arXiv subjects

Jeffrey Shaman

Publications and source records attributed to Jeffrey Shaman.

10 recordsLinked to original sources

arXiv2022

Age-specific transmission dynamics of SARS-CoV-2 during the first two years of the pandemic

During its first two years, the SARS-CoV-2 pandemic manifested as multiple waves shaped by complex interactions between variants of concern, non-pharmaceutical interventions, and the immunological landscape of the population. Understanding how the age-specific epidemiology of SARS-CoV-2 has evolved throughout the pandemic is crucial for informing policy decisions. We developed an inference-based modelling approach to reconstruct the burden of true infections and hospital admissions in children, adolescents and adults over the seven waves of four variants (wild-type, Alpha, Delta, Omicron BA.1) during the first two years of the pandemic, using the Netherlands as the motivating example. We find that reported cases are a considerable underestimate and a generally poor predictor of true infection burden, especially because case reporting differs by age. The contribution of children and adolescents to total infection and hospitalization burden increased with successive variants and was largest during the Omicron BA.1 period. Before the Delta period, almost all infections were primary infections occurring in naive individuals. During the Delta and Omicron BA.1 periods, primary infections were common in children but relatively rare in adults who experienced either re-infections or breakthrough infections. Our approach can be used to understand age-specific epidemiology through successive waves in other countries where random community surveys uncovering true SARS-CoV-2 dynamics are absent but basic surveillance and statistics data are available.

physics.soc-ph↗

arXiv2022

Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City prove the concepts. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population ($\gtrsim 75\%$) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.

stat.AP↗

arXiv2021

Viral replication dynamics could critically modulate vaccine effectiveness and should be accounted for when assessing new SARS-CoV-2 variants

In this article, we propose a theory to explain the reduction in vaccine effectiveness (VE) against the Delta SARS-CoV-2 variant and decreasing VE over time reported in recent studies. Using a model illustration, we show that in-host viral replication dynamics and delays in immune response could play a key role in VE. Given this, current laboratory approaches solely measuring reductions in neutralizing ability cannot fully represent the potential impact of new SARS-CoV-2 variants. We instead propose an alternative approach that incorporates viral replication dynamics into evaluations of SARS-CoV-2 variant impact on immunity and VE. This more robust assessment may better inform public health response to new variants like the newly detected Omicron variant.

q-bio.PE↗

arXiv2017

Efficient collective influence maximization in cascading processes with first-order transitions

In social networks, the collective behavior of large populations can be shaped by a small set of influencers through a cascading process induced by "peer pressure". For large-scale networks, efficient identification of multiple influential spreaders with a linear algorithm in threshold models that exhibit a first-order transition still remains a challenging task. Here we address this issue by exploring the collective influence in general threshold models of behavior cascading. Our analysis reveals that the importance of spreaders is fixed by the subcritical paths along which cascades propagate: the number of subcritical paths attached to each spreader determines its contribution to global cascades. The concept of subcritical path allows us to introduce a linearly scalable algorithm for massively large-scale networks. Results in both synthetic random graphs and real networks show that the proposed method can achieve larger collective influence given same number of seeds compared with other linearly scalable heuristic approaches.

physics.soc-ph↗

arXiv2014

A simple modification for improving inference of non-linear dynamical systems

Particle and ensemble filters are increasingly utilized for inference, optimization, and forecast; however, both filtering methods use discrete distributions to simulate continuous state space, a drawback that can lead to degraded performance for non-linear dynamical systems. Here we propose a simple modification, applicable to both particle and ensemble filters, that compensates for this problem. The method randomly replaces one or more model variables or parameters within a fraction of simulated trajectories at each filtering cycle. This modification, termed space re-probing, expands the state space covered by the filter through the introduction of outlying trajectories. We apply the space re-probing modification to three particle filters and three ensemble filters, and use these modified filters to model and forecast influenza epidemics. For both filter types, the space re-probing improves simulation of influenza epidemic curves and the prediction of influenza outbreak peak timing. Further, as fewer particles are needed for the particle filters, the proposed modification reduces the computational cost of these filters.

stat.ME↗

arXiv2013

Week 1 Influenza Forecast for the 2012-2013 U.S. Season

This is part of a series of weekly influenza forecasts made during the 2012-2013 influenza season. Here we present results of forecasts initiated following assimilation of observations for Week 1 (i.e. the forecast begins January 6, 2013) for municipalities in the United States. These forecasts were performed on January 11, 2013. Results from forecasts initiated the six previous weeks (Weeks 47-52) are also presented. The accuracy of these predictions will not be known for certain until the conclusion of the current influenza season; however, at the moment a number of the forecasted peaks appear to be inaccurate. This inaccuracy may be due to the virulence of influenza this season, which appears to be sending more influenza-infected persons to seek medical attention and inflates ILI levels (and possibly the proportion testing influenza positive) relative to years with milder flu strains. New forecasts that adjust, or scale, for this difference and match the two focus cities that appear to have already peaked are identified. These new forecasts will be used, in addition to the previously scaled forms, to make influenza predictions for the remainder of the season.

q-bio.PE↗

arXiv2013

Week 52 Influenza Forecast for the 2012-2013 U.S. Season

This document is another installment in a series of near real-time weekly influenza forecasts made during the 2012-2013 influenza season. Here we present some of the results of forecasts initiated following assimilation of observations for Week 52 (i.e. the forecast begins December 30, 2012) for municipalities in the United States. The forecasts were made on January 4, 2013. Results from forecasts initiated the five previous weeks (Weeks 47-51) are also presented.

q-bio.PE↗

arXiv2012

Week 51 Influenza Forecast for the 2012-2013 U.S. Season

This document is part of a series of near real-time weekly influenza forecasts made during the 2012-2013 influenza season. Here we present results of a forecast initiated following assimilation of observations for Week 51 (i.e. the forecast begins December 23, 2012) for municipalities in the United States. The forecast was made on December 28, 2012. Results from forecasts initiated the four previous weeks (Weeks 47-50) are also presented. Predictions generated with an alternate SIRS model, run without absolute humidity forcing (no AH), are also presented.

q-bio.PE↗

arXiv2012

Week 50 Influenza Forecast for the 2012-2013 U.S. Season

We present results of a forecast initiated following assimilation of observations for week Week 50 (i.e. the forecast begins December 16, 2012) of the 2012-2013 influenza season for municipalities in the United States. The forecast was made on December 21, 2012. Results from forecasts initiated the three previous weeks (Weeks 47-49) are also presented. Also results from forecasts generated with an SIRS model without absolute humidity forcing (no AH) are shown.

q-bio.PE↗

arXiv2012

Week 49 Influenza Forecast for the 2012-2013 U.S. Season

We present results of a forecast initiated Week 49 (beginning December 9, 2012) of the 2012-2013 influenza season for municipalities in the United States. The forecast was made on December 14, 2012. Results from forecasts initiated the two previous weeks (Weeks 47 and 48) are also presented. Also results from the forecast generated with the SIRS model without AH forcing (no AH) are shown

q-bio.PE↗