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Fleur Meijer,1 Petra Cornelissen,2 Corina Sie,3 Michel Wagemans,4 Anja Mars,5 Tiny Hobma,1,6 Marieke Niesters,1 Albert Dahan,1 J Seppe Koopman,6 Monique AH Steegers2 1Department of Anesthesiology, Leiden University Medical Centre, Leiden, the Netherlands; 2Department of Anesthesiology Pain and Palliative Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands; 3Department of Anesthesiology, Erasmus Medical Centre, Rotterdam, the Netherlands; 4Department of Anesthesiology and Pain Medicine, Reinier de Graaf Hospital, Delft, the Netherlands; 5Department of Anesthesiology and Acute Pain Service, Alrijne Hospital, Leiderdorp, the Netherlands; 6Department of Anesthesiology, Maasstad Hospital, Rotterdam, the Netherlands Background: The sublingual sufentanil tablet system (SSTS) is a novel hand-held patient-controlled analgesia device developed for treatment of moderate-to-severe postoperative pain. Here we present the first results of its clinical use. Methods: Adult patients undergoing major surgery in five hospitals in the Netherlands received the SSTS for postoperative pain relief as part of multimodal pain management that further included paracetamol and a nonsteroidal anti-inflammatory drug (NSAID). The following variables were collected: postoperative pain scores using the 11-point numerical rating score (NRS) at rest, number of tablets used, occurrence of nausea, and patient satisfaction scores. Results: We included 280 patients in the study; the majority underwent laparoscopic abdominal (49%) or orthopedic (knee replacement) surgery (34%). The median NRS was 3.5 (interquartile range 2.3–4.0) on the day of surgery, 3.3 (2.3–4.0) on the first postoperative day, and 2.8 (2.0–4.0) on the second postoperative day; pain scores did not differ between surgery types. Mean number of tablets used was 19 (range 0–86). Nausea occurred in 34% of patients, more often in women (45% vs 19%). Overall satisfaction was high in 73% of patients. Satisfaction was correlated with pain relief (p

Leonie M Becker,1,2 Aart Jan W Teunissen,2 Joseph SHA Koopman2 1Department of Cardiology, St. Antonius Ziekenhuis, Nieuwegein, The Netherlands; 2Department of Anaesthesiology, Maasstad Ziekenhuis, Rotterdam, The NetherlandsCorrespondence: Leonie M Becker, Department of Cardiology, St. Antonius Hospital, Koekoekslaan 1, Nieuwegein, 3435 CM, The Netherlands, Tel +31 88 320 09 22, Email l.becker@antoniusziekenhuis.nlAbstract: The addition of morphine to neuraxial anaesthesia leads to improved postoperative analgesia and lower opioid consumption, but is often accompanied by pruritus. Studies on preventing or treating pruritus show contradictory results. Our objective was to identify effective drugs for the prevention or treatment of pruritus by a scoping review of clinical trials. A systematic literature search was conducted in PubMed, Embase and Web of Science. We identified clinical trials investigating the prevention or treatment of neuraxial morphine-induced pruritus in adults. Systematic reviews and meta-analyses were screened for eligible studies. One-hundred-and-four articles were included covering 13 pharmacological groups. We conclude that dopamine antagonists, μ-opioid agonist/antagonists and neuraxial or orally administered μ-opioid antagonists prevent pruritus caused by neuraxial morphine regardless of the timing of administration. In the reviewed literature, 5HT3-antagonists prevent neuraxial morphine-induced pruritus when administered before morphine administration. For the treatment of neuraxial morphine-induced pruritus, only nalbuphine appears to be consistently effective. More research is needed to find the most effective doses and the optimal timing of the effective medication.Keywords: pruritus, prevention, treatment, morphine, epidural, spinal

David E van Veen,1 Christiaan CWM Verhelst,2 Roelof T van Dellen,3 JSHA Koopman4 1Erasmus University Rotterdam, Rotterdam, The Netherlands; 2Department of Orthopedic Surgery, Maasstad Hospital, Rotterdam, The Netherlands; 3Department of Hospital Pharmacy, Maasstad Hospital, Rotterdam, The Netherlands; 4Department of Anesthesiology, Maasstad Hospital, Rotterdam, The Netherlands Abstract: Postoperative pain is a major problem, especially in orthopedic surgery. Our data suggest suboptimal pain management after total knee arthroplasty. This study evaluated a sufentanil sublingual tablet system (Zalviso) to optimize postoperative pain treatment. This retrospective, single-center, cohort study was conducted between January 2017 and September 2017. Zalviso as standard treatment was compared with a cohort receiving oxycodone (Oxy) immediate release and Oxy extended release and another receiving Oxy immediate release, Oxy extended release, and dexamethasone (Dexa + Oxy). The primary end point, pain intensity, was assessed on a numeric rating scale (NRS). Highest, lowest, and number of NRS scores >7 were collected. Secondary end points included length of hospital stay, nausea, and mobilization on the day of surgery. Patients receiving Dexa + Oxy had a lower lowest-pain intensity on day 0 (median 0, IQR 0–0) when compared to patients receiving Oxy (median 2, IQR 0–3; P 7. Patients treated with Dexa + Oxy or Zalviso were discharged earlier compared to patients treated with Oxy (P

Polio can circulate unobserved in regions that are challenging to monitor. To assess the probability of silent circulation, simulation models can be used to understand transmission dynamics when detection is unreliable. Model assumptions, however, impact the estimated probability of silent circulation. Here, we examine the impact of having distinct populations, rather than a single well-mixed population, with a discrete-individual model including environmental surveillance. We show that partitioning a well-mixed population into networks of distinct communities may result in a higher probability of silent circulation as a result of the time it takes for the detection of a circulation event. Population structure should be considered when assessing polio control in a region with many loosely interacting communities., (© 2022. The Author(s), under exclusive licence to Society for Mathematical Biology.)

