Visualizing Clinical Evidence: Citation Networks for the Incubation Periods of Respiratory Viral Infections

Simply by repetition, medical facts can become enshrined as truth even when there is little empirical evidence supporting them. We present an intuitive and clear visual design for tracking the citation history of a particular scientific fact over time. We apply this method to data from a previously published literature review on the incubation period of nine respiratory viral infections. The resulting citation networks reveal that the conventional wisdom about the incubation period for these diseases was based on a small fraction of available data and in one case, on no retrievable empirical evidence. Overall, 50% of all incubation period statements did not provide a source for their estimate and 65% of original sources for incubation period data were not incorporated into subsequent publications. More standardized and widely available methods for visualizing these histories of medical evidence are needed to ensure that conventional wisdom cannot stray too far from empirically supported knowledge

Local variations in spatial synchrony of influenza epidemics

Background: Understanding the mechanism of influenza spread across multiple geographic scales is not complete. While the mechanism of dissemination across regions and states of the United States has been described, understanding the determinants of dissemination between counties has not been elucidated. The paucity of high resolution spatial-temporal influenza incidence data to evaluate disease structure is often not available. Methodology and Findings: We report on the underlying relationship between the spread of influenza and human movement between counties of one state. Significant synchrony in the timing of epidemics exists across the entire state and decay with distance (regional correlation = 62%). Synchrony as a function of population size display evidence of hierarchical spread with more synchronized epidemics occurring among the most populated counties. A gravity model describing movement between two populations is a stronger predictor of influenza spread than adult movement to and from workplaces suggesting that non-routine and leisure travel drive local epidemics. Conclusions: These findings highlight the complex nature of influenza spread across multiple geographic scales. © 2012 Stark et al

Antibodies to Aedes spp. salivary proteins: a systematic review and pooled analysis

Aedes spp. mosquitos are responsible for transmitting several viruses that pose significant public health risks, including dengue, Zika, yellow fever, chikungunya, and West Nile viruses. However, quantifying the number of individuals at risk and their exposure to Aedes spp. mosquitos over time is challenging due to various factors. Even accurate estimation of mosquito numbers at the population level may not fully capture the fluctuations in human exposure based on factors that affect biting rates of mosquitoes. Measuring the antibody response of humans to mosquito salivary proteins (MSP) has been proposed as a method to assess human exposure to mosquito bites and predict disease risk. The presence of antibodies to MSP can be quantified using the enzyme-linked immunosorbent assay (ELISA). While there is known variability in laboratory methods, the consistency of MSP measurements across different research groups has not been quantitatively examined. Variation in laboratory protocols, antigens used, and the human populations sampled all may contribute to differences observed in measured anti-MSP responses. In this study, we conducted a systematic review of the published literature focusing on antibody responses to MSP in humans and other vertebrate hosts. Whenever possible, we extracted individual-level anti-MSP IgG data from these studies and performed a pooled analysis of quantitative outcomes obtained from ELISAs, specifically optical densities (OD). We analyzed the pooled data to quantify variation between studies and identify sample and study characteristics associated with OD scores. Our candidate list of characteristics included the type of antigen used, age of human subjects, mosquito species, population-level mosquito exposure, collection season, Köppen-Geiger climate classification, and OD reporting method. Our findings revealed that the type of antigen, population-level mosquito exposure, and Köppen-Geiger climate classification were significantly associated with ELISA values. Furthermore, we developed a classification algorithm based on OD scores, which successfully distinguished samples from individuals living in areas where a specific mosquito species was present from those where it was not, with a high degree of accuracy. The pooled analysis we conducted provides a harmonized assessment of ELISA testing, which can be utilized to refine the use of antibody responses as markers for mosquito exposure. In conclusion, our study contributes to the understanding of antibody responses to MSP and their utility as indicators of mosquito exposure. By identifying the factors associated with variations in ELISA values, we have provided valuable insights for future research and the refinement of antibody-based assessments of mosquito exposure

The Respiratory Protection Effectiveness Clinical Trial (ResPECT): a cluster-randomized comparison of respirator and medical mask effectiveness against respiratory infections in healthcare personnel.

