パンデミックの社会人口統計学的グループへの影響の違い：数理モデルフレームワーク

Deprivation and age can both drive disparities in infectious disease transmission and outcomes; however, few models capture their combined effects. We developed a deterministic ordinary differential equation model stratified by age and deprivation decile coupled with time-dependent testing proportion to examine how mixing patterns shape inequalities in disease burden, using COVID-19 in England as a case study. The framework allows three mixing scenarios-diagonal, preferred, and proportionate, and we simulated the epidemic with movement restrictions to reflect lockdown measures. We assessed the effectiveness of these restrictions in reducing transmission and explored their implications for different social groups. Results show that under diagonal mixing, disease outcomes are significantly higher than under the other mixing scenarios, with the most deprived deciles experiencing disproportionately greater burdens. Lockdown measures substantially reduced overall disease outcomes across all deciles, but relative inequalities persisted, indicating that generalised restrictions alone were insufficient to eliminate these inequalities. Our age-deprivation structured model provides a foundation for designing targeted interventions, such as deprivation-focused testing, vaccination campaigns, or localised movement restrictions, to mitigate unequal health outcomes during epidemics.