Enhancing COVID-19 Epidemic Forecasting Accuracy by Combining Real-time and Historical Data From Multiple Internet-Based Sources: Analysis of Social Media Data,Online News Articles,and Search Queries

BackgroundThe SARS-COV-2 virus and its variants pose extraordinary challenges for public health worldwide. Timely and accurate forecasting of the COVID-19 epidemic is key to sustaining interventions and policies and efficient resource allocation. Internet-based data sources have shown great potential to supplement traditional infectious disease surveillance, and the combination of different Internet-based data sources has shown greater power to enhance epidemic forecasting accuracy than using a single Internet-based data source. However, existing methods incorporating multiple Internet-based data sources only used real-time data from these sources as exogenous inputs but did not take all the historical data into account. Moreover, the predictive power of different Internet-based data sources in providing early warning for COVID-19 outbreaks has not been fully explored.ObjectiveThe main aim of our study is to explore whether combining real-time and historical data from multiple Internet-based sources could improve the COVID-19 forecasting accuracy over the existing baseline models. A secondary aim is to explore the COVID-19 forecasting timeliness based on different Internet-based data sources.MethodsWe first used core terms and symptom-related keyword-based methods to extract COVID-19–related Internet-based data from December 21, 2019, to February 29, 2020. The Internet-based data we explored included 90,493,912 online news articles, 37,401,900 microblogs, and all the Baidu search query data during that period. We then proposed an autoregressive model with exogenous inputs, incorporating real-time and historical data from multiple Internet-based sources. Our proposed model was compared with baseline models, and all the models were tested during the first wave of COVID-19 epidemics in Hubei province and the rest of mainland China separately. We also used lagged Pearson correlations for COVID-19 forecasting timeliness analysis.ResultsOur proposed model achieved the highest accuracy in all 5 accuracy measures, compared with all the baseline models of both Hubei province and the rest of mainland China. In mainland China, except for Hubei, the COVID-19 epidemic forecasting accuracy differences between our proposed model (model i) and all the other baseline models were statistically significant (model 1, t₁₉₈=–8.722, P<.001; model 2, t₁₉₈=–5.000, P<.001, model 3, t₁₉₈=–1.882, P=.06; model 4, t₁₉₈=–4.644, P<.001; model 5, t₁₉₈=–4.488, P<.001). In Hubei province, our proposed model''s forecasting accuracy improved significantly compared with the baseline model using historical new confirmed COVID-19 case counts only (model 1, t₁₉₈=–1.732, P=.09). Our results also showed that Internet-based sources could provide a 2- to 6-day earlier warning for COVID-19 outbreaks.ConclusionsOur approach incorporating real-time and historical data from multiple Internet-based sources could improve forecasting accuracy for epidemics of COVID-19 and its variants, which may help improve public health agencies'' interventions and resource allocation in mitigating and controlling new waves of COVID-19 or other relevant epidemics.     

宁波市新型冠状病毒肺炎疫情动态清零精准防控策略和措施效果评价

目的:对宁波市新型冠状病毒肺炎（COVID-19）疫情动态清零精准防控策略和措施效果进行评价。方法:根据2021年12月宁波市报告的COVID-19确诊病例和个案流行病学调查报告绘制流行曲线,建立传播动力学模型,预测不同干预措施下的累计确诊病例数,计算基本再生数（ R0）和实时再生数（ R ...     

The Effectiveness of Community-based Social Distancing for Mitigating the Spread of the COVID-19 Pandemic in Turkey

