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.

Impact of the coronavirus disease 2019 interventions on the incidence of hand, foot, and mouth disease in mainland China.

BACKGROUND: In early 2020, non-pharmaceutical interventions (NPIs) were implemented in China to reduce and contain the coronavirus disease 2019 (COVID-19) transmission. These NPIs might have also reduced the incidence of hand, foot, and mouth disease (HFMD). METHODS: The weekly numbers of HFMD cases and meteorological factors in 31 provincial capital cities and municipalities in mainland China were obtained from Chinese Center for Disease Control and Prevention (CCDC) and National Meteorological Information Center of China from 2016 to 2020. The NPI data were collected from local CDCs. The incidence rate ratios (IRRs) were calculated for the entire year of 2020, and for January-July 2020 and August-December 2020. The expected case numbers were estimated using seasonal autoregressive integrated moving average models. The relationships between kindergarten closures and incidence of HFMD were quantified using a generalized additive model. The estimated associations from all cities were pooled using a multivariate meta-regression model. FINDINGS: Stringent NPIs were widely implemented for COVID-19 control from January to July 2020, and the IRRs for HFMD were less than 1 in all 31 cities, and less than 0·1 for 23 cities. Overall, the proportion of HFMD cases reduced by 52·9% (95% CI: 49·3-55·5%) after the implementation of kindergarten closures in 2020, and this effect was generally consistent across subgroups. INTERPRETATION: The decrease in HFMD incidence was strongly associated with the NPIs for COVID-19. HFMD epidemic peaks were either absent or delayed, and the final epidemic size was reduced. Kindergarten closure is an intervention to prevent HFMD outbreaks. FUNDING: This research was supported by the National Natural Science Foundation of China (81973102 & 81773487), Public Health Talents Training Program of Shanghai Municipality (GWV-10.2-XD21), the Shanghai New Three-year Action Plan for Public Health (GWV-10.1-XK16), the Major Project of Scientific and Technical Winter Olympics from National Key Research and Development Program of China (2021YFF0306000), 13th Five-Year National Science and Technology Major Project for Infectious Diseases (2018ZX10725-509) and Key projects of the PLA logistics Scientific research Program (BHJ17J013).

Braking Force Model on Virus Transmission to Evaluate Interventions Including the Administration of COVID-19 Vaccines - Worldwide, 2019-2021.

INTRODUCTION: Assessing the effects of non-pharmaceutical interventions (NPIs) and vaccines on controlling the coronavirus disease 2019 (COVID-19) is key for each government to optimize the anti-contagion policy according to their situation. METHODS: We proposed the Braking Force Model on Virus Transmission to evaluate the validity and efficiency of NPIs and vaccines. This model classified the NPIs and the administration of vaccines at different effectiveness levels and forecasted the duration required to control the pandemic, providing an indication of the future trends of the pandemic wave. RESULTS: This model was applied to study the effectiveness of the most commonly used NPIs according to the historic pandemic waves in different countries and regions. It was found that when facing an outbreak, only strict lockdown would give efficient control of the pandemic; the other NPIs were insufficient to promptly and effectively reduce virus transmission. Meanwhile, our results showed that NPIs would likely only slow down the pandemic's progression and maintain a low transmission level but fail to eradicate the disease. Only vaccination would likely have had a better chance of success in ending the pandemic. DISCUSSION: Based on the Braking Force Model, a pandemic control strategy framework has been devised for policymakers to determine the commencement and duration of appropriate interventions, with the aim of obtaining a balance between public health risk management and economic recovery.

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.

Meteorological conditions are heterogeneous factors for COVID-19 risk in China.

Whether meteorological factors influence COVID-19 transmission is an issue of major public health concern, but available evidence remains unclear and limited for several reasons, including the use of report date which can lag date of symptom onset by a considerable period. We aimed to generate reliable and robust evidence of this relationship based on date of onset of symptoms. We evaluated important meteorological factors associated with daily COVID-19 counts and effective reproduction number (Rt) in China using a two-stage approach with overdispersed generalized additive models and random-effects meta-analysis. Spatial heterogeneity and stratified analyses by sex and age groups were quantified and potential effect modification was analyzed. Nationwide, there was no evidence that temperature and relative humidity affected COVID-19 incidence and Rt. However, there were heterogeneous impacts on COVID-19 risk across different regions. Importantly, there was a negative association between relative humidity and COVID-19 incidence in Central China: a 1% increase in relative humidity was associated with a 3.92% (95% CI, 1.98%-5.82%) decrease in daily counts. Older population appeared to be more sensitive to meteorological conditions, but there was no obvious difference between sexes. Linear relationships were found between meteorological variables and COVID-19 incidence. Sensitivity analysis confirmed the robustness of the association and the results based on report date were biased. Meteorological factors play heterogenous roles on COVID-19 transmission, increasing the possibility of seasonality and suggesting the epidemic is far from over. Considering potential climatic associations, we should maintain, not ease, current control measures and surveillance.