<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Owek CJ</submitter><funding>World Health Organization</funding><funding>Bill and Melinda Gates Foundation</funding><funding>Wellcome Trust</funding><funding>Gates Foundation</funding><pagination>e015247</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11552008</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>9(11)</volume><pubmed_abstract>&lt;h4>Introduction&lt;/h4>The COVID-19 pandemic had devastating health and socioeconomic effects, partly due to policy decisions to mitigate them. Little evidence exists of approaches that guided decisions in settings with limited pre-pandemic modelling capacity. We thus sought to identify knowledge translation mechanisms, enabling factors and structures needed to effectively translate modelled evidence into policy decisions.&lt;h4>Methods&lt;/h4>We used convergent mixed methods in a participatory action approach, with quantitative data from a survey and qualitative data from a scoping review, in-depth interviews and workshop notes. Participants included researchers and policy actors involved in COVID-19 evidence generation and decision-making. They were mostly from lower- and middle-income countries</pubmed_abstract><journal>BMJ global health</journal><pubmed_title>Lessons learned from COVID-19 modelling efforts for policy decision-making in lower- and middle-income countries.</pubmed_title><pmcid>PMC11552008</pmcid><funding_grant_id>001</funding_grant_id><funding_grant_id>INV-034291</funding_grant_id><pubmed_authors>Cw Hutubessy R</pubmed_authors><pubmed_authors>Owek CJ</pubmed_authors><pubmed_authors>Guleid FH</pubmed_authors><pubmed_authors>Maluni J</pubmed_authors><pubmed_authors>Jepkosgei J</pubmed_authors><pubmed_authors>Were VO</pubmed_authors><pubmed_authors>Sim SY</pubmed_authors><pubmed_authors>Oliwa J</pubmed_authors><pubmed_authors>Nzinga J</pubmed_authors><pubmed_authors>Hagedorn BL</pubmed_authors></additional><is_claimable>false</is_claimable><name>Lessons learned from COVID-19 modelling efforts for policy decision-making in lower- and middle-income countries.</name><description>&lt;h4>Introduction&lt;/h4>The COVID-19 pandemic had devastating health and socioeconomic effects, partly due to policy decisions to mitigate them. Little evidence exists of approaches that guided decisions in settings with limited pre-pandemic modelling capacity. We thus sought to identify knowledge translation mechanisms, enabling factors and structures needed to effectively translate modelled evidence into policy decisions.&lt;h4>Methods&lt;/h4>We used convergent mixed methods in a participatory action approach, with quantitative data from a survey and qualitative data from a scoping review, in-depth interviews and workshop notes. Participants included researchers and policy actors involved in COVID-19 evidence generation and decision-making. They were mostly from lower- and middle-income countries</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Nov</publication><modification>2026-06-03T18:58:10.891Z</modification><creation>2025-04-04T00:33:34.351Z</creation></dates><accession>S-EPMC11552008</accession><cross_references><pubmed>39521455</pubmed><doi>10.1136/bmjgh-2024-015247</doi></cross_references></HashMap>