Environmental pathogen surveillance is a sensitive tool that can detect early-stage outbreaks, and it is being used to track poliovirus and other pathogens. However, interpretation of longitudinal environmental surveillance signals is difficult because the relationship between infection incidence and viral load in wastewater depends on time-varying shedding intensity. We developed a mathematical model of time-varying poliovirus shedding intensity consistent with expert opinion across a range of immunization states. Incorporating this shedding model into an infectious disease transmission model, we analysed quantitative, polymerase chain reaction data from seven sites during the 2013 Israeli poliovirus outbreak. Compared to a constant shedding model, our time-varying shedding model estimated a slower peak (four weeks later), with more of the population reached by a vaccination campaign before infection and a lower cumulative incidence. We also estimated the population shed virus for an average of 29 days (95% CI 28-31), longer than expert opinion had suggested for a population that was purported to have received three or more inactivated polio vaccine (IPV) doses. One explanation is that IPV may not substantially affect shedding duration. Using realistic models of time-varying shedding coupled with longitudinal environmental surveillance may improve our understanding of outbreak dynamics of poliovirus, SARS-CoV-2, or other pathogens.

We present methods for building a Java Runtime-Alterable-Model Platform (RAMP) of complex dynamical systems. We illustrate our methods by building a multivariant SEIR (epidemic) RAMP. Underlying our RAMP is an individual-based model that includes adaptive contact rates, pathogen genetic drift, waning and cross-immunity. Besides allowing parameter values, process descriptions and scriptable runtime drivers to be easily modified during simulations, our RAMP can used within R-Studio and other computational platforms. Process descriptions that can be runtime altered within our SEIR RAMP include pathogen variant-dependent host shedding, environmental persistence, host transmission and within-host pathogen mutation and replication. They also include adaptive social distancing and adaptive application of vaccination rates and variant-valency of vaccines. We present simulation results using parameter values and process descriptions relevant to the current COVID-19 pandemic. Our results suggest that if waning immunity outpaces vaccination rates, then vaccination rollouts may fail to contain the most transmissible variants, particularly if vaccine valencies are not adapted to deal with escape mutations. Our SEIR RAMP is designed for easy use by others. More generally, our RAMP concept facilitates construction of highly flexible complex systems models of all types, which can then be easily shared as stand-alone application programs.

SARS-Cov-2 escape mutations (EM) have been detected and are spreading. Vaccines may need adjustment to respond to these or future mutations. We designed a population level model integrating both waning immunity and EM. We also designed a set of criteria for elaborating and fitting this model to cross-neutralization and other data with a goal of minimizing vaccine decision errors. We formulated four related models. These differ regarding which strains can drift to escape immunity in the host when that immunity was elicited by different strains. Across changing waning and escape mutation parameter values, these model variations led to patterns where: 1) EM are rare in the first epidemic, 2) rebound outbreaks after the first outbreak are accelerated by increasing waning and by increasing drifting, 3) the long term endemic level of infection is determined mostly by waning rates with small effects of the drifting parameter, 4) EM caused loss of vaccine effectiveness, and under some conditions: vaccines induced EM that caused higher levels of infection with vaccines than without them. The differences and similarities across the four models suggest paths for developing models specifying the epitopes where EM act. This model provides a base on which to construct epitope specific evolutionary models using new high-throughput assay data from population samples to guide vaccine decisions., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Israel experienced an outbreak of wild poliovirus type 1 (WPV1) in 2013-2014, detected through environmental surveillance of the sewage system. No cases of acute flaccid paralysis were reported, and the epidemic subsided after a bivalent oral polio vaccination (bOPV) campaign. As we approach global eradication, polio will increasingly be detected only through environmental surveillance. We developed a framework to convert quantitative polymerase chain reaction (qPCR) cycle threshold data into scaled WPV1 and OPV1 concentrations for inference within a deterministic, compartmental infectious disease transmission model. We used this approach to estimate the epidemic curve and transmission dynamics, as well as assess alternate vaccination scenarios. Our analysis estimates the outbreak peaked in late June, much earlier than previous estimates derived from analysis of stool samples, although the exact epidemic trajectory remains uncertain. We estimate the basic reproduction number was 1.62 (95% CI 1.04-2.02). Model estimates indicate that 59% (95% CI 9-77%) of susceptible individuals (primarily children under 10 years old) were infected with WPV1 over a little more than six months, mostly before the vaccination campaign onset, and that the vaccination campaign averted 10% (95% CI 1-24%) of WPV1 infections. As we approach global polio eradication, environmental monitoring with qPCR can be used as a highly sensitive method to enhance disease surveillance. Our analytic approach brings public health relevance to environmental data that, if systematically collected, can guide eradication efforts., Competing Interests: The authors declare no conflict of interest.

To achieve complete polio eradication, the live oral poliovirus vaccine (OPV) currently used must be phased out after the end of wild poliovirus transmission. However, poorly understood threats may arise when OPV use is stopped. To counter these threats, better models than those currently available are needed. Two articles recently published in BMC Medicine address these issues. Mercer et al. (BMC Med 15:180, 2017) developed a statistical model analysis of polio case data and characteristics of cases occurring in several districts in Pakistan to inform resource allocation decisions. Nevertheless, despite having the potential to accelerate the elimination of polio cases, their analyses are unlikely to advance our understanding OPV cessation threats. McCarthy et al. (BMC Med 15:175, 2017) explored one such threat, namely the emergence and transmission of serotype 2 circulating vaccine derived poliovirus (cVDPV2) after OPV2 cessation, and found that the risk of persistent spread of cVDPV2 to new areas increases rapidly 1-5 years after OPV2 cessation. Thus, recently developed models and analysis methods have the potential to guide the required steps to surpass these threats. 'Big data' scientists could help with this; however, datasets covering all eradication efforts should be made readily available.Please see related articles: https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-017-0937-y and https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-017-0941-2 .