BACKGROUND: Although N95 filtering facepiece respirators and medical masks are commonly used for protection against respiratory infections in healthcare settings, more clinical evidence is needed to understand the optimal settings and exposure circumstances for healthcare personnel to use these devices. A lack of clinically germane research has led to equivocal, and occasionally conflicting, healthcare respiratory protection recommendations from public health organizations, professional societies, and experts. METHODS: The Respiratory Protection Effectiveness Clinical Trial (ResPECT) is a prospective comparison of respiratory protective equipment to be conducted at multiple U.S. study sites. Healthcare personnel who work in outpatient settings will be cluster-randomized to wear N95 respirators or medical masks for protection against infections during respiratory virus season. Outcome measures will include laboratory-confirmed viral respiratory infections, acute respiratory illness, and influenza-like illness. Participant exposures to patients, coworkers, and others with symptoms and signs of respiratory infection, both within and beyond the workplace, will be recorded in daily diaries. Adherence to study protocols will be monitored by the study team. DISCUSSION: ResPECT is designed to better understand the extent to which N95s and MMs reduce clinical illness among healthcare personnel. A fully successful study would produce clinically relevant results that help clinician-leaders make reasoned decisions about protection of healthcare personnel against occupationally acquired respiratory infections and prevention of spread within healthcare systems. TRIAL REGISTRATION: The trial is registered at clinicaltrials.gov, number NCT01249625 (11/29/2010)

The impact of the demographic transition on dengue in Thailand: Insights from a statistical analysis and mathematical modeling

Background: An increase in the average age of dengue hemorrhagic fever (DHF) cases has been reported in Thailand. The cause of this increase is not known. Possible explanations include a reduction in transmission due to declining mosquito populations, declining contact between human and mosquito, and changes in reporting. We propose that a demographic shift toward lower birth and death rates has reduced dengue transmission and lengthened the interval between large epidemics. Methods and Findings: Using data from each of the 72 provinces of Thailand, we looked for associations between force of infection (a measure of hazard, defined as the rate per capita at which susceptible individuals become infected) and demographic and climactic variables. We estimated the force of infection from the age distribution of cases from 1985 to 2005. We find that the force of infection has declined by 2% each year since a peak in the late 1970s and early 1980s. Contrary to recent findings suggesting that the incidence of DHF has increased in Thailand, we find a small but statistically significant decline in DHF incidence since 1985 in a majority of provinces. The strongest predictor of the change in force of infection and the mean force of infection is the median age of the population. Using mathematical simulations of dengue transmission we show that a reduced birth rate and a shift in the population's age structure can explain the shift in the age distribution of cases, reduction of the force of infection, and increase in the periodicity of multiannual oscillations of DHF incidence in the absence of other changes. Conclusions: Lower birth and death rates decrease the flow of susceptible individuals into the population and increase the longevity of immune individuals. The increase in the proportion of the population that is immune increases the likelihood that an infectious mosquito will feed on an immune individual, reducing the force of infection. Though the force of infection has decreased by half, we find that the critical vaccination fraction has not changed significantly, declining from an average of 85% to 80%. Clinical guidelines should consider the impact of continued increases in the age of dengue cases in Thailand. Countries in the region lagging behind Thailand in the demographic transition may experience the same increase as their population ages. The impact of demographic changes on the force of infection has been hypothesized for other diseases, but, to our knowledge, this is the first observation of this phenomenon

Social contact networks and mixing among students in K-12 schools in Pittsburgh, PA

Students attending schools play an important role in the transmission of influenza. In this study, we present a social network analysis of contacts among 1,828 students in eight different schools in urban and suburban areas in and near Pittsburgh, Pennsylvania, United States of America, including elementary, elementary-middle, middle, and high schools. We collected social contact information of students who wore wireless sensor devices that regularly recorded other devices if they are within a distance of 3 meters. We analyzed these networks to identify patterns of proximal student interactions in different classes and grades, to describe community structure within the schools, and to assess the impact of the physical environment of schools on proximal contacts. In the elementary and middle schools, we observed a high number of intra-grade and intra-classroom contacts and a relatively low number of inter-grade contacts. However, in high schools, contact networks were well connected and mixed across grades. High modularity of lower grades suggests that assumptions of homogeneous mixing in epidemic models may be inappropriate; whereas lower modularity in high schools suggests that homogenous mixing assumptions may be more acceptable in these settings. The results suggest that interventions targeting subsets of classrooms may work better in elementary schools than high schools. Our work presents quantitative measures of age-specific, school-based contacts that can be used as the basis for constructing models of the transmission of infections in schools

Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates

Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39–4.13), indicating that 58–76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6–7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090–33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’

Interior-Point Methods for Estimating Seasonal Parameters in Discrete-Time Infectious Disease Models

Infectious diseases remain a significant health concern around the world. Mathematical modeling of these diseases can help us understand their dynamics and develop more effective control strategies. In this work, we show the capabilities of interior-point methods and nonlinear programming (NLP) formulations to efficiently estimate parameters in multiple discrete-time disease models using measles case count data from three cities. These models include multiplicative measurement noise and incorporate seasonality into multiple model parameters. Our results show that nearly identical patterns are estimated even when assuming seasonality in different model parameters, and that these patterns show strong correlation to school term holidays across very different social settings and holiday schedules. We show that interior-point methods provide a fast and flexible approach to parameterizing models that can be an alternative to more computationally intensive methods