Mutationism and the Dual Causation of Evolutionary Change

The rediscovery of Mendel's laws a century ago launched the science that William Bateson called "genetics," and led to a new view of evolution combining selection, particulate inheritance, and the newly characterized phenomenon of "mutation." This "mutationist" view clashed with the earlier view of Darwin, and the later "Modern Synthesis," by allowing discontinuity, and by recognizing mutation (or more properly, mutation-and-altered-development) as a source of creativity, direction, and initiative. By the mid-20th century, the opposing Modern Synthesis view was a prevailing orthodoxy: under its influence, "evolution" was redefined as "shifting gene frequencies," that is, the sorting out of pre-existing variation without new mutations; and the notion that mutation-and-altered-development can exert a predictable influence on the course of evolutionary change was seen as heretical. Nevertheless, mutationist ideas re-surfaced: the notion of mutational determinants of directionality emerged in molecular evolution by 1962, followed in the 1980s by an interest among evolutionary developmental biologists in a shaping or creative role of developmental propensities of variation, and more recently, a recognition by theoretical evolutionary geneticists of the importance of discontinuity and of new mutations in adaptive dynamics. The synthetic challenge presented by these innovations is to integrate mutation-and-altered-development into a new understanding of the dual causation of evolutionary change--a broader and more predictive understanding that already can lay claim to important empirical and theoretical results--and to develop a research program appropriately emphasizing the emergence of variation as a cause of propensities of evolutionary change

tjhladish/covid-abm: Active Vaccination Experiment 2023-02-02

Agent-Based Model of SARS-CoV-2 transmissio

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