Evidence map›Paper›PMID 40522921›Full record

ArticlePloS one2025

Inter-agency collaboration to support the COVID-19 response and maintain malaria services in Guinea and Sierra Leone: A social network analysis.

Lukas Feddern, Ibrahima Kaba, Hanna-Tina Fischer, Brogan Geurts, Habibata Balde, Andrea Bernasconi, Vitali Merhi, Rike Böttcher, Karim Dumbuya, Francisco Pozo-Martin and 7 more

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Lukas FeddernEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Ibrahima KabaAfrican Center of Excellence for the Prevention and Control of Infectious Diseases, University Gamal Abdel Nasser of Conakry, Guinea.
Hanna-Tina FischerEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Brogan GeurtsEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Habibata BaldeAfrican Center of Excellence for the Prevention and Control of Infectious Diseases, University Gamal Abdel Nasser of Conakry, Guinea.ORCID 0000-0003-0574-7958
Andrea BernasconiEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Vitali MerhiEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Rike BöttcherEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Karim DumbuyaCollege of Medicine and Allied Health Sciences (COMAHS), University of Sierra Leone, Freetown, Sierra Leone.
Francisco Pozo-MartinEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Thurid BahrEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Sara Menelik-ObbariusEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Karoline SteinEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Abdul Karim MbawahCollege of Medicine and Allied Health Sciences (COMAHS), University of Sierra Leone, Freetown, Sierra Leone.
Alexandre DelamouAfrican Center of Excellence for the Prevention and Control of Infectious Diseases, University Gamal Abdel Nasser of Conakry, Guinea.
Heide WeishaarEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.
Charbel El-BcheraouiEvidence-Based Public Health, Centre for International Health Protection, Robert Koch Institute, Berlin, Germany.ORCID 0000-0003-3117-9966

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inter-agency networks are believed to be essential to maintaining essential health services during pandemics and to strengthening the resilience of health systems. Focusing on malaria during the COVID-19 pandemic as a case study, this paper investigates the network of organisations fighting malaria in Guinea and Sierra Leone, the changes in their interactions during the pandemic, and their collaboration with organisations addressing the COVID-19 response. We used a mixed-methods approach to analyse data from 36 semi-structured interviews with respondents involved in the COVID-19 response and malaria service provision in Guinea and Sierra Leone, as well as data from a social network survey conducted with a subsample of 21 out of the 36 respondents. Results from the thematic analysis of key informant interviews were used to contextualize the social network data, which was visualized as sociograms. We found that malaria stakeholders worked closely with key stakeholders involved in containing the COVID-19 pandemic. Their ability to support the COVID-19 response and contribute to maintaining essential malaria services was facilitated by the fact that established networks of malaria stakeholders had existed in both countries which comprised a variety of national and international organisations from the public, private and civil society sector working in different areas of malaria elimination. These stakeholders were able to maintain or even intensify their collaboration during the COVID-19 pandemic. The findings suggest that in resource-limited settings, the promotion of coordination mechanisms among stakeholders working on specific issues can strengthen linkages and consistency, thereby contributing to overall health system resilience and pandemic response.

Indexed as

COVID-19MalariaSocial Network AnalysisCooperative BehaviorGuineaHumansPandemicsSARS-CoV-2Sierra Leone

Identifiers

PMID40522921
PMCPMC12169549

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.