Waning immunity could allow transmission of polioviruses without causing poliomyelitis by promoting silent circulation (SC). Undetected SC when oral polio vaccine (OPV) use is stopped could cause difficult to control epidemics. Little is known about waning. To develop theory about what generates SC, we modeled a range of waning patterns. We varied both OPV and wild polio virus (WPV) transmissibility, the time from beginning vaccination to reaching low polio levels, and the infection to paralysis ratio (IPR). There was longer SC when waning continued over time rather than stopping after a few years, when WPV transmissibility was higher or OPV transmissibility was lower, and when the IPR was higher. These interacted in a way that makes recent emergence of prolonged SC a possibility. As the time to reach low infection levels increased, vaccine rates needed to eliminate polio increased and a threshold was passed where prolonged low-level SC emerged. These phenomena were caused by increased contributions to the force of infection from reinfections. The resulting SC occurs at low levels that would be difficult to detect using environmental surveillance. For all waning patterns, modest levels of vaccination of adults shortened SC. Previous modeling studies may have missed these phenomena because (1) they used models with no or very short duration waning and (2) they fit models to paralytic polio case counts. Our analyses show that polio case counts cannot predict SC because nearly identical polio case count patterns can be generated by a range of waning patterns that generate different patterns of SC. We conclude that the possibility of prolonged SC is real but unquantified, that vaccinating modest fractions of adults could reduce SC risk, and that joint analysis of acute flaccid paralysis and environmental surveillance data can help assess SC risks and ensure low risks before stopping OPV., (Copyright © 2017. Published by Elsevier B.V.)

Human immunodeficiency virus (HIV) transmission models that include variability in sexual behavior over time have shown increased incidence, prevalence, and acute-state transmission rates for a given population risk profile. This raises the question of whether dynamic variation in individual sexual behavior is a real phenomenon that can be observed and measured. To study this dynamic variation, we developed a model incorporating heterogeneity in both between-person and within-person sexual contact patterns. Using novel methodology that we call iterated filtering for longitudinal data, we fitted this model by maximum likelihood to longitudinal survey data from the Centers for Disease Control and Prevention's Collaborative HIV Seroincidence Study (1992-1995). We found evidence for individual heterogeneity in sexual behavior over time. We simulated an epidemic process and found that inclusion of empirically measured levels of dynamic variation in individual-level sexual behavior brought the theoretical predictions of HIV incidence into closer alignment with reality given the measured per-act probabilities of transmission. The methods developed here provide a framework for quantifying variation in sexual behaviors that helps in understanding the HIV epidemic among gay men., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Choosing between strategies to control HIV transmission with antivirals requires understanding both the dynamics affecting those strategies' effectiveness and what causes those dynamics. Alternating episodes of high and low contact rates (episodic risk) interact with increased transmission probabilities during early infection to strongly influence HIV transmission dynamics. To elucidate the mechanics of this interaction and how these alter the effectiveness of universal test and treat (UT8T) strategies, we formulated a model of UT8T effects. Analysis of this model shows how and why changing the dynamics of episodic risk changes the fraction of early transmissions (FET) and the basic reproduction number (R0) and consequently causes UT8T to vary from easily eliminating transmission to having little effect. As the length of risk episodes varies from days to lifetimes, FET first increases, then falls. Endemic prevalence varies similarly. R0, in contrast, increases monotonically and is the major determinant of UT8T effects. At some levels of episodic risk, FET can be high, but eradication is easy because R0 is low. At others FET is lower, but a high R0 makes eradication impossible and control ineffective. Thus changes in individual risk over time must be measured and analyzed to plan effective control strategies with antivirals.

Background: Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of healthcare-associated infections. An important control strategy is hand hygiene; however, non-compliance has been a major problem in healthcare settings. Furthermore, modeling studies have suggested that the law of diminishing return applies to hand hygiene. Other additional control strategies such as environmental cleaning may be warranted, given that MRSA-positive individuals constantly shed contaminated desquamated skin particles to the environment., Methods: We constructed and analyzed a deterministic environmental compartmental model of MRSA fate, transport, and exposure between two hypothetical hospital rooms: one with a colonized patient, shedding MRSA; another with an uncolonized patient, susceptible to exposure. Healthcare workers (HCWs), acting solely as vectors, spread MRSA from one patient room to the other., Results: Although porous surfaces became highly contaminated, their low transfer efficiency limited the exposure dose to HCWs and the uncolonized patient. Conversely, the high transfer efficiency of nonporous surfaces allows greater MRSA transfer when touched. In the colonized patient's room, HCW exposure occurred more predominantly through the indirect (patient to surfaces to HCW) mode compared to the direct (patient to HCW) mode. In contrast, in the uncolonized patient's room, patient exposure was more predominant in the direct (HCW to patient) mode compared to the indirect (HCW to surfaces to patient) mode. Surface wiping decreased MRSA exposure to the uncolonized patient more than daily surface decontamination. This was because wiping allowed higher cleaning frequency and cleaned more total surface area per day., Conclusions: Environmental cleaning should be considered as an integral component of MRSA infection control in hospitals. Given the previously under-appreciated role of surface contamination in MRSA transmission, this intervention mode can contribute to an effective multiple barrier approach in concert with hand hygiene.

