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This work demonstrates the efficiency of using iterative ensemble smoothers to estimate the parameters of an SEIR model. We have extended a standard SEIR model with age-classes and compartments of sick, hospitalized, and dead. The data conditioned on are the daily numbers of accumulated deaths and the number of hospitalized. Also, it is possible to condition the model on the number of cases obtained from testing. We start from a wide prior distribution for the model parameters; then, the ensemble conditioning leads to a posterior ensemble of estimated parameters yielding model predictions in close agreement with the observations. The updated ensemble of model simulations has predictive capabilities and include uncertainty estimates. In particular, we estimate the effective reproductive number as a function of time, and we can assess the impact of different intervention measures. By starting from the updated set of model parameters, we can make accurate short-term predictions of the epidemic development assuming knowledge of the future effective reproductive number. Also, the model system allows for the computation of long-term scenarios of the epidemic under different assumptions. We have applied the model system on data sets from several countries, i.e., the four European countries Norway, England, The Netherlands, and France; the province of Quebec in Canada; the South American countries Argentina and Brazil; and the four US states Alabama, North Carolina, California, and New York. These countries and states all have vastly different developments of the epidemic, and we could accurately model the SARS-CoV-2 outbreak in all of them. We realize that more complex models, e.g., with regional compartments, may be desirable, and we suggest that the approach used here should be applicable also for these models. 

Abstract Plants have unique chemical and physical traits that can reduce infections in animals ranging from primates to caterpillars. Sunflowers (Helianthus annuus; Asteraceae) are one striking example, with pollen that suppresses infections by the trypanosomatid gut pathogenCrithidia bombiin the common eastern bumble bee (Bombus impatiens). However, the mechanism underlying this effect has remained elusive, and we do not know whether pollens from other Asteraceae species have similar effects.We evaluated whether mechanisms mediating sunflower pollen's antipathogenic effects are physical (due to its spiny exine), chemical (due to metabolites) or both. We also evaluated the degree to which pollen from seven other Asteraceae species reducedC. bombiinfection relative to pollen from sunflower and two non‐Asteraceae species, and whether pollen spine length predicted pathogen suppression.We found that sunflower exines alone reduced infection as effectively as whole sunflower pollen, while sunflower pollen metabolites did not. Furthermore, bees fed pollen from four of seven other Asteraceae had 62%–92% lowerC. bombiinfections than those fed non‐Asteraceae pollen. Spine length, however, did not explain variation in bumble bee infection.Our study indicates that sunflower pollen's capacity to suppressC. bombiis driven by its spiny exine, and that this phenomenon extends to several other Asteraceae species. Our results indicate that sunflower pollen exines are as effective as whole pollen in reducing infection, suggesting that future studies should expand to assess the effects of other species with spiny pollen on pollinator–pathogen dynamics. Read the freePlain Language Summaryfor this article on the Journal blog.