Differential passage of fluids and different-sized particles in fistulated oxen (Bos primigenius f. taurus), muskoxen (Ovibos moschatus), reindeer (Rangifer tarandus) and moose (Alces alces): Rumen particle size discrimination is independent from contents stratification

Ruminant species differ in the degree that their rumen contents are stratified but are similar insofar that only very fine particles are passed from the forestomach to the lower digestive tract. We investigated the passage kinetics of fluid and particle markers (2, 10 and 20 mm) in fistulated cattle (Bos primigenius f. taurus), muskoxen (Ovibos moschatus), reindeer (Rangifer tarandus) and moose (Alces alces) on different diets. The distribution of dry matter in the rumen and the viscosity of rumen fluids suggested that the rumen contents were more stratified in muskoxen than moose. Correspondingly, as in previous studies, the species differed in the ratio of mean retention times of small particles to fluids in the reticulorumen, which was highest in cattle (2.03) and muskoxen (1.97-1.98), intermediate in reindeer (1.70) and lowest in moose (0.98-1.29). However, the ratio of large to small particle retention did not differ between the species, indicating similarity in the efficiency of the particle sorting mechanism. Passage kinetics of the two largest particle classes did not differ, indicating that particle retention is not a continuous function of particle size but rather threshold-dependent. Overall, the results suggest that fluid flow through the forestomach differs between ruminant species. A lower relative fluid passage, such as in moose, might limit species to a browse-based dietary niche, whereas a higher relative fluid passage broadens the dietary niche options and facilitates the inclusion of, or specialization on, grass. The function of fluid flow in the ruminant forestomach should be further investigated

Multiple models for outbreak decision support in the face of uncertainty

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020

Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020