> for - addiction - disease model fails addicts - turns them into patient - therefore creates powerlessness - which is a primary reason leading to replase

> for - addiction - belief in the disease model - correlated to relapse

> for - stats - addiction - rehab centers - 85% are based on disease model - and 85% use a religious oriented 12 step program

> for - addiction - disease model rationale

> for - addiction - failure of rehabilitation is proof of the wrong model - the disease model - quote - the disease model of addiction is not only wrong, but harmful - Marc Lewis

I came here after recalling a critique by Bessel van der Kolk's "The Body Keeps the Score: Brain, Mind, and Body in the Healing of Trauma" regarding the disease model and it's negative impact on adequately helping people with trauma. van der Kolk's critique was similar to Marc Lewis' critique of the disease model as it applies to addiction from "The Biology of Desire: Why Addiction Is Not a Disease". This made me wonder what the term "disease" actually means and whether or not some general consensus existed within the medical community. This article suggests there is no such consensus.

This article is by Jackie Leach Scully who holds a "PhD in cellular pathology, University of Cambridge; BA (Hons) in biochemistry, University of Oxford; MA in psychoanalytic studies, Sheffield University".

Scully does several insightful things in this paper the following are the ones that were most salient to me upon the first read: - distinguishes "disease" from "disability" - contrasts the "social model" and "medical model" perspectives on "disability" - The "medical model" referred to here is probably what Lewis & van der Kolk are critiquing as the "disease model".<br /> - Are the "medical" and "disease" model different? - the social model seems to have arisen as a response to the inadequacy of the medical model

    - "The social model's fundamental criticism of the medical model is that it wrongly locates 'the problem' of disability in biological constraints, considering it only from the point of view of the individual and neglecting the social and systemic frameworks that contribute to it. The social model distinguishes between impairment (the biological substrate, such as impaired hearing) and the disabled experience. In this view the presence of impaired hearing is one thing, while the absence of subtitling on TV is quite another, and it is the refusal of society to make the necessary accommodations that is the real site of disability. A social model does not ignore biology, but contends that societal, economic and environmental factors are at least as important in producing disability."

• brings up a subtle point that there are two jumps "from gene to phenotype, and from phenotype to experience" and that some of the arguments mentioned "suggest that the 'harm' of the impairment is not straightforwardly related to phenotype. What ought to concern us about disease and disability is the disadvantage, pain or suffering involved, and in a sense the impairment is always a kind of surrogate marker for this experience."

Wang, B., Gou, M., Guo, Y., Tanaka, G., & Han, Y. (2020). Network structure-based interventions on spatial spread of epidemics in metapopulation networks. Physical Review E, 102(6), 062306. https://doi.org/10.1103/PhysRevE.102.062306

Lee Kennedy-Shaffer. (2021, May 6). .@rebeccajk13 and @mlipsitch showed how to use that to estimate efficacy against prevalent infection (https://t.co/LeXxqcumGS). [Tweet]. @LeeKShaffer. https://twitter.com/LeeKShaffer/status/1390322501817880581

Callaway, E. (2021). Mice with severe COVID symptoms could speed vaccine effort. Nature. https://doi.org/10.1038/d41586-021-01251-0

Geng, X., Katul, G. G., Gerges, F., Bou-Zeid, E., Nassif, H., & Boufadel, M. C. (2021). A kernel-modulated SIR model for Covid-19 contagious spread from county to continent. Proceedings of the National Academy of Sciences, 118(21). https://doi.org/10.1073/pnas.2023321118

Cepelewicz, J. (n.d.). The Hard Lessons of Modeling the Coronavirus Pandemic. Quanta Magazine. Retrieved February 11, 2021, from https://www.quantamagazine.org/the-hard-lessons-of-modeling-the-coronavirus-pandemic-20210128/

Nsoesie, E. O., Oladeji, O., Abah, A. S. A., & Ndeffo-Mbah, M. L. (2021). Forecasting influenza-like illness trends in Cameroon using Google Search Data. Scientific Reports, 11(1), 6713. https://doi.org/10.1038/s41598-021-85987-9

Holme, P. (2021). Fast and principled simulations of the SIR model on temporal networks. PLOS ONE, 16(2), e0246961. https://doi.org/10.1371/journal.pone.0246961

Gupta, R. K., Marks, M., Samuels, T. H. A., Luintel, A., Rampling, T., Chowdhury, H., Quartagno, M., Nair, A., Lipman, M., Abubakar, I., Smeden, M. van, Wong, W. K., Williams, B., & Noursadeghi, M. (2020). Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: An observational cohort study. MedRxiv, 2020.07.24.20149815. https://doi.org/10.1101/2020.07.24.20149815

Ye, Y., Zhang, Q., Ruan, Z., Cao, Z., Xuan, Q., & Zeng, D. D. (2020). Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission. Physical Review E, 102(4), 042314. https://doi.org/10.1103/PhysRevE.102.042314

Anderson, S. C., Edwards, A. M., Yerlanov, M., Mulberry, N., Stockdale, J. E., Iyaniwura, S. A., Falcao, R. C., Otterstatter, M. C., Irvine, M. A., Janjua, N. Z., Coombs, D., & Colijn, C. (2020). Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing. PLOS Computational Biology, 16(12), e1008274. https://doi.org/10.1371/journal.pcbi.1008274

Karatayev, Vadim A., Madhur Anand, and Chris T. Bauch. ‘Local Lockdowns Outperform Global Lockdown on the Far Side of the COVID-19 Epidemic Curve’. Proceedings of the National Academy of Sciences 117, no. 39 (29 September 2020): 24575–80. https://doi.org/10.1073/pnas.2014385117.