Actual Infection Research Articles

PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data

BackgroundPathogenic microorganisms cause disease by invading, colonizing, and damaging their host. Virulence factors including bacterial toxins contribute to pathogenicity. Additionally, antimicrobial resistance genes allow pathogens to evade otherwise curative treatments. To understand causal relationships between microbiome compositions, functioning, and disease, it is essential to identify virulence factors and antimicrobial resistance genes in situ. At present, there is a clear lack of computational approaches to simultaneously identify these factors in metagenomic datasets.ResultsHere, we present PathoFact, a tool for the contextualized prediction of virulence factors, bacterial toxins, and antimicrobial resistance genes with high accuracy (0.921, 0.832 and 0.979, respectively) and specificity (0.957, 0.989 and 0.994). We evaluate the performance of PathoFact on simulated metagenomic datasets and perform a comparison to two other general workflows for the analysis of metagenomic data. PathoFact outperforms all existing workflows in predicting virulence factors and toxin genes. It performs comparably to one pipeline regarding the prediction of antimicrobial resistance while outperforming the others. We further demonstrate the performance of PathoFact on three publicly available case-control metagenomic datasets representing an actual infection as well as chronic diseases in which either pathogenic potential or bacterial toxins are hypothesized to play a role. In each case, we identify virulence factors and AMR genes which differentiated between the case and control groups, thereby revealing novel gene associations with the studied diseases.ConclusionPathoFact is an easy-to-use, modular, and reproducible pipeline for the identification of virulence factors, bacterial toxins, and antimicrobial resistance genes in metagenomic data. Additionally, our tool combines the prediction of these pathogenicity factors with the identification of mobile genetic elements. This provides further depth to the analysis by considering the genomic context of the pertinent genes. Furthermore, PathoFact’s modules for virulence factors, toxins, and antimicrobial resistance genes can be applied independently, thereby making it a flexible and versatile tool. PathoFact, its models, and databases are freely available at https://pathofact.lcsb.uni.lu.C2FGScqUUFdTvmU6TgDvEJVideo abstract

Information on Averted Infections Increased Perceived Efficacy of Regulations and Intentions to Follow Them

The coronavirus 2019 (COVID-19) pandemic presented policymakers with the need to change people’s behavior in a fundamental way and for an extended period of time. Changing habits is difficult and requires sustained effort, and sustaining effort is especially difficult when it does not seem to yield conspicuous results. The COVID-19 pandemic presented exactly this difficulty, as numbers of infected people continued to rise despite the public’s efforts. In a representative sample from Israel (N = 600), collected online during the first outbreak of the pandemic, we found that compared to control conditions in which information on only actual infection cases was presented, counterfactual information on the number of averted cases enhances the perceived effectiveness of following the guidelines, which, in turn, enhances perceived importance and intention of doing so (e.g., intention to restrict mobility), but only among those who understood the information. The findings align with self-regulation theories and have practical implications for policymakers.

SARS-CoV-2 Vaccines: Much Accomplished, Much to Learn

As COVID vaccines roll out, internists and other health care providers are being turned to as trusted sources of information for patients and communities. Here, experts from NIAID outline the current state of knowledge regarding such vaccines. They contrast vaccine platforms, summarize clinical trial data regarding efficacy and safety, and comment on key questions including the ability of current vaccines to protect against infection and to decrease the prevalence of virus in the community.

Dynamical SEIR Model With Information Entropy Using COVID-19 as a Case Study.

Social network information is a measure of the number of infections. Understanding the effect of social network information on disease spread can help improve epidemic forecasting and uncover preventive measures. Many driving factors for the transmission mechanism of infectious diseases remain unclear. Some experts believe that redundant information on social media may increase people's panic to evade the restrictions or refuse to report their symptoms, which increases the actual infection rate. We analyze the engagement in the COVID-19 topics on the Internet and find that the infection rate is not only related to the total amount of information. In our research, information entropy is introduced into the quantification of the impact of social network information. We find that the amount of information with different distributions has different effects on disease transmission. Furthermore, we build a new dynamic susceptible-exposed-infected-recovered (SEIR) model with information entropy to simulate the epidemic situation in China. Simulation results show that our modified model is effective in predicting the COVID-19 epidemic peaks and sizes.

