Summary
Infectious diseases pose one of the greatest risks for a global catastrophe. Just like controlling the spread of wildfires, an early detection of infectious diseases is instrumental to containing outbreaks. Nearly all infections start silently, and gradually progress until clinical symptoms appear. In this silent period, the incubation period (IP), pathogens inhibit major pathways of the innate immune system, allowing an extended period of unhindered replication. The rate of replication, as well as the type and length of suppressed symptoms vary considerably between pathogens, creating a unique signature for each pathogen. Thus, improved understanding of the IP is pivotal for early detection, prevention and control of infectious diseases. Previous studies estimating IPs have used aggregated retrospective data, and are subject to the biases of patient self-reporting. I hypothesise that the actual onset of clinical symptoms occurs earlier than previously known, can be identified more accurately, and can be used in real-time for patient empowerment. Focusing on respiratory infections, my methodological approach includes: 1) real-time evaluation of the prior risk for respiratory infections by integrating transmission models with individual-level data from electronic medical records of 4.5 Million individuals, 2) identification of micro-changes in patients’ behaviour during the early phase of an infection by prospectively analysing digital sensory data from wearable devices and mobile phones of 5000 selected participants, 3) early detection of the causing pathogen validated with self-swab kits that are tested using RT-PCR. Our preliminary work that combined an analysis of EMR and transmission modelling led to a change in public health policy in Israel. The proposed study has the potential to open new research directions on the hidden side of infectious diseases and to initiate a new era of personalized medicine through dramatic changes in patient-doctor interaction.
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| Web resources: | https://cordis.europa.eu/project/id/949850 |
| Start date: | 01-11-2020 |
| End date: | 31-10-2025 |
| Total budget - Public funding: | 1 986 875,00 Euro - 1 986 875,00 Euro |
Cordis data
Original description
Infectious diseases pose one of the greatest risks for a global catastrophe. Just like controlling the spread of wildfires, an early detection of infectious diseases is instrumental to containing outbreaks. Nearly all infections start silently, and gradually progress until clinical symptoms appear. In this silent period, the incubation period (IP), pathogens inhibit major pathways of the innate immune system, allowing an extended period of unhindered replication. The rate of replication, as well as the type and length of suppressed symptoms vary considerably between pathogens, creating a unique signature for each pathogen. Thus, improved understanding of the IP is pivotal for early detection, prevention and control of infectious diseases. Previous studies estimating IPs have used aggregated retrospective data, and are subject to the biases of patient self-reporting. I hypothesise that the actual onset of clinical symptoms occurs earlier than previously known, can be identified more accurately, and can be used in real-time for patient empowerment. Focusing on respiratory infections, my methodological approach includes: 1) real-time evaluation of the prior risk for respiratory infections by integrating transmission models with individual-level data from electronic medical records of 4.5 Million individuals, 2) identification of micro-changes in patients’ behaviour during the early phase of an infection by prospectively analysing digital sensory data from wearable devices and mobile phones of 5000 selected participants, 3) early detection of the causing pathogen validated with self-swab kits that are tested using RT-PCR. Our preliminary work that combined an analysis of EMR and transmission modelling led to a change in public health policy in Israel. The proposed study has the potential to open new research directions on the hidden side of infectious diseases and to initiate a new era of personalized medicine through dramatic changes in patient-doctor interaction.Status
SIGNEDCall topic
ERC-2020-STGUpdate Date
27-04-2024
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