Temporal and Probabilistic Comparisons of Epidemic Interventions.
Branching process
Disease modeling
Forecasting
Networks
Stochastic process
Journal
Bulletin of mathematical biology
ISSN: 1522-9602
Titre abrégé: Bull Math Biol
Pays: United States
ID NLM: 0401404
Informations de publication
Date de publication:
19 10 2023
19 10 2023
Historique:
received:
08
03
2023
accepted:
26
09
2023
medline:
23
10
2023
pubmed:
20
10
2023
entrez:
19
10
2023
Statut:
epublish
Résumé
Forecasting disease spread is a critical tool to help public health officials design and plan public health interventions. However, the expected future state of an epidemic is not necessarily well defined as disease spread is inherently stochastic, contact patterns within a population are heterogeneous, and behaviors change. In this work, we use time-dependent probability generating functions (PGFs) to capture these characteristics by modeling a stochastic branching process of the spread of a disease over a network of contacts in which public health interventions are introduced over time. To achieve this, we define a general transmissibility equation to account for varying transmission rates (e.g. masking), recovery rates (e.g. treatment), contact patterns (e.g. social distancing) and percentage of the population immunized (e.g. vaccination). The resulting framework allows for a temporal and probabilistic analysis of an intervention's impact on disease spread, which match continuous-time stochastic simulations that are much more computationally expensive. To aid policy making, we then define several metrics over which temporal and probabilistic intervention forecasts can be compared: Looking at the expected number of cases and the worst-case scenario over time, as well as the probability of reaching a critical level of cases and of not seeing any improvement following an intervention. Given that epidemics do not always follow their average expected trajectories and that the underlying dynamics can change over time, our work paves the way for more detailed short-term forecasts of disease spread and more informed comparison of intervention strategies.
Identifiants
pubmed: 37857996
doi: 10.1007/s11538-023-01220-w
pii: 10.1007/s11538-023-01220-w
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
118Subventions
Organisme : NIGMS NIH HHS
ID : P20 GM125498
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
© 2023. The Author(s), under exclusive licence to Society for Mathematical Biology.
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