What are emerging infectious diseases?

Emerging infectious diseases (EIDs) are diseases whose incidence has increased in the last two decades. EIDs can be caused by both previously unrecognized pathogens and pathogens whose incidence continues to rise, re-surge, re-emerge, or expand in range. Current examples include diseases such as Ebola, Chikungunya, Dengue, SARS, and MERS. They pose significant human health, economic, and security risks.

Pathogen surveillance systems try to prevent disease epidemics by detecting pathogens before many people become sick. However, EIDs are difficult to monitor because: (1) between 60% and 75% of EID outbreaks are caused pathogens residing in animal populations, and animals are difficult to monitor; (2) many EIDs are caused by previously unknown pathogens, such as MERS, Heartland virus, and Bourbon virus, so existing surveillance systems cannot detect them; and (3) movements of humans populations and changes in climate create more places where diseases can emerge.

What is Project Premonition?

Project Premonition seeks to detect pathogens in animals before these pathogens make people sick. It does this by treating a mosquito as a device that can find animals and sample their blood. Project Premonition uses drones and new robotic mosquito traps to capture many more mosquitoes from the environment than previously possible, and then analyzes their body contents for pathogens. Pathogens are detected by gene sequencing collected mosquitoes and computationally searching for known and unknown pathogens in sequenced genetic material.

Learn about the technologies

Grenada Feasibility Study:
Catching mosquitoes and training drones

In March 2015, Microsoft Research and Saint George's University of Grenada partnered to catch mosquitoes manually across the 133-square-mile island of Grenada, so drones can be trained to do this instead of people. Data from this study will be used to design autonomous deployment systems and new algorithms for detecting pathogens in mosquitoes.

Watch the video

Watch the video: Project Premonition: detecting early signs of infectious diseases

This is at least a five-year vision, no doubt about it. But along the way, the advances we make in each of these areas have a lot of value in their own right.

– Ethan Jackson
Lead Researcher, Project Premonition

Key technologies and methods

  • Mosquito-as-a-device. Small-scale mosquito metagenomic studies have shown that a wide range of host and pathogen genes can be obtained from body contents, even for pathogens that do not infect mosquitoes. In summary, instead of sampling animal populations directly, we sample them indirectly through wild mosquito populations.
  • Autonomous mosquito traps. Our goal is to build smarter and cheaper autonomous mosquito traps that catch only mosquitoes and preserve them for later analysis. Our designs will use low-cost odor lures that are attractive to a broad variety of mosquitoes. Our traps will be smarter, using low-cost, low-power sensors and signal processing that detect and distinguish mosquitoes from other insects. Like existing traps, our traps are placed into the environment and mosquitoes are caught when they come to investigate the trap.
  • Autonomous deployment. Sending people to collect mosquitoes is surprisingly slow and expensive. Ideal collection sites in rural environments include areas of dense vegetation miles away from roads. Urban sites include areas of clutter, such as alleys and neglected structures, where mosquitoes lay eggs, and building openings where mosquitoes seek hosts. Sites must be chosen carefully for traps to catch anything at all. We aim to use drones to autonomously decide on ideal trapping locations and to place and retrieve traps from the environment. Drones are fast, efficient, and inexpensive. We need to make them smart, safe, and robust.
  • Big metagenomics. Once mosquitoes are collected, they will be gene sequenced producing gigabytes of metagenomic data, which may contain the genes of pathogens the mosquitoes encountered. New algorithms must be developed to quickly search for virus and microbes, which are needles in this haystack of data. Cloud computing will be essential to handle the large volumes of data and to provide the computational power required to analyze the data. By analyzing so much information about microbes and viruses in space and time, we hope to see the movement and evolution of possible pathogens before they cause disease in humans.

Related links

Meet the research team

Amy Baldwin

Amy Baldwin

St. George's University
Department of Microbiology
Jonathan Carlson

Jonathan Carlson

Microsoft Research
Computational Genomics
Ethan Jackson

Ethan Jackson

Microsoft Research
Research in Software Engineering (RiSE) group
Ashish Kapoor

Ashish Kapoor

Microsoft Research
Adaptive Systems and Interaction Group
Eamonn Keogh

Eamonn Keogh

University of California Riverside
Department of Computer Science and Engineering
Shawn Keshmiri

Shawn Keshmiri

University of Kansas
Department of Aerospace Engineering
Vijay Kumar

Vijay Kumar

University of Pennsylvania
GRASP Laboratory
Douglas Norris

Douglas Norris

Johns Hopkins Bloomberg School of Public Health
Deparment of Microbiology and Immunology
James M. Pipas

James M. Pipas

University of Pittsburgh
Deparment of Molecular Biology
Shaz Qadeer

Shaz Qadeer

Microsoft Research
Research in Software Engineering (RiSE) group
Anandansankar Ray

Anandansankar Ray

University of California Riverside
Department of Entomology
Janos Sztipanovits

Janos Sztipanovits

Vanderbilt University
Institute for Software Integrated Systems
Michael Zyskowski

Michael Zyskowski

Microsoft Research
Outreach