ObjectivesThe objective of this study was to demonstrate the effects of community-based social distancing interventions after the first coronavirus disease 2019 (COVID-19) case in Turkey on the course of the pandemic and to determine the number of prevented cases.MethodsIn this ecological study, the interventions implemented in response to the first COVID-19 cases in Turkey were evaluated and the effect of the interventions was demonstrated by calculating the effective reproduction number (R_t) of severe acute respiratory syndrome coro navirus 2 (SARS-CoV-2) when people complied with community-based social distancing rules.ResultsGoogle mobility scores decreased by an average of 36.33±22.41 points (range, 2.60 to 84.80) and a median of 43.80 points (interquartile range [IQR], 24.90 to 50.25). The interventions caused the calculated R_t to decrease to 1.88 (95% confidence interval, 1.87 to 1.89). The median growth rate was 19.90% (IQR, 10.90 to 53.90). A positive correlation was found between Google mobility data and R_t (r=0.783; p<0.001). The expected number of cases if the growth rate had not changed was predicted according to Google mobility categories, and it was estimated to be 1 381 922 in total. Thus, community-based interventions were estimated to have prevented 1 299 593 people from being infected.ConclusionsCommunity-based social distancing interventions significantly decreased the R_t of COVID-19 by reducing human mobility, and thereby prevented many people from becoming infected. Another important result of this study is that it shows health policy-makers that data on human mobility in the community obtained via mobile phones can be a guide for measures to be taken.     

Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method

BackgroundThe COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA.ObjectiveThe purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19.MethodsWe collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages.ResultsWe found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic.ConclusionsThe analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.     

基于动态基本再生数的新型冠状病毒肺炎疫情防控现状评估

目的 对全国各省份的新型冠状病毒肺炎(COVID-19)疫情防控现状进行分析,建立预测模型预估现有防控措施预期成效,为决策部门提供科学信息。方法 基于COVID-19疫情网络公开数据,估计全国、各省份以及武汉市不同时间基本再生数(R0)的动态变化R₀(t),以评估在现有防控措施下,COVID-19传染速率随时间变化的趋势,预估现有防控措施的预期成效。结果 从结果稳定性考虑,选择累积确诊病例数>100例的地区进行分析,共24个省份纳入分析。在疫情初期,全国整体R₀(t)不稳定,数值较大,误差也较大。随着防控措施的进一步加强,R₀(t)普遍在1月下旬开始呈现下降趋势,2月始下降趋势稳定。截至数据分析日,纳入分析的24个省份中已有18个省份(75%)R₀(t)降到1以下。这为有条件地开放人员流动提供了信息。结论 动态R₀(t)有助于动态评估COVID-19传染速率变化情况,本次疫情防控措施已初显成效,如能继续保持,全国疫情有望短期内得到全面控制。     

The impact of non-pharmaceutical interventions on COVID-19 epidemic growth in the 37 OECD member states

We estimated the impact of a comprehensive set of non-pharmeceutical interventions on the COVID-19 epidemic growth rate across the 37 member states of the Organisation for Economic Co-operation and Development during the early phase of the COVID-19 pandemic and between October and December 2020. For this task, we conducted a data-driven, longitudinal analysis using a multilevel modelling approach with both maximum likelihood and Bayesian estimation. We found that during the early phase of the epidemic: implementing restrictions on gatherings of more than 100 people, between 11 and 100 people, and 10 people or less was associated with a respective average reduction of 2.58%, 2.78% and 2.81% in the daily growth rate in weekly confirmed cases; requiring closing for some sectors or for all but essential workplaces with an average reduction of 1.51% and 1.78%; requiring closing of some school levels or all school levels with an average reduction of 1.12% or 1.65%; recommending mask wearing with an average reduction of 0.45%, requiring mask wearing country-wide in specific public spaces or in specific geographical areas within the country with an average reduction of 0.44%, requiring mask-wearing country-wide in all public places or all public places where social distancing is not possible with an average reduction of 0.96%; and number of tests per thousand population with an average reduction of 0.02% per unit increase. Between October and December 2020 work closing requirements and testing policy were significant predictors of the epidemic growth rate. These findings provide evidence to support policy decision-making regarding which NPIs to implement to control the spread of the COVID-19 pandemic.