Background: Conventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. We conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates by stage of infection., Methods and Findings: We analyzed 662 HIV-1 subtype B sequences collected between October 14, 2004, and February 24, 2012, from MSM in the Detroit metropolitan area, Michigan. These sequences were cross-referenced with a database of 30,200 patients diagnosed with HIV infection in the state of Michigan, which includes clinical information that is informative about the recency of infection at the time of diagnosis. These data were analyzed using recently developed population genetic methods that have enabled the estimation of transmission rates from the population-level genetic diversity of the virus. We found that genetic data are highly informative about HIV donors in ways that standard surveillance data are not. Genetic data are especially informative about the stage of infection of donors at the point of transmission. We estimate that 44.7% (95% CI, 42.2%-46.4%) of transmissions occur during the first year of infection., Conclusions: In this study, almost half of transmissions occurred within the first year of HIV infection in MSM. Our conclusions may be sensitive to un-modeled intra-host evolutionary dynamics, un-modeled sexual risk behavior, and uncertainty in the stage of infected hosts at the time of sampling. The intensity of transmission during early infection may have significance for public health interventions based on early treatment of newly diagnosed individuals.

Polio eradication is on the cusp of success, with only a few regions still maintaining transmission. Improving our understanding of why some regions have been successful and others have not will help with both global eradication of polio and development of more effective vaccination strategies for other pathogens. To examine the past 25 years of eradication efforts, we constructed a transmission model for wild poliovirus that incorporates waning immunity (which affects both infection risk and transmissibility of any resulting infection), age-mediated vaccination rates, and transmission of oral polio vaccine. The model produces results consistent with the 4 country categories defined by the Global Polio Eradication Program: elimination with no subsequent outbreaks; elimination with subsequent transient outbreaks; elimination with subsequent outbreaks and transmission detected for more than 12 months; and endemic polio transmission. Analysis of waning immunity rates and oral polio vaccine transmissibility reveals that higher waning immunity rates make eradication more difficult because of increasing numbers of infectious adults, and that higher oral polio vaccine transmission rates make eradication easier as adults become reimmunized. Given these dynamic properties, attention should be given to intervention strategies that complement childhood vaccination. For example, improvement in sanitation can reduce the reproduction number in problematic regions, and adult vaccination can lower adult transmission.

Episodic high-risk sexual behavior is common and can have a profound effect on HIV transmission. In a model of HIV transmission among men who have sex with men (MSM), changing the frequency, duration and contact rates of high-risk episodes can take endemic prevalence from zero to 50% and more than double transmissions during acute HIV infection (AHI). Undirected test and treat could be inefficient in the presence of strong episodic risk effects. Partner services approaches that use a variety of control options will be likely to have better effects under these conditions, but the question remains: What data will reveal if a population is experiencing episodic risk effects? HIV sequence data from Montreal reveals genetic clusters whose size distribution stabilizes over time and reflects the size distribution of acute infection outbreaks (AIOs). Surveillance provides complementary behavioral data. In order to use both types of data efficiently, it is essential to examine aspects of models that affect both the episodic risk effects and the shape of transmission trees. As a demonstration, we use a deterministic compartmental model of episodic risk to explore the determinants of the fraction of transmissions during acute HIV infection (AHI) at the endemic equilibrium. We use a corresponding individual-based model to observe AIO size distributions and patterns of transmission within AIO. Episodic risk parameters determining whether AHI transmission trees had longer chains, more clustered transmissions from single individuals, or different mixes of these were explored. Encouragingly for parameter estimation, AIO size distributions reflected the frequency of transmissions from acute infection across divergent parameter sets. Our results show that episodic risk dynamics influence both the size and duration of acute infection outbreaks, thus providing a possible link between genetic cluster size distributions and episodic risk dynamics., (Copyright © 2012 Elsevier B.V. All rights reserved.)

HIV transmission models include heterogeneous individuals with different sexual behaviors including contact rates, mixing patterns, and sexual practices. However, heterogeneity can also exist within individuals over time. In this paper we analyze a two year prospective cohort of 882 gay men with observations at six month intervals focusing on heterogeneity both within and between individuals in sexual contact rates and sexual roles. The total number of sexual contacts made over the course of the study (mean 1.55 per month) are highly variable between individuals (standard deviation 9.82 per month) as expected. At the individual level, contacts were also heterogeneous over time. For a homogeneous count process the variance should scale with the mean; however, at the individual level the variance scaled with the square root of the mean implying the presence of heterogeneity within individuals over time. We also observed a high level of movement between dichotomous sexual roles (insertive/receptive, protected/unprotected, anal/oral, and HIV status of partners). On average periods of exclusively unprotected sexual contacted lasted 16 months. Our results suggest that future HIV models should consider heterogeneities both between and within individuals in sexual contact rates and sexual roles.

A deterministic compartmental model was explored that relaxed the unrealistic assumption in most HIV transmission models that behaviors of individuals are constant over time. A simple model was formulated to better explain the effects observed. Individuals had a high and a low contact rate and went back and forth between them. This episodic risk behavior interacted with the short period of high transmissibility during acute HIV infection to cause dramatic increases in prevalence as the differences between high and low contact rates increased and as the duration of high risk better matched the duration of acute HIV infection. These same changes caused a considerable increase in the fraction of all transmissions that occurred during acute infection. These strong changes occurred despite a constant total number of contacts and a constant total transmission potential from acute infection. Two phenomena played a strong role in generating these effects. First, people were infected more often during their high contact rate phase and they remained with high contact rates during the highly contagious acute infection stage. Second, when individuals with previously low contact rates moved into an episodic high-risk period, they were more likely to be susceptible and thus provided more high contact rate susceptible individuals who could get infected. These phenomena make test and treat control strategies less effective and could cause some behavioral interventions to increase transmission. Signature effects on genetic patterns between HIV strains could make it possible to determine whether these episodic risk effects are acting in a population.

