Data Driven Contagion Risk Management in Low-Income Countries using Machine Learning Applications with COVID-19 in South Asia

Abu Shonchoy, Moogdho Mahzab (), Towhid Mahmood () and Manhal Ali ()
Additional contact information
Moogdho Mahzab: Stanford University
Towhid Mahmood: Texas Tech University
Manhal Ali: University of Leeds

No 2302, Working Papers from Florida International University, Department of Economics

Abstract: 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 2020-2022, 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.

Pages: 58 pages
Date: 2023-02
New Economics Papers: this item is included in nep-big, nep-cmp and nep-rmg
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://economics.fiu.edu/research/pdfs/2023_working_papers/2302.pdf First version, 2023 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fiu:wpaper:2302

Access Statistics for this paper

More papers in Working Papers from Florida International University, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sheng Guo ().