arXiv subjects

Benjamin M. Althouse

Publications and source records attributed to Benjamin M. Althouse.

13 recordsLinked to original sources

arXiv2023

Spatial scales of COVID-19 transmission in Mexico

During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing non-pharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases, deaths and hospitalizations at the municipality level in Mexico to investigate how behavioural changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March - June 2020). We find that the epidemic dynamics in Mexico were initially driven by SARS-CoV-2 exports from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronised. Our results provide actionable and dynamic insights into how to use network science and epidemiological modelling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.

physics.soc-ph↗

arXiv2022

The role of directionality, heterogeneity and correlations in epidemic risk and spread

Most models of epidemic spread, including many designed specifically for COVID-19, implicitly assume mass-action contact patterns and undirected contact networks, meaning that the individuals most likely to spread the disease are also the most at risk to receive it from others. Here, we review results from the theory of random directed graphs which show that many important quantities, including the reproduction number and the epidemic size, depend sensitively on the joint distribution of in- and out-degrees ("risk" and "spread"), including their heterogeneity and the correlation between them. By considering joint distributions of various kinds, we elucidate why some types of heterogeneity cause a deviation from the standard Kermack-McKendrick analysis of SIR models, i.e., so-called mass-action models where contacts are homogeneous and random, and some do not. We also show that some structured SIR models informed by realistic complex contact patterns among types of individuals (age or activity) are simply mixtures of Poisson processes and tend not to deviate significantly from the simplest mass-action model. Finally, we point out some possible policy implications of this directed structure, both for contact tracing strategy and for interventions designed to prevent superspreading events. In particular, directed graphs have a forward and backward version of the classic "friendship paradox" -- forward edges tend to lead to individuals with high risk, while backward edges lead to individuals with high spread -- such that a combination of both forward and backward contact tracing is necessary to find superspreading events and prevent future cascades of infection.

physics.soc-ph↗

arXiv2021

The network epidemiology of an Ebola epidemic

Connecting the different scales of epidemic dynamics, from individuals to communities to nations, remains one of the main challenges of disease modeling. Here, we revisit one of the largest public health efforts deployed against a localized epidemic: the 2014-2016 Ebola Virus Disease (EVD) epidemic in Sierra Leone. We leverage the data collected by the surveillance and contact tracing protocols of the Sierra Leone Ministry of Health and Sanitation, the US Centers for Disease Control and Prevention, and other responding partners to validate a network epidemiology framework connecting the population (incidence), community (local forecasts), and individual (secondary infections) scales of disease transmission. In doing so, we gain a better understanding of what brought the EVD epidemic to an end: Reduction of introduction in new clusters (primary cases), and not reduction in local transmission patterns (secondary infections). We also find that the first 90 days of the epidemic contained enough information to produce probabilistic forecasts of EVD cases; forecasts which we show are confirmed independently by both disease surveillance and contact tracing. Altogether, using data available two months before the start of the international support to the local response, network epidemiology could have inferred heterogeneity in local transmissions, the risk for superspreading events, and probabilistic forecasts of eventual cases per community. We expect that our framework will help connect large data collection efforts with individual behavior, and help reduce uncertainty during health emergencies and emerging epidemics.

q-bio.PE↗

arXiv2021

Immunity-induced criticality of the genotype network of influenza A (H3N2) hemagglutinin

Seasonal influenza kills hundreds of thousands every year, with multiple constantly-changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity, suggesting critical-like behavior. We argue that: (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure, and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay suggests a self-organized criticality where the epidemic dynamics of influenza locates critical-like regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.

q-bio.PE↗

arXiv2020

The unintended consequences of inconsistent pandemic control policies

Controlling the spread of COVID-19 - even after a licensed vaccine is available - requires the effective use of non-pharmaceutical interventions: physical distancing, limits on group sizes, mask wearing, etc. To date, such interventions have neither been uniformly nor systematically implemented in most countries. For example, even when under strict stay-at-home orders, numerous jurisdictions granted exceptions and/or were in close proximity to locations with entirely different regulations in place. Here, we investigate the impact of such geographic inconsistencies in epidemic control policies by coupling search and mobility data to a simple mathematical model of SARS-COV2 transmission. Our results show that while stay-at-home orders decrease contacts in most areas of the US, some specific activities and venues often see an increase in attendance. Indeed, over the month of March 2020, between 10 and 30% of churches in the US saw increases in attendance; even as the total number of visits to churches declined nationally. This heterogeneity, where certain venues see substantial increases in attendance while others close, suggests that closure can cause individuals to find an open venue, even if that requires longer-distance travel. And, indeed, the average distance travelled to churches in the US rose by 13% over the same period. Strikingly, our model reveals that across a broad range of model parameters, partial measures can often be worse than none at all where individuals not complying with policies by traveling to neighboring areas can create epidemics when the outbreak would otherwise have been controlled. Taken together, our data analysis and modelling results highlight the potential unintended consequences of inconsistent epidemic control policies and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic.

