Strengthening Policy Coding Methodologies to Improve COVID-19 Disease Modeling and Policy Responses: A Proposed Coding Framework and Recommendations

In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but most have relied on binary coding that may not adequately describe the gradient in social distancing policies as states "re-open." We developed a COVID-19 social distancing intensity framework that is sufficiently specific and sensitive to capture this gradient. Based on a review of policies from a 12-state sample, we developed a social distancing intensity framework consisting of 16 domains and intensity scales of 0-5 for each domain. We found that the states with the highest average daily intensity from our sample were Pennsylvania, Washington, Colorado, California, and New Jersey, with Georgia, Florida, Massachusetts, and Texas having the lowest. While some domains (such as restaurants and movie theaters) showed bimodal policy intensity distributions compatible with binary (yes/no) coding, others (such as childcare and religious gatherings) showed broader variability that would be missed without more granular coding. We also present a range of methodological recommendations to strengthen COVID-19 comparative policy coding efforts.

Individualized Prediction of Heat Stress in Firefighters: A Data-Driven Approach Using Classification and Regression Trees.

The purpose of this study was to explore data-driven models, based on decision trees, to develop practical and easy to use predictive models for early identification of firefighters who are likely to cross the threshold of hyperthermia during live-fire training. Predictive models were created for three consecutive live-fire training scenarios. The final predicted outcome was a categorical variable: will a firefighter cross the upper threshold of hyperthermia - Yes/No. Two tiers of models were built, one with and one without taking into account the outcome (whether a firefighter crossed hyperthermia or not) from the previous training scenario. First tier of models included age, baseline heart rate and core body temperature, body mass index, and duration of training scenario as predictors. The second tier of models included the outcome of the previous scenario in the prediction space, in addition to all the predictors from the first tier of models. Classification and regression trees were used independently for prediction. The response variable for the regression tree was the quantitative variable: core body temperature at the end of each scenario. The predicted quantitative variable from regression trees was compared to the upper threshold of hyperthermia (38°C) to predict whether a firefighter would enter hyperthermia. The performance of classification and regression tree models was satisfactory for the second (success rate = 79%) and third (success rate = 89%) training scenarios but not for the first (success rate = 43%). Data-driven models based on decision trees can be a useful tool for predicting physiological response without modeling the underlying physiological systems. Early prediction of heat stress coupled with proactive interventions, such as pre-cooling, can help reduce heat stress in firefighters.

Manikin-based performance evaluation of elastomeric respirators against combustion particles.

This study investigated the effects of faceseal leakage, breathing flow, and combustion material on the overall (non-size-selective) penetration of combustion particles into P-100 half and full facepiece elastomeric respirators used by firefighters. Respirators were tested on a breathing manikin exposed to aerosols produced by combustion of three materials (wood, paper, and plastic) in a room-size exposure chamber. Testing was performed using a single constant flow (inspiratory flow rate = 30 L/min) and three cyclic flows (mean inspiratory flow rates = 30, 85, and 135 L/min). Four sealing conditions (unsealed, nose-only sealed, nose and chin sealed, and fully sealed) were examined to evaluate the respirator faceseal leakage. Total aerosol concentration was measured inside (C(in)) and outside (C(out)) the respirator using a condensation particle counter. The total penetration through the respirator was determined as a ratio of the two (P = C(in) / C(out)). Faceseal leakage, breathing flow type and rate, and combustion material were all significant factors affecting the performance of the half mask and full facepiece respirators. The efficiency of P-100 respirator filters met the NIOSH certification criteria (penetration ≤0.03%); it was not significantly influenced by the challenge aerosol and flow type, which supports the current NIOSH testing procedure using a single challenge aerosol and a constant airflow. However, contrary to the NIOSH total inward leakage (TIL) test protocol assuming that the result is independent on the type of the tested aerosol, this study revealed that the challenge aerosol significantly affects the particle penetration through unsealed and partially sealed half mask respirators. Increasing leak size increased total particle penetration. The findings point to some limitations of the existing TIL test in predicting protection levels offered by half mask elastomeric respirators.