BackgroundInfluenza causes an estimated 3000 to 50,000 deaths per year in the United States of America (US). Timely and representative data can help local, state, and national public health officials monitor and respond to outbreaks of seasonal influenza. Data from cloud-based electronic health records (EHR) and crowd-sourced influenza surveillance systems have the potential to provide complementary, near real-time estimates of influenza activity. The objectives of this paper are to compare two novel influenza-tracking systems with three traditional healthcare-based influenza surveillance systems at four spatial resolutions: national, regional, state, and city, and to determine the minimum number of participants in these systems required to produce influenza activity estimates that resemble the historical trends recorded by traditional surveillance systems.MethodsWe compared influenza activity estimates from five influenza surveillance systems: 1) patient visits for influenza-like illness (ILI) from the US Outpatient ILI Surveillance Network (ILINet), 2) virologic data from World Health Organization (WHO) Collaborating and National Respiratory and Enteric Virus Surveillance System (NREVSS) Laboratories, 3) Emergency Department (ED) syndromic surveillance from Boston, Massachusetts, 4) patient visits for ILI from EHR, and 5) reports of ILI from the crowd-sourced system, Flu Near You (FNY), by calculating correlations between these systems across four influenza seasons, 2012–16, at four different spatial resolutions in the US. For the crowd-sourced system, we also used a bootstrapping statistical approach to estimate the minimum number of reports necessary to produce a meaningful signal at a given spatial resolution.ResultsIn general, as the spatial resolution increased, correlation values between all influenza surveillance systems decreased. Influenza-like Illness rates in geographic areas with more than 250 crowd-sourced participants or with more than 20,000 visit counts for EHR tracked government-lead estimates of influenza activity.ConclusionsWith a sufficient number of reports, data from novel influenza surveillance systems can complement traditional healthcare-based systems at multiple spatial resolutions.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3322-3) contains supplementary material, which is available to authorized users.

Artificial selection offers a powerful tool for the exploration of how selection and development shape the evolution of morphological scaling relationships. An emerging approach models the expression and evolution of morphological scaling relationships as a function of variation among individuals in the developmental mechanisms that regulate trait growth. These models posit the existence of genotype-specific morphological scaling relationships that are unseen or “cryptic.” Within-population allelic variation at growth-regulating loci determines how these individual cryptic scaling relationships are distributed, and exposure to environmental factors that affect growth determines the size phenotype expressed by each individual on their cryptic, genotype-specific scaling relationship. These models reveal that evolution of the intercept and slope of the population-level static allometry is determined, often in counterintuitive ways, largely by the shape of the distribution of these underlying individual-level scaling relationships. Here we review this modeling framework and present the wing-body size individual cryptic scaling relationships from a population of Drosophila melanogaster. To determine how these models might inform interpretation of published work on scaling relationship evolution, we review studies where artificial selection was applied to alter the parameters of population-level static allometries. Finally, motivated by our review, we outline areas in need of empirical work and describe a research program to address these topics; the approach includes describing the distribution of individual cryptic scaling relationships across populations and environments, empirical testing of the model’s predictions, and determining the effects of environmental heterogeneity on realized trait distributions and how this affects allometry evolution.

When Hurricane Harvey landed along the Texas coast on August 25, 2017, it caused massive flooding and damage and displaced tens of thousands of residents of Harris County, Texas. Between August 29 and September 23, Harris County, along with community partners, operated a megashelter at NRG Center, which housed 3365 residents at its peak. Harris County Public Health conducted comprehensive public health surveillance and response at NRG, which comprised disease identification through daily medical record reviews, nightly “cot-to-cot” resident health surveys, and epidemiological consultations; messaging and communications; and implementation of control measures including stringent isolation and hygiene practices, vaccinations, and treatment. Despite the lengthy operation at the densely populated shelter, an early seasonal influenza A (H3) outbreak of 20 cases was quickly identified and confined. Influenza outbreaks in large evacuation shelters after a disaster pose a significant threat to populations already experiencing severe stressors. A holistic surveillance and response model, which consists of coordinated partnerships with onsite agencies, in-time epidemiological consultations, predesigned survey tools, trained staff, enhanced isolation and hygiene practices, and sufficient vaccines, is essential for effective disease identification and control. The lessons learned and successes achieved from this outbreak may serve for future disaster response settings. (Disaster Med Public Health Preparedness. 2019;13:97-101)

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