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Population Dynamics of Pathogens
Published in Leonhard Held, Niel Hens, Philip O’Neill, Jacco Wallinga, Handbook of Infectious Disease Data Analysis, 2019
The “initially exponential” method arise from the observation that initially during invasion into a susceptible host population the infection will spread exponentially at a rate , where is the serial interval. The serial interval is the time taken for the secondary cases to be infected once the primary case is infected (see Chapter 5). The epidemic is thus initially expected to double after time units. Hence, if a pathogen with a mean serial interval of 10 days doubles in 3 weeks, is estimated to be 1.3. Alternatively we can regress log(cumulative incidence) on time to estimate the rate of exponential increase by the slope, (), and calculate or some version thereoff [14]. The “initial exponential” approach is detailed in Chapter 5.
Definitions
Published in Johan Giesecke, Modern Infectious Disease Epidemiology, 2017
This term also applies to person-to-person spread. It is defined as the time from infection in the primary case until time of infection in his secondary case. You see that this is a more theoretical concept than serial interval, since time of infection may not be directly observable, whilst appearance of symptoms usually is. Depending on temporal and biological variations in infector and infected (such as varying infectivity over the infectious period), the two time periods may not always be quite the same. (The concepts of serial intervals and generation times are actually more complex than one would first think, but the above definitions will suffice for this book. For good discussions, refer to [4] and [5].)
Modeling the heterogeneity in COVID-19's reproductive number and its impact on predictive scenarios
Published in Journal of Applied Statistics, 2023
Our model is similar to the one previously used in the experimental section of this paper and based on the non-parametric model developed by Fraser [14] and later used for estimating the et al. [8]. This model is well-established and implemented in the R-package EarlyR [41], and it has been used in recent studies [45] to infer COVID 19's 8]. In this setting, each infected case is expected to contaminate on average of s elapsed since infection. One could indeed imagine a patient becoming increasingly contagious over the first few days of the infection as the viral load builds up, and decreasingly so after the peak of the illness. This infectious profile is typically modeled as the quantiles from a gamma distribution. Since this quantity is generally unknown and hard to estimate from available data, Cori et al. [8] propose the use of the parameters of the serial interval (for which we typically have much more substantial observational data and means of estimation) as a good proxy.
Positivity rate: an indicator for the spread of COVID-19
Published in Current Medical Research and Opinion, 2021
Ahmed Al Dallal, Usama AlDallal, Jehad Al Dallal
There are several methods to estimate Re35. In this research, we applied the method implemented by a ready-to-use, Microsoft Excel based tool36. The tool requires only the daily incidence data and the distribution of the mean and standard deviation of the serial interval (i.e. the time interval between the beginning of symptoms in the infector and infected cases). In addition, the tool allows the user to set the time window, in terms of the number of days, to calculate smooth Re daily values. In our study and based on an earlier study37, we set the mean and standard deviation of the serial interval to be 4.7 and 2.9, respectively, and we set the step time window to be 7 days. We ran the tool twice for each considered country to perform the study: one using the daily positivity rate and one using the daily number of confirmed cases. We obtained the Re results and compared them statistically.
Transmission dynamics and timing of key events for SARS-CoV-2 infection in healthcare workers
Published in Infectious Diseases, 2021
Ahmet Naci Emecen, Ecem Basoglu Sensoy, Edanur Sezgin, Buket Yildirim Ustuner, Salih Keskin, Neslisah Siyve, Saadet Goksu Celik, Gamze Bayrak, Nurcan Senturk Durukan, Ayse Coskun Beyan, Alp Ergor, Belgin Unal, Gul Ergor
Serial interval is the time between the symptom onsets of a primary case and a secondary case. It defines the time between the appearance of similar symptoms in successive generations. Incubation period is the time from infection to symptom onset. Along with the infectious period, both are key parameters in determining the control strategies for COVID-19. The exact timing of SARS-CoV-2 infection and/or symptom onset could not be observed precisely. Generally, real-life data provides a period of exposure times to viral pathogens causing respiratory infections and is often referred to as coarse data. Coarse data emerges when the exact value of the data lies in a subset of the complete data (in this study: period) that contains the exact value [12]. Taking the coarseness in our data into account, we censored the exposure intervals and the exact symptom onset dates with the possible left and right endpoints. The left endpoint of the exposure intervals was set at 3 days prior to symptom onset of the primary case for the pairs who had continuous contact with each other. It is generally considered that during the presymptomatic period, infected persons can be contagious for 1–3 days prior to symptom onset [13].