This app teaches the topic of Infectious disease surveillance and demonstrates the effect of the type and extent of surveillance on the dynamics of an infectious disease in a population, including the course and outcome of an epidemic. Read about the model in the “Model” tab. Then do the tasks described in the “What to do” tab.
Understand the influence of infectious disease surveillance on the dynamics of an infectious disease in a population by comparing disease dynamics with and without surveillance.
Understand the effect of the extent of surveillance and underreporting/incomplete surveillance (Brabazon et al. 2015; Kazerooni et al. 2018; Jelastopulu, Merekoulias, and Alexopoulos 2010)on the course and outcome of the epidemic. The effect of the extent of surveillance on the number of susceptibles left at the end of the simulation and the total number of deaths that occurred will be explored. In addition, users will explore which surveillance parameters prevent the occurrence of an outbreak.
Understand influence of the type of surveillance (e.g., one that detects presymptomatic, asymptomatic or symptomatic infections or pairwise combinations of the three or all) on disease dynamics. The effect of the type of surveillance will be explored in the case where individuals in the different stages of infection (presymptomatic, asymptomatic or symptomatic) transmit or don’t.
For this compartmental model, we track the following compartments/stages:
The model can be extended to include a reported compartment. This compartment would represent for example the US Centers for Disease Control and Prevention (CDC) to which health systems report the observed cases of disease at some rate(s).
In addition to specifying the compartments of a model, we need to specify the dynamics determining the changes for each compartment. In general, more compartments leads to more processes and more parameters governing these processes.
For this model, we include the following processes:
The flow diagram and the set of ordinary differential equations (ODE) which are used to implement this model are as follows:
Flow diagram for this model.
\[\dot S = m - S(b_P P + b_A A + b_I I) + wR - nS\] \[\dot P = S (b_P P + b_A A + b_I I) - P(g_P + r_P + n) \] \[\dot A = f g_P P - A(r_A + n) \] \[\dot I = (1-f) g_P P - I (r_I + n + d) \] \[\dot R = r_PP + r_A A + r_I I - wR \] \[\dot D = dD\]
The tasks below are described in a way that assumes everything is in units of months (rate parameters, therefore, have units of inverse months). If any quantity is not given in those units, you need to convert it first (e.g. if it says a year, you need to convert it to 12 months).
simulate_idsurveillance_ode
. You can call them directly, without going through the shiny app. Use the help()
command for more information on how to use the functions directly. If you go that route, you need to use the results returned from this function and produce useful output (such as a plot) yourself.vignette('DSAIDE')
into the R console.Brabazon, E. D., A. Sheridan, P. Finnegan, M. W. Carton, and D. Bedford. 2015. “Under-reporting of notifiable infectious disease hospitalizations: Significant improvements in the Irish context.” Epidemiology and Infection 143 (6): 1166–74. https://doi.org/10.1017/S0950268814001733.
Jelastopulu, E., G. Merekoulias, and E. C. Alexopoulos. 2010. “Underreporting of communicable diseases in the prefecture of Achaia, Western Greece, 1999-2004 - Missed opportunities for early intervention.” Eurosurveillance 15 (21): 1999–2004. https://doi.org/10.2807/ese.15.21.19579-en.
Kazerooni, P. A., M. Fararouei, M. Nejat, M. Akbarpoor, and Z. Sedaghat. 2018. “Under-ascertainment, under-reporting and timeliness of Iranian communicable disease surveillance system for zoonotic diseases.” Public Health 154: 130–35. https://doi.org/10.1016/j.puhe.2017.10.029.