COVID-19: Optimal Allocation of Ventilator Supply Under Uncertainty and Risk

This study presents a new risk-averse multi-stage stochastic epidemics-ventilator-logistics compartmental model to address the resource allocation challenges of mitigating COVID-19. This epidemiological logistics model involves the uncertainty of untested asymptomatic infections and incorporates short-term human migration. Disease transmission is also forecasted through a new formulation of transmission rates that evolve over space and time with respect to various non-pharmaceutical interventions, such as wearing masks, social distancing, and lockdown. The proposed multi-stage stochastic model overviews different scenarios on the number of asymptomatic individuals while optimizing the distribution of resources, such as ventilators, to minimize the total expected number of newly infected and deceased people. The Conditional Value at Risk (CVaR) is also incorporated into the multi-stage mean-risk model to allow for a trade-off between the weighted expected loss due to the outbreak and the expected risks associated with experiencing disastrous pandemic scenarios. We apply our multi-stage mean-risk epidemics-ventilator-logistics model to the case of controlling the COVID-19 in highly-impacted counties of New York and New Jersey. We calibrate, validate, and test our model using actual infection, population, and migration data. The results indicate that short-term migration influences the transmission of the disease significantly. The optimal number of ventilators allocated to each region depends on various factors, including the number of initial infections, disease transmission rates, initial ICU capacity, the population of a geographical location, and the availability of ventilator supply. Our data-driven modeling framework can be adapted to study the disease transmission dynamics and logistics of other similar epidemics and pandemics.

Modelling lockdown measures in epidemic outbreaks using selective socio-economic containment with uncertainty.

After the introduction of drastic containment measures aimed at stopping the epidemic contagion from SARS-CoV2, many governments have adopted a strategy based on a periodic relaxation of such measures in the face of a severe economic crisis caused by lockdowns. Assessing the impact of such openings in relation to the risk of a resumption of the spread of the disease is an extremely difficult problem due to the many unknowns concerning the actual number of people infected, the actual reproduction number and infection fatality rate of the disease. In this work, starting from a SEIRD compartmental model with a social structure based on the age of individuals and stochastic inputs that account for data uncertainty, the effects of containment measures are introduced via an optimal control problem dependent on specific social activities, such as home, work, school, etc. Through a short time horizon approximation, we derive models with multiple feedback controls depending on social activities that allow us to assess the impact of selective relaxation of containment measures in the presence of uncertain data. After analyzing the effects of the various controls, results from different scenarios concerning the first wave of the epidemic in some major countries, including Germany, France, Italy, Spain, the United Kingdom and the United States, are presented and discussed. Specific contact patterns in the home, work, school and other locations have been considered for each country. Numerical simulations show that a careful strategy of progressive relaxation of containment measures, such as that adopted by some governments, may be able to keep the epidemic under control by restarting various productive activities.

LB-14. CovidIQ- a Text Message-Based Symptom Surveillance Tracker that Predicts New Areas of Increased Incidence of Covid-19 Disease

BackgroundTesting for SARS-CoV-2 is limited, making it difficult to estimate the true prevalence of disease and control the spread of new cases. Therefore, finding other ways to diagnose new cases of Covid-19 early is essential for preventing further spread of SARS-CoV-2 to other people in the community and prevent further outbreaks from occurring.MethodsCovidIQ is a confidential and secure text messaging platform that works by collecting participants’ self-reported symptoms. Upon agreeing to participate, users are asked some basic demographic questions including gender, age range, ethnic background, and zip code. Participants are then queried via text message on a weekly or biweekly basis as to what symptoms they are experiencing: none, temperature >99.6F, cough, shortness of breath, headache, fatigue, loss of appetite, loss of sense of smell or taste, diarrhea, body ache, sore throat, and/or chills. The symptoms are further broken down into major and minor criteria, allowing presumptive cases to be identified with more accuracy. The major criteria include elevated body temperature, cough, and shortness of breath. If a participant has any 2 major criteria, 1 major or 2 minor, or 3 minor criteria, they are considered a presumptive positive case.While CovidIQ cannot be used to diagnose individuals, the combined results from many individuals show real-time changes in rates of infection for entire counties. Not all people who develop Covid-19 will need hospitalization. They may remain out in the community unaware of the risk they pose to others. And, because the official count of confirmed cases is delayed by 2–4 weeks from the time of actual infection, CovidIQ can sound the alarm much earlier when rates of infection begin to spike. Advanced warning can help communities and individuals make informed decisions about how they should conduct themselves.ResultsCovidIQ identified the spike in COVID-19 cases in Jacksonville/Duval County, Florida a full two weeks before it was reported by the Florida Department of Health and Johns Hopkins University.Prevalence of Suspected COVID-19 Cases Through the Use of the CovidIQ Platform in Relation to Dates of OfficiallyReported Cases, Duval County, FL ConclusionCovidIQ is a novel tool developed to augment the public health response to this ongoing crisis by informing the public sector of potential new hot spots before areas experience a surge as compared to the current reporting structure.Disclosures All Authors: No reported disclosures

