Data driven contagion risk management in low-income countries using machine learning applications with COVID-19 in South Asia

Aug 5, 2026

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In the absence of real-time surveillance data, early warning and identification of likely outbreak locations are difficult for resource-constrained settings. A contagion risk index (CR-Index) is developed using publicly available national statistics and communicable disease spreadability vectors, then parameterized with daily COVID-19 positive cases and deaths from 2020–2022 for South Asia (India, Pakistan, Bangladesh). Week-by-week and fixed-effects regression show strong correlation with district-level outcomes. Out-of-sample machine-learning validation predicts high-incidence districts with over 85% accuracy, enabling interpretable hotspot targeting and efficient resource prioritization for crisis management and future pandemics.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Data limitations in LMICs

Need for early warning and targeted interventions

Proposed CR-Index Framework

Contagion risk index based on spreadability vectors

Composite index for resource prioritization

Validation and Predictive Performance

Regression and fixed-effects estimates

Out-of-sample machine learning validation

Policy Use: Hotspots and Mitigation Planning

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Data driven contagion risk management in low‑income countries using machine learning applications with COVID‑19 in South Asia Abu S. Shonchoy1*, Moogdho M. Mahzab2, Towhid I. Mahmood 3 & Manhal Ali4 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 ofCOVID‑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. Coronavirus Disease 2019 (COVID-19), a highly contagious respiratory borne infection, and its variants are still spreading rapidly and have caused more than 6.4 million global deaths. Although scientists have developed pharmaceutical treatments (medicine, therapeutics) and vaccines, it is still uncertain how effective these pharmaceutical measures are against the future variants ofCOVID-19, as evident from the recent astounding surge in global caseloads with the latest variant of concern—Omicron. The series ofCOVID-19 waves in the last two years have generated tremendous stress on the health care systems, prompting public health professionals to recommend universal vaccination and boosting. This situation is more acute in Low and Middle-Income Countries (LMICs), where a major share of the unvaccinated population resides (about 19.87% of the low-income country population received only one dose of vaccine compared to the global full vaccination rate of 62%)1. In the face of widespread poverty, deficient safetynet measures, high self-employment, and a sizable informal economy, LMICs remained vulnerable imposing mobility restrictions that showed adverse consequences—leading to unemployment, poverty, and starvation2. Moreover, limited data capacity, insufficient health infrastructure, resource limitations, and inadequate COVID- 19 testing ability made it further difficult for the LMIC governments to mitigate and manage the COVID-19 spread (for example, facilitating widespread testing and vaccinations), which demonstrate that conventional crisis management policies by LMICs are not equipped to tackle a pandemic of such scale. Efforts to contain future pandemics (and epidemics) and manage their far-reaching adverse consequences require smart crisis management—employing early warning systems, efficient planning, and targeted interventions. To this end, we proposed a data-driven strategy to prioritize the allocation of limited resources of LMICs 1Florida International University, 11200 SW 8th Street, Miami, FL 33199, USA. 2Stanford University, 473 Via Ortega, Stanford, CA 94305, USA. 3Texas Tech University, 2625 Memorial Circle, Lubbock, TX 79409, USA. 4University of Leeds, M

FAQs

Why is real-time outbreak forecasting difficult in low-income countries?

Real-time surveillance data are limited, making it hard to build early warning systems and identify potential outbreak locations using existing epidemiological models.

How is the contagion risk index (CR-Index) constructed?

CR-Index is based on publicly available national statistics and communicable disease spreadability vectors, then tailored using daily COVID-19 positive cases and deaths for each country and sub-national area.

How is the CR-Index validated and how accurate is it?

Validation uses regression and out-of-sample machine learning predictive testing on district-level data; machine-learning results correctly identify high-incident districts with more than 85% accuracy.

Aug 5, 2026

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