     

新型冠状病毒肺炎疫情多次暴发的动力学机制分析

目的:新型冠状病毒肺炎（新冠肺炎）自暴发以来,世界各国疫情形势、非药物防控措施、疫情下的民众行为模式及疫苗接种等差异巨大。我国疫情也在经历较长时间无本土病例后,由于境外输入等因素引起局部疫情暴发,给疫情防控带来巨大挑战。深入探讨疫情多次暴发的机制和差异性是极为必要的。方法:本研究基于SEIR传染病动力学模型,构建新颖的...     

Correlation of Population SARS-CoV-2 Cycle Threshold Values to Local Disease Dynamics: Exploratory Observational Study

BackgroundDespite the limitations in the use of cycle threshold (CT) values for individual patient care, population distributions of CT values may be useful indicators of local outbreaks.ObjectiveWe aimed to conduct an exploratory analysis of potential correlations between the population distribution of cycle threshold (CT) values and COVID-19 dynamics, which were operationalized as percent positivity, transmission rate (R_t), and COVID-19 hospitalization count.MethodsIn total, 148,410 specimens collected between September 15, 2020, and January 11, 2021, from the greater El Paso area were processed in the Dascena COVID-19 Laboratory. The daily median CT value, daily R_t, daily count of COVID-19 hospitalizations, daily change in percent positivity, and rolling averages of these features were plotted over time. Two-way scatterplots and linear regression were used to evaluate possible associations between daily median CT values and outbreak measures. Cross-correlation plots were used to determine whether a time delay existed between changes in daily median CT values and measures of community disease dynamics.ResultsDaily median CT values negatively correlated with the daily R_t values (P<.001), the daily COVID-19 hospitalization counts (with a 33-day time delay; P<.001), and the daily changes in percent positivity among testing samples (P<.001). Despite visual trends suggesting time delays in the plots for median CT values and outbreak measures, a statistically significant delay was only detected between changes in median CT values and COVID-19 hospitalization counts (P<.001).ConclusionsThis study adds to the literature by analyzing samples collected from an entire geographical area and contextualizing the results with other research investigating population CT values.     

新型冠状病毒肺炎两种不同流行模式及其防控效果比较：基于广州和温州市的分析

目的 比较广州、温州市两个城市新型冠状病毒肺炎（COVID-19）的流行模式,并评估两个城市疫情的防控效果。方法 获取截至2020年2月29日广州和温州市COVID-19确诊病例的个案数据,绘制两个城市疫情的流行曲线,收集不同时间的防控措施,计算在两个城市的实时再生数。结果 广州和温州市分别纳入确诊病例346例和465例,两个城市病例均集中在30～59岁（广州市：54.9%;温州市：70.3%）。流行曲线显示广州和温州市的每日发病数分别在1月27日与1月26日到达峰值,随后出现下降趋势。两个城市的发病高峰均出现在湖北省输入病例的抵达高峰后,且温州市的湖北省输入病例的抵达高峰早于广州市。广州市一直以输入病例为主,温州市从前期的以输入病例为主转变为后期以本地病例为主。虽然两个城市流行模式存在差异,在采取了有力的防控措施后,两个城市均取得了较好的防控效果。结论 COVID-19输入疫情可导致两种不同的流行模式,但采取强有力的防控措施,均能有效控制疫情蔓延。     

The Impact of Public Health Events on COVID-19 Vaccine Hesitancy on Chinese Social Media: National Infoveillance Study

