Novel Challenges in Emerging Infectious Disease Surveillance
The reality of today’s global human mobility, wars and political unrest, and climate change require methods of surveillance that meet such challenges, but it is the pace at which these challenges are transforming the world that calls for novel methods to surveillance. Such novel surveillance methods must meet, keep pace with, and advance our understanding and protection of internally displaced and stateless persons, as well as communities at the animal-human interface (i.e., those most at risk of animal-human spillover and ecological havoc). Our aim is to advance novel methods to tracking excess mortality, accelerate predictive models to allow us to protect vulnerable populations and areas of the world, and overcome the challenges inherent to such methods to more fully understand the impact of emerging infectious disease threats and the most effective countermeasures to meet them.
Our Current Work In This Space:
Predicting and Preventing Epidemic to Pandemic Transitions (NSF)
The Predicting and Preventing Epidemic to Pandemic Transitions study, funded by the National Science Foundation (NSF),seeks to develop a rich model that predicts both disease emergence and potential for significant spread, and formulate pandemic prevention strategies. This project aims to evaluate factors that impact disease emergence and spread by incorporating input from multidisciplinary fields that span biology, ecology, epidemiology, medicine, computer & information science & engineering, and social sciences. The detailed knowledge base gathered from this work aims to generate novel predictive models, provide insight to strengthen current models, and formulate rapid pandemic mitigation strategies.
CEID Project Faculty & Collaborators
- Dr. Nahid Bhadelia, MD, MALD, CEID Founding Director
- John Connor ,PhD, Associate Professor Microbiology, BU School of Medicine
- Michael Dietze, PhD, Professor, Ecological Forecasting Lab at BU
- Traci Hong , PhD, Associate Professor, Media Science, BU College of Communication
- Gerald Keusch, MD, Professor, Medicine & International Health
BU School of Medicine
- Nina Mazar, PhD, Professor of Marketing, BU Questrom School of Business
- Andrew Stokes, PhD, Assistant Professor, Department of Global Health, BU School of Public Health
- Gianluca Stringhini, PhD, Assistant Professor, Electrical and Computer Engineering, BU College of Engineering
- Laura White, PhD, Associate Professor of Biostatistics,
BU School of Public Health
- Kevin Gallagher , PhD, Professor & Director, Global Development Policy Center
Additional Co-PIs include Drs. Yannis Paschalidis, Eric Kolaczyk, and Diane Joseph-McCarthy of Boston University, as well as Dr. Jon Epstein of EcoHealth Alliance.
Modeling Antimicrobial Resistance (AMR) Outbreaks in Wastewater
Overview
Wastewater (comprising industrial, hospital and agricultural wastewater or run-offs and domestic wastewater or sewage) systems are major reservoirs of multi-drug resistant bacterial populations that put the lives of vulnerable communities at risk. This is particularly of concern in low and middle income countries, and in urban informal settlements, where wastewater systems represent a leading driver of antimicrobial resistance (AMR). The dynamics of AMR emergence in wastewater environments is currently not fully understood. Existing studies have only surveyed the presence of antibiotic resistant organisms, genes and antibiotic residue in wastewater environments; the evolution and causative drivers of antibiotic resistant infectious disease outbreaks remain unknown. This has been a challenge due to both the lack of quantitative studies and the fact that experimental studies that mimic the wastewater environment can be difficult with standard laboratory culture techniques.
The goal of this project is to develop a novel, integrated mathematical modeling and experimental approach that will serve as a rigorous, data-driven, robust and scalable method allowing for the simultaneous study of several bacterial and environmental factors. The researchers will focus on several informal urban settlements in Pakistan, Bangladesh and South Africa for data collection and optimization of our model. This work will lead to a model to be used as a surveillance tool which can not only shed light on the evolution of antibiotic resistance, but also inform specific interventions, including surveillance, regulations, wastewater treatment plans and targeted antibiotic stewardship programs. Furthermore, the researchers believe that the outcome of this work in the form of an experimentally- and field-validated mathematical model is readily customizable and scalable, and can therefore be adapted to study context-specific wastewater systems in varying geographies and socioeconomic environments. Closing this gap through quantitative modeling and analysis is critical to fundamentally understanding the drivers of AMR and creating effective interventions to prevent outbreaks of multi-drug resistant pathogens. This research builds on CEID’s core pillar of innovation.
Project Faculty & Collaborators
- Muhammad Zaman, PhD, Professor, Biomedical Engineering Boston University College of Engineering, CEID Faculty
- David Hamer, MD, Professor, Global Health Boston University School of Medicine, CEID Faculty
Other Related Resources
Related CEID Faculty Publications