Most models assessing relative transmissions during different progressive stages of human immunodeficiency virus (HIV) infection assume that infections are transmitted through instantaneous sexual contacts. In reality, however, HIV will often be transmitted through repeated sex acts during partnerships that form and dissolve at varying rates. We sought to understand how dynamic sexual partnerships would influence transmissions during different progression stages of HIV infection: primary HIV infection (PHI) and chronic stage. Using a system of ordinary differential equations with a pair approximation technique, we constructed a model of HIV transmission in a homogeneous population in which sexual partnerships form and dissolve. We derived analytical expressions for useful epidemiological quantities such as basic reproduction number and also did simulation runs of the model. Partnership dynamics strongly influence transmissions during progressive stages of HIV infection. The fraction of transmissions during PHI has a U-shaped relationship with respect to the rate of partnership change, where the minimum and maximum occur given partnerships of about 100 days and fixed partnerships, respectively. Models that assume instantaneous contacts may overestimate transmissions during PHI for real, dynamic sexual partnerships with varying (non-zero) durations., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Fomites involved in influenza transmission are either hand- or droplet-contaminated. We evaluated the interactions of fomite characteristics and human behaviors affecting these routes using an Environmental Infection Transmission System (EITS) model by comparing the basic reproduction numbers (R(0)) for different fomite mediated transmission pathways. Fomites classified as large versus small surface sizes (reflecting high versus low droplet contamination levels) and high versus low touching frequency have important differences. For example, 1) the highly touched large surface fomite (public tables) has the highest transmission potential and generally strongest control measure effects; 2) transmission from droplet-contaminated routes exceed those from hand-contaminated routes except for highly touched small surface fomites such as door knob handles; and 3) covering a cough using the upper arm or using tissues effectively removes virus from the system and thus decreases total fomite transmission. Because covering a cough by hands diverts pathogens from the droplet-fomite route to the hand-fomite route, this has the potential to increase total fomite transmission for highly touched small surface fomites. An improved understanding and more refined data related to fomite mediated transmission routes will help inform intervention strategies for influenza and other pathogens that are mediated through the environment.

Phylogenies of highly genetically variable viruses such as HIV-1 are potentially informative of epidemiological dynamics. Several studies have demonstrated the presence of clusters of highly related HIV-1 sequences, particularly among recently HIV-infected individuals, which have been used to argue for a high transmission rate during acute infection. Using a large set of HIV-1 subtype B pol sequences collected from men who have sex with men, we demonstrate that virus from recent infections tend to be phylogenetically clustered at a greater rate than virus from patients with chronic infection ('excess clustering') and also tend to cluster with other recent HIV infections rather than chronic, established infections ('excess co-clustering'), consistent with previous reports. To determine the role that a higher infectivity during acute infection may play in excess clustering and co-clustering, we developed a simple model of HIV infection that incorporates an early period of intensified transmission, and explicitly considers the dynamics of phylogenetic clusters alongside the dynamics of acute and chronic infected cases. We explored the potential for clustering statistics to be used for inference of acute stage transmission rates and found that no single statistic explains very much variance in parameters controlling acute stage transmission rates. We demonstrate that high transmission rates during the acute stage is not the main cause of excess clustering of virus from patients with early/acute infection compared to chronic infection, which may simply reflect the shorter time since transmission in acute infection. Higher transmission during acute infection can result in excess co-clustering of sequences, while the extent of clustering observed is most sensitive to the fraction of infections sampled.

The most commonly used dose-response models implicitly assume that accumulation of dose is a time-independent process where each pathogen has a fixed risk of initiating infection. Immune particle neutralization of pathogens, however, may create strong time dependence; i.e. temporally clustered pathogens have a better chance of overwhelming the immune particles than pathogen exposures that occur at lower levels for longer periods of time. In environmental transmission systems, we expect different routes of transmission to elicit different dose-timing patterns and thus potentially different realizations of risk. We present a dose-response model that captures time dependence in a manner that incorporates the dynamics of initial immune response. We then demonstrate the parameter estimation of our model in a dose-response survival analysis using empirical time-series data of inhalational anthrax in monkeys in which we find slight dose-timing effects. Future dose-response experiments should include varying the time pattern of exposure in addition to varying the total doses delivered. Ultimately, the dynamic dose-response paradigm presented here will improve modelling of environmental transmission systems where different systems have different time patterns of exposure.

Influenza can be transmitted through respirable (small airborne particles), inspirable (intermediate size), direct-droplet-spray, and contact modes. How these modes are affected by features of the virus strain (infectivity, survivability, transferability, or shedding profiles), host population (behavior, susceptibility, or shedding profiles), and environment (host density, surface area to volume ratios, or host movement patterns) have only recently come under investigation. A discrete-event, continuous-time, stochastic transmission model was constructed to analyze the environmental processes through which a virus passes from one person to another via different transmission modes, and explore which factors increase or decrease different modes of transmission. With the exception of the inspiratory route, each route on its own can cause high transmission in isolation of other modes. Mode-specific transmission was highly sensitive to parameter values. For example, droplet and respirable transmission usually required high host density, while the contact route had no such requirement. Depending on the specific context, one or more modes may be sufficient to cause high transmission, while in other contexts no transmission may result. Because of this, when making intervention decisions that involve blocking environmental pathways, generic recommendations applied indiscriminately may be ineffective; instead intervention choice should be contextualized, depending on the specific features of people, virus strain, or venue in question.

Molecular typing and mathematical modeling have gone through rapid development in the past decade. Both offer new insights into the epidemiology of infectious diseases, thereby contributing to a better understanding of transmission dynamics. Infectious disease surveillance and control benefit from the optimum use of these techniques. In this paper, we review recent developments and propose methods to integrate pathogen ecology and molecular evolution based on their common dependence on the underlying host contact patterns., (Copyright 2010 Elsevier Ltd. All rights reserved.)