q-bio.PE↗

arXiv2020

Stochasticity and heterogeneity in the transmission dynamics of SARS-CoV-2

SARS-CoV-2 causing COVID-19 disease has moved rapidly around the globe, infecting millions and killing hundreds of thousands. The basic reproduction number, which has been widely used and misused to characterize the transmissibility of the virus, hides the fact that transmission is stochastic, is dominated by a small number of individuals, and is driven by super-spreading events (SSEs). The distinct transmission features, such as high stochasticity under low prevalence, and the central role played by SSEs on transmission dynamics, should not be overlooked. Many explosive SSEs have occurred in indoor settings stoking the pandemic and shaping its spread, such as long-term care facilities, prisons, meat-packing plants, fish factories, cruise ships, family gatherings, parties and night clubs. These SSEs demonstrate the urgent need to understand routes of transmission, while posing an opportunity that outbreak can be effectively contained with targeted interventions to eliminate SSEs. Here, we describe the potential types of SSEs, how they influence transmission, and give recommendations for control of SARS-CoV-2.

q-bio.PE↗

arXiv2020

Beyond $R_0$: Heterogeneity in secondary infections and probabilistic epidemic forecasting

The basic reproductive number -- $R_0$ -- is one of the most common and most commonly misapplied numbers in public health. Although often used to compare outbreaks and forecast pandemic risk, this single number belies the complexity that two different pathogens can exhibit, even when they have the same $R_0$. Here, we show how to predict outbreak size using estimates of the distribution of secondary infections, leveraging both its average $R_0$ and the underlying heterogeneity. To do so, we reformulate and extend a classic result from random network theory that relies on contact tracing data to simultaneously determine the first moment ($R_0$) and the higher moments (representing the heterogeneity) in the distribution of secondary infections. Further, we show the different ways in which this framework can be implemented in the data-scarce reality of emerging pathogens. Lastly, we demonstrate that without data on the heterogeneity in secondary infections for emerging infectious diseases like COVID-19, the uncertainty in outbreak size ranges dramatically. Taken together, our work highlights the critical need for contact tracing during emerging infectious disease outbreaks and the need to look beyond $R_0$ when predicting epidemic size.

q-bio.PE↗

arXiv2020

Spread of infectious disease and social awareness as parasitic contagions on clustered networks

There is a rich history of models for the interaction of a biological contagion like influenza with the spread of related information such as an influenza vaccination campaign. Recent work on the spread of interacting contagions on networks has highlighted that these interacting contagions can have counter-intuitive interplay with network structure. Here we generalize one of these frameworks to tackle three important features of the spread of awareness and disease: one, we model the dynamics on highly clustered, cliquish, networks to mimic the role of workplaces and households; two, the awareness contagion affects the spread of the biological contagion by reducing its transmission rate where an aware or vaccinated individual is less likely to be infected; and three, the biological contagion also affects the spread of the awareness contagion but by increasing its transmission rate where an infected individual is more receptive and more likely to share information related to the disease. Under these conditions, we find that increasing network clustering, which is known to hinder disease spread, can actually allow them to sustain larger epidemics of the disease in models with awareness. This counter-intuitive result goes against the conventional wisdom suggesting that random networks are justifiable as they provide worst-case scenario forecasts. To further investigate this result, we provide a closed-form criterion based on a two-step branching process (i.e., the numbers of expected tertiary infections) to identify different regions in parameter space where the net effect of clustering and co-infection varies. Altogether, our results highlight once again the need to go beyond random networks in disease modeling and illustrate the type of analysis that is possible even in complex models of interacting contagions.

