bachelors students in Psychology and Medicine, and science journalists.

Conclusions

We created a simple, easy-to-use tool for helping scientifically literate non-experts navigate preprints with a critical mind and facilitate discussions within, for example, a beginner-level lecture on research methods. We believe that our checklist has potential to help guide decisions about the quality of preprints on COVID-19 in our target audience and that this extends beyond COVID-19.

Estimating age-stratified transmission and reproduction numbers during the early exponential phase of an epidemic: A case study with COVID-19 data

In a pending pandemic, early knowledge of age-specific disease parameters, e.g., susceptibility, infectivity, and the clinical fraction (the fraction of infections coming to clinical attention), supports targeted public health responses like school closures or sequestration of the elderly. The earlier the knowledge, the more useful it is, so the present article examines an early phase of many epidemics, exponential growth. Using age-stratified COVID-19 case counts collected in Canada, China, Israel, Italy, the Netherlands, and the United Kingdom before April 23, 2020, we present a linear analysis of the exponential phase that attempts to estimate the age-specific disease parameters given above. Some combinations of the parameters can be estimated by requiring that they change smoothly with age. The estimation yielded: (1) the case susceptibility, defined for each age-group as the product of susceptibility to infection and the clinical fraction; (2) the mean number of transmissions of infection per contact within each age-group; and (3) the reproduction number of infection within each age-group, i.e., the diagonal of the age-stratified next-generation matrix. Our restriction to data from the exponential phase indicates the combinations of epidemic parameters that are intrinsically easiest to estimate with early age-stratified case counts. For example, conclusions concerning the age-dependence of case susceptibility appeared more robust than corresponding conclusions about infectivity. Generally, the analysis produced some results consistent with conclusions confirmed much later in the COVID-19 pandemic. Notably, our analysis showed that in some countries, the reproduction number of infection within the half-decade 70-75 was unusually large compared to other half-decades. Our analysis therefore could have anticipated that without countermeasures, COVID-19 would spread rapidly once seeded in homes for the elderly.

“We are Tired but Do Not Give Up” the Dilemma and Challenges of Primary Nurses Facing the Omicron Variant: Qualitative Research

Background

Everyone becomes aware due to the epidemic, especially primary department nurses. The significance of their experiences teaches nurses how to take care of themselves and be successful in their caregiving.

Objective

The purpose of this study was to investigate the perceptions of nurses working in primary care settings in rural regions during the omicron variant pandemic.

Methods

On the basis of the Nvivo 12 analytic approach, extensive semi-structured interviews were used to perform this qualitative study. 20 interviews later, data saturation had been reached. Data collecting was place from February to March 2022 for a month. The following participant characteristics were discovered through semi-structured interviews with 20 nurse participants. Ages of the participants, who were split between eight men and twelve women, varied from 28 to 43 years (average age 36.4 years). The majority (75% of them) had a vocational education, and their years of experience ranged from five to fifteen (average 11 years).

Results

4 topics and 7 sub-themes' results. The results' fundamental message is: The Nursing Clinical Practice Dilemma, school district, virus type uncertainty Indigenous peoples do not adhere to the concept of the afterlife. Must Be Excited and Alert; School Cluster; Virus Type Confusion; Non-Belief in Covid; and the Dilemma of Nursing in Clinical Practice are the Overarching Themes of This Study.

Conclusion

Making innovations to increase motivation thereby reducing mental and physical fatigue are the implications of the results of this study. Further exploration of the readiness of nurses to treat patients in the main department is believed to be beneficial for the results of this study.

Semantic wikis as flexible database interfaces for biomedical applications

Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm .

Coronavirus (COVID-19): ARIMA-based Time-series Analysis to Forecast near Future and the Effect of School Reopening in India

COVID-19, a novel coronavirus, is currently a major worldwide threat. It has infected more than a million people globally leading to hundred-thousands of deaths. In such grave circumstances, it is very important to predict future scenario to support prevention and recurrence of the disease, aid in healthcare service preparation and help in decision making process. Following that notion, a model has been developed for forecasting future COVID-19 cases in India. The time series analysis indicates that the cases will keep on increasing in India in the coming month as the peak has not been attained until now. A statistical analysis based on the effect of reopening of schools has also been performed. It is revealed that there will be a minor increase in the active cases when pre-/primary schools are opened. The present prediction models will assist the government and medical personnel in gaining insight and planning for forthcoming conditions.

Who are the ‘silent spreaders’?: contact tracing in spatio-temporal memory models

The COVID-19 epidemic has swept the world for over two years. However, a large number of infectious asymptomatic COVID-19 cases (ACCs) are still making the breaking up of the transmission chains very difficult. Efforts by epidemiological researchers in many countries have thrown light on the clinical features of ACCs, but there is still a lack of practical approaches to detect ACCs so as to help contain the pandemic. To address the issue of ACCs, this paper presents a neural network model called Spatio-Temporal Episodic Memory for COVID-19 (STEM-COVID) to identify ACCs from contact tracing data. Based on the fusion Adaptive Resonance Theory (ART), the model encodes a collective spatio-temporal episodic memory of individuals and incorporates an effective mechanism of parallel searches for ACCs. Specifically, the episodic traces of the identified positive cases are used to map out the episodic traces of suspected ACCs using a weighted evidence pooling method. To evaluate the efficacy of STEM-COVID, a realistic agent-based simulation model for COVID-19 spreading is implemented based on the recent epidemiological findings on ACCs. The experiments based on rigorous simulation scenarios, manifesting the current situation of COVID-19 spread, show that the STEM-COVID model with weighted evidence pooling has a higher level of accuracy and efficiency for identifying ACCs when compared with several baselines. Moreover, the model displays strong robustness against noisy data and different ACC proportions, which partially reflects the effect of breakthrough infections after vaccination on the virus transmission.

The first COVID-19 new graduate nurses generation: findings from an Italian cross-sectional study

Background

Nursing education has been disrupted by the onset of the COronaVIrus Disease 19 (COVID-19) pandemic, potentially impacting learning experiences and perceived competencies at the time of graduation. However, the learning experiences of students since the onset of COVID-19, their perceived competences achieved and the employment status one month after graduation, have not been traced to date.

Methods

A cross sectional online survey measured the individual profile, the learning experience in the last academic year and the perceived competences of the first COVID-19 new nursing graduates in two Italian universities. Details relating to employment status and place of employment (Covid-19 versus non-COVID-19 units) one month after graduation were also collected and the data compared with those reported by a similar cohort of new graduates pre-pandemic in 2018–2019. All those who graduated in November 2020 and attended their third year after the onset of the COVID-19 pandemic were eligible. The online survey included individual, nursing programme and first working experience variables alongside the Nurse Competence Scale (NCS). Descriptive and inferential statistical analyses were performed.

