The study of human factors in forensic science informs our understanding of the interaction between humans and the systems they use. The Expert Working Group (EWG) on Human Factors in Forensic DNA Interpretation used a systems approach to conduct a scientific assessment of the effects of human factors on forensic DNA interpretation with the goal of recommending approaches to improve practice and reduce the likelihood and consequence of errors. This effort resulted in 44 recommendations. The EWG designed many of these recommendations to improve the production, interpretation, evaluation, documentation, and communication of DNA comparison results.
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Abstract Background: COVID-19 was first identified in December 2019 in the city of Wuhan, China. The virus quickly spread and was declared a pandemic on March 11, 2020. After infection, symptoms such as fever, a (dry) cough, nasal congestion, and fatigue can develop. In some cases, the virus causes severe complications such as pneumonia and dyspnea and could result in death. The virus also spread rapidly in the Netherlands, a small and densely populated country with an aging population. Health care in the Netherlands is of a high standard, but there were nevertheless problems with hospital capacity, such as the number of available beds and staff. There were also regions and municipalities that were hit harder than others. In the Netherlands, there are important data sources available for daily COVID-19 numbers and information about municipalities. Objective: We aimed to predict the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands, using a data set with the properties of 355 municipalities in the Netherlands and advanced modeling techniques. Methods: We collected relevant static data per municipality from data sources that were available in the Dutch public domain and merged these data with the dynamic daily number of infections from January 1, 2020, to May 9, 2021, resulting in a data set with 355 municipalities in the Netherlands and variables grouped into 20 topics. The modeling techniques random forest and multiple fractional polynomials were used to construct a prediction model for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants per municipality in the Netherlands. Results: The final prediction model had an R2 of 0.63. Important properties for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality in the Netherlands were exposure to particulate matter with diameters <10 μm (PM10) in the air, the percentage of Labour party voters, and the number of children in a household. Conclusions: Data about municipality properties in relation to the cumulative number of confirmed infections in a municipality in the Netherlands can give insight into the most important properties of a municipality for predicting the cumulative number of confirmed COVID-19 infections per 10,000 inhabitants in a municipality. This insight can provide policy makers with tools to cope with COVID-19 and may also be of value in the event of a future pandemic, so that municipalities are better prepared.
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Forensic reports use various types of conclusions, such as a categorical (CAT) conclusion or a likelihood ratio (LR). In order to correctly assess the evidence, users of forensic reports need to understand the conclusion and its evidential strength. The aim of this paper is to study the interpretation of the evidential strength of forensic conclusions by criminal justice professionals. In an online questionnaire 269 professionals assessed 768 reports on fingerprint examination and answered questions that measured self-proclaimed and actual understanding of the reports and conclusions. The reports entailed CAT, verbal LR and numerical LR conclusions with low or high evidential strength and were assessed by crime scene investigators, police detectives, public prosecutors, criminal lawyers, and judges. The results show that about a quarter of all questions measuring actual understanding of the reports were answered incorrectly. The CAT conclusion was best understood for the weak conclusions, the three strong conclusions were all assessed similarly. The weak CAT conclusion correctly emphasizes the uncertainty of any conclusion type used. However, most participants underestimated the strength of this weak CAT conclusion compared to the other weak conclusion types. Looking at the self-proclaimed understanding of all professionals, they in general overestimated their actual understanding of all conclusion types.
