Given progressive developments and demands on clinical trials, accurate enrollment timeline forecasting is increasingly crucial for both strategic decision-making and trial execution excellence. Naïve approach assumes flat rates on enrollment using average of historical data, while traditional statistical approach applies simple Poisson-Gamma model using time-invariant rates for site activation and subject recruitment. Both of them are lack of non-trivial factors such as time and location. We propose a novel two-segment statistical approach based on Quasi-Poisson regression for subject accrual rate and Poisson process for subject enrollment and site activation. The input study-level data are publicly accessible and it can be integrated with historical study data from user\'s organization to prospectively predict enrollment timeline. The new framework is neat and accurate compared to preceding works. We validate the performance of our proposed enrollment model and compare the results with other frameworks on 7 curated studies.

This work presents a model-agnostic evaluation of four different models that estimate a disease\'s basic reproduction number. The evaluation presented is twofold: first, the theory behind each of the models is reviewed and compared; then, each model is tested with eight impartial simulations. All scenarios were constructed in an experimental framework that allows each model to fulfill its assumptions and hence, obtain unbiased results for each case. Among these models is the one proposed by Thompson et al. (Epidemics 29:100356, 2019), i.e., a Bayesian estimation method well established in epidemiological practice. The other three models include a novel state-space method and two simulation-based approaches based on a Poisson infection process. The advantages and flaws of each model are discussed from both theoretical and practical standpoints. Finally, we present the evolution of Covid-19 outbreak in Colombia as a case study for computing the basic reproduction number with each one of the reviewed methods.

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BACKGROUND: Disasters occur randomly and can severely tax the health care delivery system of affected and surrounding regions. A significant proportion of disaster survivors are children, who have unique medical, psychosocial, and logistical needs after a mass casualty event. Children are often transported to specialty centers after disasters for a higher level of pediatric care, but this can also lead to separation of these survivors from their families. In a recent theoretical article, we showed that the availability of a pediatric trauma center after a mass casualty event would decrease the time needed to definitively treat the pediatric survivor cohort and decrease pediatric mortality. However, we also found that if the pediatric center was too slow in admitting and discharging patients, these benefits were at risk of being lost as children became \"trapped\" in the slow center. We hypothesized that this effect could result in further increased mortality and greater costs.
METHODS: Here, we expand on these ideas to test this hypothesis via mathematical simulation. We examine how a delay in discharge of part of the pediatric cohort is predicted to affect mortality and the cost of inpatient care in the setting of our model.
RESULTS: We find that mortality would increase slightly (from 14.2%-16.1%), and the cost of inpatient care increases dramatically (by a factor of 21) if children are discharged at rates consistent with reported delays to reunification after a disaster from the literature.
CONCLUSIONS: Our results argue for the ongoing improvement of identification technology and logistics for rapid reunification of pediatric survivors with their families after mass casualty events.