Experience with a hybrid recruitment approach of patient-facing web portal screening and subsequent phone and medical record review for a neurosurgical intervention trial for chronic ischemic stroke disability (PISCES III).

BACKGROUND: Recruitment of participants is the greatest risk to completion of most clinical trials, with 20-40% of trials failing to reach the targeted enrollment. This is particularly true of trials of central nervous system (CNS) therapies such as intervention for chronic stroke. The PISCES III trial was an invasive trial of stereotactically guided intracerebral injection of CTX0E03, a fetal derived neural stem cell line, in patients with chronic disability due to ischemic stroke. We report on the experience using a novel hybrid recruitment approach of a patient-facing portal to self-identify and perform an initial screen for general trial eligibility (tier 1), followed by phone screening and medical records review (tier 2) prior to a final in-person visit to confirm eligibility and consent. METHODS: Two tiers of screening were established: an initial screen of general eligibility using a patient-facing web portal (tier 1), followed by a more detailed screen that included phone survey and medical record review (tier 2). If potential participants passed the tier 2 screen, they were referred directly to visit 1 at a study site, where final in-person screening and consent were performed. Rates of screening were tracked during the period of trial recruitment and sources of referrals were noted. RESULTS: The approach to screening and recruitment resulted in 6125 tier 1 screens, leading to 1121 referrals to tier 2. The tier 2 screening resulted in 224 medical record requests and identification of 86 qualifying participants for referral to sites. The study attained a viable recruitment rate of 6 enrolled per month prior to being disrupted by COVID 19. CONCLUSIONS: A tiered approach to eligibility screening using a hybrid of web-based portals to self-identify and screen for general eligibility followed by a more detailed phone and medical record review allowed the study to use fewer sites and reduce cost. Despite the difficult and narrow population of patients suffering moderate chronic disability from stroke, this strategy produced a viable recruitment rate for this invasive study of intracranially injected neural stem cells. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03629275.

Modeling targeted layered containment of an influenza pandemic in the United States.

Planning a response to an outbreak of a pandemic strain of influenza is a high public health priority. Three research groups using different individual-based, stochastic simulation models have examined the consequences of intervention strategies chosen in consultation with U.S. public health workers. The first goal is to simulate the effectiveness of a set of potentially feasible intervention strategies. Combinations called targeted layered containment (TLC) of influenza antiviral treatment and prophylaxis and nonpharmaceutical interventions of quarantine, isolation, school closure, community social distancing, and workplace social distancing are considered. The second goal is to examine the robustness of the results to model assumptions. The comparisons focus on a pandemic outbreak in a population similar to that of Chicago, with approximately 8.6 million people. The simulations suggest that at the expected transmissibility of a pandemic strain, timely implementation of a combination of targeted household antiviral prophylaxis, and social distancing measures could substantially lower the illness attack rate before a highly efficacious vaccine could become available. Timely initiation of measures and school closure play important roles. Because of the current lack of data on which to base such models, further field research is recommended to learn more about the sources of transmission and the effectiveness of social distancing measures in reducing influenza transmission.

Evaluating the impact of international airline suspensions on the early global spread of COVID-19.

Global airline networks play a key role in the global importation of emerging infectious diseases. Detailed information on air traffic between international airports has been demonstrated to be useful in retrospectively validating and prospectively predicting case emergence in other countries. In this paper, we use a well-established metric known as effective distance on the global air traffic data from IATA to quantify risk of emergence for different countries as a consequence of direct importation from China, and compare it against arrival times for the first 24 countries. Using this model trained on official first reports from WHO, we estimate time of arrival (ToA) for all other countries. We then incorporate data on airline suspensions to recompute the effective distance and assess the effect of such cancellations in delaying the estimated arrival time for all other countries. Finally we use the infectious disease vulnerability indices to explain some of the estimated reporting delays.

A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation.

We describe a large-scale simulation of the aftermath of a hypothetical 10kT improvised nuclear detonation at ground level, near the White House in Washington DC. We take a synthetic information approach, where multiple data sets are combined to construct a synthesized representation of the population of the region with accurate demographics, as well as four infrastructures: transportation, healthcare, communication, and power. In this article, we focus on the model of agents and their behavior, which is represented using the options framework. Six different behavioral options are modeled: household reconstitution, evacuation, healthcare-seeking, worry, shelter-seeking, and aiding & assisting others. Agent decision-making takes into account their health status, information about family members, information about the event, and their local environment. We combine these behavioral options into five different behavior models of increasing complexity and do a number of simulations to compare the models.

A Simulation Optimization Approach to Epidemic Forecasting.

