Statistical Communications in Infectious Diseases

Published by De Gruyter

Volume 12 Issue 1

January 2020

Issue of Statistical Communications in Infectious Diseases

Contents
Journal Overview

Research Articles

Open Access November 13, 2020

Estimation of time-dependent arbovirus infection risk in blood and tissue donations

Brad J. Biggerstaff

Article number: 20200001

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Abstract

West Nile virus (WNV) outbreaks raise the concern of WNV infection in donated blood and blood products destined for transfusion. We describe methods we developed to estimate time-dependent risk of WNV infection in donated blood, including improvements not previously detailed. The methods are then extended for use in estimation of the risk of WNV infection in donated cadaveric tissues by introducing stratification and stratum-specific weighting to address novel aspects of this application. Data from the WNV outbreak in Colorado in 2003 are used to estimate risk for donated cardiac tissue.

July 5, 2020

Accounting for informative sampling in estimation of associations between sexually transmitted infections and hormonal contraceptive methods

Anu Mishra, Petra Bůžková, Jennifer E. Balkus, Elizabeth R. Brown

Article number: 20190010

Abstract

The relationship between hormonal contraceptive method use and sexually transmitted infections is not well understood. Studies that implement routine screening for STIs among different contraceptive users, such as the ASPIRE HIV-1 prevention trial, can be useful for identifying potential risk factors of STIs. However, the complex nature of non-random data can lead to challenges in estimation of associations for potential risk factors. In particular, if screening for the disease is not random (i. e. it is driven by symptoms or other clinical indicators), estimates of association can suffer from bias, often referred to as informative sampling bias. Time-varying predictors and potential stratification variables can further contribute to difficulty in obtaining unbiased estimates. In this paper, we estimate the association between time- varying contraceptive use and Sexually transmitted infections acquisition, in the presence of informative sampling, by extending the work Buzkova (2010). We use a two-step procedure to jointly model the non-random screening process and sexually transmitted infection risk. In the first step, inverse intensity rate ratios (IIRR) weights are estimated. In the second step, a weighted proportional rate model is fit to estimate the IIRR weighted hazard ratio. We apply the method to evaluate the relationship between hormonal contraception and risk of sexually transmitted infections among women participating in a biomedical HIV-1 prevention trial. We compare our results using the proposed weighted method to those generated using conventional approaches that do not account for potential informative sampling bias or do not use the full potential of the data. Using the IIRR weighted approach we found depot medroxyprogesterone acetate users have a significantly decreased hazard of Trichomonas vaginalis acquisition compared to IUD users (hazard ratio: 0.44, 95% CI: (0.25, 0.83)), which is consistent with the literature. We did not find significant increased or decreased hazard of other STIs for hormonal contraceptive users compared to non-hormonal IUD users.

October 23, 2019

Bayesian Design of Agricultural Disease Transmission Experiments for Individual Level Models

Grace P. S. Kwong, Rob Deardon, Scott Hunt, Michele T. Guerin

Article number: 20180005

Abstract

Here, we address the issue of experimental design for animal and crop disease transmission experiments, where the goal is to identify some characteristic of the underlying infectious disease system via a mechanistic disease transmission model. Design for such non-linear models is complicated by the fact that the optimal design depends upon the parameters of the model, so the problem is set in simulation-based, Bayesian framework using informative priors. This involves simulating the experiment over a given design repeatedly using parameter values drawn from the prior, calculating a Monte Carlo estimate of the utility function from those simulations for the given design, and then repeating this over the design space in order to find an optimal design or set of designs. Here we consider two agricultural scenarios. The first involves an experiment to characterize the effectiveness of a vaccine-based treatment on an animal disease in an in-barn setting. The design question of interest is on which days to make observations if we are limited to being able to observe the disease status of all animals on only two days. The second envisages a trial being carried out to estimate the spatio-temporal transmission dynamics of a crop disease. The design question considered here is how far apart to space the plants from each other to best capture those dynamics. In the in-barn animal experiment, we see that for the prior scenarios considered, observations taken very close to the beginning of the experiment tend to lead to designs with the highest values of our chosen utility functions. In the crop trial, we see that over the prior scenarios considered, spacing between plants is important for experimental performance, with plants being placed too close together being particularly deleterious to that performance.

