Significance This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict the COVID-19 disease course and compare the effectiveness of mitigation measures across countries to inform policy decision making using a robust and parsimonious survival-convolution model. We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number ( R t ) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the transmission rate using a natural experiment design and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only early phase data (2–3 weeks after the outbreak). A fast rate of decline in R t was observed, and adopting mitigation strategies early in the epidemic was effective in reducing the transmission rate in these two countries. The nationwide lockdown in Italy did not accelerate the speed at which the transmission rate decreases. In the United States, R t significantly decreased during a 2-week period after the declaration of national emergency, but it declined at a much slower rate afterwards. If the trend continues after May 1, COVID-19 may be controlled by late July. However, a loss of temporal effect (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (mid-November with fewer than 100 daily cases) and a total of more than 2 million cases.

LHRH receptor, is over-expressed in a variety of human tumors and, is a potential binding site for targeted metastatic prostate cancer therapy. The objectives of our study were to synthesize a bioconjugate of the LHRH analog [DLys6]-LHRH and the anti-tumor agent methotrexate and test the hypothesis that [DLys6]-LHRH-MTX targets and inhibits prostate cancer cell growth in vitro and in vivo. The results of in vitro studies, showed that both [DLys6]-LHRH-MTX and MTX displayed superior cytotoxicity against prostate cancer cells in a concentration-dependent manners, with IC50 concentrations for PC-3 cells of, 1.02 ± 0.18 μmol/L and 6.34 ± 1.01 μmol/L; for DU-145 cells, 1.53 ± 0.27 μmol/L and 8.03 ± 1.29 μmol/L; and for LNCaP cells, 1.93 ± 0.19 μmol/L and 9.68 ± 1.24 μmol/L, respectively. The IC50 values of [DLys6]-LHRH-MTX and MTX were 110.77 ± 15.31 μmol/L and 42.33 ± 7.25 μmol/L, respectively. Finally, [DLys6]-LHRH-MTX significantly improved the anti-tumor activity of MTX in nude mice bearing PC-3 tumor xenografts. The inhibition ratios of tumor volume and tumor weight in the [DLys6]-LHRH-MTX treated group were significantly higher than those in the MTX-treated group. Tumor volume doubling time was also significantly extended from 6.13 days in control animals to 9.67 days in mice treated with [DLys6]-LHRH-MTX. In conclusion, [DLys6]-LHRH -MTX may be useful in treating prostate cancer.

medRxiv preprint South Korea have reduced the infection rate faster than Italy and the US. Italy's R t has remained around 1·0 for more than two weeks since March 26, while in the US R t continues to decrease. Implications of all the available evidenceImplementing response measures earlier in the disease epidemic reduces the disease transmission measured by R t at a faster speed. Thus, for regions at early stage of disease epidemic (e.g., South America), mitigation measures should be introduced early. Nation-wide lockdown may not further reduce the speed of R t reduction compared to regional quarantine measures. In countries where disease transmission has slowed down, lifting of quarantine measures may lead to a persistent infection rate delaying full control of epidemic and thus should be implemented with caution.