Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks

The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook—involving more than 500,000 respondents from 64 countries—showing that there is a “one-to-one” relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease—sharing epidemiological features with COVID-19—that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks.     

Get the News Out Loudly and Quickly: The Influence of the Media on Limiting Emerging Infectious Disease Outbreaks

During outbreaks of infectious diseases with high morbidity and mortality, individuals closely follow media reports of the outbreak. Many will attempt to minimize contacts with other individuals in order to protect themselves from infection and possibly death. This process is called social distancing. Social distancing strategies include restricting socializing and travel, and using barrier protections. We use modeling to show that for short-term outbreaks, social distancing can have a large influence on reducing outbreak morbidity and mortality. In particular, public health agencies working together with the media can significantly reduce the severity of an outbreak by providing timely accounts of new infections and deaths. Our models show that the most effective strategy to reduce infections is to provide this information as early as possible, though providing it well into the course of the outbreak can still have a significant effect. However, our models for long-term outbreaks indicate that reporting historic infection data can result in more infections than with no reporting at all. We examine three types of media influence and we illustrate the media influence with a simulated outbreak of a generic emerging infectious disease in a small city. Social distancing can never be complete; however, for a spectrum of outbreaks, we show that leaving isolation (stopping applying social distancing measures) for up to 4 hours each day has modest effect on the overall morbidity and mortality.     

Primary amoebic meningoencephalitis in Britain

Primary amoebic meningoencephalitis is caused by amoebae of the genera Naegleria and Hartmannella (Acanthamoeba), which ordinarily are free-living saprophytes. The infection may be acquired from fresh water—for example, while bathing—the amoebae invading the nasal mucosa and reaching the meninges and brain along the olfactory nerve filaments. The disease is designated “primary” to distinguish it from meningocerebral infection caused by the parasitic amoebae, particularly Entamoeba histolytica, which invade the central nervous system only as a result of dissemination in the blood stream from lesions in other parts of the body.During histological reappraisal of old specimens in a medical museum in London an instance of amoebic meningoencephalitis histologically indistinguishable from the published cases has been found. The specimen dates from 1909. The patient was said to be from Essex. What may have been another case, seen in Northern Ireland in 1937, is also described briefly. These observations may indicate that this disease occurs in the British Isles.Primary amoebic meningoencephalitis should be considered in the differential diagnosis of every case of acute meningitis.     

The Impact of Contact Tracing in Clustered Populations

The tracing of potentially infectious contacts has become an important part of the control strategy for many infectious diseases, from early cases of novel infections to endemic sexually transmitted infections. Here, we make use of mathematical models to consider the case of partner notification for sexually transmitted infection, however these models are sufficiently simple to allow more general conclusions to be drawn. We show that, when contact network structure is considered in addition to contact tracing, standard “mass action” models are generally inadequate. To consider the impact of mutual contacts (specifically clustering) we develop an improvement to existing pairwise network models, which we use to demonstrate that ceteris paribus, clustering improves the efficacy of contact tracing for a large region of parameter space. This result is sometimes reversed, however, for the case of highly effective contact tracing. We also develop stochastic simulations for comparison, using simple re-wiring methods that allow the generation of appropriate comparator networks. In this way we contribute to the general theory of network-based interventions against infectious disease.     

Can the battle against tuberculosis gain from epigenetic research?

A healthy immune system needs to be highly plastic to cope with host defense and surveillance. What mechanisms provide this plasticity? Considering the threat of infectious diseases to a large part of the world's population, can these mechanisms possibly be of use in the ongoing battle against infectious diseases? Against the backdrop of the pandemic nature of tuberculosis, we discuss whether and how epigenetic mechanisms can shed light on our understanding of infectious disease, and if epigenetic marks can be employed to monitor latent infection, disease reactivation or treatment response.     

Revisiting Metchnikoff's work in light of the COVID-19 pandemic

Revisiting Metchnikoff''s work in light of the COVID-19 pandemic illustrates how much this amazing scientist was a polymath, and one could speculate how much he would have been fascinated and most interested in following the course of the pandemic. Since he coined the word “gerontology”, he would have been intrigued by the high mortality among the elderly, and by the concepts of immunosenescence and inflammaging that characterize the SARS-CoV-2 infection. While Metchnikoff''s work is mainly associated with the discovery of the phagocytes and the birth of cellular innate immunity, he regularly invited his closest collaborators to investigate humoral immunity, and it was in his laboratory that Jules Bordet made his major discovery of the complement system. While Metchnikoff and his team investigated many infectious diseases, he also contributed to studies linked to vaccination, such as those on typhoid fever performed in chimpanzees, illustrating that non-human primates can provide animal models which are potentially helpful for understanding the pathophysiology of the COVID-19 virus. In the present review, we illustrate how much his own work and the investigations of his trainees were pertinent to this new disease.     

