Eric T. Lofgren

Influenza Seasonality: Underlying Causes and Modeling Theories

Influenza (or “flu”) leads to the hospitalization of more than 200,000 people yearly and results in 36,000 deaths from flu or flu-related complications in the United States ([15][1]), striking both the elderly and infant populations particularly hard ([24][2]). Two members of the

What Factors Might Have Led to the Emergence of Ebola in West Africa

An Ebola outbreak of unprecedented scope emerged in West Africa in December 2013 and presently continues unabated in the countries of Guinea, Sierra Leone, and Liberia. Ebola is not new to Africa, and outbreaks have been confirmed as far back as 1976. The current West African Ebola outbreak is the largest ever recorded and differs dramatically from prior outbreaks in its duration, number of people affected, and geographic extent. The emergence of this deadly disease in West Africa invites many questions, foremost among these: why now, and why in West Africa? Here, we review the sociological, ecological, and environmental drivers that might have influenced the emergence of Ebola in this region of Africa and its spread throughout the region. Containment of the West African Ebola outbreak is the most pressing, immediate need. A comprehensive assessment of the drivers of Ebola emergence and sustained human-to-human transmission is also needed in order to prepare other countries for importation or emergence of this disease. Such assessment includes identification of country-level protocols and interagency policies for outbreak detection and rapid response, increased understanding of cultural and traditional risk factors within and between nations, delivery of culturally embedded public health education, and regional coordination and collaboration, particularly with governments and health ministries throughout Africa. Public health education is also urgently needed in countries outside of Africa in order to ensure that risk is properly understood and public concerns do not escalate unnecessarily. To prevent future outbreaks, coordinated, multiscale, early warning systems should be developed that make full use of these integrated assessments, partner with local communities in high-risk areas, and provide clearly defined response recommendations specific to the needs of each community.

The untapped potential of virtual game worlds to shed light on real world epidemics

Simulation models are of increasing importance within the field of applied epidemiology. However, very little can be done to validate such models or to tailor their use to incorporate important human behaviours. In a recent incident in the virtual world of online gaming, the accidental inclusion of a disease-like phenomenon provided an excellent example of the potential of such systems to alleviate these modelling constraints. We discuss this incident and how appropriate exploitation of these gaming systems could greatly advance the capabilities of applied simulation modelling in infectious disease research.

Opinion: Mathematical models: A key tool for outbreak response

The 2014 outbreak of Ebola in West Africa is unprecedented in its size and geographic range, and demands swift, effective action from the international community. Understanding the dynamics and spread of Ebola is critical for directing interventions and extinguishing the epidemic; however, observational studies of local conditions have been incomplete and limited by the urgent need to direct resources to patient care.

Impact of Change to Molecular Testing for Clostridium difficile Infection on Healthcare Facility–Associated Incidence Rates

BACKGROUND Change from nonmolecular to molecular testing techniques is thought to contribute to the increasing trend in incidence of Clostridium difficile infection (CDI); however the degree of effect attributed to this versus other time-related epidemiologic factors is unclear. METHODS We compared the relative change in incidence rate (IRR) of healthcare facility-associated (HCFA) CDI among hospitals in the Duke Infection Control Outreach Network before and after the date of switch from nonmolecular tests to polymerase chain reaction (PCR) using prospectively collected surveillance data from July 2009 to December 2011. Data from 10 hospitals that switched and 22 control hospitals were included. Individual hospital estimates were determined using Poisson regression. We used an interrupted time series approach to develop a Poisson mixed-effects model. Additional regression adjustments were made for clustering and proportion of intensive care unit patient-days. The variable for PCR was treated as a fixed effect; other modeled variables were random effects. RESULTS For those hospitals that switched to PCR, mean incidence rate of HCFA CDI before the switch was 6.0 CDIs per 10,000 patient-days compared with 9.6 CDIs per 10,000 patient-days after the switch. Estimates of hospital-specific IRR that compared after the switch with before the switch ranged from 0.89 (95% confidence interval [CI], 0.32-2.44) to 6.91 (95% CI, 1.12-42.54). After adjustment in the mixed-effects model, the overall IRR comparing CDI incidence after the switch to before the switch was 1.56 (95% CI, 1.28-1.90). Time-trend variables did not reach statistical significance. CONCLUSION Hospitals that switched from nonmolecular to molecular tests experienced an approximate 56% increase in the rate of HCFA CDI after testing change.

