Advance Warning and Response Systems in Kenya: A Scoping Review

2025 · doi:10.1101/2025.04.23.25326250
preprint OA: closed
📄 Open PDF Full text JSON View at publisher
⚙ AI-generated deep summary by claude@2026-06, 2026-06-24 · read from full text ⓘ

This scoping review mapped evidence on Kenya’s advance warning and response systems (AW&RS) for infectious disease outbreak detection, focusing on system enablers and barriers, including climate-sensitive diseases, using searches of major databases and Kenyan grey literature up to August 26, 2024. Across 166 included studies, Integrated Disease Surveillance and Response and cohort surveillance were most common, with evidence concentrated in Nairobi County and focused largely on malaria, and most systems relied on hospital-based burden data and automated alerts; the review reports temporal associations between environmental factors and disease trends (e.g., malaria, Rift Valley Fever, and cholera increasing with precipitation and related climate/vegetation measures). System performance was evaluated using prediction accuracy/reliability and effectiveness measures such as acceptability and timeliness, with health system factors dominating (skilled personnel as key enablers, inadequate finances as a major barrier). The paper concludes that observed climate-linked trends require further studies to establish causal links and that insufficient funding limits effective implementation, and it explicitly does not discuss endometriosis or adenomyosis; it was included via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Introduction Infectious diseases (IDs) cause approximately 13.7 million deaths globally. The Kenyan Advance Warning and Response Systems (AW&RS) against ID outbreaks is a core capacity of the 2005 International Health Regulations and a key indicator of health security. We mapped evidence on Kenya’s AW&RS and their enablers, and barriers for successfully detecting IDs, including climate-sensitive IDs. Methods We searched Cochrane Library, MEDLINE, EMBASE, Web of Science, Africa Index Medicus, and SCOPUS before August 26th, 2024. We also searched for grey literature on the Google Scholar search engine alongside the main repositories of Kenyan Universities. Two independent reviewers conducted study selection, while one reviewer extracted data. Discrepancies were resolved through discussion. Results were synthesised narratively and thematically. Results The search yielded 4,379 records from databases and 1,363 articles from websites, university repositories, and citations; we included 166 articles in the analysis. Integrated Disease Surveillance and Response (IDSR) and cohort surveillance systems were the most common (37.2%). Most studies were concentrated in Nairobi County (25.7%) and reported on malaria (23.6%). Most systems (82.4%) monitored the disease burden and outbreaks using hospital-based data (35.1%) and automated alert mechanisms (27.7%). National bulletins report a temporal association between environmental factors and disease prevalence. Malaria, Rift Valley Fever (RVF), and cholera cases increased with higher precipitation, lower temperatures and increased vegetative index. AW&RS used the accuracy and reliability of the model prediction to measure the system’s performance. Effectiveness was evaluated based on system acceptability and timeliness. Health system factors were predominant, with 121 enablers and 127 barriers. Key enablers included skilled personnel (13 studies), whereas inadequate finances were a major barrier (21 studies). Conclusion Most AW&RS were IDSR and cohort-based surveillance. Climate changes have resulted in observed trends in diseases such as malaria and RVF, but further studies are needed to determine causal links. Insufficient funding hinders the effective implementation of AW&RS. Future research should assess the cost drivers influencing system effectiveness.
Full text 4,146 characters · extracted from oa-doi-fallback · 4 sections · click to expand

Abstract

Introduction Infectious diseases (IDs) cause approximately 13.7 million deaths globally. The Kenyan Advance Warning and Response Systems (AW&RS) against ID outbreaks is a core capacity of the 2005 International Health Regulations and a key indicator of health security. We mapped evidence on Kenya’s AW&RS and their enablers, and barriers for successfully detecting IDs, including climate-sensitive IDs.

Methods

We searched Cochrane Library, MEDLINE, EMBASE, Web of Science, Africa Index Medicus, and SCOPUS before August 26th, 2024. We also searched for grey literature on the Google Scholar search engine alongside the main repositories of Kenyan Universities. Two independent reviewers conducted study selection, while one reviewer extracted data. Discrepancies were resolved through discussion. Results were synthesised narratively and thematically.

Results

The search yielded 4,379 records from databases and 1,363 articles from websites, university repositories, and citations; we included 166 articles in the analysis. Integrated Disease Surveillance and Response (IDSR) and cohort surveillance systems were the most common (37.2%). Most studies were concentrated in Nairobi County (25.7%) and reported on malaria (23.6%). Most systems (82.4%) monitored the disease burden and outbreaks using hospital-based data (35.1%) and automated alert mechanisms (27.7%). National bulletins report a temporal association between environmental factors and disease prevalence. Malaria, Rift Valley Fever (RVF), and cholera cases increased with higher precipitation, lower temperatures and increased vegetative index. AW&RS used the accuracy and reliability of the model prediction to measure the system’s performance. Effectiveness was evaluated based on system acceptability and timeliness. Health system factors were predominant, with 121 enablers and 127 barriers. Key enablers included skilled personnel (13 studies), whereas inadequate finances were a major barrier (21 studies).

Conclusion

Most AW&RS were IDSR and cohort-based surveillance. Climate changes have resulted in observed trends in diseases such as malaria and RVF, but further studies are needed to determine causal links. Insufficient funding hinders the effective implementation of AW&RS. Future research should assess the cost drivers influencing system effectiveness. Competing Interest Statement The authors have declared no competing interest. Clinical Protocols Funding Statement The review was funded by an award from Gates Ventures through an award to Brown University and a sub-award to WSU (award number: 00002570,FEA). IN, EO, DO and MKN each received additional support from the National Institutes of Health (NIH), grant number U01AI151799, through the Centre for Research in Emerging Infectious Diseases-East and Central Africa. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The supplementary files provide additional data that support our findings. The corresponding author will provide further details upon request via email.

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

⚙ Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback ⓘ

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2025) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00