Mosquito species identification accuracy of early deployed algorithms in IDX, A vector identification tool.

Mosquito-borne diseases continue to pose a great threat to global public health systems due to increased insecticide resistance and climate change. Accurate vector identification is crucial for effective control, yet it presents significant challenges. IDX - an automated computer vision-based device capable of capturing mosquito images and outputting mosquito species ID has been deployed globally resulting in algorithms currently capable of identifying 53 mosquito species.

The epidemiology of pediatric outpatient acute respiratory tract infections in the US: a multi-facility analysis of multiplex PCR testing from 2018 to 2023.

Post-pandemic, it is essential to understand the epidemiology of pediatric acute respiratory tract infections (ARTIs). Our multi-facility study elucidates the outpatient epidemiology of pediatric ARTI using highly multiplexed PCR testing, providing critical insights into the evolving landscape of the etiological agents with a particular focus on the years following the emergence of SARS-CoV-2. Utilizing data from two different multiplex PCR panels, our research provides a comprehensive analysis of respiratory pathogen positivity from 2018 to 2023.

Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection.

With over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation.

Development of a low-cost imaging system for remote mosquito surveillance.

Targeted vector control strategies aiming to prevent mosquito borne disease are severely limited by the logistical burden of vector surveillance, the monitoring of an area to understand mosquito species composition, abundance and spatial distribution. We describe development of an imaging system within a mosquito trap to remotely identify caught mosquitoes, including selection of the image resolution requirement, a design to meet that specification, and evaluation of the system. The necessary trap image resolution was determined to be 16 lp/mm, or 31.

Age, Race, and At-Risk Drinking in an HIV-infected U.S. Military Cohort.

Introduction: There is a high prevalence of at-risk drinking in the U.S. military.

A novel personal protective equipment coverall was rated higher than standard Ebola virus personal protective equipment in terms of comfort, mobility and perception of safety when tested by health care workers in Liberia and in a United States biocontainment unit.

Background: During the 2014-2016 Ebola virus epidemic, more than 500 health care workers (HCWs) died in spite of the use of personal protective equipment (PPE). The Johns Hopkins University Center for Bioengineering Innovation and Design (CBID) and Jhpiego, an international nongovernmental organization affiliate of Johns Hopkins, collaborated to create new PPE to improve the ease of the doffing process.

Methods: HCWs in Liberia and a US biocontainment unit compared standard Médecins Sans Frontière PPE (PPE A) with the new PPE (PPE B).

Design Improvements for Personal Protective Equipment Used in Ebola and Other Epidemic Outbreaks.

We redesigned the personal protective equipment ensemble widely used during the 2014 Ebola outbreak into a relatively simpler and more versatile coverall and hood, to improve protection and usability for frontline workers treating patients in infectious disease outbreaks around the world.

Epidemiologic and Environmental Risk Factors of Rift Valley Fever in Southern Africa from 2008 to 2011.

Background: Rift Valley fever (RVF) outbreaks have been associated with periods of widespread and above-normal rainfall over several months. Knowledge on the environmental factors influencing disease transmission dynamics has provided the basis for developing models to predict RVF outbreaks in Africa. From 2008 to 2011, South Africa experienced the worst wave of RVF outbreaks in almost 40 years.