Models of epidemics: when contact repetition and clustering should be included

Fiebig, Lena; Scholz, R. D.

doi:10.1055/s-0030-1266299

Cited by 3 publications

(4 citation statements)

References 21 publications

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“…Once two persons are identified to have contact, and one of them is contagious and the other is susceptible, there is a probability of an infection. For this, we use the mechanical model by Smieszek [60, 71]: Infected persons generate a “viral load” that they exhale, cough or sneeze into the environment, and people close by are exposed. Overall, the probability for person n to become infected by this process in a time step t is described as where m is a sum over all other persons, sh is the shedding rate (∼ microbial load), ci the contact intensity, in the intake (reduced, e.g., by a mask), τ the duration of interaction between the two individuals, and Θ a calibration parameter.…”

Section: Model Detailsmentioning

confidence: 99%

Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data

Müller

Balmer²,

Charlton

et al. 2021

Preprint

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Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19.This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. The model is validated against the infection dynamics in Berlin (Germany).The model can be used to understand the contributions of different activity types to the infection dynamics over time. The model predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reinfection rate R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%.Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions.Author summaryEvidently, there is an interest in models that are able to predict the effect of interventions in the face of pandemic diseases. The so-called compartmental models have difficulties to include effects that stem from spatial, demographic or temporal inhomongeneities. Person-centric models, often using social contact matrices, are difficult and time-consuming to build up. In the present paper, we describe how we built a largely data-driven person-centric infection model within less than a month when COVID-19 took hold in Germany. The model is based on our extensive experience with mobility modelling, and a synthetic data pipeline that starts with mobile phone data, while taking the infection dynamics and the disease progression from the literature. The approach makes the model portable to all places that have similar so-called activity-based models of travel in place, which are many places world-wide, and the number is continuously increasing. The model has been used since its inception to regularly advise the German government on expected consequences of interventions.

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Section: Model Detailsmentioning

confidence: 99%

Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data

Müller

Balmer²,

Charlton

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…Also, we include the effects of public transport. Finally, we include a mechanistic infection model, taken from Smieszek (2010). Ferguson et al (2020) do not describe their microscopic infection model in that paper; we cannot tell if the reported so-called “R” values (= reinfection values) are model input or model output.…”

Section: Discussionmentioning

confidence: 99%

“…The present paper goes beyond the above-mentioned studies (Smieszek 2010; Hackl and Dubernet 2019) in the following aspects:…”

Section: Introductionmentioning

confidence: 91%

“…droplet infection), and infection by touch (smear infection). Smieszek (Smieszek 2009, 2010) has described in particular the first process in detail: Infected persons generate a “viral load” that they exhale or cough into the environment, and people close by have an exposure. Overall, the probability to become infected by this process in a time step t is described as where m is a sum over all other persons, q is the shedding rate (∼microbial load), i contact intensity, and τ the duration of interaction between the two individuals.…”

Section: Introductionmentioning

confidence: 99%

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Mobility traces and spreading of COVID-19

Müller

Balmer²,

Neumann³

et al. 2020

Preprint

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We use human mobility models, for which we are experts, and attach a virus infection dynamics to it, for which we are not experts but have taken it from the literature, including recent publications. This results in a virus spreading dynamics model. The results should be verified, but because of the current time pressure, we publish them in their current state. Recommendations for improvement are welcome. We come to the following conclusions:

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Kovi̇d-19 Pandemi̇ Süreci̇ni̇n Değerlendi̇ri̇lmesi̇nde Sosyal Ağ Anali̇zi̇ni̇n Kullanimi

UNUTULMAZ

Dulupçu

2020

Türkiye Mesleki Ve Sosyal Bilimler Dergisi

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İnsanlık tarihinde geniş coğrafyalara yayılan ve toplu ölümlere neden olan pek çok salgın hastalık yaşanmıştır. Bu hastalıkların ortaya çıkmasının önlenmesi, izlenmesi ve kontrol altına alınması yıllarca birincil öneme sahip halk sağlığı sorunu olarak görülmüştür. Temas ağları, enfeksiyonların popülasyonda yayılmasını görselleştirmek, modelleme yaklaşımlarının temelini oluşturmak ve müdahale için kilit bireyleri tanımlamak amacıyla kullanılmaktadır. Bu çalışmada bulaşıcı hastalıkların yayılmasını anlamak için sosyal ağ analizlerinin sağladığı olanaklar ele alınmaktadır. Hem epidemiyolojide kullanılan tekniklere hem de sosyal ağ analizinde yeni perspektifler açan son çalışmalara odaklanılmıştır. Sosyal ağ analizi temaslar arasındaki çok sayıda ara bağlantı ve yoğun döngüsel şekilleri göstererek enfekte vaka hastaları ve temaslar arasında yakın ilişkiler olduğunu ortaya koymuştur. Ayrıca temas ağırlıkları kullanılarak, bireysel enfeksiyon riskinin tahmin edilebileceği ve koruyucu aşılama gibi hedefe yönelik müdahalelerin etkin bir şekilde uygulanabileceği görülmüştür. Sosyal ağ analizi aracılığı ile elde edilen kilit bilgiler Kovid-19 denetleyicileri için ileriyi tahmin etmeye olanak sağlayarak salgın hastalıların yayılımını önlemeye yardımcı olabilir.

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Models of epidemics: when contact repetition and clustering should be included

Cited by 3 publications

References 21 publications

Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data

Predicting the effects of COVID-19 related interventions in urban settings by combining activity-based modelling, agent-based simulation, and mobile phone data

Mobility traces and spreading of COVID-19

Kovi̇d-19 Pandemi̇ Süreci̇ni̇n Değerlendi̇ri̇lmesi̇nde Sosyal Ağ Anali̇zi̇ni̇n Kullanimi

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