BackgroundThe ongoing COVID-19 pandemic has brought unprecedented challenges to every country worldwide. A call for global vaccination for COVID-19 plays a pivotal role in the fight against this virus. With the development of COVID-19 vaccines, public willingness to get vaccinated has become an important public health concern, considering the vaccine hesitancy observed worldwide. Social media is powerful in monitoring public attitudes and assess the dissemination, which would provide valuable information for policy makers.ObjectiveThis study aimed to investigate the responses of vaccine positivity on social media when major public events (major outbreaks) or major adverse events related to vaccination (COVID-19 or other similar vaccines) were reported.MethodsA total of 340,783 vaccine-related posts were captured with the poster’s information on Weibo, the largest social platform in China. After data cleaning, 156,223 posts were included in the subsequent analysis. Using pandas and SnowNLP Python libraries, posts were classified into 2 categories, positive and negative. After model training and sentiment analysis, the proportion of positive posts was computed to measure the public positivity toward the COVID-19 vaccine.ResultsThe positivity toward COVID-19 vaccines in China tends to fluctuate over time in the range of 45.7% to 77.0% and is intuitively correlated with public health events. In terms of gender, males were more positive (70.0% of the time) than females. In terms of region, when regional epidemics arose, not only the region with the epidemic and surrounding regions but also the whole country showed more positive attitudes to varying degrees. When the epidemic subsided temporarily, positivity decreased with varying degrees in each region.ConclusionsIn China, public positivity toward COVID-19 vaccines fluctuates over time and a regional epidemic or news on social media may cause significant variations in willingness to accept a vaccine. Furthermore, public attitudes toward COVID-19 vaccination vary from gender and region. It is crucial for policy makers to adjust their policies through the use of positive incentives with prompt responses to pandemic-related news to promote vaccination acceptance.     

Changes in Temporal Properties of Notifiable Infectious Disease Epidemics in China During the COVID-19 Pandemic: Population-Based Surveillance Study

BackgroundCOVID-19 was first reported in 2019, and the Chinese government immediately carried out stringent and effective control measures in response to the epidemic.ObjectiveNonpharmaceutical interventions (NPIs) may have impacted incidences of other infectious diseases as well. Potential explanations underlying this reduction, however, are not clear. Hence, in this study, we aim to study the influence of the COVID-19 prevention policies on other infectious diseases (mainly class B infectious diseases) in China.MethodsTime series data sets between 2017 and 2021 for 23 notifiable infectious diseases were extracted from public data sets from the National Health Commission of the People’s Republic of China. Several indices (peak and trough amplitudes, infection selectivity, preferred time to outbreak, oscillatory strength) of each infectious disease were calculated before and after the COVID-19 outbreak.ResultsWe found that the prevention and control policies for COVID-19 had a strong, significant reduction effect on outbreaks of other infectious diseases. A clear event-related trough (ERT) was observed after the outbreak of COVID-19 under the strict control policies, and its decreasing amplitude is related to the infection selectivity and preferred outbreak time of the disease before COVID-19. We also calculated the oscillatory strength before and after the COVID-19 outbreak and found that it was significantly stronger before the COVID-19 outbreak and does not correlate with the trough amplitude.ConclusionsOur results directly demonstrate that prevention policies for COVID-19 have immediate additional benefits for controlling most class B infectious diseases, and several factors (infection selectivity, preferred outbreak time) may have contributed to the reduction in outbreaks. This study may guide the implementation of nonpharmaceutical interventions to control a wider range of infectious diseases.     

北京市新型冠状病毒Omicron变异株的传播力研究

目的 评估新型冠状病毒Omicron变异株在北京市现有防控措施下的传播力,为做好疫情防控工作提供参考依据。方法 收集北京市2022年3月7-25日报告的78例具有明确传播链的Omicron变异株感染者信息,分别采用Gamma和Weibull分布拟合潜伏期和序列间隔时间,使用马尔科夫链蒙特卡罗算法估计实时再生数（R_t）。结果 Omicron变异株感染者潜伏期M（Q₁,Q₃）为4.0（3.0,6.0）d,序列间隔时间3.0（2.0,5.0）d,序列间隔时间在未完成和已完成全程疫苗接种感染者中M（Q₁,Q₃）分别为2.0（1.0,4.0）d和4.0（2.0,6.0）d（Z=-2.12,P=0.034）,儿童和成年人感染者分别为2.0（1.5,3.0）d和4.0（2.0,6.0）d（Z=-2.02,P=0.044）,差异均有统计学意义。本轮疫情R_t初始值为4.98（95%CI： 2.22~9.04）。结论 与既往Delta变异株相比,北京市Omicron变异株的传播力较强,应持续做好常态化疫情防控和新型冠状病毒疫苗接种工作,关注儿童易感人群。     

