Bangladesh is one of the most vulnerable countries to climate change (CC) with higher temperatures reducing crop yields and sea level rise decreasing arable land supply. The Government of Bangladesh aspires to offer its people a comparable standard of living to that of middle-income countries by 2021. Bangladesh's population will reach 247 million by 2050 and GDP is projected to grow annually by 7.9%. With increasing population density, greater demand for resources, and CC impacts, adaptation and mitigation strategies will be required for agricultural output to meet growing food demand. We develop a dynamic computable general equilibrium model linked with a food security module to explore CC impacts on agriculture and food security. Although CC impacts had a relatively small effect on GDP, reducing it by $29,925 million Taka (-0.11%) by 2030, agricultural sector impacts were felt more acutely, reducing output by -1.23%, increasing imports by 1.52%, and reducing total caloric consumption by 17%, with some households remaining underfed due to inequitable food distribution. Evidence generated here can guide policy to ensure economic growth contributes to meeting national development and food security targets.

In the absence of real-time surveillance data, it is difficult to derive an early warning system and potential outbreak locations with the existing epidemiological models, especially in resource-constrained countries. We proposed a contagion risk index (CR-Index)—based on publicly available national statistics—founded on communicable disease spreadability vectors. Utilizing the daily COVID-19 data (positive cases and deaths) from 2020 to 2022, we developed country-specific and sub-national CR-Index for South Asia (India, Pakistan, and Bangladesh) and identified potential infection hotspots—aiding policymakers with efficient mitigation planning. Across the study period, the week-by-week and fixed-effects regression estimates demonstrate a strong correlation between the proposed CR-Index and sub-national (district-level) COVID-19 statistics. We validated the CR-Index using machine learning methods by evaluating the out-of-sample predictive performance. Machine learning driven validation showed that the CR-Index can correctly predict districts with high incidents of COVID-19 cases and deaths more than 85% of the time. This proposed CR-Index is a simple, replicable, and easily interpretable tool that can help low-income countries prioritize resource mobilization to contain the disease spread and associated crisis management with global relevance and applicability. This index can also help to contain future pandemics (and epidemics) and manage their far-reaching adverse consequences.

Objectives To derive and validate a data-driven Contagion Risk Index (CR-Index) at sub-national level for low-income countries – detecting potential infection hotspots – aiding policymakers with efficient mitigation planning for COVID-19 and future epidemics and pandemics. Methods We utilize daily district-level COVID-19 data (positive cases and deaths) from South Asia (India, Pakistan, and Bangladesh) from 2020–2022 to derive the CR-Index – founded on commutable disease spreadability vectors across four domains: urbanization, informality, migration, and health infrastructure. We validated CR-Index based risk-zoning by utilizing time-series regressions and machine learning (ML) estimates (Random Forests and a battery of cross-validation) for predictive accuracy. Results Regressions demonstrate a strong association between the CR-Index and sub-national COVID-19 epidemiology data. ML driven validation show strong predictive support for the CR-Index that can distinguish districts with high-risk COVID-19 cases/deaths for more than 85% of the time. Conclusion Our proposed simple and replicable CR-Index is an easily interpretable tool that can help low-income countries to prioritize resource mobilization (such as vaccination roll-out or free in-home test-kits) to contain the disease spread and associated crisis management, with global relevance and applicability.

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.

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