@article{AdnanSrsicVenticichetal.2020,
  author    = {Adnan, Hassan Sami and Srsic, Amanda and Venticich, Pete Milos and Townend, David M.R.},
  title     = {Using AI for mental health analysis and prediction in school surveys},
  series = {European journal of public health},
  volume    = {30},
  journal   = {European journal of public health},
  publisher = {Oxford Univ. Press},
  address   = {Oxford [u.a.]},
  issn      = {1101-1262},
  doi       = {10.1093/eurpub/ckaa165.336},
  pages     = {V125 -- V125},
  year      = {2020},
  abstract  = {Background: Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods: For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results: We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions: This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects.},
  language  = {en}
}
@article{SchatzOhlendorfBusseetal.2014,
  author    = {Schatz, J. and Ohlendorf, B. and Busse, P. and Pelz, G. and Dolch, D. and Teubner, J. and Encarnacao, Jorge A. and Muehle, Ralf -Udo and Fischer, M. and Hoffmann, B. and Kwasnitschka, L. and Balkema-Buschmann, Anne and Mettenleiter, Thomas Christoph and Mueller, T. and Freuling, C. M.},
  title     = {Twenty years of active bat rabies surveillance in Germany},
  series = {Epidemiology and infection},
  volume    = {142},
  journal   = {Epidemiology and infection},
  number    = {6},
  publisher = {Cambridge Univ. Press},
  address   = {New York},
  issn      = {0950-2688},
  doi       = {10.1017/S0950268813002185},
  pages     = {1155 -- 1166},
  year      = {2014},
  abstract  = {In Germany, active bat rabies surveillance was conducted between 1993 and 2012. A total of 4546 oropharyngeal swab samples from 18 bat species were screened for the presence of EBLV-1- , EBLV-2- and BBLV-specific RNA. Overall, 0 center dot 15\% of oropharyngeal swab samples tested EBLV-1 positive, with the majority originating from Eptesicus serotinus. Interestingly, out of seven RT-PCR-positive oropharyngeal swabs subjected to virus isolation, viable virus was isolated from a single serotine bat (E. serotinus). Additionally, about 1226 blood samples were tested serologically, and varying virus neutralizing antibody titres were found in at least eight different bat species. The detection of viral RNA and seroconversion in repeatedly sampled serotine bats indicates long-term circulation of the virus in a particular bat colony. The limitations of random-based active bat rabies surveillance over passive bat rabies surveillance and its possible application of targeted approaches for future research activities on bat lyssavirus dynamics and maintenance are discussed.},
  language  = {en}
}
@article{AbramovaWagnerOltetal.2022,
  author    = {Abramova, Olga and Wagner, Amina and Olt, Christian M. and Buxmann, Peter},
  title     = {One for all, all for one},
  series = {International Journal of Information Management},
  volume    = {64},
  journal   = {International Journal of Information Management},
  publisher = {Elsevier},
  address   = {Kidlington},
  issn      = {0268-4012},
  doi       = {10.1016/j.ijinfomgt.2022.102473},
  pages     = {1 -- 16},
  year      = {2022},
  abstract  = {We propose a conceptual model of acceptance of contact tracing apps based on the privacy calculus perspective. Moving beyond the duality of personal benefits and privacy risks, we theorize that users hold social considerations (i.e., social benefits and risks) that underlie their acceptance decisions. To test our propositions, we chose the context of COVID-19 contact tracing apps and conducted a qualitative pre-study and longitudinal quantitative main study with 589 participants from Germany and Switzerland. Our findings confirm the prominence of individual privacy calculus in explaining intention to use and actual behavior. While privacy risks are a significant determinant of intention to use, social risks (operationalized as fear of mass surveillance) have a notably stronger impact. Our mediation analysis suggests that social risks represent the underlying mechanism behind the observed negative link between individual privacy risks and contact tracing apps' acceptance. Furthermore, we find a substantial intention-behavior gap.},
  language  = {en}
}
@article{UrbachAwiszusLeissetal.2020,
  author    = {Urbach, Dietmar and Awiszus, Friedemann and Leiß, Sven and Venton, Tamsin and De Specht, Alexander Vincent and Apfelbacher, Christian},
  title     = {Associations of medications with lower odds of typical COVID-19 symptoms},
  series = {JMIR public health and surveillance},
  volume    = {6},
  journal   = {JMIR public health and surveillance},
  number    = {4},
  publisher = {JMIR Publications},
  address   = {Toronto},
  issn      = {2369-2960},
  doi       = {10.2196/22521},
  pages     = {10},
  year      = {2020},
  abstract  = {Background: As the COVID-19 pandemic continues to spread across the globe, the search for an effective medication to treat the symptoms of COVID-19 continues as well. It would be desirable to identify a medication that is already in use for another condition and whose side effect profile and safety data are already known and approved. Objective: The objective of this study was to evaluate the effect of different medications on typical COVID-19 symptoms by using data from an online surveillance survey. Methods: Between early April and late-July 2020, a total of 3654 individuals in Lower Saxony, Germany, participated in an online symptom-tracking survey conducted through the app covid-nein-danke.de. The questionnaire comprised items on typical COVID-19 symptoms, age range, gender, employment in patient-facing healthcare, housing status, postal code, previous illnesses, permanent medication, vaccination status, results of reverse transcription polymerase chain reaction (RT-PCR) and antibody tests for COVID-19 diagnosis, and consequent COVID-19 treatment if applicable. Odds ratio estimates with corresponding 95\% CIs were computed for each medication and symptom by using logistic regression models. Results: Data analysis suggested a statistically significant inverse relationship between typical COVID-19 symptoms self-reported by the participants and self-reported statin therapy and, to a lesser extent, antihypertensive therapy. When COVID-19 diagnosis was based on restrictive symptom criteria (ie, presence of 4 out of 7 symptoms) or a positive RT-PCR test, a statistically significant association was found solely for statins (odds ratio 0.28, 95\% CI 0.1-0.78). Conclusions: Individuals taking statin medication are more likely to have asymptomatic COVID-19, in which case they may be at an increased risk of transmitting the disease unknowingly. We suggest that the results of this study be incorporated into symptoms-based surveillance and decision-making protocols in regard to COVID-19 management. Whether statin therapy has a beneficial effect in combating COVID-19 cannot be deduced based on our findings and should be investigated by further study.},
  language  = {en}
}