Measurability of the epidemic reproduction number in data-driven contact networks

Abstract

SignificanceThe analysis of real epidemiological data has raised issues of the adequacy of the classic homogeneous modeling framework and quantities, such as the basic reproduction number in real-world situations. Based on high-quality sociodemographic data, here we generate a multiplex network describing the contact pattern of the Italian and Dutch populations. By using a microsimulation approach, we show that, for epidemics spreading on realistic contact networks, it is not possible to define a steady exponential growth phase and a basic reproduction number. We show the operational use of the instantaneous reproduction rate as a good descriptor of the transmission dynamics.

Epidemiological characteristics of COVID-19 cases in Italy and estimates of the reproductive numbers one month into the epidemic

“…Case fatality rates (CFR), not accounting for delays, were calculated by age and sex and smoothed with the locally weighted regression method. CFRs by age were also calculated by calendar period of diagnosis (i.e., before 4 th , [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. A multilevel (clustered by Region/AP) multivariable logistic model was applied to evaluate characteristics associated with death, including age group (i.e., ≤40, 40-49, 50-59, 60-69, 70-79, 80-89, 90+ years), sex, HCW status, and week of diagnosis.…”

Evolving epidemiology of novel coronavirus diseases 2019 and possible interruption of local transmission outside Hubei Province in China: a descriptive and modeling study

“…By leveraging the estimated distribution of the serial interval, we provide estimates of the net reproduction number (Rt), which is the average number of secondary cases generated by a typical primary case at time t. Consecutive generations of cases arise after a period measured by the serial interval or by the generation time. We use a Bayesian approach to estimate Rt from the time series of symptom onset dates and the distribution of the serial interval 8,9 . For this analysis, the last 9 days of the dataset were not considered to deal with the possible incompleteness of the dataset due to reporting delays.…”

Mathematical model of infection kinetics and its analysis for COVID-19, SARS and MERS

“…Zhang et al (2018)) proposed a novel susceptible-infected-susceptible-recovered-susceptible virus transmission model based on partial immunity and immune inefficiency in complex networks and studied the epidemic dynamics behavior of this model in unified networks and scale-free networks based on mean field theory. Liu et al (2018)) simulated an infection transmission process on multiplex contact networks accounting for the natural history of influenza and found that the classical concept of the basic reproduction number was untenable in realistic populations, and it did not provide any conceptual understanding of the epidemic evolution. Based on the complex network theory, Fan et al (2020)) established the SEIR dynamic model of 2019-ncov epidemic with incubation period, and predicted the epidemic inflection point through model parameter simulation.Based on Wuhan migration data, Yang et al (2020)) estimated the number of people infected with SARS-CoV-2 in Wuhan by the number of confirmed people and found that the rate of confirmed patients in 15 cities in Hubei province was lower than that in 35 cities outside the province in terms of the mean and median.…”