Real-time risk ranking of emerging epidemics based on optimized moving average prediction limit-taking the COVID-19 pandemic as an example.

BACKGROUND: Mathematical models to forecast the risk trend of the COVID-19 pandemic timely are of great significance to control the pandemic, but the requirement of manual operation and many parameters hinders their efficiency and value for application. This study aimed to establish a convenient and prompt one for monitoring emerging infectious diseases online and achieving risk assessment in real time. METHODS: The Optimized Moving Average Prediction Limit (Op-MAPL) algorithm model analysed real-time COVID-19 data online and was validated using the data of the Delta variant in India and the Omicron in the United States. Then, the model was utilized to determine the infection risk level of the Omicron in Shanghai and Beijing. RESULTS: The Op-MAPL model can predict the epidemic peak accurately. The daily risk ranking was stable and predictive, with an average accuracy of 87.85% within next 7 days. Early warning signals were issued for Shanghai and Beijing on February 28 and April 23, 2022, respectively. The two cities were rated as medium-high risk or above from March 27 to April 20 and from April 24 to May 5, indicating that the pandemic had entered a period of rapid increase. After April 21 and May 26, the risk level was downgraded to medium and became stable by the algorithm, indicating that the pandemic had been controlled well and mitigated gradually. CONCLUSIONS: The Op-MAPL relies on nothing but an indicator to assess the risk level of the COVID-19 pandemic with different data sources and granularities. This forward-looking method realizes real-time monitoring and early warning effectively to provide a valuable reference to prevent and control infectious diseases.

2021-2022 monitoring, early warning, and forecasting of global infectious diseases.

COVID-19 has had a considerable impact on society since 2019, and the disease has high mortality and infection rates. There has been a particular focus on how to best manage COVID-19 and how to analyze and predict the epidemic status of infectious diseases in general. Methods The present study analyzed the COVID-19 epidemic patterns and made predictions of future trends based on the statistics obtained from a global infectious disease network data monitoring and early warning system (OBN, http://27.115.41.130:8888/OBN/). The development trends of other major infectious diseases were also examined. Results The global COVID-19 pandemic showed periodic increases throughout 2021. At present, there is a high incidence in European countries, especially in Eastern Europe, followed by in Africa. The risk of contracting COVID-19 was divided into high, medium-high, medium, medium-low, and low grades depending on the stage of the epidemic in each examined region over the current period. The occurrence and prevalence of major infectious diseases throughout the world did not significantly change in 2021. Conclusions The COVID-19 pandemic has strongly impacted people's lives and the economy. The effects of global infectious diseases can be ameliorated by strengthening monitoring and early warning systems and by facilitating the international exchange of information.

The characteristics and implications of epidemic reports during COVID-19 in the United States.

We summarized the basic practices and characteristics of epidemic reporting during the COVID-19 pandemic in the United States. Based on the analysis of the advantages and disadvantages of epidemic data reporting, we put forward some suggestions that should be used for reference and thus improve the epidemic data reports of infectious diseases.

Ambient air pollution and COVID-19 risk: Evidence from 35 observational studies.

BACKGROUND AND AIMS: The coronavirus disease 2019 (COVID-19) pandemic is severely threatening and challenging public health worldwide. Epidemiological studies focused on the influence of outdoor air pollution (AP) on COVID-19 risk have produced inconsistent conclusions. We aimed to quantitatively explore this association using a meta-analysis. METHODS: We searched for studies related to outdoor AP and COVID-19 risk in the Embase, PubMed, and Web of Science databases. No language restriction was utilized. The search date entries were up to August 13, 2021. Pooled estimates and 95% confidence intervals (CIs) were obtained with random-/fixed-effects models. PROSPERO registration number: CRD42021244656. RESULTS: A total of 35 articles were eligible for the meta-analysis. For long-term exposure to AP, COVID-19 incidence was positively associated with 1 µg/m3 increase in nitrogen dioxide (NO2; effect size = 1.042, 95% CI 1.017-1.068), particulate matter with diameter <2.5 µm (PM2.5; effect size = 1.056, 95% CI 1.039-1.072), and sulfur dioxide (SO2; effect size = 1.071, 95% CI 1.002-1.145). The COVID-19 mortality was positively associated with 1 µg/m3 increase in nitrogen dioxide (NO2; effect size = 1.034, 95% CI 1.006-1.063), PM2.5 (effect size = 1.047, 95% CI 1.025-1.1071). For short-term exposure to air pollutants, COVID-19 incidence was positively associated with 1 unit increase in air quality index (effect size = 1.001, 95% CI 1.001-1.002), 1 µg/m3 increase NO2 (effect size = 1.014, 95% CI 1.011-1.016), particulate matter with diameter <10 µm (PM10; effect size = 1.005, 95% CI 1.003-1.008), PM2.5 (effect size = 1.003, 95% CI 1.002-1.004), and SO2 (effect size = 1.015, 95% CI 1.007-1.023). CONCLUSIONS: Outdoor air pollutants are detrimental factors to COVID-19 outcomes. Measurements beneficial to reducing pollutant levels might also reduce the burden of the pandemic.