Results

A total of 323 new graduates participated. In their last academic year, they experienced a single, long clinical placement in non-COVID-19 units. One month after graduation, 54.5% (n = 176) were working in COVID-19 units, 22.9% (n = 74) in non-COVID-19 units and 22.6 (n = 73) were unemployed. There was no statistical difference among groups regarding individual variables and the competences perceived. Fewer new graduates working in COVID-19 units experienced a transition programme compared to those working in non-COVID-19 units (p = 0.053). At the NCS, the first COVID-19 new graduate generation perceived significantly lower competences than the pre-COVID-19 generation in the ‘Helping role’ factor and a significant higher in ‘Ensuring quality’ and ‘Therapeutic interventions’ factors.

Conclusions

The majority of the first COVID-19 new graduate generation had been employed in COVID-19 units without clinical experience and transition programmes, imposing an ethical debate regarding (a) the role of education in graduating nurses in challenging times with limited clinical placements; and (b) that of nurse managers and directors in ensuring safe transitions for new graduates. Despite the profound clinical placement revision, the first COVID-19 new graduate generation reported competences similar to those of the pre-COVID-19 generation, suggesting that the pandemic may have helped them to optimise the clinical learning process.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-022-00885-3.

Stochastic mathematical models for the spread of COVID-19: a novel epidemiological approach

In this paper, three stochastic mathematical models are developed for the spread of the coronavirus disease (COVID-19). These models take into account the known special characteristics of this disease such as the existence of infectious undetected cases and the different social and infectiousness conditions of infected people. In particular, they include a novel approach that considers the social structure, the fraction of detected cases over the real total infected cases, the influx of undetected infected people from outside the borders, as well as contact-tracing and quarantine period for travellers. Two of these models are discrete time-discrete state space models (one is simplified and the other is complete) while the third one is a continuous time-continuous state space stochastic integro-differential model obtained by a formal passing to the limit from the proposed simplified discrete model. From a numerical point of view, the particular case of Lebanon has been studied and its reported data have been used to estimate the complete discrete model parameters, which can be of interest in estimating the spread of COVID-19 in other countries. The obtained simulation results have shown a good agreement with the reported data. Moreover, a parameters' analysis is presented in order to better understand the role of some of the parameters. This may help policy makers in deciding on different social distancing measures.

Effect of Repeated Home Quarantine on Anxiety, Depression, and PTSD Symptoms in a Chinese Population During the COVID-19 Pandemic: A Cross-sectional Study

BACKGROUND

Strict quarantines can prevent the spread of the COVID-19 pandemic, but also increase the risk of mental illness. This study examined whether the people who have experienced repeated home quarantine performance more negative emotions such as anxiety, depression, and post-traumatic stress disorder (PTSD) in a Chinese population.

METHODS

We collected data from 2,514 participants in Pi County, Chengdu City, and stratified them into two groups. Group 1 comprised 1,214 individuals who were quarantined only once in early 2020, while Group 2 comprised 1,300 individuals who were quarantined in early 2020 and again in late 2020. Both groups were from the same community. The GAD-7, PHQ-9, and PCL-C scales were used to assess symptoms of anxiety, depression, and PTSD between the two groups.

RESULTS

Analyses showed that total PHQ-9 scores were significantly higher in Group 2 than in Group 1 (p < 0.001) and the quarantine times and age are independent predictors of symptoms of depression (p < 0.001). The two groups did not differ significantly in total GAD-7 or PCL-C scores.

CONCLUSION

Increasing quarantine times was associated with moderate to severe depression symptoms, but not with an increase in symptoms of anxiety or PTSD.

COVID-19 pandemic: Impact on gestational diabetes mellitus prevalence

AIM

Although an increased risk of gestational diabetes mellitus (GDM) has been noted in women exposed to stressful conditions and traumatic events, limited information is available about such risk in the context of the COVID-19 pandemic.

METHODS

The study was designed as a non-concurrent case-control study on the prevalence of GDM, defined according to IADPSG 2010, in women giving birth during the COVID-19 pandemic in the hot spot of Northeast Italy from March 9th to May 18th, 2020, with an antecedent puerperae-matched group whose women had given birth in 2019.

RESULTS

Analysis revealed that during the COVID-19 pandemic in 2020, GDM prevalence was significantly higher than in 2019 (GDM, 48/533, 9 vs 86/637, 13.5%, p = 0.01), as illustrated by a higher GDM prevalence in 5/6 months of the final semester of 2020. In addition, logistic regression analysisconfirmed a statistically significant temporal relationship between experiencing the lockdown during the first trimester of gestation and later GDM incidence (t = 2.765, P = 0.012), with an 34% increase in mean number of GDM diagnoses per month (antilog of the parameter = 1.34).

CONCLUSION

The COVID-19 pandemic negatively impacted GDM prevalence in 2020 compared to 2019, especially for pregnant women in the 1st trimester of gestation.

Clinical and epidemiological characteristics and effectiveness of antiviral therapy for COVID-19 in children: The experience of the first year of the pandemic

Background. The use of antiviral agents can shorten the duration of the viral infection. The aim: to study the clinical and epidemiological features and the effectiveness of antiviral therapy for new coronavirus infection (COVID-19) in outpatient children.Materials and methods. From April 2020 to March 2021, 9334 outpatient children aged from 0 months to 17 years were randomly tested for new coronavirus infection. SARS-CoV-2 RNA was detected in oropharyngeal and nasal material by PCR. Patients with confirmed new coronavirus infection were prescribed interferon-alpha (IFN-α) intranasally, antiviral agents of systemic action. The control group consisted of children with COVID-19 who did not receive treatment.Results. When examining clinically healthy contact children, SARS-CoV-2 RNA was detected in 7.4 % of cases. In the structure of ARI, the specific weight of COVID-19 was 12.3 % with the peak incidence in April-May (up to 22.8 %) and NovemberDecember (up to 30.0 %). In half of the cases, children became infected in the family, and usually adults were the index patient. In 47.7 % of cases, an asymptomatic form of COVID-19 was registered without significant differences in patients of different ages. In one third of children with concomitant pathology, the disease was asymptomatic, in half it was mild, in other cases moderate severity was diagnosed. The clinical picture of COVID-19 did not differ from other ARIs. Anosmia (9.4 %) in half of the cases was combined with ageusia (4.4 %) and was significantly more common in boys. The duration of clinical manifestations in children of the control group and those who received antiviral therapy did not statistically significantly differ in mild and severity of the disease. Also, various antiviral therapy options did not significantly affect the duration of SARS-CoV-2 detection in children with various forms of COVID-19.Conclusion. In the first year of the pandemic, the novel coronavirus infection did not dominate the pattern of respiratory diseases in outpatient children. Further research is required to develop pediatric guidelines for the treatment of COVID-19 at the outpatient stage.

