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The design of the German statutory long-term care insurance (LTCI) is deficient in many respects. One of the major flaws in nursing home care is the inherent incentive problem concerning the relationship of the insured, the insurers and the nursing homes: For one, there is no competition amongst the insurers which influences the negotiation behaviour towards the nursing homes concerning the fixing of the daily payment rates. The nursing homes in turn are more or less unrestricted at setting the daily payment rate as the insured are mostly not in a position to fully practice their consumer sovereignty in case of the need of long-term care treatment. In the framework of this paper we try to quantify the efficiency reserve behind these disincentives in nursing home care and try to assess to what extent the contribution rate to LTCI could be reduced, if the efficieny reserves were exhausted.

Timely detection of an invasion event, or a pest outbreak, is an extremely challenging operation of major importance for implementing management action toward eradication and/or containment. Fruit flies—FF—(Diptera: Tephritidae) comprise important invasive and quarantine species that threaten the world fruit and vegetables production. The current manuscript introduces a recently developed McPhail-type electronic trap (e-trap) and provides data on its field performance to surveil three major invasive FF (Ceratitis capitata, Bactrocera dorsalis and B. zonata). Using FF male lures, the e-trap attracts the flies and retains them on a sticky surface placed in the internal part of the trap. The e-trap captures frames of the trapped adults and automatically uploads the images to the remote server for identification conducted on a novel algorithm involving deep learning. Both the e-trap and the developed code were tested in the field in Greece, Austria, Italy, South Africa and Israel. The FF classification code was initially trained using a machine-learning algorithm and FF images derived from laboratory colonies of two of the species (C. capitata and B. zonata). Field tests were then conducted to investigate the electronic, communication and attractive performance of the e-trap, and the model accuracy to classify FFs. Our results demonstrated a relatively good communication, electronic performance and trapping efficacy of the e-trap. The classification model provided average precision results (93–95%) for the three target FFs from images uploaded remotely from e-traps deployed in field conditions. The developed and field tested e-trap system complies with the suggested attributes required for an advanced camera-based smart-trap.

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