Estimating the population-level effects of non-pharmaceutical interventions when transmission rates of COVID-19 vary by orders of magnitude from one contact to another

Publication date: Oct 03, 2024

Statistical physicists have long studied systems where the variable of interest spans many orders of magnitude, the classic example is the relaxation times of glassy materials, which are often found to follow power laws. A power-law dependence has been found for the probability of transmission of COVID-19, as a function of length of time a susceptible person is in contact with an infected person. This is in data from the United Kingdom’s COVID-19 app. The amount of virus in infected people spans many orders of magnitude. Inspired by this I assume that the power-law behaviour found in COVID-19 transmission, is due to the effective transmission rate varying over orders of magnitude from one contact to another. I then use a model from statistical physics to estimate that if a population all wear FFP2/N95 masks, this reduces the effective reproduction number for COVID-19 transmission by a factor of approximately nine.

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Concepts Keywords
Donald Air
E2200109119 Al
Mazzarella Contact
Physicists Covid
Tuberculosis Fig
Infected
Infection
Law
Person
Probability
Rate
Rates
Transmission
Viral
Virus

Semantics

Type Source Name
drug DRUGBANK Ranitidine
drug DRUGBANK Water
disease MESH viral infection
disease IDO intervention
pathway KEGG Tuberculosis
disease MESH tuberculosis
disease MESH infection
disease IDO susceptibility
disease MESH viral load
disease IDO process
drug DRUGBANK Medical air
pathway REACTOME Reproduction
disease MESH COVID-19
disease MESH respiratory infections
drug DRUGBANK Tromethamine
disease MESH infectious diseases
drug DRUGBANK Pentaerythritol tetranitrate
disease MESH respiratory diseases
disease IDO assay
disease IDO quality
drug DRUGBANK Cysteamine
disease IDO host
disease IDO pathogen
drug DRUGBANK Vorinostat
disease MESH Inflammation
disease IDO infectivity

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