Estimating disease prevalence and temporal dynamics using biased capture serological data in a wildlife reservoir: The example of brucellosis in Alpine ibex (Capra ibex)

The monitoring of the disease prevalence in a population is an essential component of its adaptive management. However, field data often lead to biased estimates. This is the case for brucellosis infection of ibex in the Bargy massif (France). A test-and-cull program is being carried out in this area to manage the infection: captured animals are euthanized when seropositive, and marked and released when seronegative. Because this mountainous species is difficult to capture, field workers tend to focus the capture effort on unmarked animals. Indeed, marked animals are less likely to be infected, as they were controlled and negative during previous years. As the proportion of marked animals in the population becomes large, captured animals can no longer be considered as an unbiased sample of the population. We designed an integrated Bayesian model to correct this bias, by estimating the seroprevalence in the population as the combination of the separate estimates of the seroprevalence among unmarked animals (estimated from the data) and marked animals (estimated with a catalytic infection model, to circumvent the scarcity of the data). As seroprevalence may not be the most responsive parameter to management actions, we also estimated the proportion of animals in the population with an active bacterial infection. The actual infection status of captured animals was thus inferred as a function of their age and their level of antibodies, using a model based on bacterial cultures carried out for a sample of animals. Focusing on the population of adult females in the core area of the massif, i.e. with the highest seroprevalence, this observational study shows that seroprevalence has been divided by two between 2013 (51%) and 2018 (21%). Moreover, the likely estimated proportion of actively infected females in the same population, though very imprecise, has decreased from a likely estimate of 34% to less than 15%, suggesting that the management actions have been effective in reducing infection prevalence.

Person and situation effects in predicting outgroup prejudice and avoidance during the COVID-19 pandemic

Recent theories of intergroup relations suggest that factors relevant to disease, disgust, and contagion predict prejudice towards ethnic outgroups. The current research explored the influence of contextual pathogen threat and individual differences in threat sensitivity on outgroup prejudice and avoidance in the context of the COVID-19 pandemic. Data were collected from a sample of British adults in June 2020 (N = 524). A multi-level approach was employed to capture differences in confirmed COVID-19 cases across different regions in the UK. Results demonstrated that even in a “strong” pandemic context, individual differences in both disgust sensitivity (DS) and intergroup disgust sensitivity (ITG-DS) explained variability in outgroup distancing. Subjective perceptions of contextual pathogen prevalence, but not actual infection rates, also predicted greater outgroup avoidance. However, a significant cross-level interaction revealed that DS predicted outgroup distancing in regions with higher numbers of confirmed COVID-19 cases, but not in areas of lower infection. Thus, individual differences in pathogen avoidance may be especially influential under high situational pathogen stress. There was also some evidence that pathogen threat also predicted greater ingroup attraction. Results provide important insights into factors that promote or inhibit positive intergroup relations during pandemics.

SARS-CoV-2 Infections and COVID-19 Fatality: Estimation of Infection Fatality Ratio and Current Prevalence.

COVID-19 is one of the most important problems for public health, according to the number of deaths associated to this pathology reported so far. However, from the epidemiological point of view, the dimension of the problem is still unknown, since the number of actual cases of SARS-CoV-2 infected people is underestimated, due to limited testing. This paper aims at estimating the actual Infection Fatality Ratio (number of deaths with respect to the number of infected people) and the actual current prevalence (number of infected people with respect to the entire population), both in a specific population and all over the world. With this aim, this paper proposes a method to estimate Infection Fatality Ratio of a still ongoing infection, based on a daily estimation, and on the relationship between this estimation and the number of tests performed per death. The method has been applied using data about COVID-19 from Italy. Results show a fatality ratio of about 0.9%, which is lower than previous findings. The number of actual infected people in Italy is also estimated, and results show that (i) infection started at the end of January 2020; (ii) a maximum number of about 100,000 new cases in one day was reached at the beginning of March 2020; (iii) the estimated cumulative number of infections at the beginning of October 2020 is about 4.2 million cases in Italy (more than 120 million worldwide, if a generalization is conjectured as reasonable). Therefore, the prevalence at the beginning of October 2020 is estimated at about 6.9% in Italy (1.6% worldwide, if a generalization is conjectured).

COVID-19: a one-way ticket to a global childhood obesity crisis?

