New Model Sheds Light on Patterns of Infectious Illnesses
The COVID-19 pandemic was a major reminder of just how problematic infectious diseases can be, especially if we aren’t intimately aware of their spread patterns. Only a few months after the disease was discovered in Wuhan, China, significant chunks of the world were shut down to avoid mass infections that would place undue strain on their medical systems.
Even though we are now out of the eye of the storm, it is important that we understand how such infectious diseases behave to prevent future pandemics. Fortunately, a new modeling approach capable of accounting for contact patterns between different age groups has given scientists new insight into how infectious diseases evolve. The model also showed researchers how they may be able to predict the spread of a disease and its case numbers over a specific region.
This model used data collected during the coronavirus pandemic and leveraged two approaches to fine tune and improve predictions on the spread of infectious diseases. One common modeling approach uses the SIR model, which splits the population into susceptible, infected and recovered persons before modeling the rates of how people move from one compartment to another.
Taking age-specific contact patterns into account, researchers from the King Abdullah University of Science and Technology integrated SIR compartment modeling with a point process modeling approach. Paula Moraga, an assistant professor of statistics at the institution who is also the principal investigator of the Geospatial Statistics and Health Surveillance (GeoHealth) research group, led the team of researchers.
Moraga and her team leveraged a two-step framework to model infectious location data for different age groups over time and found that the new modeling approach provided more accurate predictions compared to other approaches. Furthermore, André Amaral, lead researcher of the study, stated that the novel model allowed the group to treat different age groups separately by accounting for different age classes. As a result, the researchers were able to gain tighter control over the number of infectious disease cases.
The model made better predictions in a case study of coronavirus cases in Cali, Columbia, and gave similar results for previous time points compared to other predictive models.
Amaral noted that the model’s features had the potential to help decision makers identify locations that are high-risk and populations that are vulnerable, allowing them to come up with better disease control strategies in case of future outbreaks. He also noted that the research team would try to improve the predictive capabilities of the model via ensemble approaches that combine predictions from different models while accounting for potential time delays during data collection.
As researchers work to better understand how infectious diseases on the scale of COVID-19 spread, other teams such as those at BiondVax Pharmaceuticals Ltd. (NASDAQ: BVXV) are focused on finding novel treatments that can tame the onslaught of infectious ailments which are now posing an ever-increasing threat to populations worldwide.
NOTE TO INVESTORS: The latest news and updates relating to BiondVax Pharmaceuticals Ltd. (NASDAQ: BVXV) are available in the company’s newsroom at https://ibn.fm/BVXV
Please see full terms of use and disclaimers on the BioMedWire website applicable to all content provided by BMW, wherever published or re-published: http://BMW.fm/Disclaimer
The COVID-19 pandemic was a major reminder of just how problematic infectious diseases can be, especially if we aren’t intimately aware of their spread patterns. Only a few months after the disease was discovered in Wuhan, China, significant chunks of the world were shut down to avoid mass infections that would place undue strain on their medical systems.
Even though we are now out of the eye of the storm, it is important that we understand how such infectious diseases behave to prevent future pandemics. Fortunately, a new modeling approach capable of accounting for contact patterns between different age groups has given scientists new insight into how infectious diseases evolve. The model also showed researchers how they may be able to predict the spread of a disease and its case numbers over a specific region.
This model used data collected during the coronavirus pandemic and leveraged two approaches to fine tune and improve predictions on the spread of infectious diseases. One common modeling approach uses the SIR model, which splits the population into susceptible, infected and recovered persons before modeling the rates of how people move from one compartment to another.
Taking age-specific contact patterns into account, researchers from the King Abdullah University of Science and Technology integrated SIR compartment modeling with a point process modeling approach. Paula Moraga, an assistant professor of statistics at the institution who is also the principal investigator of the Geospatial Statistics and Health Surveillance (GeoHealth) research group, led the team of researchers.
Moraga and her team leveraged a two-step framework to model infectious location data for different age groups over time and found that the new modeling approach provided more accurate predictions compared to other approaches. Furthermore, André Amaral, lead researcher of the study, stated that the novel model allowed the group to treat different age groups separately by accounting for different age classes. As a result, the researchers were able to gain tighter control over the number of infectious disease cases.
The model made better predictions in a case study of coronavirus cases in Cali, Columbia, and gave similar results for previous time points compared to other predictive models.
Amaral noted that the model’s features had the potential to help decision makers identify locations that are high-risk and populations that are vulnerable, allowing them to come up with better disease control strategies in case of future outbreaks. He also noted that the research team would try to improve the predictive capabilities of the model via ensemble approaches that combine predictions from different models while accounting for potential time delays during data collection.
As researchers work to better understand how infectious diseases on the scale of COVID-19 spread, other teams such as those at BiondVax Pharmaceuticals Ltd. (NASDAQ: BVXV) are focused on finding novel treatments that can tame the onslaught of infectious ailments which are now posing an ever-increasing threat to populations worldwide.
NOTE TO INVESTORS: The latest news and updates relating to BiondVax Pharmaceuticals Ltd. (NASDAQ: BVXV) are available in the company’s newsroom at https://ibn.fm/BVXV
Please see full terms of use and disclaimers on the BioMedWire website applicable to all content provided by BMW, wherever published or re-published: http://BMW.fm/Disclaimer
