Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

Nande, Anjalika; Adlam, Ben; Sheen, Justin; Levy, Michael Z.; Hill, Alison L.

doi:10.1371/journal.pcbi.1008684

Cited by 87 publications

(72 citation statements)

References 68 publications

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“…Another compartmental epidemic model SEIR (susceptible-exposed-infectious-recovered) 15 forecast for few countries and the impact of the quarantine on the COVID-19 is investigated. A stochastic epidemic model is presented in Reference [16], where the effect of clinical progression and transmission network structure on the outcome of social distancing is investigated. An adaptive and improved version of the SIR model is illustrated in Reference [17].…”

Section: Introductionmentioning

confidence: 99%

Estimation of undetected symptomatic and asymptomatic cases of COVID‐19 infection and prediction of its spread in the USA

Mahajan

Solanki

Sivadas

2021

Journal of Medical Virology

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The reported COVID-19 cases in the United States of America have crossed over 10 million and a large number of infected cases are undetected whose estimation can be done if country-wide antibody testing is performed. In this study, we estimate this undetected fraction of the population by a modeling and simulation approach. We employ an epidemic model SIPHERD in which three categories of infection carriers, symptomatic, purely asymptomatic, and exposed are considered with different transmission rates that are taken dependent on the social distancing conditions, and the detection rate of the infected carriers is taken dependent on the tests done per day. The model is first validated for Germany and South Korea and then applied for prediction of the total number of confirmed, active and dead, and daily new positive cases in the United States. Our study predicts the possible outcomes of the infection if social distancing conditions are relaxed or kept stringent. We estimate that around 30.1 million people are already infected, and in the absence of any vaccine, 66.2 million (range: 64.3-68.0) people, or 20% (range: 19.4-20.5) of the population will be infected by mid-February 21 if social distancing conditions are not made stringent. We find the infection-to-fatality ratio to be 0.65% (range: 0.63-0.67).

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Section: Introductionmentioning

confidence: 99%

Estimation of undetected symptomatic and asymptomatic cases of COVID‐19 infection and prediction of its spread in the USA

Mahajan

Solanki

Sivadas

2021

Journal of Medical Virology

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“…24 Indeed, employees in some occupations (eg, healthcare, social assistance, agriculture, construction, manufacturing) have been shown to be at higher risk of COVID-19 exposure, which has major implications considering that racial/ethnic minority populations are overrepresented in these industry sectors. 24,25 Related, transmission of COV-ID-19 often occurs within the household, [26][27][28] and average US household size differs considerably by race/ethnicity. 29 Public health programs seeking to eliminate or reduce inequities in viral transmission need to bolster testing uptake among the most vulnerable populations disproportionally affected by COVID-19.…”

Section: Discussionmentioning

confidence: 99%

County-Level Factors That Influenced the Trajectory of COVID-19 Incidence in the New York City Area

Kranjac

2021

Health Security

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More than a century of research has shown that sociodemographic conditions affect infectious disease transmission. In the late spring and early summer of 2020, reports of the effects of sociodemographic variables on the spread of COVID-19 were used in the media with minimal scientific proof attached. With new cases of COVID-19 surging in the United States at that time, it became essential to better understand how the spread of COVID-19 was varying across all segments of the population. We used hierarchical exponential growth curve modeling techniques to examine whether community socioeconomic characteristics uniquely influence the incidence of reported COVID-19 cases in the urban built environment. We show that as of July 19, 2020, confirmed coronavirus infections in New York City and surrounding areas—one of the early epicenters of the COVID-19 pandemic in the United States—were concentrated along demographic and socioeconomic lines. Furthermore, our data provides evidence that after the onset of the pandemic, timely enactment of physical distancing measures such as school closures was essential to limiting the extent of the coronavirus spread in the population. We conclude that in a pandemic, public health authorities must impose physical distancing measures early on as well as consider community-level factors that associate with a greater risk of viral transmission.

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“…Effects of different contact networks were studied in [25,[72][73][74]. Contact networks are being used to build realistic simulations of a society, for instance by creating household-structures with various types of inter-household connections [75][76][77][78]. The flexibility to control networks modeling NPIs easy [76,77,79,80].…”

Section: Network-based Modelsmentioning

confidence: 99%

Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics

2021

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In the recent COVID-19 pandemic, mathematical modeling constitutes an important tool to evaluate the prospective effectiveness of non-pharmaceutical interventions (NPIs) and to guide policy-making. Most research is, however, centered around characterizing the epidemic based on point estimates like the average infectiousness or the average number of contacts. In this work, we use stochastic simulations to investigate the consequences of a population’s heterogeneity regarding connectivity and individual viral load levels. Therefore, we translate a COVID-19 ODE model to a stochastic multi-agent system. We use contact networks to model complex interaction structures and a probabilistic infection rate to model individual viral load variation. We observe a large dependency of the dispersion and dynamical evolution on the population’s heterogeneity that is not adequately captured by point estimates, for instance, used in ODE models. In particular, models that assume the same clinical and transmission parameters may lead to different conclusions, depending on different types of heterogeneity in the population. For instance, the existence of hubs in the contact network leads to an initial increase of dispersion and the effective reproduction number, but to a lower herd immunity threshold (HIT) compared to homogeneous populations or a population where the heterogeneity stems solely from individual infectivity variations.

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Dynamics of COVID-19 under social distancing measures are driven by transmission network structure

Cited by 87 publications

References 68 publications

Estimation of undetected symptomatic and asymptomatic cases of COVID‐19 infection and prediction of its spread in the USA

Estimation of undetected symptomatic and asymptomatic cases of COVID‐19 infection and prediction of its spread in the USA

County-Level Factors That Influenced the Trajectory of COVID-19 Incidence in the New York City Area

Heterogeneity matters: Contact structure and individual variation shape epidemic dynamics

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