Taxi Cab Syndrome: A Review of the Extensive Genitourinary Pathology Experienced by Taxi Cab Drivers and What We Can Do to Help

This review consolidates knowledge regarding the extensive genitourinary pathology experienced by taxi cab drivers. Taxi cab, livery, truck, and other drivers all objectively and subjectively may have more voiding dysfunction, infertility, urolithiasis, bladder cancer, and urinary infections as compared with nonprofessional drivers; this is called taxi cab syndrome. Together with governmental and medical assistance, simple interventions—such as education, the addition of taxi relief stations, and possibly the use of sanitary urinary collection devices—to curb the progression of genitourinary disease in taxi drivers should be prospectively studied. It is postulated that many of these interventions may also benefit other groups of occupationally related infrequent voiders.Key words: Taxi cab syndrome, Infrequent voiders syndrome, Occupational hazard, Voiding dysfunction, Prostodynia, Infertility, UrolithiasisIn New York City, where taxi cabs flow like erythrocytes in a vast net of arteries, the drivers, New York’s backbone, often do not have sufficient flow themselves. This article demonstrates how the lack of adequate and accessible bathroom facilities in New York likely accounts for most of the genitourinary pathology that taxi drivers have. In fact, drivers represent only one of many occupations that contribute to voiding dysfunction as a result of inadequate bathroom access or other factors that lead to the inability to void regularly throughout the workday. These drivers represent the ultimate case study on how, using simple interventions, those who move us through New York City at an often bewildering speed may be helped.In the aptly worded article by Gany and colleagues,¹ “Every disease…man can get can start in this cab,” the cardiovascular risk factors of New York City taxi cabs are described in great detail. Gany and colleagues followed a cohort of South Asian immigrant taxi drivers, holding focus groups and administering surveys about the drivers’ perceptions of their own cardiovascular health. Musculoskeletal pain, diabetes, hypertension, vision problems, stress, obesity, and constipation were pervasive throughout the group. Additionally, urinary tract issues such as kidney problems, bladder dysfunction, and prostatism were noted. One of the participants attributed his onset of kidney problems to intentional infrequent urination, and said this was mostly “because you don’t have facilities at most places where you can stand and urinate.” Two other drivers with diabetes-induced polyuria also attributed their severe problem with this issue.¹     

Quantifying Trends in Disease Impact to Produce a Consistent and Reproducible Definition of an Emerging Infectious Disease

The proper allocation of public health resources for research and control requires quantification of both a disease''s current burden and the trend in its impact. Infectious diseases that have been labeled as “emerging infectious diseases” (EIDs) have received heightened scientific and public attention and resources. However, the label ‘emerging’ is rarely backed by quantitative analysis and is often used subjectively. This can lead to over-allocation of resources to diseases that are incorrectly labelled “emerging,” and insufficient allocation of resources to diseases for which evidence of an increasing or high sustained impact is strong. We suggest a simple quantitative approach, segmented regression, to characterize the trends and emergence of diseases. Segmented regression identifies one or more trends in a time series and determines the most statistically parsimonious split(s) (or joinpoints) in the time series. These joinpoints in the time series indicate time points when a change in trend occurred and may identify periods in which drivers of disease impact change. We illustrate the method by analyzing temporal patterns in incidence data for twelve diseases. This approach provides a way to classify a disease as currently emerging, re-emerging, receding, or stable based on temporal trends, as well as to pinpoint the time when the change in these trends happened. We argue that quantitative approaches to defining emergence based on the trend in impact of a disease can, with appropriate context, be used to prioritize resources for research and control. Implementing this more rigorous definition of an EID will require buy-in and enforcement from scientists, policy makers, peer reviewers and journal editors, but has the potential to improve resource allocation for global health.     

