A community-based contact isolation strategy to reduce the spread of Ebola virus disease: an analysis of the 2018–2020 outbreak in the Democratic Republic of the Congo

Keïta, Mory; Polonsky, Jonathan; Ahuka‐Mundeke, Steve; Ilumbulumbu, Michel Kalongo; Dakissaga, Adama; Boiro, Hamadou; Anoko, Julienne Ngoundoung; Diassy, Lamine; Ngwama, John Kombe; Bah, Hawa; Tosalisana, Michel Kasereka; Omasumbu, Richard Kitenge; Chérif, Ibrahima Sory; Boland, Samuel; Délamou, Alexandre; Yam, Abdoulaye; Flahault, Antoine; Dagron, Stéphanie; Guèye, Abdou Salam; Keiser, Olivia; Fall, Ibrahima Socé

doi:10.1136/bmjgh-2023-011907

Cited by 3 publications

References 38 publications

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Factors Associated with Reliable Contact Tracing During the 2021 Ebola Virus Disease Outbreak in Guinea

Keita,

Cherif,

Polonsky

et al. 2024

J Epidemiol Glob Health

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Background In 2021, an Ebola virus disease (EVD) outbreak was declared in Guinea, linked to persistent virus from the 2014–2016 West Africa Epidemic. This paper analyzes factors associated with contact tracing reliability (defined as completion of a 21-day daily follow-up) during the 2021 outbreak, and transitively, provides recommendations for enhancing contact tracing reliability in future. Methods We conducted a descriptive and analytical cross-sectional study using multivariate regression analysis of contact tracing data from 1071 EVD contacts of 23 EVD cases (16 confirmed and 7 probable). Results Findings revealed statistically significant factors affecting contact tracing reliability. Unmarried contacts were 12.76× more likely to miss follow-up than those married (OR = 12.76; 95% CI [3.39–48.05]; p < 0.001). Rural-dwelling contacts had 99% lower odds of being missed during the 21-day follow-up, compared to those living in urban areas (OR = 0.01; 95% CI [0.00–0.02]; p < 0.01). Contacts who did not receive food donations were 3× more likely to be missed (OR = 3.09; 95% CI [1.68–5.65]; p < 0.001) compared to those who received them. Contacts in health areas with a single team were 8× more likely to be missed (OR = 8.16; 95% CI [5.57–11.96]; p < 0.01) than those in health areas with two or more teams (OR = 1.00; 95% CI [1.68–5.65]; p < 0.001). Unvaccinated contacts were 30.1× more likely to be missed compared to vaccinated contacts (OR = 30.1; 95% CI [5.12–176.83]; p < 0.001). Conclusion Findings suggest that contact tracing reliability can be significantly influenced by various demographic and organizational factors. Considering and understanding these factors—and where possible addressing them—may be crucial when designing and implementing contact tracing strategies during future outbreaks in low-resource settings.

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Factors Associated with Reliable Contact Tracing During the 2021 Ebola Virus Disease Outbreak in Guinea

Keita,

Cherif,

Polonsky

et al. 2024

J Epidemiol Glob Health

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Toward optimal disease surveillance with graph-based active learning

Tsui,

Zhang,

Sambaturu

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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Tracking the spread of emerging pathogens is critical to the design of timely and effective public health responses. Policymakers face the challenge of allocating finite resources for testing and surveillance across locations, with the goal of maximizing the information obtained about the underlying trends in prevalence and incidence. We model this decision-making process as an iterative node classification problem on an undirected and unweighted graph, in which nodes represent locations and edges represent movement of infectious agents among them. To begin, a single node is randomly selected for testing and determined to be either infected or uninfected. Test feedback is then used to update estimates of the probability of unobserved nodes being infected and to inform the selection of nodes for testing at the next iterations, until certain test budget is exhausted. Using this framework, we evaluate and compare the performance of previously developed active learning policies for node selection, including Node Entropy and Bayesian Active Learning by Disagreement. We explore the performance of these policies under different outbreak scenarios using simulated outbreaks on both synthetic and empirical networks. Further, we propose a policy that considers the distance-weighted average entropy of infection predictions among neighbors of each candidate node. Our proposed policy outperforms existing ones in most outbreak scenarios given small test budgets, highlighting the need to consider an exploration–exploitation trade-off in policy design. Our findings could inform the design of cost-effective surveillance strategies for emerging and endemic pathogens and reduce uncertainties associated with early risk assessments in resource-constrained situations.

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Optimal disease surveillance with graph-based Active Learning

Tsui,

Zhang,

Sambaturu

et al. 2024

Preprint

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Tracking the spread of emerging pathogens is critical to the design of timely and effective public health responses. Policymakers face the challenge of allocating finite resources for testing and surveillance across locations, with the goal of maximising the information obtained about the underlying trends in prevalence and incidence. We model this decision-making process as an iterative node classification problem on an undirected and unweighted graph, in which nodes represent locations and edges represent movement of infectious agents among them. To begin, a single node is randomly selected for testing and determined to be either infected or uninfected. Test feedback is then used to update estimates of the probability of unobserved nodes being infected and to inform the selection of nodes for testing at the next iterations, until a certain resource budget is exhausted. Using this framework we evaluate and compare the performance of previously developed Active Learning policies, including node-entropy and Bayesian Active Learning by Disagreement. We explore the performance of these policies under different outbreak scenarios using simulated outbreaks on both synthetic and empirical networks. Further, we propose a novel policy that considers the distance-weighted average entropy of infection predictions among the neighbours of each candidate node. Our proposed policy outperforms existing ones in most outbreak scenarios, leading to a reduction in the number of tests required to achieve a certain predictive accuracy. Our findings could inform the design of cost-effective surveillance strategies for emerging and endemic pathogens, and reduce the uncertainties associated with early risk assessments in resource-constrained situations.

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A community-based contact isolation strategy to reduce the spread of Ebola virus disease: an analysis of the 2018–2020 outbreak in the Democratic Republic of the Congo

Cited by 3 publications

References 38 publications

Factors Associated with Reliable Contact Tracing During the 2021 Ebola Virus Disease Outbreak in Guinea

Factors Associated with Reliable Contact Tracing During the 2021 Ebola Virus Disease Outbreak in Guinea

Toward optimal disease surveillance with graph-based active learning

Optimal disease surveillance with graph-based Active Learning

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