Coronavirus in water media: Analysis, fate, disinfection and epidemiological applications

Considerable attention has been recently given to possible transmission of SARS-CoV-2 via water media. This review addresses this issue and examines the fate of coronaviruses (CoVs) in water systems, with particular attention to the recently available information on the novel SARS-CoV-2. The methods for the determination of viable virus particles and quantification of CoVs and, in particular, of SARS-CoV-2 in water and wastewater are discussed with particular regard to the methods of concentration and to the emerging methods of detection. The analysis of the environmental stability of CoVs, with particular regard of SARS-CoV-2, and the efficacy of the disinfection methods are extensively reviewed as well. This information provides a broad view of the state-of-the-art for researchers involved in the investigation of CoVs in aquatic systems, and poses the basis for further analyses and discussions on the risk associated to the presence of SARS-CoV-2 in water media. The examined data indicates that detection of the virus in wastewater and natural water bodies provides a potentially powerful tool for quantitative microbiological risk assessment (QMRA) and for wastewater-based epidemiology (WBE) for the evaluation of the level of circulation of the virus in a population. Assays of the viable virions in water media provide information on the integrity, capability of replication (in suitable host species) and on the potential infectivity. Challenges and critical issues relevant to the detection of coronaviruses in different water matrixes with both direct and surrogate methods as well as in the implementation of epidemiological tools are presented and critically discussed.

The impact of digital contact tracing on the SARS-CoV-2 pandemic-a comprehensive modelling study

Contact tracing is one of several strategies employed in many countries to curb the spread of SARS-CoV-2. Digital contact tracing (DCT) uses tools such as cell-phone applications to improve tracing speed and reach. We model the impact of DCT on the spread of the virus for a large epidemiological parameter space consistent with current literature on SARS-CoV-2. We also model DCT in combination with random testing (RT) and social distancing (SD). Modelling is done with two independently developed individual-based (stochastic) models that use the Monte Carlo technique, benchmarked against each other and against two types of deterministic models. For current best estimates of the number of asymptomatic SARS-CoV-2 carriers (approximately 40%), their contagiousness (similar to that of symptomatic carriers), the reproductive number before interventions ( R 0 at least 3) we find that DCT must be combined with other interventions such as SD and/or RT to push the reproductive number below one. At least 60% of the population would have to use the DCT system for its effect to become significant. On its own, DCT cannot bring the reproductive number below 1 unless nearly the entire population uses the DCT system and follows quarantining and testing protocols strictly. For lower uptake of the DCT system, DCT still reduces the number of people that become infected. When DCT is deployed in a population with an ongoing outbreak where O (0.1%) of the population have already been infected, the gains of the DCT intervention come at the cost of requiring up to 15% of the population to be quarantined (in response to being traced) on average each day for the duration of the epidemic, even when there is sufficient testing capability to test every traced person.

Children's role in the COVID-19 pandemic: a systematic review of early surveillance data on susceptibility, severity, and transmissibility

SARS-CoV-2 infections have been reported in all age groups including infants, children, and adolescents. However, the role of children in the COVID-19 pandemic is still uncertain. This systematic review of early studies synthesises evidence on the susceptibility of children to SARS-CoV-2 infection, the severity and clinical outcomes in children with SARS-CoV-2 infection, and the transmissibility of SARS-CoV-2 by children in the initial phases of the COVID-19 pandemic. A systematic literature review was conducted in PubMed. Reviewers extracted data from relevant, peer-reviewed studies published up to July 4th 2020 during the first wave of the SARS-CoV-2 outbreak using a standardised form and assessed quality using the NIH Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. For studies included in the meta-analysis, we used a random effects model to calculate pooled estimates of the proportion of children considered asymptomatic or in a severe or critical state. We identified 2775 potential studies of which 128 studies met our inclusion criteria; data were extracted from 99, which were then quality assessed. Finally, 29 studies were considered for the meta-analysis that included information of symptoms and/or severity, these were further assessed based on patient recruitment. Our pooled estimate of the proportion of test positive children who were asymptomatic was 21.1% (95% CI: 14.0-28.1%), based on 13 included studies, and the proportion of children with severe or critical symptoms was 3.8% (95% CI: 1.5-6.0%), based on 14 included studies. We did not identify any studies designed to assess transmissibility in children and found that susceptibility to infection in children was highly variable across studies. Children's susceptibility to infection and onward transmissibility relative to adults is still unclear and varied widely between studies. However, it is evident that most children experience clinically mild disease or remain asymptomatically infected. More comprehensive contact-tracing studies combined with serosurveys are needed to quantify children's transmissibility relative to adults. With children back in schools, testing regimes and study protocols that will allow us to better understand the role of children in this pandemic are critical.

A Continuous Model of Three Scenarios of the Infection Process with Delayed Immune Response Factors

The course of an infection was modeled as a controlled nonlinear process. Understanding the substantial differences observed in the trajectory of the disease caused by the new coronavirus SARS-CoV-2 is of critical importance at the moment. Numerous factors have been considered to explain the fact that symptoms vary highly among different people and the infection transmission rate varies among local populations. Virus replication within the host cell and the development of an immune response to virus antigens in the body are two interdependent processes, which have aftereffects and depend on the preexisting states of the cell and virus populations. Different scenarios with the same input parameters are necessary to consider for modeling the properties of the states. The efficiency of the immune response is the most important factor, including the time it takes to develop defense responses from three levels of the immune system, which is a complex system of the body. A computational description of infection scenarios was proposed on the basis of a delay differential equation with two values of the time lag. In the new model, transitions between phases of infectious disease depend on the initial virus dose and the delayed immune response to infection. A variation in the dose of the virus and response time can lead to a transition from an acute phase of the disease with overt symptoms to a chronic phase or fatal outcome. Asymptomatic transmission of viral infection was calculated and described in the model as a situation where the virus is rapidly and efficiently suppressed after a short replication phase, while still persisting in the body in minor amounts. An analysis of the model behavior is consistent with the theory that the initial number of virions can affect the quality of the immune response. The reasons that high individual differences are observed in the trajectory of COVID-19 disease and the formation of types of the immune response to coronavirus are still poorly understood. Known trajectories of hepatitis C virus (HCV) infection were used as a basis for model scenarios.

Rapid Assessment of SARS-CoV-2 Transmission Risk for Fecally Contaminated River Water

Following the outbreak of severe acute respiratory syndrome coronavirus (SARS-CoV-2), airborne water droplets have been identified as the main transmission route. Identifying and breaking all viable transmission routes are critical to stop future outbreaks, and the potential of transmission by water has been highlighted. By modifying established approaches, we provide a method for the rapid assessment of the risk of transmission posed by fecally contaminated river water and give example results for 39 countries. The country relative risk of transmission posed by fecally contaminated river water is related to the environment and the populations' infection rate and water usage. On the basis of in vitro data and using temperature as the primary controller of survival, we then demonstrate how viral loads likely decrease after a spill. These methods using readily available data suggest that sewage spills into rivers within countries with high infection rates could provide infectious doses of >40 copies per 100 mL of water. The approach, implemented in the supplementary spreadsheet, can provide a fast estimate of the upper and lower viral load ranges following a riverine spill. The results enable evidence-based research recommendations for wastewater epidemiology and could be used to evaluate the significance of fecal-oral transmission within freshwater systems.