Risk Assessment of Importation and Local Transmission of COVID-19 in South Korea: Statistical Modeling Approach

BackgroundDespite recent achievements in vaccines, antiviral drugs, and medical infrastructure, the emergence of COVID-19 has posed a serious threat to humans worldwide. Most countries are well connected on a global scale, making it nearly impossible to implement perfect and prompt mitigation strategies for infectious disease outbreaks. In particular, due to the explosive growth of international travel, the complex network of human mobility enabled the rapid spread of COVID-19 globally.ObjectiveSouth Korea was one of the earliest countries to be affected by COVID-19. In the absence of vaccines and treatments, South Korea has implemented and maintained stringent interventions, such as large-scale epidemiological investigations, rapid diagnosis, social distancing, and prompt clinical classification of severely ill patients with appropriate medical measures. In particular, South Korea has implemented effective airport screenings and quarantine measures. In this study, we aimed to assess the country-specific importation risk of COVID-19 and investigate its impact on the local transmission of COVID-19.MethodsThe country-specific importation risk of COVID-19 in South Korea was assessed. We investigated the relationships between country-specific imported cases, passenger numbers, and the severity of country-specific COVID-19 prevalence from January to October 2020. We assessed the country-specific risk by incorporating country-specific information. A renewal mathematical model was employed, considering both imported and local cases of COVID-19 in South Korea. Furthermore, we estimated the basic and effective reproduction numbers.ResultsThe risk of importation from China was highest between January and February 2020, while that from North America (the United States and Canada) was high from April to October 2020. The R₀ was estimated at 1.87 (95% CI 1.47-2.34), using the rate of α=0.07 for secondary transmission caused by imported cases. The R_t was estimated in South Korea and in both Seoul and Gyeonggi.ConclusionsA statistical model accounting for imported and locally transmitted cases was employed to estimate R₀ and R_t. Our results indicated that the prompt implementation of airport screening measures (contact tracing with case isolation and quarantine) successfully reduced local transmission caused by imported cases despite passengers arriving from high-risk countries throughout the year. Moreover, various mitigation interventions, including social distancing and travel restrictions within South Korea, have been effectively implemented to reduce the spread of local cases in South Korea.     

Optimal non-pharmaceutical intervention policy for Covid-19 epidemic via neuroevolution algorithm

BackgroundNational responses to the Covid-19 pandemic varied markedly across countries, from business-as-usual to complete shutdowns. Policies aimed at disrupting the viral transmission cycle and preventing the overwhelming of healthcare systems inevitably exact an economic toll.MethodologyWe developed an intervention policy model that comprised the relative human, implementation and healthcare costs of non-pharmaceutical epidemic interventions and identified the optimal strategy using a neuroevolution algorithm. The proposed model finds the minimum required reduction in transmission rates to maintain the burden on the healthcare system below the maximum capacity.ResultsWe find that such a policy renders a sharp increase in the control strength during the early stages of the epidemic, followed by a steady increase in the subsequent ten weeks as the epidemic approaches its peak, and finally the control strength is gradually decreased as the population moves towards herd immunity. We have also shown how such a model can provide an efficient adaptive intervention policy at different stages of the epidemic without having access to the entire history of its progression in the population.Conclusions and implicationsThis work emphasizes the importance of imposing intervention measures early and provides insights into adaptive intervention policies to minimize the economic impacts of the epidemic without putting an extra burden on the healthcare system.Lay SummaryWe developed an intervention policy model that comprised the relative human, implementation and healthcare costs of non-pharmaceutical epidemic interventions and identified the optimal strategy using a neuroevolution algorithm. Our work emphasizes the importance of imposing intervention measures early and provides insights into adaptive intervention policies to minimize the economic impacts of the epidemic without putting an extra burden on the healthcare system.     

