ORCID Profile
0000-0002-3973-017X
Current Organisation
University of Oxford
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Publisher: Springer Science and Business Media LLC
Date: 15-10-2021
DOI: 10.1007/S11538-021-00947-8
Abstract: The contact structure of a population plays an important role in transmission of infection. Many ‘structured models’ capture aspects of the contact pattern through an underlying network or a mixing matrix. An important observation in unstructured models of a disease that confers immunity is that once a fraction $$1-1/{\\mathcal {R}}_0$$ 1 - 1 / R 0 has been infected, the residual susceptible population can no longer sustain an epidemic. A recent observation of some structured models is that this threshold can be crossed with a smaller fraction of infected in iduals, because the disease acts like a targeted vaccine, preferentially immunising higher-risk in iduals who play a greater role in transmission. Therefore, a limited ‘first wave’ may leave behind a residual population that cannot support a second wave once interventions are lifted. In this paper, we set out to investigate this more systematically. While networks offer a flexible framework to model contact patterns explicitly, they suffer from several shortcomings: (i) high-fidelity network models require a large amount of data which can be difficult to harvest, and (ii) very few, if any, theoretical contact network models offer the flexibility to tune different contact network properties within the same framework. Therefore, we opt to systematically analyse a number of well-known mean-field models. These are computationally efficient and provide good flexibility in varying contact network properties such as heterogeneity in the number contacts, clustering and household structure or differentiating between local and global contacts. In particular, we consider the question of herd immunity under several scenarios. When modelling interventions as changes in transmission rates, we confirm that in networks with significant degree heterogeneity, the first wave of the epidemic confers herd immunity with significantly fewer infections than equivalent models with less or no degree heterogeneity. However, if modelling the intervention as a change in the contact network, then this effect may become much more subtle. Indeed, modifying the structure disproportionately can shield highly connected nodes from becoming infected during the first wave and therefore make the second wave more substantial. We strengthen this finding by using an age-structured compartmental model parameterised with real data and comparing lockdown periods implemented either as a global scaling of the mixing matrix or age-specific structural changes. Overall, we find that results regarding (disease-induced) herd immunity levels are strongly dependent on the model, the duration of the lockdown and how the lockdown is implemented in the model.
Publisher: Public Library of Science (PLoS)
Date: 18-03-2021
DOI: 10.1371/JOURNAL.PCBI.1008763
Abstract: The interventions and outcomes in the ongoing COVID-19 pandemic are highly varied. The disease and the interventions both impose costs and harm on society. Some interventions with particularly high costs may only be implemented briefly. The design of optimal policy requires consideration of many intervention scenarios. In this paper we investigate the optimal timing of interventions that are not sustainable for a long period. Specifically, we look at at the impact of a single short-term non-repeated intervention (a “one-shot intervention”) on an epidemic and consider the impact of the intervention’s timing. To minimize the total number infected, the intervention should start close to the peak so that there is minimal rebound once the intervention is stopped. To minimise the peak prevalence, it should start earlier, leading to initial reduction and then having a rebound to the same prevalence as the pre-intervention peak rather than one very large peak. To delay infections as much as possible (as might be appropriate if we expect improved interventions or treatments to be developed), earlier interventions have clear benefit. In populations with distinct subgroups, synchronized interventions are less effective than targeting the interventions in each subcommunity separately.
Publisher: IOP Publishing
Date: 02-02-2021
Abstract: On May 28th and 29th, a two day workshop was held virtually, facilitated by the Beyond Center at ASU and Moogsoft Inc. The aim was to bring together leading scientists with an interest in network science and epidemiology to attempt to inform public policy in response to the COVID-19 pandemic. Epidemics are at their core a process that progresses dynamically upon a network, and are a key area of study in network science. In the course of the workshop a wide survey of the state of the subject was conducted. We summarize in this paper a series of perspectives of the subject, and where the authors believe fruitful areas for future research are to be found.
Publisher: Cold Spring Harbor Laboratory
Date: 06-03-2020
DOI: 10.1101/2020.03.02.20030007
Abstract: The apparent early success in China’s large-scale intervention to control the COVID-19 epidemic has led to interest in whether other countries can replicate it as well as concerns about a resurgence of the epidemic if or when China relaxes the interventions. In this paper we look at the impact of a single short-term intervention on an epidemic. We see that if an intervention cannot be sustained long-term, it has the greatest impact if it is imposed once infection levels have become large enough that there is an appreciable number of infections present. For minimising the total number infected it should start close to the peak so that there is no rebound once the intervention is stopped, while to minimise the peak prevalence, it should start earlier, allowing two peaks of comparable size rather than one very large peak. In populations with distinct subgroups, synchronized interventions are less effective than targeting the interventions in each sub-population separately. We do not attempt to clearly determine what makes an intervention sustainable or not. We believe that is a policy question. If an intervention is sustainable, it should be kept in place. Our intent is to offer insight into how best to time an intervention whose impact on society is too great to maintain.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Francesco Di Lauro.