Reliable forecasts of influenza can aid in the control of both seasonal and pandemic outbreaks. We introduce a simulation optimization (SIMOP) approach for forecasting the influenza epidemic curve. This study represents the final step of a project aimed at using a combination of simulation, classification, statistical and optimization techniques to forecast the epidemic curve and infer underlying model parameters during an influenza outbreak. The SIMOP procedure combines an individual-based model and the Nelder-Mead simplex optimization method. The method is used to forecast epidemics simulated over synthetic social networks representing Montgomery County in Virginia, Miami, Seattle and surrounding metropolitan regions. The results are presented for the first four weeks. Depending on the synthetic network, the peak time could be predicted within a 95% CI as early as seven weeks before the actual peak. The peak infected and total infected were also accurately forecasted for Montgomery County in Virginia within the forecasting period. Forecasting of the epidemic curve for both seasonal and pandemic influenza outbreaks is a complex problem, however this is a preliminary step and the results suggest that more can be achieved in this area.

Sensitivity analysis of an individual-based model for simulation of influenza epidemics.

Individual-based epidemiology models are increasingly used in the study of influenza epidemics. Several studies on influenza dynamics and evaluation of intervention measures have used the same incubation and infectious period distribution parameters based on the natural history of influenza. A sensitivity analysis evaluating the influence of slight changes to these parameters (in addition to the transmissibility) would be useful for future studies and real-time modeling during an influenza pandemic.In this study, we examined individual and joint effects of parameters and ranked parameters based on their influence on the dynamics of simulated epidemics. We also compared the sensitivity of the model across synthetic social networks for Montgomery County in Virginia and New York City (and surrounding metropolitan regions) with demographic and rural-urban differences. In addition, we studied the effects of changing the mean infectious period on age-specific epidemics. The research was performed from a public health standpoint using three relevant measures: time to peak, peak infected proportion and total attack rate. We also used statistical methods in the design and analysis of the experiments. The results showed that: (i) minute changes in the transmissibility and mean infectious period significantly influenced the attack rate; (ii) the mean of the incubation period distribution appeared to be sufficient for determining its effects on the dynamics of epidemics; (iii) the infectious period distribution had the strongest influence on the structure of the epidemic curves; (iv) the sensitivity of the individual-based model was consistent across social networks investigated in this study and (v) age-specific epidemics were sensitive to changes in the mean infectious period irrespective of the susceptibility of the other age groups. These findings suggest that small changes in some of the disease model parameters can significantly influence the uncertainty observed in real-time forecasting and predicting of the characteristics of an epidemic.

Prediction of an Epidemic Curve: A Supervised Classification Approach.

Classification methods are widely used for identifying underlying groupings within datasets and predicting the class for new data objects given a trained classifier. This study introduces a project aimed at using a combination of simulations and classification techniques to predict epidemic curves and infer underlying disease parameters for an ongoing outbreak.Six supervised classification methods (random forest, support vector machines, nearest neighbor with three decision rules, linear and flexible discriminant analysis) were used in identifying partial epidemic curves from six agent-based stochastic simulations of influenza epidemics. The accuracy of the methods was compared using a performance metric based on the McNemar test.The findings showed that: (1) assumptions made by the methods regarding the structure of an epidemic curve influences their performance i.e. methods with fewer assumptions perform best, (2) the performance of most methods is consistent across different individual-based networks for Seattle, Los Angeles and New York and (3) combining classifiers using a weighting approach does not guarantee better prediction.

Same influenza vaccination strategies but different outcomes across US cities?

OBJECTIVES: This research aimed to determine if the same influenza vaccination strategies would have the same level of effectiveness when applied to two different US metropolitan areas, Miami and Seattle, where the composition of the population differs significantly in age distribution and household size distribution. METHODS: We used an individual-based network modeling approach in which every pair of individuals connected in the social network is represented. Factorial design experiments were performed to estimate the impact of age-targeted vaccination strategies to control the transmission of a 'flu-like' virus. RESULTS: The findings showed that: (1) age composition of the city matters in determining the effectiveness of a vaccination strategy and (2) vaccinating school children outperforms every other strategy. CONCLUSIONS: The most significant policy implication of this research is that there may not be a universal vaccination strategy that works across all cities with the same level of effectiveness. Secondly, given the important role of school children in the transmission of influenza, the US Government should consider the vaccination of school children a top priority.

Detail in network models of epidemiology: are we there yet?

Network models of infectious disease epidemiology can potentially provide insight into how to tailor control strategies for specific regions, but only if the network adequately reflects the structure of the region's contact network. Typically, the network is produced by models that incorporate details about human interactions. Each detail added renders the models more complicated and more difficult to calibrate, but also more faithful to the actual contact network structure. We propose a statistical test to determine when sufficient detail has been added to the models and demonstrate its application to the models used to create a synthetic population and contact network for the USA.