Hospital-acquired Clostridium difficile infections: estimating all-cause mortality and length of stay.

Background: Clostridium difficile is a health care–associated infection of increasing importance. The purpose of this study was to estimate the time until death from any cause and time until release among patients with C. difficile, comparing the burden of those in the intensive care unit (ICU) with those in the general hospital population. Methods: A parametric mixture model was used to estimate event times, as well as the case-fatality ratio in ICU and non-ICU patients within a cohort of 609 adult incident cases of C. difficile in the Southeastern United States between 1 July 2009 and 31 December 2010. Results: ICU patients had twice the median time to death (relative time = 1.97 [95% confidence interval (CI) = 0.96–4.01]) and nearly twice the median time to release (1.88 [1.40–2.51]) compared with non-ICU patients. ICU patients also experienced 3.4 times the odds of mortality (95% CI = 1.8–6.2). Cause-specific competing risks analysis underestimated the relative survival time until death (0.65 [0.36–1.17]) compared with the mixture model. Conclusions: Patients with C. difficile in the ICU experienced higher mortality and longer lengths of stay within the hospital. ICU patients with C. difficile infection represent a population in need of particular attention, both to prevent adverse patient outcomes and to minimize transmission of C. difficile to other hospitalized patients.

Performance of rapid influenza H1N1 diagnostic tests: A meta-analysis

Please cite this paper as: Chu et al. (2011) Performance of rapid influenza H1N1 diagnostic tests: a meta‐analysis. Influenza and Other Respiratory Viruses DOI: 10.1111/j.1750‐2659.2011.00284.x.

A Mathematical Model to Evaluate the Routine Use of Fecal Microbiota Transplantation to Prevent Incident and Recurrent Clostridium difficile Infection

OBJECTIVE Fecal microbiota transplantation (FMT) has been suggested as a new treatment to manage Clostridium difficile infection (CDI). With use of a mathematical model of C. difficile within an intensive care unit (ICU), we examined the potential impact of routine FMT. DESIGN, SETTING, AND PATIENTS A mathematical model of C. difficile transmission, supplemented with prospective cohort, surveillance, and billing data from hospitals in the southeastern United States. METHODS Cohort, surveillance, and billing data as well as data from the literature were used to construct a compartmental model of CDI within an ICU. Patients were defined as being in 1 of 6 potential health states: uncolonized and at low risk; uncolonized and at high risk; colonized and at low risk; colonized and at high risk; having CDI; or treated with FMT. RESULTS The use of FMT to treat patients after CDI was associated with a statistically significant reduction in recurrence but not with a reduction in incident cases. Treatment after administration of high-risk medications, such as antibiotics, did not result in a decrease in recurrence but did result in a statistically significant difference in incident cases across treatment groups, although whether this difference was clinically relevant was questionable. CONCLUSIONS Our study is a novel mathematical model that examines the effect of FMT on the prevention of recurrent and incident CDI. The routine use of FMT represents a promising approach to reduce complex recurrent cases, but a reduction in CDI incidence will require the use of other methods to prevent transmission.

Ebola: Mobility data

Understanding human movement and mobility is important for characterizing, forecasting, and controlling the spatial and temporal spread of infectious diseases. Unfortunately, the current West African Ebola outbreak is taking place in a region where mobility has changed considerably in recent years.

Risks of Death and Severe Disease in Patients With Middle East Respiratory Syndrome Coronavirus, 2012–2015

Abstract Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging pathogen, first recognized in 2012, with a high case fatality risk, no vaccine, and no treatment beyond supportive care. We estimated the relative risks of death and severe disease among MERS-CoV patients in the Middle East between 2012 and 2015 for several risk factors, using Poisson regression with robust variance and a bootstrap-based expectation maximization algorithm to handle extensive missing data. Increased age and underlying comorbidity were risk factors for both death and severe disease, while cases arising in Saudi Arabia were more likely to be severe. Cases occurring later in the emergence of MERS-CoV and among health-care workers were less serious. This study represents an attempt to estimate risk factors for an emerging infectious disease using open data and to address some of the uncertainty surrounding MERS-CoV epidemiology.