Policy Stringency,Handwashing and COVID-19 cases: Evidence from Global dataset

Objective: Since the COVID-19 pandemic, many governments globally have introduced policy measures to contain the spread of the virus. Popular COVID-19 containment measures include lockdowns of various forms (aggregated into government response stringency index [GRSI]) and handwashing (HWF). The effectiveness of these policy measures remains unclear in the academic literature. This study, therefore, examines the effect of government policy stringency and handwashing on total daily reported COVID-19 cases.Method: We use a comprehensive dataset of 176 countries to investigate the effect of government policy stringency and handwashing on daily reported COVID-19 cases. In this study, we apply the Lewbel (2012) two-stage least squares technique to control endogeneity.Results: Our results indicated that GRSI significantly contributes to the increase in the total and new confirmed cases of COVI-19. Sensitivity analyses revealed that the 1st, 4th, and 5th quintiles of GRIS significantly reduce total confirmed cases of COVID-19. Also, the result indicated that while the 1st quintile of GRIS contributes significantly to reducing the new confirmed cases of COVID-19, the 3rd, 4th, and 5th quintiles of GRSI contribute significantly to increasing the new confirmed cases of COVID-19. The results indicated that HWF reduces total and new confirmed cases of COVID-19; however, such effect is not robust to income and regional effects. Nonlinear analysis revealed that while GRSI has an inverted U-shaped relationship with total and new confirmed cases of COVID-19, HWF has a U-shaped relationship.Conclusion: We suggest that policymakers should focus on raising awareness and full engagement of all members of society in implementing public health policies rather than using stringent lockdown measures.     

广州市2起由新型冠状病毒奥密克戎变异株引起的本地疫情流行病学参数研究

目的 本研究旨在估计广州市2起由新型冠状病毒（新冠病毒）奥密克戎变异株（BA.2）引起的本地疫情的潜伏期、序列间隔和基本再生数（R₀）等流行病学参数,探索不同场所聚集性对R₀的影响,为奥密克戎变异株疫情防控提供科学依据。方法 收集2022年4-5月广州市2起新冠病毒奥密克戎变异株本地疫情病例数据,使用Weibull、Gamma和lognormal分布对奥密克戎变异株本地疫情的潜伏期、序列间隔分布进行估计,采用指数增长法和极大似然法估计R₀。结果 两起疫情中位潜伏期为2.94（95%CI：2.52~3.38）d;中位序列间隔为3.32（95%CI：2.89~3.81）d。小型场所聚集性疫情R₀为4.40（95%CI：3.95~4.85）,机场聚集性疫情R₀为11.35（95%CI：11.02~11.67）。结论 广州市2起由新冠病毒奥密克戎变异株引起的本地疫情潜伏期较德尔塔变异株明显缩短。场所聚集程度越高,R₀越大,传播速度越快,易呈现暴发疫情,应及时调整防控策略。     

中国新型冠状病毒肺炎疫情基本再生数评估

目的 目前湖北省的新型冠状病毒肺炎(COVID-19)确诊和疑似病例的数量仍在增加。国内外多个团队对疫情发展进行了模型预测,但结论并不统一。因此,开展本次疫情的预测模型研究、评估COVID-19的基本再生数(basic reproduction number,R₀),对于评估病毒的传播能力以及一系列控制措施的效果具有重要意义。方法 收集从湖北省2020年1月17日到2月8日期间每天报告的确诊病例数等数据,分别采用指数增长方法(exponential growth,EG)、极大似然法(maximum likelihood estimation,ML)、序贯贝叶斯方法(sequential Bayesian method,SB)和时间相关基本再生数(time dependent reproduction numbers,TD)估计R₀值。结果 由观测病例数和4种方法预测的病例数的拟合情况可知,EG方法拟合效果最优。EG方法估计COVID-19湖北省R₀的值为3.49(95%CI:3.42~3.58)。采取封城控制手段期间,EG方法估算R₀值为2.95(95%CI:2.86~3.03)。结论 在传染病流行初期,适合采用EG方法估算R₀。同时需要采取及时有效的控制措施,进一步降低COVID-19的传播速率。     

