Comparison of crowd-sourced, electronic health records based, and traditional health-care based influenza-tracking systems at multiple spatial resolutions in the United States of America.

BACKGROUND: Influenza 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. METHODS: We 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. RESULTS: In 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. CONCLUSIONS: With a sufficient number of reports, data from novel influenza surveillance systems can complement traditional healthcare-based systems at multiple spatial resolutions.

Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis.

BACKGROUND: Influenza outbreaks pose major challenges to public health around the world, leading to thousands of deaths a year in the United States alone. Accurate systems that track influenza activity at the city level are necessary to provide actionable information that can be used for clinical, hospital, and community outbreak preparation. OBJECTIVE: Although Internet-based real-time data sources such as Google searches and tweets have been successfully used to produce influenza activity estimates ahead of traditional health care-based systems at national and state levels, influenza tracking and forecasting at finer spatial resolutions, such as the city level, remain an open question. Our study aimed to present a precise, near real-time methodology capable of producing influenza estimates ahead of those collected and published by the Boston Public Health Commission (BPHC) for the Boston metropolitan area. This approach has great potential to be extended to other cities with access to similar data sources. METHODS: We first tested the ability of Google searches, Twitter posts, electronic health records, and a crowd-sourced influenza reporting system to detect influenza activity in the Boston metropolis separately. We then adapted a multivariate dynamic regression method named ARGO (autoregression with general online information), designed for tracking influenza at the national level, and showed that it effectively uses the above data sources to monitor and forecast influenza at the city level 1 week ahead of the current date. Finally, we presented an ensemble-based approach capable of combining information from models based on multiple data sources to more robustly nowcast as well as forecast influenza activity in the Boston metropolitan area. The performances of our models were evaluated in an out-of-sample fashion over 4 influenza seasons within 2012-2016, as well as a holdout validation period from 2016 to 2017. RESULTS: Our ensemble-based methods incorporating information from diverse models based on multiple data sources, including ARGO, produced the most robust and accurate results. The observed Pearson correlations between our out-of-sample flu activity estimates and those historically reported by the BPHC were 0.98 in nowcasting influenza and 0.94 in forecasting influenza 1 week ahead of the current date. CONCLUSIONS: We show that information from Internet-based data sources, when combined using an informed, robust methodology, can be effectively used as early indicators of influenza activity at fine geographic resolutions.

Burkholderia pseudomallei infection in a child with cystic fibrosis: acquisition in the Western Hemisphere.

Melioidosis, an infection caused by the bacterium Burkholderia pseudomallei, is endemic to Southeast Asia and northern Australia but is only very rarely seen in patients in the United States. We report pulmonary B pseudomallei infection in a young girl with cystic fibrosis (CF) who had never traveled to Asia or Australia. Biochemical and epidemiologic investigation determined Aruba as the likely site of disease acquisition. This report highlights the ability of patients with CF to acquire this organism outside of Southeast Asia and describes an aggressive treatment regimen that has kept this patient culture-negative for the organism over a long period of time.

Serial nosocomial transmission of Plasmodium falciparum malaria from patient to nurse to patient.

BACKGROUND: Nosocomial transmission of malaria is a rare phenomenon in the United States. OBJECTIVE: To describe the probable transmission of Plasmodium falciparum malaria from a patient to a healthcare worker and then from the healthcare worker to another patient. DESIGN: Case series. SETTING: Two community hospitals in Massachusetts. INTERVENTION: Routine medical and supportive care. MEASUREMENTS: Clinical and laboratory evaluation. RESULTS: A nurse developed falciparum malaria after a needlestick injury from a patient with documented falciparum malaria. Three days prior to her diagnosis, she cared for another patient, who subsequently developed falciparum malaria. That patient's parasite isolate genetically matched the nurse's isolate by two independent DNA fingerprinting techniques. CONCLUSION: After extensive evaluation, we believe that a nurse who had acquired falciparum malaria via needlestick subsequently transmitted malaria to another patient via a break in standard precautions. The implications of this mechanism of transmission are discussed.