Modelling COVID-19 transmission in Africa: countrywise projections of total and severe infections under different lockdown scenarios

OBJECTIVES

As of 13 January 2021, there have been 3 113 963 confirmed cases of SARS-CoV-2 and 74 619 deaths across the African continent. Despite relatively lower numbers of cases initially, many African countries are now experiencing an exponential increase in case numbers. Estimates of the progression of disease and potential impact of different interventions are needed to inform policymaking decisions. Herein, we model the possible trajectory of SARS-CoV-2 in 52 African countries under different intervention scenarios.

DESIGN

We developed a compartmental model of SARS-CoV-2 transmission to estimate the COVID-19 case burden for all African countries while considering four scenarios: no intervention, moderate lockdown, hard lockdown and hard lockdown with continued restrictions once lockdown is lifted. We further analysed the potential impact of COVID-19 on vulnerable populations affected by HIV/AIDS and tuberculosis (TB).

RESULTS

In the absence of an intervention, the most populous countries had the highest peaks in active projected number of infections with Nigeria having an estimated 645 081 severe infections. The scenario with a hard lockdown and continued post-lockdown interventions to reduce transmission was the most efficacious strategy for delaying the time to the peak and reducing the number of cases. In South Africa, projected peak severe infections increase from 162 977 to 2 03 261, when vulnerable populations with HIV/AIDS and TB are included in the analysis.

CONCLUSION

The COVID-19 pandemic is rapidly spreading across the African continent. Estimates of the potential impact of interventions and burden of disease are essential for policymakers to make evidence-based decisions on the distribution of limited resources and to balance the economic costs of interventions with the potential for saving lives.

[SARS-CoV-2, influenza and norovirus infection : A direct epidemiologic comparison]

Hintergrund

Hospitalisierungsraten, Notaufnahmeprävalenzen und Fallsterblichkeiten (CFP) stationärer SARS-CoV-2-Patienten und wie sich diese von anderen pandemischen oder saisonalen Viruserkrankungen, wie Influenza A/B oder Norovirusinfektionen unterscheiden, wurden bisher nicht untersucht. Diese Arbeit gibt einen Überblick aus einer Kommune darüber und vergleicht diesen auch mit den negativ getesteten stationären Verdachtsfällen.

Methoden

Im Rahmen retrospektiver Kohortenanalysen von 67.000 Krankenhausfällen eines Klinikums mit umfassender Notfallversorgung und Meldedaten des regionalen Gesundheitsamts wurden für die Virusinfektionen SARS-CoV‑2, Influenza A/B und Norovirus Genotyp 1/2 Hospitalisierungsraten, Notaufnahmeprävalenzen und CFP berechnet.

Ergebnisse

In Fürth (Stadt‑/Landkreis) wurden 0,34 % der Bevölkerung, 824 Personen, bis 07.05.2020 positiv auf SARS-CoV‑2 getestet, wovon 162 (19,7 %) stationär behandelt wurden. 91 der Infizierten verstarben (CFP 11,0 %), davon 48 stationär. In der aktuellen Grippe‑/Norovirussaison wurden 992 Einwohner als influenzapositiv und 135 als noroviruspositiv gemeldet, davon 202 (20,3 %) bzw. 125 (91,9 %) stationär behandelt. Die Notaufnahmeprävalenzen waren 4,1 %, 2,0 % und 0,6 %. Die CFP der SARS-CoV-2-, influenza- und noroviruspositiven Krankenhauspopulationen betrugen 29,1 %, 3,0 % und 1,6 %, die der testnegativen Verdachtsfälle 5,9 %, 4,8 % und 6,9 % bei einer Krankenhausmortalität von 2,1 % für 2020.

Schlussfolgerungen

Bei gleichen Hospitalisierungsraten von SARS-CoV-2- und Influenzapatienten unterschieden sich die CFP massiv, während die CFP der testnegativen Verdachtsfälle aller 3 Infektionserkrankungen sich nicht signifikant unterschieden.

A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing

Motivated by the current COVID-19 epidemic, this work introduces an epidemiological model in which separate compartments are used for susceptible and asymptomatic "socially distant" populations. Distancing directives are represented by rates of flow into these compartments, as well as by a reduction in contacts that lessens disease transmission. The dynamical behavior of this system is analyzed, under various different rate control strategies, and the sensitivity of the basic reproduction number to various parameters is studied. One of the striking features of this model is the existence of a critical implementation delay (CID) in issuing distancing mandates: while a delay of about two weeks does not have an appreciable effect on the peak number of infections, issuing mandates even slightly after this critical time results in a far greater incidence of infection. Thus, there is a nontrivial but tight "window of opportunity" for commencing social distancing in order to meet the capacity of healthcare resources. However, if one wants to also delay the timing of peak infections - so as to take advantage of potential new therapies and vaccines - action must be taken much faster than the CID. Different relaxation strategies are also simulated, with surprising results. Periodic relaxation policies suggest a schedule which may significantly inhibit peak infective load, but that this schedule is very sensitive to parameter values and the schedule's frequency. Furthermore, we considered the impact of steadily reducing social distancing measures over time. We find that a too-sudden reopening of society may negate the progress achieved under initial distancing guidelines, but the negative effects can be mitigated if the relaxation strategy is carefully designed.

A novel COVID-19 epidemiological model with explicit susceptible and asymptomatic isolation compartments reveals unexpected consequences of timing social distancing

Motivated by the current COVID-19 epidemic, this work introduces an epidemiological model in which separate compartments are used for susceptible and asymptomatic "socially distant" populations. Distancing directives are represented by rates of flow into these compartments, as well as by a reduction in contacts that lessens disease transmission. The dynamical behavior of this system is analyzed, under various different rate control strategies, and the sensitivity of the basic reproduction number to various parameters is studied. One of the striking features of this model is the existence of a critical implementation delay (CID) in issuing distancing mandates: while a delay of about two weeks does not have an appreciable effect on the peak number of infections, issuing mandates even slightly after this critical time results in a far greater incidence of infection. Thus, there is a nontrivial but tight "window of opportunity" for commencing social distancing in order to meet the capacity of healthcare resources. However, if one wants to also delay the timing of peak infections - so as to take advantage of potential new therapies and vaccines - action must be taken much faster than the CID. Different relaxation strategies are also simulated, with surprising results. Periodic relaxation policies suggest a schedule which may significantly inhibit peak infective load, but that this schedule is very sensitive to parameter values and the schedule's frequency. Furthermore, we considered the impact of steadily reducing social distancing measures over time. We find that a too-sudden reopening of society may negate the progress achieved under initial distancing guidelines, but the negative effects can be mitigated if the relaxation strategy is carefully designed.

Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification

To increase situational awareness and support evidence-based policy-making, we formulated a mathematical model for COVID-19 transmission within a regional population. This compartmental model accounts for quarantine, self-isolation, social distancing, a non-exponentially distributed incubation period, asymptomatic individuals, and mild and severe forms of symptomatic disease. Using Bayesian inference, we have been calibrating region-specific models daily for consistency with new reports of confirmed cases from the 15 most populous metropolitan statistical areas in the United States and quantifying uncertainty in parameter estimates and predictions of future case reports. This online learning approach allows for early identification of new trends despite considerable variability in case reporting.

ARTICLE SUMMARY LINE

We report models for regional COVID-19 epidemics and use of Bayesian inference to quantify uncertainty in daily predictions of expected reporting of new cases, enabling identification of new trends in surveillance data.

Bidirectional contact tracing could dramatically improve COVID-19 control

Contact tracing is critical to controlling COVID-19, but most protocols only "forward-trace" to notify people who were recently exposed. Using a stochastic branching-process model, we find that "bidirectional" tracing to identify infector individuals and their other infectees robustly improves outbreak control. In our model, bidirectional tracing more than doubles the reduction in effective reproduction number (Reff) achieved by forward-tracing alone, while dramatically increasing resilience to low case ascertainment and test sensitivity. The greatest gains are realised by expanding the manual tracing window from 2 to 6 days pre-symptom-onset or, alternatively, by implementing high-uptake smartphone-based exposure notification; however, to achieve the performance of the former approach, the latter requires nearly all smartphones to detect exposure events. With or without exposure notification, our results suggest that implementing bidirectional tracing could dramatically improve COVID-19 control.

Robust estimation of diagnostic rate and real incidence of COVID-19 for European policymakers

Policymakers need clear, fast assessment of the real spread of the COVID-19 epidemic in each of their respective countries. Standard measures of the situation provided by the governments include reported positive cases and total deaths. While total deaths indicate immediately that countries like Italy and Spain had the worst situation as of mid-April, 2020, reported cases alone do not provide a complete picture of the situation. Different countries diagnose differently and present very distinctive reported case fatality ratios. Similar levels of reported incidence and mortality might hide a very different underlying pictures. Here we present a straightforward and robust estimation of the diagnostic rate in each European country. From that estimation we obtain a uniform, unbiased incidence of the epidemic. The method to obtain the diagnostic rate is transparent and empirical. The key assumption of the method is that the infection fatality ratio of COVID-19 in Europe is not strongly country-dependent. We show that this number is not expected to be biased due to demography nor to the way total deaths are reported. The estimation protocol is dynamic, and it has been yielding converging numbers for diagnostic rates in all European countries as from mid-April, 2020. Using this diagnostic rate, policy makers can obtain Effective Potential Growth updated every day, providing an unbiased assessment of the countries at greater risk of experiencing an uncontrolled situation. The method developed has been and will be used to track possible improvements in the diagnostic rate in European countries as the epidemic evolves.

Cost effective reproduction number based strategies for reducing deaths from COVID-19

In epidemiology, the effective reproduction number R e is used to characterize the growth rate of an epidemic outbreak. If R e > 1 , the epidemic worsens, and if R e < 1 , then it subsides and eventually dies out. In this paper, we investigate properties of R e for a modified SEIR model of COVID-19 in the city of Houston, TX USA, in which the population is divided into low-risk and high-risk subpopulations. The response of R e to two types of control measures (testing and distancing) applied to the two different subpopulations is characterized. A nonlinear cost model is used for control measures, to include the effects of diminishing returns. Lowest-cost control combinations for reducing instantaneous R e to a given value are computed. We propose three types of heuristic strategies for mitigating COVID-19 that are targeted at reducing R e , and we exhibit the tradeoffs between strategy implementation costs and number of deaths. We also consider two variants of each type of strategy: basic strategies, which consider only the effects of controls on R e , without regard to subpopulation; and high-risk prioritizing strategies, which maximize control of the high-risk subpopulation. Results showed that of the three heuristic strategy types, the most cost-effective involved setting a target value for R e and applying sufficient controls to attain that target value. This heuristic led to strategies that begin with strict distancing of the entire population, later followed by increased testing. Strategies that maximize control on high-risk individuals were less cost-effective than basic strategies that emphasize reduction of the rate of spreading of the disease. The model shows that delaying the start of control measures past a certain point greatly worsens strategy outcomes. We conclude that the effective reproduction can be a valuable real-time indicator in determining cost-effective control strategies.

True COVID-19 mortality rates from administrative data

In this paper, I use administrative data to estimate the number of deaths, the number of infections, and mortality rates from COVID-19 in Lombardia, the hot spot of the disease in Italy and Europe. The information will assist policy makers in reaching correct decisions and the public in adopting appropriate behaviors. As the available data suffer from sample selection bias, I use partial identification to derive the above quantities. Partial identification combines assumptions with the data to deliver a set of admissible values or bounds. Stronger assumptions yield stronger conclusions but decrease the credibility of the inference. Therefore, I start with assumptions that are always satisfied, then I impose increasingly more restrictive assumptions. Using my preferred bounds, during March 2020 in Lombardia, there were between 10,000 and 18,500 more deaths than in previous years. The narrowest bounds of mortality rates from COVID-19 are between 0.1 and 7.5%, much smaller than the 17.5% discussed in earlier reports. This finding suggests that the case of Lombardia may not be as special as some argue.

Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures

Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus's infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.

Behavioral dynamics of COVID-19: estimating underreporting, multiple waves, and adherence fatigue across 92 nations

Effective responses to the COVID-19 pandemic require integrating behavioral factors such as risk-driven contact reduction, improved treatment, and adherence fatigue with asymptomatic transmission, disease acuity, and hospital capacity. We build one such model and estimate it for all 92 nations with reliable testing data. Cumulative cases and deaths through 22 December 2020 are estimated to be 7.03 and 1.44 times official reports, yielding an infection fatality rate (IFR) of 0.51 percent, which has been declining over time. Absent adherence fatigue, cumulative cases would have been 47 percent lower. Scenarios through June 2021 show that modest improvement in responsiveness could reduce cases and deaths by about 14 percent, more than the impact of vaccinating half of the population by that date. Variations in responsiveness to risk explain two orders of magnitude difference in per-capita deaths despite reproduction numbers fluctuating around one across nations. A public online simulator facilitates scenario analysis over the coming months. © 2021 System Dynamics Society.

Estimating the COVID-19 infection rate: Anatomy of an inference problem

As a consequence of missing data on tests for infection and imperfect accuracy of tests, reported rates of cumulative population infection by the SARS CoV-2 virus are lower than actual rates of infection. Hence, reported rates of severe illness conditional on infection are higher than actual rates. Understanding the time path of the COVID-19 pandemic has been hampered by the absence of bounds on infection rates that are credible and informative. This paper explains the logical problem of bounding these rates and reports illustrative findings, using data from Illinois, New York, and Italy. We combine the data with assumptions on the infection rate in the untested population and on the accuracy of the tests that appear credible in the current context. We find that the infection rate might be substantially higher than reported. We also find that, assuming accurate reporting of deaths, the infection fatality rates in Illinois, New York, and Italy are substantially lower than reported.

Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

A contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure-including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.

The Natural History and Transmission Potential of Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 Infection

BACKGROUND

Little is known about the natural history of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

METHODS

We conducted a prospective study at a quarantine center for coronavirus disease 2019 in Ho Chi Minh City, Vietnam. We enrolled quarantined people with reverse-transcription polymerase chain reaction (RT-PCR)-confirmed SARS-CoV-2 infection, collecting clinical data, travel and contact history, and saliva at enrollment and daily nasopharyngeal/throat swabs (NTSs) for RT-PCR testing. We compared the natural history and transmission potential of asymptomatic and symptomatic individuals.

RESULTS

Between 10 March and 4 April 2020, 14 000 quarantined people were tested for SARS-CoV-2; 49 were positive. Of these, 30 participated in the study: 13 (43%) never had symptoms and 17 (57%) were symptomatic. Seventeen (57%) participants imported cases. Compared with symptomatic individuals, asymptomatic people were less likely to have detectable SARS-CoV-2 in NTS collected at enrollment (8/13 [62%] vs 17/17 [100%]; P = .02). SARS-CoV-2 RNA was detected in 20 of 27 (74%) available saliva samples (7 of 11 [64%] in the asymptomatic group and 13 of 16 [81%] in the symptomatic group; P = .56). Analysis of RT-PCR positivity probability showed that asymptomatic participants had faster viral clearance than symptomatic participants (P < .001 for difference over the first 19 days). This difference was most pronounced during the first week of follow-up. Two of the asymptomatic individuals appeared to transmit SARS-CoV-2 to 4 contacts.

CONCLUSIONS

Asymptomatic SARS-CoV-2 infection is common and can be detected by analysis of saliva or NTSs. The NTS viral loads fall faster in asymptomatic individuals, but these individuals appear able to transmit the virus to others.

Epidemiological characterisation of asymptomatic carriers of COVID-19 in Colombia: a cross-sectional study

INTRODUCTION

Asymptomatic carriers (AC) of the new SARS-CoV-2 represent an important source of spread for COVID-19. Early diagnosis of these cases is a powerful tool to control the pandemic. Our objective was to characterise patients with AC status and identify associated sociodemographic factors.

METHODS

Using a cross-sectional design and the national database of daily occurrence of COVID-19, we characterised both socially and demographically all ACs. Additional correspondence analysis and logistic regression model were performed to identify characteristics associated with AC state (OR, 95% CI).

RESULTS

76.162 ACs (12.1%; 95% CI 12.0% to 12.2%) were identified, mainly before epidemiological week 35. Age≤26 years (1.18; 1.09 to 1.28), male sex (1.51; 1.40 to 1.62), cases imported from Venezuela, Argentina, Brazil, Germany, Puerto Rico, Spain, USA or Mexico (12.6; 3.03 to 52.5) and autochthonous cases (22.6; 5.62 to 91.4) increased the risk of identifying ACs. We also identified groups of departments with moderate (1.23; 1.13 to 1.34) and strong (19.8; 18.6 to 21.0) association with ACs.

CONCLUSION

Sociodemographic characteristics strongly associated with AC were identified, which may explain its epidemiological relevance and usefulness to optimise mass screening strategies and prevent person-to-person transmission.

Age-stratified discrete compartment model of the COVID-19 epidemic with application to Switzerland

Compartmental models enable the analysis and prediction of an epidemic including the number of infected, hospitalized and deceased individuals in a population. They allow for computational case studies on non-pharmaceutical interventions thereby providing an important basis for policy makers. While research is ongoing on the transmission dynamics of the SARS-CoV-2 coronavirus, it is important to come up with epidemic models that can describe the main stages of the progression of the associated COVID-19 respiratory disease. We propose an age-stratified discrete compartment model as an alternative to differential equation based S-I-R type of models. The model captures the highly age-dependent progression of COVID-19 and is able to describe the day-by-day advancement of an infected individual in a modern health care system. The fully-identified model for Switzerland not only predicts the overall histories of the number of infected, hospitalized and deceased, but also the corresponding age-distributions. The model-based analysis of the outbreak reveals an average infection fatality ratio of 0.4% with a pronounced maximum of 9.5% for those aged ≥ 80 years. The predictions for different scenarios of relaxing the soft lockdown indicate a low risk of overloading the hospitals through a second wave of infections. However, there is a hidden risk of a significant increase in the total fatalities (by up to 200%) in case schools reopen with insufficient containment measures in place.

Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2

A long-standing question in infectious disease dynamics concerns the role of transmission heterogeneities, driven by demography, behavior and interventions. Based on detailed patient and contact tracing data in Hunan, China we find 80% of secondary infections traced back to 15% of SARS-CoV-2 primary infections, indicating substantial transmission heterogeneities. Transmission risk scales positively with the duration of exposure and the closeness of social interactions and is modulated by demographic and clinical factors. The lockdown period increases transmission risk in the family and households, while isolation and quarantine reduce risks across all types of contacts. The reconstructed infectiousness profile of a typical SARS-CoV-2 patient peaks just before symptom presentation. Modeling indicates SARS-CoV-2 control requires the synergistic efforts of case isolation, contact quarantine, and population-level interventions, owing to the specific transmission kinetics of this virus.

One Sentence Summary

Public health measures to control SARS-CoV-2 could be designed to block the specific transmission characteristics of the virus.

Infection fatality rate of SARS-CoV2 in a super-spreading event in Germany

A SARS-CoV2 super-spreading event occurred during carnival in a small town in Germany. Due to the rapidly imposed lockdown and its relatively closed community, this town was seen as an ideal model to investigate the infection fatality rate (IFR). Here, a 7-day seroepidemiological observational study was performed to collect information and biomaterials from a random, household-based study population. The number of infections was determined by IgG analyses and PCR testing. We found that of the 919 individuals with evaluable infection status, 15.5% (95% CI:[12.3%; 19.0%]) were infected. This is a fivefold higher rate than the reported cases for this community (3.1%). 22.2% of all infected individuals were asymptomatic. The estimated IFR was 0.36% (95% CI:[0.29%; 0.45%]) for the community and 0.35% [0.28%; 0.45%] when age-standardized to the population of the community. Participation in carnival increased both infection rate (21.3% versus 9.5%, p < 0.001) and number of symptoms (estimated relative mean increase 1.6, p = 0.007). While the infection rate here is not representative for Germany, the IFR is useful to estimate the consequences of the pandemic in places with similar healthcare systems and population characteristics. Whether the super-spreading event not only increases the infection rate but also affects the IFR requires further investigation.