Niche theory‐based modeling of assembly processes of viral communities in bats

Understanding the assembly processes of symbiont communities, including viromes and microbiomes, is important for improving predictions on symbionts’ biogeography and disease ecology. Here, we use phylogenetic, functional, and geographic filters to predict the similarity between symbiont communities, using as a test case the assembly process in viral communities of Mexican bats. We construct generalized linear models to predict viral community similarity, as measured by the Jaccard index, as a function of differences in host phylogeny, host functionality, and spatial co‐occurrence, evaluating the models using the Akaike information criterion. Two model classes are constructed: a “known” model, where virus–host relationships are based only on data reported in Mexico, and a “potential” model, where viral reports of all the Americas are used, but then applied only to bat species that are distributed in Mexico. Although the “known” model shows only weak dependence on any of the filters, the “potential” model highlights the importance of all three filter types—phylogeny, functional traits, and co‐occurrence—in the assemblage of viral communities. The differences between the “known” and “potential” models highlight the utility of modeling at different “scales” so as to compare and contrast known information at one scale to another one, where, for example, virus information associated with bats is much scarcer.     

Obesity and Respiratory Infection in Infants and Young Children

A controlled trial has been carried out to test the widely held “clinical impression” that overweight infants have a greater liability to respiratory infections than those of normal weight. Two matched groups of children aged between 3 months and 2 years were studied, containing children whose weight was above the 90th percentile, or was between the 25th and 75th percentiles, at the start of the trial.Data from 120 children of the overweight group were available for analysis, of whom 47 experienced at least one respiratory infection during the trial and 73 did not. Of children remaining over the 90th percentile at the end of the trial 19 had suffered respiratory infections and 28 had not. One baby in the overweight group suffered a “cot death” from acute bronchiolitis. In the control group, 103 cases were analysed—23 had suffered a respiratory infection and 80 had remained free of respiratory infection throughout the trial. These figures are statistically significant and suggest that obesity in infants and young children is associated with an increased incidence of acute respiratory infections.     

Spatially Explicit Modeling of Schistosomiasis Risk in Eastern China Based on a Synthesis of Epidemiological,Environmental and Intermediate Host Genetic Data

Schistosomiasis japonica is a major parasitic disease threatening millions of people in China. Though overall prevalence was greatly reduced during the second half of the past century, continued persistence in some areas and cases of re-emergence in others remain major concerns. As many regions in China are approaching disease elimination, obtaining quantitative data on Schistosoma japonicum parasites is increasingly difficult. This study examines the distribution of schistosomiasis in eastern China, taking advantage of the fact that the single intermediate host serves as a major transmission bottleneck. Epidemiological, population-genetic and high-resolution ecological data are combined to construct a predictive model capable of estimating the probability that schistosomiasis occurs in a target area (“spatially explicit schistosomiasis risk”). Results show that intermediate host genetic parameters are correlated with the distribution of endemic disease areas, and that five explanatory variables—altitude, minimum temperature, annual precipitation, genetic distance, and haplotype diversity—discriminate between endemic and non-endemic zones. Model predictions are correlated with human infection rates observed at the county level. Visualization of the model indicates that the highest risks of disease occur in the Dongting and Poyang lake regions, as expected, as well as in some floodplain areas of the Yangtze River. High risk areas are interconnected, suggesting the complex hydrological interplay of Dongting and Poyang lakes with the Yangtze River may be important for maintaining schistosomiasis in eastern China. Results demonstrate the value of genetic parameters for risk modeling, and particularly for reducing model prediction error. The findings have important consequences both for understanding the determinants of the current distribution of S. japonicum infections, and for designing future schistosomiasis surveillance and control strategies. The results also highlight how genetic information on taxa that constitute bottlenecks to disease transmission can be of value for risk modeling.     

Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today''s important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a “middle-out” strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from “-omics” signatures are identified as key elements of a successful systems biology modeling approach in nutrition research—one that integrates physiological mechanisms and data at multiple space and time scales.     

Parameterizing Spatial Models of Infectious Disease Transmission that Incorporate Infection Time Uncertainty Using Sampling-Based Likelihood Approximations

A class of discrete-time models of infectious disease spread, referred to as individual-level models (ILMs), are typically fitted in a Bayesian Markov chain Monte Carlo (MCMC) framework. These models quantify probabilistic outcomes regarding the risk of infection of susceptible individuals due to various susceptibility and transmissibility factors, including their spatial distance from infectious individuals. The infectious pressure from infected individuals exerted on susceptible individuals is intrinsic to these ILMs. Unfortunately, quantifying this infectious pressure for data sets containing many individuals can be computationally burdensome, leading to a time-consuming likelihood calculation and, thus, computationally prohibitive MCMC-based analysis. This problem worsens when using data augmentation to allow for uncertainty in infection times. In this paper, we develop sampling methods that can be used to calculate a fast, approximate likelihood when fitting such disease models. A simple random sampling approach is initially considered followed by various spatially-stratified schemes. We test and compare the performance of our methods with both simulated data and data from the 2001 foot-and-mouth disease (FMD) epidemic in the U.K. Our results indicate that substantial computation savings can be obtained—albeit, of course, with some information loss—suggesting that such techniques may be of use in the analysis of very large epidemic data sets.     

