New modelling framework developed to improve infectious disease control


Date:
March 7, 2022
Source:
University of Nottingham
Summary:
A new model to analyse infectious disease outbreak data has been
developed by mathematicians that could be used to improve disease
tracking and control.



FULL STORY ==========================================================================
A new model to analyse infectious disease outbreak data has been developed
by mathematicians that could be used to improve disease tracking and
control.


========================================================================== Researchers from the University of Nottingham developed a new data-driven framework for modelling how infectious diseases spread through a
population that could reduce errors in decisions made about disease
control measures.

Their findings have been published in PNAS.

The COVID-19 pandemic has highlighted that the ability to unravel the
dynamics of the spread of infectious diseases is profoundly important
for designing effective control strategies and assessing existing
ones. Mathematical models of how infectious diseases spread continue to
play a vital role in understanding, mitigating, and preventing outbreaks.

Dr Rowland Seymour led the study and explains: "Most of the infectious
disease models contain specific assumptions about how transmission occurs within a population. These assumptions can be arbitrary, particularly
when it comes to describing how transmission varies between individuals
of different types or in different locations and can be lacking in
appropriate biological or epidemiological justification. this can
lead to erroneous scientific conclusions and misleading predictions."
The researchers developed a data-driven framework for modelling how
infectious diseases spread through a population by avoiding strict
modelling assumptions which are often difficult to justify. The
researchers used the method to enhance understanding of the 2001 UK Foot
and Mouth outbreak in which over 6 million animals were culled with a
cost to the public and private purse of over -L-8 billion.

The proposed methodology is very general making it applicable to a wide
class of models, including those which take into account the population's structure (e.g. households, workplaces) and individual's characteristics
(e.g. location and age).

Dr Rowland Seymour continues: "Infectious diseases both within human and
animal populations continue to pose serious health and socioeconomic
risks. We have developed a suite of contemporary statistical methods
that dispenses with the need for the underlying transmission assumptions
of existing models. Our approach enables instead the analysis to be
driven by evidence in the data and hence allowing policy makers to make data-driven decisions about controlling the spread of a disease. Our work
is another tool in the fight against the spread of infectious diseases and
we are excited to develop this framework further." This work has opened several avenues for further research in this area, including improving its computationally efficiency and being applicable in real-time, i.e. when
the outbreak is still ongoing. The latter is of material importance for
policy makers and government authorities, so they can be responsive to
the data that is emerging from the outbreak.

========================================================================== Story Source: Materials provided by University_of_Nottingham. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Rowland G. Seymour, Theodore Kypraios, Philip D. O'Neill. Bayesian
nonparametric inference for heterogeneously mixing infectious
disease models. Proceedings of the National Academy of Sciences,
2022; 119 (10) DOI: 10.1073/pnas.2118425119 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220307113120.htm

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