write a draft for an abstract for odissei

[n400peanuts]

@Astrid-p @jiqicn @kristinathompson

I have made a first attempt for an abstract for the ODISSEI conference. This is mostly focused on the model rather than the scientific side (i.e., the results of the simulation). I will try to add another one that is modelled more on Kristina's abstract based on the relevance of the type of data you can get with comma... not ready yet, but when it will be, it is going to be uploaded in the same file :)

we can discuss it on monday! cheers

[kristinathompson]

Thanks, Eva!

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From: Eva Viviani @.> Sent: Thursday, June 8, 2023 2:07:12 PM To: covid19ABM/comma @.> Cc: Thompson, Kristina @.>; Mention @.> Subject: Re: [covid19ABM/comma] write a draft for an abstract for odissei (Issue #46)

@Astrid-phttps://github.com/Astrid-p @jiqicnhttps://github.com/jiqicn @kristinathompsonhttps://github.com/kristinathompson

I have made a first attempthttps://github.com/covid19ABM/comma/blob/submissions/submissions/odissei.md for an abstract for the ODISSEI conference. This is mostly focused on the model rather than the scientific side (i.e., the results of the simulation). I will try to add another one that is modelled more on Kristina's abstract based on the relevance of the type of data you can get with comma... not ready yet, but when it will be, it is going to be uploaded in the same file :)

we can discuss it on monday! cheers

— Reply to this email directly, view it on GitHubhttps://github.com/covid19ABM/comma/issues/46#issuecomment-1582465793, or unsubscribehttps://github.com/notifications/unsubscribe-auth/A4VBXNZV7RNUCGLAV7TOKCTXKG57BANCNFSM6AAAAAAYYK744Q. You are receiving this because you were mentioned.Message ID: @.***>

[n400peanuts]

Final abstract for ODISSEI (poster):

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COMMA: A microsimulation model exploring the relationship between lockdown policies and depression rates during the COVID-19 pandemic

During the COVID-19 pandemic, lockdowns decreased COVID-19-related mortality and hospitalizations, but also took a large toll on populations' mental health, particularly on depression. We hypothesized that depression rates during the pandemic could be associated with the duration, sequence, and severity of lockdown policies, as evidenced by the growing literature on the effects of lockdown on mental health issues.

To test this, we developed COMMA (COvid Mental-health Model with Agents), a microsimulation model based on an individual-level synthetic population derived from data of the Groningen area's lifelines biobank ((n=2,556), https://www.lifelines.nl/). This biobank provides rich socio-economic, demographic, activity, and health data, including depression onset and progression from pre- to post- pandemic periods. COMMA operates on a daily timestep, simulating a series of daily activities reflective of individual's characteristics and current lockdown policies. The type of daily activities allowed in the model is configured to reflect actual lockdown policies applied in the Netherlands, but the model accepts also hypothetical/alternative lockdown scenarios. These activities, varying in accordance with actual or hypothetical lockdown conditions, were calibrated to influence depression rates positively or negatively, based on the literature surrounding lockdown-induced mental health issues.

COMMA not only aids in confirming existing literature on mental health impacts of lockdown measures, but also offers a platform to explore potential alternative lockdown scenarios. This may help to shed light into mechanisms linking experiencing lockdowns and depression, and thus potentially inform future policies in case of a future pandemic.

[kristinathompson]

Nice - thanks for doing so! I'll upload the 'public health' abstract in the coming days.

Best wishes,

Kristina

From: Eva Viviani @.> Sent: 26 June 2023 11:09 To: covid19ABM/comma @.> Cc: Thompson, Kristina @.>; Mention @.> Subject: Re: [covid19ABM/comma] write a draft for an abstract for odissei (Issue #46)

Final abstract for ODISSEI (poster):

---

COMMA: A microsimulation model exploring the relationship between lockdown policies and depression rates during the COVID-19 pandemic

During the COVID-19 pandemic, lockdowns decreased COVID-19-related mortality and hospitalizations, but also took a large toll on populations' mental health, particularly on depression. We hypothesized that depression rates during the pandemic could be associated with the duration, sequence, and severity of lockdown policies, as evidenced by the growing literature on the effects of lockdown on mental health issues.

To test this, we developed COMMA (COvid Mental-health Model with Agents), a microsimulation model based on an individual-level synthetic population derived from data of the Groningen area's lifelines biobank ((n=2,556), https://www.lifelines.nl/). This biobank provides rich socio-economic, demographic, activity, and health data, including depression onset and progression from pre- to post- pandemic periods. COMMA operates on a daily timestep, simulating a series of daily activities reflective of individual's characteristics and current lockdown policies. The type of daily activities allowed in the model is configured to reflect actual lockdown policies applied in the Netherlands, but the model accepts also hypothetical/alternative lockdown scenarios. These activities, varying in accordance with actual or hypothetical lockdown conditions, were calibrated to influence depression rates positively or negatively, based on the literature surrounding lockdown-induced mental health issues.

COMMA not only aids in confirming existing literature on mental health impacts of lockdown measures, but also offers a platform to explore potential alternative lockdown scenarios. This may help to shed light into mechanisms linking experiencing lockdowns and depression, and thus potentially inform future policies in case of a future pandemic.

- Reply to this email directly, view it on GitHubhttps://github.com/covid19ABM/comma/issues/46#issuecomment-1607037002, or unsubscribehttps://github.com/notifications/unsubscribe-auth/A4VBXNYKD5TWTMJTIQGWSR3XNFGRZANCNFSM6AAAAAAYYK744Q. You are receiving this because you were mentioned.Message ID: @.**@.>>