Efforts to contain future pandemics (and epidemics) and managing their far-reaching adverse consequences require early warning systems, efficient planning, and targeted policy interventions. Lacking timely data with inadequate health capacity make resource-limited countries’ communicable disease management and planning difficult. We proposed a cost-effective and data-driven Contagion Risk Index (CR-Index) strategy founded on communicable disease spreadability vectors. Utilizing the daily district-level COVID-19 data (positive cases and deaths) from 2020–2022, we derived the CR-Index for South Asia (India, Pakistan, and Bangladesh) and identified potential infection hotspots, marked as "red zones" – aiding policymakers with efficient mitigation planning. Across the study period the week-by-week and fixed-effects regressions demonstrate a strong correlation between the proposed CR-Index and district-wise COVID-19 epidemiology data. We validated the CR-Index using machine learning methods by evaluating the out-of-sample predictive performance of the CR-Index. Machine learning driven validation shows strong predictive support for the CR-Index and can distinguish districts with high-risk COVID-19 cases/deaths for more than 85% of the time. Our proposed simple and replicable CR-Index is an easily interpretable tool that can help low-income countries to prioritize resource mobilization to contain the disease spread and associated crisis management, with global relevance and applicability.