June 22, 2019

Implementation of Power Law Network Models of Epidemic Surveillance Data for Better Evaluation of Outbreak Detection Alarms

Razvan G. Romanescu, Rob Deardon

Article number: 20180004

Abstract

Properties of statistical alarms have been well studied for simple disease surveillance models, such as normally distributed incidence rates with a sudden or gradual shift in mean at the start of an outbreak. It is known, however, that outbreak dynamics in human populations depend significantly on the heterogeneity of the underlying contact network. The rate of change in incidence for a disease such as influenza peaks early on during the outbreak, when the most highly connected individuals get infected, and declines as the average number of connections in the remaining susceptible population drops. Alarm systems currently in use for detecting the start of influenza seasons generally ignore this mechanism of disease spread, and, as a result, will miss out on some early warning signals. We investigate the performance of various alarms on epidemics simulated from an undirected network model with a power law degree distribution for a pathogen with a relatively short infectious period. We propose simple custom alarms for the disease system considered, and show that they can detect a change in the process sooner than some traditional alarms. Finally, we test our methods on observed rates of influenza-like illness from two sentinel providers (one French, one Spanish) to illustrate their use in the early detection of the flu season.

About this journal

Objective
The mission of Statistical Communications in Infectious Diseases is to serve as the primary vehicle for the communication and education of statistical thinking in infectious disease research and policy.

The infectious diseases community faces many difficult challenges. These include: (1) coping with continuing high-impact diseases such as HIV, malaria, TB, and flu; (2) dealing with infectious disease outbreaks such as Ebola, pandemic avian influenza, or SARS; and (3) preparing for the inevitable emergence of diseases that are unknown or are recognized but will reemerge in a more threatening form (e.g., antibiotic resistant [nightmare] bacteria or superbugs). Research in infectious diseases is also challenged by funding limitations, politics, and ethical dilemmas.

Increasingly complex data is also creating new challenges to the design and analysis of research studies. Enhanced statistical expertise is essential to address these challenges and to develop and evaluate medical and public health responses to potential outbreaks and epidemics.

Statistical Communications in Infectious Diseases (SCID) publishes significant research on the application of statistical ideas to problems arising from studies of infectious diseases. SCID takes a broad perspective on the role of statistics in infectious disease research including application, policy, education and theory. SCID fosters much-needed communication among statisticians on the best approaches to evolving complex infectious disease data, and is a venue for statisticians to enter a dialogue with other scientists and policy makers on the strengths and limitations of policies and methods for design, monitoring, analysis, and reporting of infectious disease research studies.

SCID goes beyond the application of statistical methods to data arising from infectious disease studies or new statistical strategies. SCID serves as a sounding board to discuss policy issues, as well as to accomplish the following goals: (1) engage and raise the quality of the discussions of important issues in the public and scientific press; (2) ensure that optimal scientific tools are utilized in infectious disease research; (3) improve the training and practice of the next generation of professionals involved in infectious diseases research through educational articles; (4) raise the profile of quantitative science in infectious disease research by demonstrating its value in decision making and public health.

Topics

How heavily should the public invest in research or implementation of different prevention modalities?
What is necessary to achieve control of epidemics (for example, do we require a vaccine to control the spread of HIV or what procedures are required to halt the spread of Ebola)?
How can statisticians help with grass roots epidemic control efforts?
How can we optimally evaluate the benefits vs. harms vs. costs of competing interventions?
How can we best characterize the trade-offs between civil liberties and public safety?
How should we adjust the traditionally required level of evidence to address areas of unmet medical need?
How do we revise our traditional approaches to the design, monitoring, analyses, and reporting of infectious disease studies to improve medical practice and public health?

Article formats
Original research articles, book reviews