Changes in Notifiable Infectious Diseases in China During the COVID-19 Pandemic: A National Surveillance Study (preprint)

Background: Nationwide nonpharmaceutical interventions (NPI) were used to combat the novel coronavirus disease (COVID-19) during 2020 in the mainland of China. These NPIs have proven effective on mitigating the spread of COVID-19, but their broad impact on other diseases remains under-investigated. In this study, we aim to assess whether such broad impact exists on notifiable diseases in China.Methods: Weekly incidence and mortality data for 31 major notifiable infectious diseases at the province level were extracted from the China Information System for Disease Control and Prevention from 2014 to 2020. We assessed the impact of NPIs by contrasting the incidences of each infectious disease in predefined COVID-19 phases during 2020 to the average incidences in the corresponding time intervals during 2014-2019.Findings: We observed decreased incidences of most diseases during the phases after the lockdown of Wuhan. In general, respiratory diseases and gastrointestinal or enteroviral diseases were more affected than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Seasonal flu and rubella were the most sensitive to the NPIs, with reductions of 67-99% in incidence rates throughout the NPI-implemented phases in China (Jan 27-Dec 31, 2020). Among gastrointestinal or enteroviral diseases, the hand, foot and mouth disease (HFMD) was subject to the largest declines during Jan 27-Aug 31, 2020, with >90% reduction in incidence rate. Phases with more stringent NPIs were associated with more reductions. Non-respiratory diseases, particularly HFMD, gonorrhea and brucellosis, rebounded towards the end of the year as the NPIs were relaxed.Interpretation: NPIs are broadly effective in containing infectious diseases. Less disruptive NPIs such as wearing face masks are still effective in mitigating respiratory diseases but are not adequate for containing non-respiratory diseases.Funding Statement: This work was supported by grants from the National Natural Science Funds [91846302, 81825019], the China Mega-Project on Infectious Disease Prevention [2018ZX10713001, 2018ZX10713002, 2018ZX10201001 and 2017ZX10103004], and the US National Institutes of Health [R56 AI148284].Declaration of Interests: All authors declare no competing interests.Ethics Approval Statement: Missing.

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US (preprint)

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. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naive baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse 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. f