Background: Most model analyses examining the role of primary HIV infection in the HIV epidemic ignore the fact that HIV is often transmitted through long-term, concurrent sexual partnerships. We sought to understand how duration and concurrency of sexual partnerships affect the role of transmissions during primary HIV infection., Methods: We constructed a stochastic individual-based model of HIV transmission in a homogeneous population where partnerships form and dissolve. Using observed contagiousness by stage of HIV infection, the fraction of transmissions during primary HIV infection at equilibrium was examined across varying partnership durations and concurrencies., Results: The fraction of transmissions during primary HIV infection has a U-shaped relationship with partnership duration. The fraction drops with increasing partnership duration for partnerships with shorter average duration but rises for partnerships with longer average duration. Partnership concurrency modifies this relationship. The fraction of transmissions during primary HIV infection increases with increasing partnership concurrency for partnerships with shorter average duration, but decreases for partnerships with longer average duration., Conclusions: Partnership patterns strongly influence the transmission of HIV and do so differentially by stage of infection. Dynamic partnerships need to be taken into account to make a robust inference on the role of different stages of HIV infection.

Background: Previous studies estimating the fraction of transmissions from persons with primary HIV have not focused on the effects of switching sex role in male homosexual populations. Such behavioral fluctuations can increase the contribution of primary HIV in the overall population., Methods: We modeled HIV transmission with 8 compartments defined by 4 behavioral groups, with different anal-insertive and anal-receptive combinations, and 2 stages of infection. We explored the effects of fluctuating behavioral categories on endemic prevalence and the fraction of transmissions from primary HIV. We varied transition rates to develop the theory on how behavioral fluctuation affects infection patterns, and we used the transition rates in a Netherlands cohort to assess overall effects in a real setting., Results: The dynamics of change in behavior-group status over time observed in the Netherlands cohort amplifies the prevalence of infection and the fraction of transmissions from primary HIV, resulting in the highest proportions of transmissions being from people with primary HIV. Fluctuation between dual- or receptive-role periods and no-anal-sex periods mainly determines this amplification. In terms of the total transmissions, the dual-role risk group is dominant. Fluctuation between insertive and receptive roles decreases the fraction of transmissions from primary HIV, but such fluctuation is infrequently observed., Conclusion: The fraction of transmissions from primary HIV is considerably raised by fluctuations in insertive and receptive anal sex behaviors. This increase occurs even when primary HIV or later infection status does not influence risk behavior. Thus, it is not simply biology but also behavior patterns and social contexts that determine the fraction of transmissions from primary HIV. Moreover, each primary HIV transmission has a larger population effect than each later infection transmission because the men to whom one transmits from primary HIV carry on more chains of transmissions than the men to whom one transmits later in infection. Reducing transmissions from primary HIV should be a primary focus of HIV control efforts.

Unlabelled: Few drugs are registered for treatment of neuropathic facial pain (NFP), and not much is known about treatment choices for NFP in daily practice. Patients with NFP were identified in the IPCI-database with longitudinal electronic general practitioner (GP) records. We described prescription patterns of pain medication following first symptoms. Off-label, off-guideline use, failure and reasons for failure were assessed. Failure was defined as treatment switch, exacerbation, adverse event, or invasive treatment for NFP. Of 203 NFP cases, 160 (79%) received pharmacological pain treatment. Most patients (90%) were initially treated by a GP with anti-epileptic drugs (55%) or NSAIDs (16%) as monotherapy. The median treatment delay was 0 days (range 0 to 2,478 days). Adverse events were experienced by 16 (10%) of patients. Sixty-two percent of first prescriptions were in adherence to guidelines and 59% were considered on-label while 34% of prescriptions were both off-label and off-guideline. Of the first therapy, 38% failed within 3 months. The median duration until failure was 251 days. General practitioners usually are the first to treat NFP. They usually prescribe drugs licensed for NFP and according to guidelines, but the extent of off-label use is substantial. Initial treatment often failed within a short period after starting therapy., Perspective: This drug-utilization study describes the pharmacological treatment of different forms of neuropathic facial pain in daily practice. Although treatment is mostly initiated rapidly by general practitioners in a correct way, it often contains off-label or off-guideline medication. Failure of the initial treatment is common and occurs rapidly as well., (Copyright 2010 American Pain Society. Published by Elsevier Inc. All rights reserved.)

Facial pain has a considerable impact on quality of life. Accurate incidence estimates in the general population are scant. The aim was therefore to estimate the incidence rate (IR) of trigeminal neuralgia (TGN), postherpetic neuralgia (PHN), cluster headache (CH), occipital neuralgia (ON), local neuralgia (LoN), atypical facial pain (AFP), glossopharyngeal neuralgia (GPN) and paroxysmal hemicrania (PH) in the Netherlands. In the population-based Integrated Primary Care Information (IPCI) medical record database potential facial pain cases were identified from codes and narratives. Two medical doctors reviewed medical records, questionnaires from general practitioners and specialist letters using criteria of the International Association for the Study of Pain. A pain specialist arbitrated if necessary and a random sample of all cases was evaluated by a neurologist. The date of onset was defined as date of first specific symptoms. The IR was calculated per 100,000PY. Three hundred and sixty-two incident cases were ascertained. The overall IR [95% confidence interval] was 38.7 [34.9-42.9]. It was more common among women compared to men. Trigeminal neuralgia and cluster headache were the most common forms among the studied diseases. Paroxysmal hemicrania and glossopharyngeal neuralgia were among the rarer syndromes. The IR increased with age for all diseases except CH and ON, peaking in the 4th and 7th decade, respectively. Postherpetic neuralgia, CH and LoN were more common in men than women. From this we can conclude that facial pain is relatively rare, although more common than estimated previously based on hospital data.