Obesity is a global epidemic based on three major pillars of (i) genetic (ii) behavioural and (iii) environmental determinants. The latter two pillars have been challenged during the course of the COVID-19 pandemic across all population age groups including children. The closure of schools resulted in decreased organised physical activity, increase in sedentary lifestyle and screen time with the possibility of stress-induced indulgence in high calorie dense and sugary foods, resulting in higher susceptibility to weight gain. The uncertainty faced by many Northern Hemisphere governments as the new scholastic year looms closer whether to open schools again or not further enhances the stress on the children and their family. Re-opening of schools is beneficial for children’s mental and physical health, and general wellbeing including the ‘combating’ of the childhood obesity epidemic. The family unit has also been challenged during this pandemic especially if the parent/s suffered redundancy. There have been attempts at seeing a silver lining as some families have embraced lockdowns as a means to strengthen their family bonds, increase homemade meals apart from the various virtual opportunities that were streamed on social media to encourage children to perform physical activity at home or in safe environments. However, curbing the viral spread while protecting population health will remain top priority until an effective COVID-19 vaccine is available. It is imperative to address other co-existing problems such as childhood obesity, which if uncontrolled may have a long-term profound health and economic consequence of higher eminence than the actual COVID-19 infection. The prevention and management of childhood obesity should be set as a priority at an individual, community and population level during this pandemic.

Scaling effect in COVID-19 spreading: The role of heterogeneity in a hybrid ODE-network model with restrictions on the inter-cities flow

The new Covid-19 pandemic has left traces of suffering and devastation to individuals of almost all countries worldwide and severe impact on the global economy. Understanding the clinical characteristics, interactions with the environment, and the variables that favor or hinder its dissemination help the public authorities in the fight and prevention, leading for a rapid response in society. Using models to estimate contamination scenarios in real time plays an important role. Population compartments models based on ordinary differential equations (ODE) for a given region assume two homogeneous premises, the contact mechanisms and diffusion rates, disregarding heterogeneous factors as different contact rates for each municipality and the flow of contaminated people among them. This work considers a hybrid model for covid-19, based on local SIR models and the population flow network among municipalities, responsible for a complex lag dynamic in their contagion curves. Based on actual infection data, local contact rates (β) are evaluated. The epidemic evolution at each municipality depends on the local SIR parameters and on the inter-municipality transport flow. When heterogeneity of β values and flow network are included, forecasts differ from those of the homogeneous ODE model. This effect is more relevant when more municipalities are considered, hinting that the latter overestimates new cases. In addition, mitigation scenarios are assessed to evaluate the effect of earlier interventions reducing the inter-municipality flux. Restricting the flow between municipalities in the initial stage of the epidemic is fundamental for flattening the contamination curve, highlighting advantages of a contamination lag between the capital curve and those of other municipalities in the territories.

Statistical Literacy Affects Risk Perceptions and Behaviors during the COVID-19 Pandemic

We study the role of statistical literacy in interpreting COVID-19-related information, assessments of infection risk, and compliance with mobility restrictions. Using a large-scale panel survey of about 4,000 Dutch households, we find that more statistically literate people adjust their risk estimates more strongly to the actual infection risk, measured by the current number of confirmed COVID-19 cases in their home province. They are also generally more likely to socially distance, less concerned about their own infection risk, and more concerned about infecting others, especially vulnerable populations such as the elderly. We also find that more statistically literate people generally consider a flu shot more important, potentially suggesting a more positive attitude toward vaccines.

Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous disease-behavior-information transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention, (b) a reasonable fraction of overreacting nodes are needed in epidemic prevention (c) the basic reproduction number R_{0} has different effects on epidemic outbreak for cases with and without asymptomatic infection, and (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people become aware of the disease and adopt self-protection to protect themselves and the whole population.

Psychiatric emergencies during the height of the COVID-19 pandemic in the suburban New York City area

