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

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.

A Community Health Worker-Based Intervention on Anthropometric Outcomes of Children Aged 3 to 21 Months in Urban Pakistan, 2019-2021.

Objectives. To evaluate the impact of a community health worker-based "in-home growth monitoring with counseling" (IHGMC) intervention on anthropometric outcomes in Pakistan, where 38% of children younger than 5 years are stunted. Methods. We used an individual, single-blind, step-wedge randomized controlled trial and a pure control group recruited at endline. We based the analysis on an intention-to-treat estimation using the coarsened exact matching (CEM) method for sample selection among treatments and the control. We conducted the baseline in July 2019 and completed endline in September-October 2021. We recruited 1639 households (treated: 1188; control: 451) with children aged 3 to 21 months who were residing in an urban informal settlement area. The CEM sample used for analysis numbered 1046 (treated: 636; control: 410). The intervention continued for 6 months. Results. Compared with the control group, the height-for-age z-score in the IHGMC group increased by 0.58 SD (95% confidence interval [CI] = 0.33, 0.83; P = .001) and the weight-for-age z-score by 0.43 SD (95% CI = 0.20, 0.67; P < .01), measured at endline. Conclusions. IHGMC substantially improved child anthropometric outcomes in disadvantaged localities, and this impact persisted during the COVID-19 pandemic. Trial Registration. AER-RCT registry (AEARCTR-0003248). (Am J Public Health. 2023;113(1):105-114. https://doi.org/10.2105/AJPH.2022.307111).

A novel index-based decision support toolkit for safe reopening following a generalized lockdown in low and middle-income countries.

While the effectiveness of lockdowns to reduce Coronavirus Disease-2019 (COVID-19) transmission is well established, uncertainties remain on the lifting principles of these restrictive interventions. World Health Organization recommends case positive rate of 5% or lower as a threshold for safe reopening. However, inadequate testing capacity limits the applicability of this recommendation, especially in the low-income and middle-income countries (LMICs). To develop a practical reopening strategy for LMICs, in this study, we first identify the optimal timing of safe reopening by exploring accessible epidemiological data of 24 countries during the initial COVID-19 surge. We find that a safe opening can occur two weeks after the crossover of daily infection and recovery rates while maintaining a negative trend in daily new cases. Epidemiologic SIRM model-based example simulation supports our findings. Finally, we develop an easily interpretable large-scale reopening (LSR) index, which is an evidence-based toolkit-to guide/inform reopening decision for LMICs.

Migration, externalities, and the diffusion of COVID-19 in South Asia☆.

The initial spread of COVID-19 halted economic activity as countries around the world restricted the mobility of their citizens. As a result, many migrant workers returned home, spreading the virus across borders. We investigate the relationship between migrant movements and the spread of COVID-19 using district-day-level data from Bangladesh, India, and Pakistan (the 1st, 6th, and 7th largest sources of international migrant workers). We find that during the initial stage of the pandemic, a 1 SD increase in prior international out-migration relative to the district-wise average in India and Pakistan predicts a 48% increase in the number of cases per capita. In Bangladesh, however, the estimates are not statistically distinguishable from zero. Domestic out-migration predicts COVID-19 diffusion in India, but not in Bangladesh and Pakistan. In all three countries, the association of COVID-19 cases per capita and measures of international out-migration increases over time. The results show how migration data can be used to predict coronavirus hotspots. More broadly, the results are consistent with large cross-border negative externalities created by policies aimed at containing the spread of COVID-19 in migrant-receiving countries.