The environment provides points for control of pathogens spread by food, water, hands, air, or fomites. These environmental transmission pathways require contact network formulations more realistically detailed than those based on social encounters or physical proximity. As a step toward improved assessment of environmental interventions, description of contact networks, and better use of environmental specimens to analyze transmission, an environmental infection transmission system model that describes the dynamics of human interaction with pathogens in the environment is presented. Its environmental parameters include the pathogen elimination rate, mu, and the rate humans pick up pathogens, rho, and deposit them, alpha. The ratio, rhoN/micro (N equals population size), indicates whether transmission is density dependent (low ratio), frequency dependent (high ratio), or in between. Transmission through frequently touched fomites, such as doorknobs, generates frequency-dependent patterns, while transmission through thoroughly mixed air or infrequently touched fomites generates density-dependent patterns. The environmental contamination ratio, alpha/gamma, reflects total agent deposition per infection and outbreak probability, where gamma is defined as the recovery rate. These insights provide theoretical contexts to examine the role of the environment in pathogen transmission and a framework to interpret environmental data to inform environmental interventions.

Characterizing infectivity as a function of pathogen dose is integral to microbial risk assessment. Dose-response experiments usually administer doses to subjects at one time. Phenomenological models of the resulting data, such as the exponential and the Beta-Poisson models, ignore dose timing and assume independent risks from each pathogen. Real world exposure to pathogens, however, is a sequence of discrete events where concurrent or prior pathogen arrival affects the capacity of immune effectors to engage and kill newly arriving pathogens. We model immune effector and pathogen interactions during the period before infection becomes established in order to capture the dynamics generating dose timing effects. Model analysis reveals an inverse relationship between the time over which exposures accumulate and the risk of infection. Data from one time dose experiments will thus overestimate per pathogen infection risks of real world exposures. For instance, fitting our model to one time dosing data reveals a risk of 0.66 from 313 Cryptosporidium parvum pathogens. When the temporal exposure window is increased 100-fold using the same parameters fitted by our model to the one time dose data, the risk of infection is reduced to 0.09. Confirmation of this risk prediction requires data from experiments administering doses with different timings. Our model demonstrates that dose timing could markedly alter the risks generated by airborne versus fomite transmitted pathogens.

We propose dynamic systems models as one component of the epidemiologic toolbox. Systems models reflect the fact that diseases are caused within complex molecular, biological, and social systems, with positive and negative feedback. Such models predict empiric observations, provide a framework for clarifying what new data is needed, allow for complex interactions between variables at levels from the subcellular to the community, and incorporate known feedbacks between systems elements at these various levels. In all of these ways, they have the capability to advance the science of epidemiology.

Vaccines to prevent acute otitis media (AOM) caused by non-typeable Haemophilus influenzae (NTHi) are under development. Because NTHi is highly variable and colonization rates are high, special vaccine characteristics and trial designs might be needed. We examined in mathematical models the equilibrium NTHi-caused AOM rate given hypothetical vaccines that generated immunity identical to corresponding maximal naturally acquired immunity. Vaccines were examined with single effects and combinations of immunity affecting (1) AOM rates given colonization (pathogenicity), (2) susceptibility to colonization, and (3) contagiousness given colonization. Percent reductions in AOM across all preschool children were (1) 34%, (2) 31%, (3) 9%, (1 and 2) 57%, (2 and 3) 50%, and (1, 2, and 3) 75%. Effects on children in daycare vs. not in daycare were (1) 18 vs. 48%, (2) -1 vs. 57%, (3) 13 vs. 5%, (1 and 2) 30 vs. 79%, (2 and 3) 33 vs. 60%, and (1, 2, and 3) 64 vs. 85%. Pure pathogenicity effects (1 alone) will need to be supplemented by transmission effects. The effects of susceptibility (2 alone) are diminished or negative because children protected against colonization have lower levels of immunity to (1) and (3) than unvaccinated children. For trials to predict population effects, both colonization and AOM outcomes must be studied and all three effects must be evaluated. This need arises because, unlike H. influenzae type B, high NTHi exposure diminishes cumulative vaccine effects and high colonization rates generate rapid accumulation of natural immunity that alters the indirect effects of vaccine immunity on transmission differently by age and daycare status.

Infection transmission systems circulate infection through complex contact patterns related to both contact patterns and patterns of factors that affect the risk of transmission given contact. The nonlinear dynamics of infection transmission cause these patterns to make big differences in population infection levels. A science of infection transmission system analysis is needed to focus on those details that affect the control of infection transmission. This science must have a strong theoretical base because there is little chance that a dominantly data based approach not using mechanistic models of transmission will have any predictive value. The theoretical base should be built on linked transmission system models that are focused on making needed inferences for both building the theoretical base and making infection control decisions. The linking of different models is needed for a strategy of inference robustness assessment that is designed to find the model that is simple enough to effectively analyze the transmission system but not so simple that realistic violation of simplifying assumptions will change an inference. Types of models that should be used in such linked analyses include deterministic and stochastic compartmental models, discrete individual models with individual event histories but structured mass action mixing, network models that provide more detail as to who has contact with whom, and intermediate model forms such as correlation models that address some aspects of contact details while preserving the flexibility of deterministic compartmental models to structure mixing and analyze the system. While transmission system science is currently weak in regards both to its data base and its theory base, many things are now coming together that could make this science flourish. On the data side these include greater ability to detect infectious agent sequences in the environment and greater ability to sequence and genetically relate agents identified at different sites in the transmission system. On the theory sides, new model construction and model analysis methods are providing new potential to use the new sources of data. Also new parameter estimation methods provide new potential to combine models and data in effective analytic strategies.