physics.soc-ph↗

arXiv2017

Asymmetric percolation drives a double transition in sexual contact networks

Zika virus (ZIKV) exhibits unique transmission dynamics in that it is concurrently spread by a mosquito vector and through sexual contact. We show that this sexual component of ZIKV transmission induces novel processes on networks through the highly asymmetric durations of infectiousness between males and females -- it is estimated that males are infectious for periods up to ten times longer than females -- leading to an asymmetric percolation process on the network of sexual contacts. We exactly solve the properties of this asymmetric percolation on random sexual contact networks and show that this process exhibits two epidemic transitions corresponding to a core-periphery structure. This structure is not present in the underlying contact networks, which are not distinguishable from random networks, and emerges because of the asymmetric percolation. We provide an exact analytical description of this double transition and discuss the implications of our results in the context of ZIKV epidemics. Most importantly, our study suggests a bias in our current ZIKV surveillance as the community most at risk is also one of the least likely to get tested.

physics.soc-ph↗

arXiv2016

Can Big Media Data Revolutionarize Gun Violence Prevention?

The scientific method drives improvements in public health, but a strategy of obstructionism has impeded scientists from gathering even a minimal amount of information to address America's gun violence epidemic. We argue that in spite of a lack of federal investment, large amounts of publicly available data offer scientists an opportunity to measure a range of firearm-related behaviors. Given the diversity of available data - including news coverage, social media, web forums, online advertisements, and Internet searches (to name a few) - there are ample opportunities for scientists to study everything from trends in particular types of gun violence to gun-related behaviors (such as purchases and safety practices) to public understanding of and sentiment towards various gun violence reduction measures. Science has been sidelined in the gun violence debate for too long. Scientists must tap the big media data stream and help resolve this crisis.

cs.CY↗

arXiv2014

Escaping the poverty trap: modeling the interplay between economic growth and the ecology of infectious disease

The dynamics of economies and infectious disease are inexorably linked: economic well-being influences health (sanitation, nutrition, treatment capacity, etc.) and health influences economic well-being (labor productivity lost to sickness and disease). Often societies are locked into "poverty traps" of poor health and poor economy. Here, using a simplified coupled disease-economic model with endogenous capital growth we demonstrate the formation of poverty traps, as well as ways to escape them. We suggest two possible mechanisms of escape both motivated by empirical data: one, through an influx of capital (development aid), and another through changing the percentage of GDP spent on healthcare. We find that a large influx of capital is successful in escaping the poverty trap, but increasing health spending alone is not. Our results demonstrate that escape from a poverty trap may be possible, and carry important policy implications in the world-wide distribution of aid and within-country healthcare spending.

physics.soc-ph↗

arXiv2014

Epidemiological consequences of an ineffective Bordetella pertussis vaccine

The recent increase in Bordetella pertussis incidence (whooping cough) presents a challenge to global health. Recent studies have called into question the effectiveness of acellular B. pertussis vaccination in reducing transmission. Here we examine the epidemiological consequences of an ineffective B. pertussis vaccine. Using a dynamic transmission model, we find that: 1) an ineffective vaccine can account for the observed increase in B. pertussis incidence; 2) asymptomatic infections can bias surveillance and upset situational awareness of B. pertussis; and 3) vaccinating individuals in close contact with infants too young to receive vaccine (so called "cocooning" unvaccinated children) may be ineffective. Our results have important implications for B. pertussis vaccination policy and paint a complicated picture for achieving herd immunity and possible B. pertussis eradication.

q-bio.PE↗

arXiv2008

Differences in Impact Factor Across Fields and Over Time

The bibliometric measure impact factor is a leading indicator of journal influence, and impact factors are routinely used in making decisions ranging from selecting journal subscriptions to allocating research funding to deciding tenure cases. Yet journal impact factors have increased gradually over time, and moreover impact factors vary widely across academic disciplines. Here we quantify inflation over time and differences across fields in impact factor scores and determine the sources of these differences. We find that the average number of citations in reference lists has increased gradually, and this is the predominant factor responsible for the inflation of impact factor scores over time. Field-specific variation in the fraction of citations to literature indexed by Thomson Scientific's Journal Citation Reports is the single greatest contributor to differences among the impact factors of journals in different fields. The growth rate of the scientific literature as a whole, and cross-field differences in net size and growth rate of individual fields, have had very little influence on impact factor inflation or on cross-field differences in impact factor.

physics.soc-ph↗