The COVID-19 pandemic in Italy: Policy and technology impact on health and non-health outcomes

BackgroundItaly was the first Western country to experience a major coronavirus outbreak and consequently faced large-scale health and socio-economic challenges. The Italian government enforced a wide set of homogeneous interventions nationally, despite the differing incidences of the virus throughout the country.ObjectiveThe paper aims to analyse the policies implemented by the government and their impact on health and non-health outcomes considering both scaling-up and scaling-down interventions.MethodsTo categorise the policy interventions, we rely on the comparative and conceptual framework developed by Moy et al. (2020). We investigate the impact of policies on the daily reported number of deaths, case fatality rate, confirmation rate, intensive care unit saturation, and financial and job market indicators across the three major geographical areas of Italy (North, Centre, and South). Qualitative and quantitative data are gathered from mixed sources: Italian national and regional institutions, National Health Research and international organisations. Our analysis contributes to the literature on the COVID-19 pandemic by comparing policy interventions and their outcomes.ResultsOur findings suggest that the strictness and timing of containment and prevention measures played a prominent role in tackling the pandemic, both from a health and economic perspective. Technological interventions played a marginal role due to the inadequacy of protocols and the delay of their implementation.ConclusionsFuture government interventions should be informed by evidence-based decision making to balance, the benefits arising from the timing and stringency of the interventions against the adverse social and economic cost, both in the short and long term.     

深圳市新型冠状病毒肺炎应急响应策略和措施效果评价

目的 对深圳市新型冠状病毒肺炎（新冠肺炎）应急响应策略和措施的效果进行评价，明确人口密度高、流动性强的超大城市新冠肺炎策略和措施的有效性。方法 绘制新冠肺炎流行曲线，划分流行期和防控阶段，观察防控措施效果。制作双层阶发病标点地图，明确不同时点传染源的分布和传播风险。建立传播动力学模型，预期发病数与实际发病数对比。新型冠状病毒核酸检测阳性率，反映人群风险水平。借助深圳市人群新冠肺炎相关知识、态度和行为调查结果，估计人群防护和响应能力。结果 深圳市新冠肺炎流行经历了上升期、平台期和下降期，疫情上升快，但高峰期持续时间短，而发病降低的速度快和幅度大；流行曲线虽然呈现人传人现象，但“拖尾”现象不明显。从发病标点地图看，在新冠肺炎防控干预期，传染源分布广泛；人口密度越高的区域，病例数越多；而防控干预措施见效后，传染源减少。从预期发病数对比可见，实际病例数大幅减少。高危险人群新型冠状病毒核酸检测阳性率呈下降趋势，2月16日之后未能检出。市民新冠肺炎知识、态度和行为水平较高，防护能力和应急响应度较高。结论 深圳市早期由部门主导的防控机制发挥了重要作用，但不足以阻断新冠肺炎流行；一级响应策略和措施效果明显，确保了春节后复工复产的启动；对超大城市，采取综合性阻断策略和措施，同样可以控制新冠肺炎的流行。     

Interpretation of the Basic and Effective Reproduction Number

In epidemiology, the basic reproduction number (R₀) is a term that describes the expected number of infections generated by 1 case in a susceptible population. At the beginning of the coronavirus disease 2019 (COVID-19) pandemic, R₀ was frequently referenced by the public health community and the wider public. However, this metric is often misused or misinterpreted. Moreover, the complexity of the process of estimating R₀ has caused difficulties for a substantial number of researchers. In this article, in order to increase the accessibility of this concept, we address several misconceptions related to the threshold characteristics of R₀ and the effective reproduction number (R_t). Moreover, the appropriate interpretation of the metrics is discussed. R₀ should be considered as a population-averaged value that pools the contact structure according to a stochastic transmission process. Furthermore, it is necessary to understand the unavoidable time lag for R_t due to the incubation period of the disease.