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1.
Math Biosci ; 338: 108645, 2021 08.
Article in English | MEDLINE | ID: covidwho-1492387

ABSTRACT

With more than 1.7 million COVID-19 deaths, identifying effective measures to prevent COVID-19 is a top priority. We developed a mathematical model to simulate the COVID-19 pandemic with digital contact tracing and testing strategies. The model uses a real-world social network generated from a high-resolution contact data set of 180 students. This model incorporates infectivity variations, test sensitivities, incubation period, and asymptomatic cases. We present a method to extend the weighted temporal social network and present simulations on a network of 5000 students. The purpose of this work is to investigate optimal quarantine rules and testing strategies with digital contact tracing. The results show that the traditional strategy of quarantining direct contacts reduces infections by less than 20% without sufficient testing. Periodic testing every 2 weeks without contact tracing reduces infections by less than 3%. A variety of strategies are discussed including testing second and third degree contacts and the pre-exposure notification system, which acts as a social radar warning users how far they are from COVID-19. The most effective strategy discussed in this work was combining the pre-exposure notification system with testing second and third degree contacts. This strategy reduces infections by 18.3% when 30% of the population uses the app, 45.2% when 50% of the population uses the app, 72.1% when 70% of the population uses the app, and 86.8% when 95% of the population uses the app. When simulating the model on an extended network of 5000 students, the results are similar with the contact tracing app reducing infections by up to 79%.


Subject(s)
COVID-19/prevention & control , Contact Tracing/statistics & numerical data , Disease Notification/standards , Models, Theoretical , Social Network Analysis , Adult , Computer Simulation , Humans , Medical Informatics Applications , Mobile Applications , Quarantine/statistics & numerical data , Students , Young Adult
2.
Dtsch Med Wochenschr ; 146(3): 198-204, 2021 Feb.
Article in German | MEDLINE | ID: covidwho-1006397

ABSTRACT

The COVID-19 illness can occur as an occupational disease or work-related accident. According to the German list of occupational diseases, recognition as an occupational disease 3101 requires occupational exposure of an insured person who has been exposed to an increased risk of infection compared to the general population as a result of their occupational activity in one of the four areas: (1) health service or (2) social welfare sector, (3) laboratory or (4) during activities with increased risk of infection comparable to (1) to (3). The insurance cover covers employees, self-employed people - if not exempted from insurance cover - and honorary workers. The COVID-19 disease is subject to legal notification, mostly in conjunction with a contemporary SARS-CoV-2 virus detection. Regarding insured people who are not included within the aforementioned areas (1) to (4), the COVID-19 illness can be acknowledged as an occupational accident if the intense and direct contact with infected people - not intended as in the case of occupational disease 3101 - but otherwise situationally results from the insured activity itself.


Subject(s)
COVID-19/economics , COVID-19/etiology , Insurance Coverage , Occupational Diseases/economics , Occupational Diseases/etiology , SARS-CoV-2/isolation & purification , Disease Notification/legislation & jurisprudence , Disease Notification/standards , Germany , Health Occupations , Humans , Insurance Coverage/economics , Insurance Coverage/standards , Laboratories , Occupational Exposure , Risk Factors , Social Welfare , Volunteers
5.
Nat Med ; 26(7): 1005-1008, 2020 07.
Article in English | MEDLINE | ID: covidwho-595980
6.
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz ; 63(6): 777-789, 2020 Jun.
Article in German | MEDLINE | ID: covidwho-245211

ABSTRACT

With the entry into force of the Infection Protection Act (IfSG) in 2001, the reporting obligations for infectious diseases and infectious agents were placed on a new foundation. For the first time, a distinction was made between an obligation for the notification of infectious diseases by physicians and a notification obligation for infectious agents by laboratories. The aim was to reduce the notification burden on physicians and thus to improve the quality of the notifications. Since then, numerous new obligations for notifications have been added.The aim of this work is to describe and discuss the mandatory notification of infectious diseases in Germany on the basis of their development - compared to previous regulations in Germany (Federal Communicable Diseases Act) as well as international and Europe-wide recommendations (IHR; decisions of the EU Commission 1999, 2018) - and to submit suggestions for improvement.Regarding the considerable increase in reporting requirements and reports in recent years, and the fact that the IfSG provides other surveillance systems in addition to mandatory reporting, the mandatory reporting system should be focused on the necessary reporting requirements. In a first step, the proposed abolition of the mandatory reporting of noroviruses and rotaviruses could relieve both the notifiers and the health authorities, thus enabling more efficient reporting and more intensive and better investigation by the health authorities.


Subject(s)
Communicable Disease Control/standards , Communicable Diseases , Disease Notification/standards , Population Surveillance/methods , Communicable Disease Control/legislation & jurisprudence , Disease Notification/legislation & jurisprudence , Germany , Humans
7.
Nat Med ; 26(7): 1037-1040, 2020 07.
Article in English | MEDLINE | ID: covidwho-232776

ABSTRACT

A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31-7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.


Subject(s)
Coronavirus Infections/diagnosis , Disease Notification/methods , Mobile Applications , Pneumonia, Viral/diagnosis , Prodromal Symptoms , Self Report , Smartphone , Adult , Aged , Betacoronavirus/physiology , COVID-19 , Computer Systems , Coronavirus Infections/epidemiology , Coronavirus Infections/pathology , Cough/diagnosis , Cough/epidemiology , Disease Notification/standards , Dyspnea/diagnosis , Dyspnea/epidemiology , Fatigue/diagnosis , Fatigue/epidemiology , Female , Humans , Male , Middle Aged , Mobile Applications/standards , Models, Biological , Olfaction Disorders/diagnosis , Olfaction Disorders/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Pneumonia, Viral/pathology , Prognosis , SARS-CoV-2 , Severity of Illness Index , Taste Disorders/diagnosis , Taste Disorders/epidemiology , United Kingdom/epidemiology , United States/epidemiology
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