SARS-CoV-2: exposure to high external doses as determinants of higher viral loads and of increased risk for COVID-19. A systematic review of the literature

The determinants of the risk of becoming infected by SARS-CoV-2, contracting COVID-19, and being affected by the more serious forms of the disease have been generally explored in merely qualitative terms. It seems reasonable to argue that the risk patterns for COVID-19 have to be usefully studied in quantitative terms too, whenever possible applying the same approach to the relationship 'dose of the exposure vs pathological response' commonly used for chemicals and already followed for several biological agents to SARS-CoV-2, too.

Risk assessment of exported risk of COVID-19 from Hubei Province

目的: 评估湖北新型冠状病毒肺炎的疫情输出风险及其他各省从湖北输入疫情的风险。 方法: 获取截至2020年2月14日我国各省报告病例数(不含临床诊断病例;不含中国香港、澳门和台湾数据)和百度迁徙指数,对各省累计报告病例数和湖北迁出指数进行相关分析,评估湖北疫情输出风险和其他省疫情输入风险。 结果: 全国累计报告确诊病例49 970例,其中湖北37 884例。湖北平均每天迁出至其他省的指数为312.09,武汉和湖北其他市分别为117.95和194.16。各省累计报告病例数与湖北、武汉及湖北其他市迁出至各省的人口迁徙指数均成正相关,相关系数分别为0.84、0.84和0.81;湖北、武汉及湖北其他市人口迁出分别可解释线性模型71.2%、70.1%和66.3%的变异。湖北高输出风险时间集中在1月27日前,其中1月23日前的疫情输出风险主要来源于武汉,之后主要来源于湖北其他市。疫情输入风险排前3位的是湖南、河南和广东,累计风险指数分别为58.61,54.75和49.62。 结论: 我国各省疫情主要由湖北输入引起,湖北限制人口流出、各省加强对湖北省迁入人员的检疫,可以较大程度降低各省(除湖北)疫情持续传播风险。

Risk assessment and early warning of imported COVID-19 in Guangdong province

Objective To assess the imported risk of COVID-19 in Guangdong province and its cities, and conduct early warning. Methods Data of reported COVID-19 cases and Baidu Migration Index of 21 cities in Guangdong province and other provinces of China as of February 25, 2020 were collected. The imported risk index of each city in Guangdong province were calculated, and then correlation analysis was performed between reported cases and the imported risk index to identify lag time. Finally, we classified the early wanning levels of epidemic by imported risk index.

Impact of returning population migration after the Chinese Spring Festival on the COVID-19 epidemic [关于春节返程人口流动对新型冠状病毒肺炎(COVID-19)疫情影响的讨论]

The outbreak of the novel coronavirus disease 2019 (COVID-19) and its spread throughout the China have caused a huge impact on China and the international community. And now it becomes a worldwide infectious disease which poses a major threat to the lives of people around the world. What is worth noting about China is five million people left Wuhan before the Spring Festival, which caused the nationwide spreading of COVID-19 epidemic. Then, it raises a question of concern, should the return of migrant workers and students after the Spring Festival cause an increase in the epidemic?

Risk assessment of global COVID-19 imported cases into China

目的: 评估全球新型冠状病毒肺炎(COVID-19)疫情对我国的输入风险。 方法: 基于收集的疫情数据(各国家每日累计确诊病例数、境外输入病例累计确诊病例数)、人口学数据(各国人口密度、人口数)、旅客潜在来源群体信息(华侨华人常住人口数、在外中国留学生数、海外务工人员数、来华留学生数、航班旅客数估计)和全球健康安全指数(GHS)等信息,进行近期(2月1日-4月25日)和未来(4月26日-)风险分析及预测,构建输入风险得分。 结果: 各国境外输入病例数、累计确诊数、罹患率、华侨华人数、境外留学生数、来华留学生数、航班乘客数和GHS变量间有较强的正相关性。近期风险分析中,俄罗斯输入病例明显较高,英国、美国、法国、西班牙次之。在未来风险预测中,通过各国罹患率指数和平均每日入境乘客数估计值两项信息,评估美国、新加坡等44个国家为未来潜在高风险国家。 结论: 通过COVID-19疫情各国家输入风险评估,可以识别近期及未来的高风险区域,为加强疫情防控,为最终战胜疫情提高帮助。.Objective: To assess the risk of COVID-19 foreign imports cases to China.

Assessment of Infection Risk Based on Travel Behavior Analysis -Example of Jiangsu Province Travel during COVID-19 [基于出行行为模型的出行感染风险评估-以新冠肺炎疫情期间江苏省地区为例]

In order to correctly evaluate the travel infection risks during the COVID-19 pandemic, this study proposed an infection risk assessment model based on the travel behavior analysis. Using the epidemiological survey data and online questionnaire data in Jiangsu, China this study developed and calibrated the travel behavior models for virus carriers and ordinary individuals. The travel behaviors of virus carriers and ordinary individuals were also compared. The infection risks were evaluated for different travel modes and travel activities.

Implications for border containment strategies when COVID-19 presents atypically

Objectives: For a large part of the coronavirus disease 2019 (COVID-19) pandemic, Singapore had managed to keep local cases in the single digits daily, with decisive measures. Yet, we saw this critical time point when the imported cases surged through our borders. The gaps which we can and have efficiently closed, using a public health approach and global border containment strategies, are aptly illustrated through this case.

Design of a mobile app with the use of machine learning for the monitoring of coronavirus patients (Covid 19) in Peru

Globally, the massive expansion of acute respiratory syndrome (COVID_19) is mainly caused by the massive agglomeration of people at the time of travel, as a person infected with the virus who does not have the respective preventive measures can infect 3 more people according to studies. For this reason, here is proposed a mobile application with the use of the Machine Learning methodology for future prediction, through the historical data learned.

Lessons learned for COVID-19 in the cruise ship industry

The coronavirus disease 2019 (COVID-19) pandemic has created widespread disruption in individuals’ personal and occupational lives all around the world. Vacationers and tourism, recreation, and leisure employees were among those who experienced substantial disruption. Cruise ships, especially, faced turmoil on a global scale for both their customers and workers. COVID-19 outbreaks were reported on cruise ships beginning in February 2020, presenting new and unique challenges for the industry.