A wide variety of bacterial typing systems are currently in use that vary greatly with respect to the effort required, cost, reliability and ability to discriminate between bacterial strains. No one technique is optimal for all forms of investigation. We discuss the desired level of discrimination and need for a biologic basis for grouping strains of apparently different types when using bacterial typing techniques for different epidemiologic applications: 1) confirming epidemiologic linkage in outbreak investigations, 2) generating hypotheses about epidemiologic relationships between bacterial strains in the absence of epidemiologic information, and 3) describing the distributions of bacterial types and identifying determinants of those distributions. Inferences made from molecular epidemiologic studies of bacteria depend upon both the typing technique selected and the study design used; thus, choice of typing technique is pivotal for increasing our understanding of the pathogenesis and transmission, and eventual disease prevention.

Background: For nontransmissible diseases, decisions between interventions focused on high-risk groups and unfocused interventions can be based on attributable-risk calculations. The assumptions of those calculations, however, are violated for infectious diseases., Methods: We used deterministic compartmental models of infection transmission having both high- and low-risk groups and both susceptible and infected states to examine intervention effects on endemic infection levels. High risk is generated by increased susceptibility or contagiousness-factors that can be reduced by interventions. Population effects of focused and unfocused interventions are compared at settings where these would be equal if there were no transmission., Results: In the most likely range of mixing between high- and low-risk groups, focused interventions have considerably larger effects than unfocused interventions. At all mixing levels, both interventions have greater effects on infectious than noninfectious diseases because a change in risk factor for one individual alters risk in others. Interventions on contagiousness in high-risk groups have greater effects than comparable interventions on susceptibility., Conclusions: Risk assessment for infectious disease requires analysis of the population system that is circulating the infection. Vaccine trials on individuals will miss important effects that trials on transmission units will detect. Focusing HIV control on contagiousness factors in high-risk groups will be especially productive.

Because uropathogenic Escherichia coli are better adapted than other E. coli to the urethra, periurethra, and vagina, the authors reasoned that uropathogenic E. coli would be more likely than commensal E. coli to be shared between sex partners. In this 1996-1999 Michigan study, the genetic identity of E. coli isolated from 166 women with E. coli urinary tract infection (UTI) and 94 women without UTI and their sex partners was determined by pulsed-field gel electrophoresis. Rectal isolates were considered uropathogenic E. coli if genetically identical to the urinary isolate causing UTI. All eight urinary isolates from men with UTI partners were identical to the E. coli found in the urine or vagina of their sex partner. When the 550 unique rectal E. coli isolates from couples were considered the unit of analysis, E. coli that caused UTI were nine times (odds ratio (OR) = 8.87, 95% confidence interval (CI): 5.41, 14.54) more likely than other E. coli to be shared between sex partners. Sharing occurred twice as frequently (OR = 1.87, 95% CI: 1.13, 3.08) if the E. coli had P pili or if the couples engaged in oral sex (OR = 2.09, 95% CI: 1.09, 4.00). Uropathogenic E. coli are more likely than commensal E. coli to be shared with a current heterosexual sex partner. Both sexual behaviors and a bacterial virulence factor, P pili, modified sharing.

The effects of two levels of mixing on endemic infection levels are shown to differ for identically conformed deterministic compartmental (DC) and stochastic compartmental (SC) models. Both DC and SC models give similar endemic levels when populations are large, immunity is short lived, and mixing is universal. But local transmissions and/or transient immunity decrease overall population infection levels in SC but not in DC models. DC models also fail to detect the greater effects of eliminating disseminating transmissions in comparison to eliminating local transmissions shown by SC models. These differences in model behavior arise because localities that encounter few infections from distant sites and that have stochastically low infection levels have decreased infection rates while localities with stochastically high levels of infection do not decrease the rate at which they lose infection. At the extreme this generates local stochastic die out with subsequent build up of susceptibility in SC but not DC models. This phenomenon should act upon all endemic infections that have changing geographic or social foci of infection. Neither standard epidemiological investigations nor sufficient-component cause models can capture these effects because they occur in the absence of differences between individuals.

Realistic population models have interactions between individuals. Such interactions cause populations to behave as systems with nonlinear dynamics. Much population data analysis is done using linear models assuming no interactions between individuals. Such analyses miss strong influences on population behavior and can lead to serious errors--especially for infectious diseases. To promote more effective population system analyses, we present a flexible and intuitive modeling framework for infection transmission systems. This framework will help population scientists gain insight into population dynamics, develop theory about population processes, better analyze and interpret population data, design more powerful and informative studies, and better inform policy decisions. Our framework uses a hierarchy of infection transmission system models. Four levels are presented here: deterministic compartmental models using ordinary differential equations (DE); stochastic compartmental (SC) models that relax assumptions about population size and include stochastic effects; individual event history models (IEH) that relax the SC compartmental structure assumptions by allowing each individual to be unique. IEH models also track each individual's history, and thus, allow the simulation of field studies. Finally, dynamic network (DNW) models relax the assumption of the previous models that contacts between individuals are instantaneous events that do not affect subsequent contacts. Eventually it should be possible to transit between these model forms at the click of a mouse. An example is presented dealing with Cryptosporidium. It illustrates how transiting model forms helps assess water contamination effects, evaluate control options, and design studies of infection transmission systems using nucleotide sequences of infectious agents.

This article describes new methods to characterize epidemiologic contact networks that involve links that are being dynamically formed and dissolved. The new social network measures are designed with an epidemiologic interpretation in mind. These methods are intended to capture dynamic aspects of networks related to their potential to spread infection. This differs from many social network measures that are based on static networks. The networks are formulated as transmission graphs (TGs), in which nodes represent relationships between two individuals and directed edges (links) represent the potential of an individual in one relationship to carry infection to an individual in another relationship. Network measures derived from transmission graphs include "source counts," which are defined as the number of prior relationships that could potentially transmit infection to a particular node or individual.