Analysis of Swiss data from the 1918-1920 influenza pandemic reveals that factors driving illness rates differ substantially from those determining death rates. Using spatial regression techniques across administrative regions, researchers found that demographic and socioeconomic variables influenced morbidity and mortality through distinct pathways, challenging the common practice of using death data alone to understand pandemic dynamics. This methodological approach demonstrates how historical morbidity data, often dismissed as unreliable, can provide crucial insights when properly harmonized across geographic levels. The findings have significant implications for contemporary pandemic preparedness and response strategies. Understanding why certain populations experience higher infection rates versus higher case fatality rates could inform targeted interventions during future health crises. The divergent patterns suggest that policies aimed at reducing transmission may need different approaches than those focused on preventing severe outcomes. However, this preprint awaits peer review, and the findings depend on the quality of century-old Swiss health records. The work represents an incremental but valuable contribution to historical epidemiology, offering a template for extracting meaningful insights from imperfect historical health data that could enhance modern pandemic modeling.