Quantifying the Risk and Cost of Active Monitoring for Infectious Diseases

Nicholas G Reich; Justin Lessler; Jay K Varma; Neil M Vora

The incubation period of a disease is the duration of time between exposure to the pathogen and symptom onset.(5) This characteristic is imperfectly observed in most settings. (6, 7) We obtained previously published incubation period observations on 145 cases of Ebola in Guinea (8) , 170 laboratory-confirmed cases of MERS-CoV in South Korea (9) and 362 cases of smallpox (10) (11) (12) . We fit parametric distributions to the observed incubation period data using maximum likelihood techniques (see Supplemental Text). We ran sensitivity analyses to evaluate the influence of several outlying observations in the Ebola dataset. Using this model, we calculate the probability of each of the four outcomes, with the associated expected costs, by combining the data on the probabilities of infection, the estimated incubation period distribution, and plausible cost ranges for different outcomes. Since limited data prevent us from formally estimating variance, we created conservative (i.e. maximally wide) intervals for possible costs by using the endpoints of the plausible cost ranges. Additionally, it determines the uncertainty associated with these estimates due to not having precise incubation period observations and having an unknown time between exposure and the beginning of monitoring for a particular individual (see Supplemental Text). For these pathogens, we assume that infectiousness coincides with the onset of symptoms. (13) (14) (15) Our model estimates the risks and costs associated with active monitoring programs for a range of active monitoring durations. To estimate the cost per person-day of monitoring, we used data on the number of individuals actively monitored by DOHMH and costs associated with the DOHMH Ebola response.