Dr. Manners

Good manners make a difference—in science and elsewhere. This includes our social media etiquette as researchers. Subject Categories: S&S: History & Philosophy of Science, Methods & Resources, S&S: Ethics

Elbows off the table, please. Don’t chew with your mouth open. Don’t blow your nose at the table. Don’t put your feet up on the chair or table. And please, do not yuck my yum. These are basic table manners that have come up at some of our lab meals, and I have often wondered if it was my job to teach my trainees social graces. A good fellow scientist and friend of mine once told me it was absolutely our place as mentors to teach our trainees not only how to do science well, but also how to be well‐mannered humans. While these Emily Post‐approved table manners might seem old‐fashioned (I’m guessing some readers will have to look up Emily Post), I strongly believe they still hold a place in modern society; being in good company never goes out of style.Speaking of modern society: upon encouragement by several of my scientist friends, I joined Twitter in 2016. My motivation was mainly to hear about pre‐prints and publications, conference announcements and relevant news, science or otherwise. I also follow people who just make me laugh (I highly recommend @ConanOBrien or @dog_rates). I (re)tweet job openings, conference announcements, and interesting new data. Occasionally, I post photos from conferences, or random science‐related art. I also appreciate the sense of community that social media brings to the table. However, social media is a venue where I have also seen manners go to die. Rapidly.It is really shocking to read what some people feel perfectly comfortable tweeting. While most of us can agree that foul language and highly offensive opinions are generally considered distasteful, there are other, subtler but nonetheless equally—if not more—cringe‐worthy offenses online when I am fairly certain these people would never utter such words in real life. In the era of pandemic, the existence of people tweeting about not being able to eat at their favorite restaurant or travel to some destination holiday because of lockdown shows an egregious lack of self‐awareness. Sure it sucks to cancel a wedding due to COVID‐19, but do you need to moan to your followers—most of whom are likely total strangers—about it while other people have lost their jobs? If I had a nickel for every first‐world complaint I have seen on Twitter, I’d have retired a long time ago; although to be honest, I would do science for free. However, these examples pale in comparison with another type of tweeter: Reader, I submit to you, “the Humblebragger.”From the MacMillan Buzzword dictionary (via Google): a humblebrag is “a statement in which you pretend to be modest but which you are really using as a way of telling people about your success or achievements.” I would further translate this definition to indicate that humblebraggers are starved for attention. After joining Twitter, I quickly found many people using social media to announce how “humble and honored” they are for receiving grant or prize X, Y, or Z. In general, these are junior faculty who have perhaps not acquired the self‐awareness more senior scientists have. Perhaps the most off‐putting posts I have seen are from people who post photos of their NIH application priority scores right after study section, or their Notice of Awards (NOA). When did we ever, before social media, send little notes to each other—let alone to complete strangers—announcing our priority scores or NOAs? (Spoiler: NEVER)Some of you reading this opinion piece might have humblebragged at one or time or another, and might not understand why it is distasteful. Please let me explain. For every person who gets a fundable score, there are dozens more people who do not, and they are sad (I speak from many years of experience). While said fundable‐score person might be by someone we like—and I absolutely, positively wish them well—there are many more people who will feel lousy because they did not get funding from the same review round. When has anyone ever felt good about other people getting something that they, too, desire? I think as children, none of us liked the kid on the playground who ran around with the best new Toy of the Season. As adults, do we feel differently? Along these lines, I have never been a fan of “best poster/talk/abstract” prizes. Trainees should not be striving for these fleeting recognitions and should focus on doing the best science for Science’s sake; I really believe this competition process sets people up for life in a negative way—there, I’ve said it.Can your friends and colleagues tweet about your honors? Sure, why not, and by all means please let your well‐wishers honor you, and do thank them and graciously congratulate your trainees or colleagues for helping you to get there. But to post things yourself? Please. Don’t be surprised if you have been muted by many of your followers.It is notable that many of our most decorated scientists are not on Twitter, or at least never tweet about their accomplishments. I do not recall ever seeing a single Nobel laureate announce how humbled and honored they are about their prize. Of course, I might be wrong, but I am willing to bet the numbers are much lower than what I have observed for junior faculty. True humility will never be demonstrated by announcing your achievements to your social media followers, and I believe humblebragging reveals insecurity more than anything. I hope that many more of us can follow the lead of our top scientists both in creativity, rigor, and social media politeness.     