BackgroundThis report characterizes patients presenting for psychiatric emergencies during the COVID-19 pandemic and describes COVID-19-related stressors. MethodsPatients seen for emergency psychiatric evaluation during the height of the COVID-19 period (March 1-April 30, 2020; N = 201) were compared with those in the immediate Pre-COVID-19 period (January 1-February 28, 2020; N = 355), on sociodemographic characteristics, psychiatric diagnoses, symptoms, and disposition. Patients tested positive for COVID-19 were compared with those that tested negative on the same outcomes. Prevalence and nature of COVID-19-stressors that influenced the emergency presentation were rated. OutcomeThe most common psychiatric diagnoses and presenting symptoms during both periods were depression and suicidal ideation. Comparing the Pre-COVID-19 and COVID-19 periods, a significant decline in emergency psychiatric volume was observed in children and adolescents (C/A), but not adults. COVID-19 period C/A patients had more new onset disorders and were more likely to be admitted to inpatient care, but were less likely to present with suicide attempts, impulse control disorders and agitation/aggression. Adults were more likely to have no access to outpatient care, present with anxiety disorders, and were also more likely to be admitted for inpatient care. COVID-19 directly affected the psychiatric emergency in 25% of patients, with the more severe stressors triggered by fear of COVID infection (including psychosis), actual COVID infection in self or family members, including death of a loved one. COVID-positive patients were more likely to have psychosis, including new-onset, and were less likely to be depressed/suicidal compared to their COVID-negative counterparts. ConclusionThis report demonstrates the need for emergency psychiatric services throughout the COVID-19 pandemic and the need for clinical and diagnostic COVID-19 screening of psychiatric emergency patients. New and severe pathology underscore the need for enhanced outpatient access to tele-mental health, crisis hotline and on-line psychotherapeutic services, as well as psychiatric inpatient services with capacity to safely care for COVID-19 patients.

Psychiatric Emergencies During the Height of the COVID-19 Pandemic in the Suburban New York City Area

Background: This report characterizes patients presenting for psychiatric emergencies during the COVID-19 pandemic and describes COVID-19-related stressors.Methods: Medical records of 556 patients that received emergency psychiatric evaluation January 1-April 30, 2020 were reviewed. Patient seen during COVID-19 (N=201) were compared with those prior (N=355), on sociodemographic characteristics, psychiatric diagnoses, symptoms, and disposition. Patients tested positive for COVID-19 were compared with those that tested negative. Prevalence and nature of COVID-19-stressors that influenced the emergency presentation were rated.Outcome: A significant decline in emergency psychiatric volume was observed in children and adolescents (C/A), but not adults. COVID-19 period C/A patients had more new onset disorders and were more likely to be admitted to inpatient care, but were less likely to present with suicide attempts, impulse control disorders and agitation/aggression. Adults were more likely to have no access to outpatient care, present with anxiety disorders, and were also more likely to be admitted for inpatient care. COVID-19 directly affected the psychiatric emergency in 25% of patients, with the more severe stressors triggered by fear of COVID infection (including psychosis), actual COVID infection in self or family members, including death of a loved one. COVID-positive patients were more likely to have psychosis, including new-onset, and were less likely to be depressed/ suicidal compared to their COVID-negative counterparts.Interpretation: This report demonstrates the need for emergency psychiatric services throughout the COVID-19 pandemic. New and severe pathology underscore the need for enhanced outpatient access and inpatient services with capacity to care for COVID-19 patients.Funding Statement: No external funding.Declaration of Interests: The authors have no conflicts of interest to disclose.Ethics Approval Statement: This study was approved by the institutional review board of New York Medical College and Westchester Medical Center Health System, Valhalla, New York.

English

There is a rising concern for the rapid increase of COVID-19 confirmed cases in Kandahar province. From zero reported cases until 17th March 2020, then Kandahar saw a sudden rise in the cases by 16th May 2020. Decreased literacy rates, poor health education, lack of facilities, inconsistent government policies, and defying coronavirus safety advisory by the public have resulted in the rapid spread of COVID-19. The awareness and practices of the people towards the COVID-19 were significantly low. Therefore, the risk of coronavirus in Kandahar province is extremely high due to the aforementioned reasons. To overcome this virus, the local government must declare strict measures and provide the public with information about the severity and prevention mechanisms of this fatal disease. Mass and random diagnostic testing are required to track the actual infection rates, which can give a realistic picture of what is occurring. In this article, the current situation of COVID-19, available medical facilities, and public response to the ongoing pandemic in Kandahar, Afghanistan was highlighted.   Key words: COVID-19, Kandahar province, public response, curfew, random testing.

The probability of the 6-week lockdown in Victoria (commencing 9 July 2020) achieving elimination of community transmission of SARS-CoV-2.

The probability of the 6-week lockdown in Victoria (commencing 9 July 2020) achieving elimination of community transmission of SARS-CoV-2.

Seasonal flu, vaccinations and COVID-19

Seasonal flu, vaccinations and COVID-19

Complications associated with closure of the linea alba using a combination of interrupted vertical mattress and simple interrupted sutures in equine laparotomies

Objectives(1) Evaluate the occurrence and variables associated with incisional morbidities (IMs) after ventral median laparotomy when using interrupted vertical mattress sutures (IVMS) and (2) determine the occurrence of abdominal bandage-associated...