Citizen-Centered Mobile Health Apps Collecting Individual-Level Spatial Data for Infectious Disease Management: Scoping Review

BACKGROUND

The novel coronavirus SARS-CoV-2 rapidly spread around the world, causing the disease COVID-19. To contain the virus, much hope is placed on participatory surveillance using mobile apps, such as automated digital contact tracing, but broad adoption is an important prerequisite for associated interventions to be effective. Data protection aspects are a critical factor for adoption, and privacy risks of solutions developed often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches.

OBJECTIVE

Our aim is to inform the current discussions and to support the development of solutions providing an optimal balance between privacy protection and pandemic control. To this end, we present a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis is that there are dependencies between the following dimensions: the use cases supported, the technology used to collect spatial data, the specific diseases focused on, and data protection measures implemented.

METHODS

We searched PubMed and IEEE Xplore with a search string combining terms from the area of infectious disease management with terms describing spatial surveillance technologies to identify studies published between 2010 and 2020. After a two-step eligibility assessment process, 27 articles were selected for the final analysis. We collected data on the four dimensions described as well as metadata, which we then analyzed by calculating univariate and bivariate frequency distributions.

RESULTS

We identified four different use cases, which focused on individual surveillance and public health (most common: digital contact tracing). We found that the solutions described were highly specialized, with 89% (24/27) of the articles covering one use case only. Moreover, we identified eight different technologies used for collecting spatial data (most common: GPS receivers) and five different diseases covered (most common: COVID-19). Finally, we also identified six different data protection measures (most common: pseudonymization). As hypothesized, we identified relationships between the dimensions. We found that for highly infectious diseases such as COVID-19 the most common use case was contact tracing, typically based on Bluetooth technology. For managing vector-borne diseases, use cases require absolute positions, which are typically measured using GPS. Absolute spatial locations are also important for further use cases relevant to the management of other infectious diseases.

CONCLUSIONS

We see a large potential for future solutions supporting multiple use cases by combining different technologies (eg, Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented. Technologies currently used in this context can probably not offer enough protection. We, therefore, recommend that future solutions should consider the use of modern privacy-enhancing techniques (eg, from the area of secure multiparty computing and differential privacy).

Describing the COVID-19 outbreak during the lockdown: fitting modified SIR models to data

In this paper, we analyse the COVID-19 outbreak data with simple modifications of the SIR compartmental model, in order to understand the time evolution of the cases in Italy and Germany, during the first half of 2020. Even if the complexity of the pandemic cannot be easily described, we show that our models are suitable for understanding the data during the application of the social distancing and the lockdown. We compare and contrast different modifications of the SIR model showing the strengths and the weaknesses of each approach. Finally, we discuss the reliability of the model predictions for estimating the near- and far-future evolution of the outbreak.

At what times during infection is SARS-CoV-2 detectable and no longer detectable using RT-PCR-based tests? A systematic review of individual participant data

BACKGROUND

Tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral ribonucleic acid (RNA) using reverse transcription polymerase chain reaction (RT-PCR) are pivotal to detecting current coronavirus disease (COVID-19) and duration of detectable virus indicating potential for infectivity.

METHODS

We conducted an individual participant data (IPD) systematic review of longitudinal studies of RT-PCR test results in symptomatic SARS-CoV-2. We searched PubMed, LitCOVID, medRxiv, and COVID-19 Living Evidence databases. We assessed risk of bias using a QUADAS-2 adaptation. Outcomes were the percentage of positive test results by time and the duration of detectable virus, by anatomical sampling sites.

RESULTS

Of 5078 studies screened, we included 32 studies with 1023 SARS-CoV-2 infected participants and 1619 test results, from - 6 to 66 days post-symptom onset and hospitalisation. The highest percentage virus detection was from nasopharyngeal sampling between 0 and 4 days post-symptom onset at 89% (95% confidence interval (CI) 83 to 93) dropping to 54% (95% CI 47 to 61) after 10 to 14 days. On average, duration of detectable virus was longer with lower respiratory tract (LRT) sampling than upper respiratory tract (URT). Duration of faecal and respiratory tract virus detection varied greatly within individual participants. In some participants, virus was still detectable at 46 days post-symptom onset.

CONCLUSIONS

RT-PCR misses detection of people with SARS-CoV-2 infection; early sampling minimises false negative diagnoses. Beyond 10 days post-symptom onset, lower RT or faecal testing may be preferred sampling sites. The included studies are open to substantial risk of bias, so the positivity rates are probably overestimated.

Serological determinants of COVID-19

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection spreaded rapidly worldwide, as far as it has become a global pandemic. Therefore, the introduction of serological tests for determination of IgM and IgG antibodies has become the main diagnostic tool, useful for tracking the spread of the virus and for consequently allowing its containment. In our study we compared point of care test (POCT) lateral flow immunoassay (FIA) vs automated chemiluminescent immunoassay (CLIA), in order to assess their specificity and sensibility for COVID-19 antibodies detection.

RESULTS

We find that different specificities and sensitivities for IgM and IgG tests. Notably IgM POCT FIA method vs CLIA method (gold standard) has a low sensitivity (0.526), while IgG POCT FIA method vs CLIA method (gold standard) test has a much higher sensitivity (0.937); further, with respect of IgG, FIA and CLIA could arguably provide equivalent information.

CONCLUSIONS

FIA method could be helpful in assessing in short time, the possible contagiousness of subjects that for work reasons cannot guarantee "social distancing".

A Bayesian approach for monitoring epidemics in presence of undetected cases

One of the key indicators used in tracking the evolution of an infectious disease is the reproduction number. This quantity is usually computed using the reported number of cases, but ignoring that many more individuals may be infected (e.g. asymptomatic carriers). We develop a Bayesian procedure to quantify the impact of undetected infectious cases on the determination of the effective reproduction number. Our approach is stochastic, data-driven and not relying on any compartmental model. It is applied to the COVID-19 outbreak in eight different countries and all Italian regions, showing that the effect of undetected cases leads to estimates of the effective reproduction numbers larger than those obtained only with the reported cases by factors ranging from two to ten.

Addendum to S3-Guidelines Crohn’s disease and ulcerative colitis: Management of Patients with Inflammatory Bowel Disease in the COVID-19 Pandemic – open questions and answers

The COVID-19 pandemic is a global outbreak of new onset infections with the SARS-CoV-2 virus. To date, more than 3.4 million people have been infected throughout the world. In Germany, approximately 450,000 patients suffer from inflammatory bowel disease; these patients generally require continuous expert care and support. Against the background of a rapidly accumulating knowledge base on SARS-CoV-2, 68 expert authors of the current DGVS guidelines for Crohn’s disease and ulcerative colitis took part in a virtual meeting to compile up-to-date, practice-orientated recommendations aimed at improving the care of patients with IBD. These recommendations address the risk of infection, including the risk for specific patient groups, the possible course of the disease, and consequences for pharmacological and surgical therapies of the underlying disease, as well as general measures for infection prevention and adjuvant prophylactic and therapeutic options.

