| Autor: |
Hart WS; Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom.; lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan., Park H; lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan., Jeong YD; lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan.; Department of Mathematics, Pusan National University, Busan 46241, South Korea., Kim KS; lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan.; Department of Scientific Computing, Pukyong National University, Busan 48513, South Korea., Yoshimura R; lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan., Thompson RN; Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom.; Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom.; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, Coventry CV4 7AL, United Kingdom., Iwami S; lnterdisciplinary Biology Laboratory, Division of Natural Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan.; Institute of Mathematics for Industry, Kyushu University, Fukuoka 819-0395, Japan.; Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto 606-8501, Japan.; Interdisciplinary Theoretical and Mathematical Sciences Program, RIKEN, Saitama 351-0198, Japan.; NEXT-Ganken Program, Japanese Foundation for Cancer Research, Tokyo 135-8550, Japan.; Science Groove Inc., Fukuoka 810-0041, Japan. |
| Abstrakt: |
In the era of living with COVID-19, the risk of localised SARS-CoV-2 outbreaks remains. Here, we develop a multiscale modelling framework for estimating the local outbreak risk for a viral disease (the probability that a major outbreak results from a single case introduced into the population), accounting for within-host viral dynamics. Compared to population-level models previously used to estimate outbreak risks, our approach enables more detailed analysis of how the risk can be mitigated through pre-emptive interventions such as antigen testing. Considering SARS-CoV-2 as a case study, we quantify the within-host dynamics using data from individuals with omicron variant infections. We demonstrate that regular antigen testing reduces, but may not eliminate, the outbreak risk, depending on characteristics of local transmission. In our baseline analysis, daily antigen testing reduces the outbreak risk by 45% compared to a scenario without antigen testing. Additionally, we show that accounting for heterogeneity in within-host dynamics between individuals affects outbreak risk estimates and assessments of the impact of antigen testing. Our results therefore highlight important factors to consider when using multiscale models to design pre-emptive interventions against SARS-CoV-2 and other viruses. |
