Evidence map›Paper›PMID 42558235›Full record

ArticleFrontiers in veterinary science2026

Integrating systems thinking for analyzing and designing national early warning surveillance for animal health: a perspective from Tanzania.

Janeth George

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

1 author.

Janeth GeorgeDepartment of Economics and Community Economic Development, Faculty of Arts and Social Sciences, The Open University of Tanzania, Dar Es Salaam, Tanzania.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Animal health surveillance has demonstrated its value in global health security. The effectiveness of a surveillance system in reducing health threats and their impact depends on complex interactions between processes, enablers and context. However, most national animal health surveillance systems, including those in Tanzania, still face complex, interconnected challenges that undermine their efficiency. This paper is presented as a conceptual perspective that synthesizes insights from previously published studies on the use of integrative approaches for analyzing, designing, and evaluating animal health surveillance systems. The studies focused on evaluating the system, identifying data sources, mapping stakeholders for collaboration, and developing a prototype of an integrated animal health surveillance system. The paper offers a structured socio-technical framework drawing on experience from Tanzania, a resource-limited setting. It highlights the complex interplay between technical and social factors that shape the implementation of animal health surveillance. Strengthening surveillance systems required the adoption of technologies that facilitate integration and interoperability of multiple data sources for continuous improvement on system attributes, decision outcomes and ultimately health outcomes. It is also critical to consider social aspects, such as investment in human and financial resources, and foster collaboration between public and private stakeholders to improve data flow from various sources. Most importantly, there should be capacity building in using data for decision-making to enhance a timely response to health threats. This paper provides additional guidance to countries in assessing their current surveillance setups and developing more efficient early warning systems.

Indexed as

animal health surveillanceearly disease detectionOne Healthsocio-technical systemsveterinary epidemiology

Identifiers

PMID42558235
PMCPMC13437301

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.