Modelling a pandemic with asymptomatic patients, impact of lockdown and herd immunity, with applications to SARS-CoV-2

The SARS-CoV-2 is a type of coronavirus that has caused the pandemic known as the Coronavirus Disease of 2019, or COVID-19. In traditional epidemiological models such as SEIR (Susceptible, Exposed, Infected, Removed), the exposed group E does not infect the susceptible group S. A distinguishing feature of COVID-19 is that, unlike with previous viral diseases, there is a distinct "asymptomatic" group A, which does not show any symptoms, but can nevertheless infect others, at the same rate as infected symptomatic patients. This situation is captured in a model known as SAIR (Susceptible, Asymptomatic, Infected, Removed), introduced in Robinson and Stillianakis (2013). The dynamical behavior of the SAIR model is quite different from that of the SEIR model. In this paper, we use Lyapunov theory to establish the global asymptotic stabililty of the SAIR model, both without and with vital dynamics. Then we develop compartmental SAIR models to cater to the migration of population across geographic regions, and once again establish global asymptotic stability. Next, we go beyond long-term asymptotic analysis and present methods for estimating the parameters in the SAIR model. We apply these estimation methods to data from several countries including India, and demonstrate that the predicted trajectories of the disease closely match actual data. We show that "herd immunity" (defined as the time when the number of infected persons is maximum) can be achieved when the total of infected, symptomatic and asymptomatic persons is as low as 25% of the population. Previous estimates are typically 50% or higher. We also conclude that "lockdown" as a way of greatly reducing inter-personal contact has been very effective in checking the progress of the disease.

Seroprevalence of SARS-CoV-2 IgM and IgG antibodies in an asymptomatic population in Sergipe, Brazil

OBJECTIVE

To estimate the prevalence of SARS-CoV-2 antibodies in an asymptomatic population in the state of Sergipe, Brazil.
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METHODS

This cross-sectional study with stratified sampling (sex and age) included serological immunofluorescent tests for IgM and IgG on samples from 3 046 asymptomatic individuals. Sample collection was performed in wet-markets of the 10 most populous cities of Sergipe, Brazil. Exclusion criteria included symptomatic individuals and health workers. The presence of comorbidities was registered.
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RESULTS

Of the 3 046 participants, 1 577 (51.8%) were female and 1 469 (48.2%) were male; the mean age was 39.76 (SD 16.83) years old. 2 921 tests were considered valid for IgM and 2 635 for IgG. Of the valid samples, 347 (11.9% [CI 10.7%-13.1%]) tested positive for IgM and 218 (8.3% [CI 7.2%-9.4%]) tested positive for IgG. Women over 40 had the highest prevalence for IgM (group C, p=0.006; group D p=0.04). The capital Aracaju displayed the highest prevalence for both antibodies; 83 (26.3% [CI 21.6%-31.6%]) tested positive for IgM and 35 (14.6% [CI 10.4%-19.7%]) for IgG. The most prevalent comorbidities were hypertension (64/123 individuals) and diabetes (29/123).
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CONCLUSIONS

A high prevalence of SARS-CoV-2 antibodies was found among asymptomatic persons in Sergipe. Women over 40 showed the highest rates. The capital, Aracaju, displayed the highest seroprevalence. Surveys like this one are important to understand how the virus spreads and to help authorities to plan measures to control it. Repeated serologic testing are required to track the progress of the epidemic.

COVID-19 Disease Severity Risk Factors for Pediatric Patients in Italy

OBJECTIVES

To describe the epidemiological and clinical characteristics of coronavirus disease (COVID-19) pediatric patients aged <18 years in Italy.

METHODS

Data from the national case-based surveillance system of confirmed COVID-19 infections until May 8, 2020, were analyzed. Demographic and clinical characteristics of subjects were summarized by age groups (0-1, 2-6, 7-12, 13-18 years), and risk factors for disease severity were evaluated by using a multilevel (clustered by region) multivariable logistic regression model. Furthermore, a comparison among children, adults, and elderly was performed.

RESULTS

Pediatric patients (3836) accounted for 1.8% of total infections (216 305); the median age was 11 years, 51.4% were male, 13.3% were hospitalized, and 5.4% presented underlying medical conditions. The disease was mild in 32.4% of cases and severe in 4.3%, particularly in children ≤6 years old (10.8%); among 511 hospitalized patients, 3.5% were admitted in ICU, and 4 deaths occurred. Lower risk of disease severity was associated with increasing age and calendar time, whereas a higher risk was associated with preexisting underlying medical conditions (odds ratio = 2.80, 95% confidence interval = 1.74-4.48). Hospitalization rate, admission in ICU, disease severity, and days from symptoms onset to recovery significantly increased with age among children, adults and elderly.

CONCLUSIONS

Data suggest that pediatric cases of COVID-19 are less severe than adults; however, age ≤1 year and the presence of underlying conditions represent severity risk factors. A better understanding of the infection in children may give important insights into disease pathogenesis, health care practices, and public health policies.

Lockdown measures in response to COVID-19 in nine sub-Saharan African countries

Lockdown measures have been introduced worldwide to contain the transmission of COVID-19. However, the term 'lockdown' is not well-defined. Indeed, WHO's reference to 'so-called lockdown measures' indicates the absence of a clear and universally accepted definition of the term 'lockdown'. We propose a definition of 'lockdown' based on a two-by-two matrix that categorises different communicable disease measures based on whether they are compulsory or voluntary; and whether they are targeted at identifiable individuals or facilities, or whether they are applied indiscriminately to a general population or area. Using this definition, we describe the design, timing and implementation of lockdown measures in nine countries in sub-Saharan Africa: Ghana, Nigeria, South Africa, Sierra Leone, Sudan, Tanzania, Uganda, Zambia and Zimbabwe. While there were some commonalities in the implementation of lockdown across these countries, a more notable finding was the variation in the design, timing and implementation of lockdown measures. We also found that the number of reported cases is heavily dependent on the number of tests carried out, and that testing rates ranged from 2031 to 63 928 per million population up until 7 September 2020. The reported number of COVID-19 deaths per million population also varies (0.4 to 250 up until 7 September 2020), but is generally low when compared with countries in Europe and North America. While lockdown measures may have helped inhibit community transmission, the pattern and nature of the epidemic remains unclear. However, there are signs of lockdown harming health by affecting the functioning of the health system and causing social and economic disruption.

Size and timescale of epidemics in the SIR framework

The most important features to assess the severity of an epidemic are its size and its timescale. We discuss these features in a systematic way in the context of SIR and SIR-type models. We investigate in detail how the size and timescale of the epidemic can be changed by acting on the parameters characterizing the model. Using these results and having as guideline the COVID-19 epidemic in Italy, we compare the efficiency of different containment strategies for contrasting an epidemic diffusion such as social distancing, lockdown, tracing, early detection and isolation.