Effektmodeller, slutrapport 2016-2018

Authors:

  • Oscar Björnham
  • Petter Lindgren
  • Håkan Grahn
  • Sofia Jonasson
  • Ulrika Bergström
  • Åsa Gustafsson
  • Christian Lejon
  • Jalil Bahar Gogani
  • Niklas Brännström
  • Jonas Näslund
  • Laila Noppa
  • Göran Bucht
  • Jan Burman

Publish date: 2019-04-01

Report number: FOI-R--4731--SE

Pages: 29

Written in: Swedish

Research areas:

  • CBRN-frågor

Abstract

This report describes briefly the results from the project Effect models during the period 2016-2018. Effect models contains several fields that mainly operate separately and independently of each other. The fields for biological, chemical and radioactive substances have all made progress by building on and improving earlier results. Secondary infections and epidemiology have been in focus in the area of biological substance. A thorough review and update have been conducted regarding probit parameters for chemical substances. This included a comparison with earlier results and with two external toxicological data sets. New and improved phantoms have been constructed to which gamma radiation dosages have been quantitatively determined for different environmental conditions. The sources are located at different distances and with a variety of radiation energies which allows for utilization in generic situations. In addition to the three substance fields, progress have been made regarding a new deposition model which is now finalized and implemented. This development promises improved input data to the effect models. The Effect module has been improved with a number of new functionalities where different forms of population representations and support for agents may be mentioned. Two new fields have been studied during this period: a review of models for lung deposition of aerosols and numerical optimization for radiation dosage calculations. Finally, two case studies have been conducted coupled to the war in Iraq/Syria. The results were encouraging and also indicated fields in need of improvement for Effect models.