Fifteen compelling open questions in plant cell biology

As scientists, we are at least as excited about the open questions—the things we do not know—as the discoveries. Here, we asked 15 experts to describe the most compelling open questions in plant cell biology. These are their questions: How are organelle identity, domains, and boundaries maintained under the continuous flux of vesicle trafficking and membrane remodeling? Is the plant cortical microtubule cytoskeleton a mechanosensory apparatus? How are the cellular pathways of cell wall synthesis, assembly, modification, and integrity sensing linked in plants? Why do plasmodesmata open and close? Is there retrograde signaling from vacuoles to the nucleus? How do root cells accommodate fungal endosymbionts? What is the role of cell edges in plant morphogenesis? How is the cell division site determined? What are the emergent effects of polyploidy on the biology of the cell, and how are any such “rules” conditioned by cell type? Can mechanical forces trigger new cell fates in plants? How does a single differentiated somatic cell reprogram and gain pluripotency? How does polarity develop de-novo in isolated plant cells? What is the spectrum of cellular functions for membraneless organelles and intrinsically disordered proteins? How do plants deal with internal noise? How does order emerge in cells and propagate to organs and organisms from complex dynamical processes? We hope you find the discussions of these questions thought provoking and inspiring.

We asked 15 experts to address what they consider to be the most compelling open questions in plant cell biology and these are their questions.     

PYOGENIC INFECTIONS IN ORTHOPEDIC SURGERY—Prevention and Management

A problem that confronts surgeons in clinical practice is that a patient may acquire new infections while in the hospital. When such infections occur they are predominantly staphylococcal and these bacteria are often, but not always resistant to penicillin, streptomycin and the tetracycline antibiotics. They are often but neither completely nor uniformly sensitive to the newer or less frequently used antimicrobial agents.The extension of antibiotic usage from proven situations to “routine” prophylaxis has been a widespread practice. There are many reasons to discourage and to reexamine the validity and purpose, as well as the safety of this practice. We now have sufficient background and experience to revert from widespread and indiscriminate use to a practice of discriminate prophylactic therapy.In general, soft tissue lacerations and clean wounds do not require operation under an “antibiotic umbrella.” Similarly, elective orthopedic surgical procedures of soft tissues such as muscle biopsy, tenorrhaphy and muscle and tendon transplants as well as plastic surgical procedures can be safely performed without antibiotic therapy if technique is good and operation not prolonged. Operations of major magnitude on the motor-skeletal system, such as open fractures, internal fixation of fractures with bone grafts, and major operations of joints are indication for antibiotic therapy for impending infection postoperatively for five days. Reliance is mainly on antistaphylococcal drugs to which hospital organisms are predominantly sensitive. The two remaining indications for antibiotic therapy against impending infection are: (1) major crush injury—for example, to the thigh—and (2) the need for a patient with a healing fracture to have other surgical procedures such as tooth extraction or excision of an infected area which might predispose to transient bacteremia and embolic infection in bone or joint.     

Use of artificial genomes in assessing methods for atypical gene detection

Parametric methods for identifying laterally transferred genes exploit the directional mutational biases unique to each genome. Yet the development of new, more robust methods—as well as the evaluation and proper implementation of existing methods—relies on an arbitrary assessment of performance using real genomes, where the evolutionary histories of genes are not known. We have used the framework of a generalized hidden Markov model to create artificial genomes modeled after genuine genomes. To model a genome, “core” genes—those displaying patterns of mutational biases shared among large numbers of genes—are identified by a novel gene clustering approach based on the Akaike information criterion. Gene models derived from multiple “core” gene clusters are used to generate an artificial genome that models the properties of a genuine genome. Chimeric artificial genomes—representing those having experienced lateral gene transfer—were created by combining genes from multiple artificial genomes, and the performance of the parametric methods for identifying “atypical” genes was assessed directly. We found that a hidden Markov model that included multiple gene models, each trained on sets of genes representing the range of genotypic variability within a genome, could produce artificial genomes that mimicked the properties of genuine genomes. Moreover, different methods for detecting foreign genes performed differently—i.e., they had different sets of strengths and weaknesses—when identifying atypical genes within chimeric artificial genomes.