Evaluation of the Development, Implementation, Maintenance, and Impact of 3 Digital Surveillance Tools Deployed in Malawi During the COVID-19 Pandemic: Protocol for a Modified Delphi Expert Consensus Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has highlighted the importance of strengthening national monitoring systems to safeguard a globally connected society, especially those in low- and middle-income countries. Africa's rapid adoption of digital technological interventions created a new frontier of digital advancement during crises or pandemics. The use of digital tools for disease surveillance can assist with rapid outbreak identification and response, handling duties such as diagnosis, testing, contact tracing, and risk communication. Malawi was one of the first countries in the region to launch a government-led coordinated effort to harmonize and streamline the necessary COVID-19 digital health implementation through an integrated system architecture. OBJECTIVE: The aim of this study is to seek expert consensus using the Delphi methodology to examine Malawi's COVID-19 digital surveillance response strategy and to assess the digital tools using the World Health Organization mHealth (mobile health) Assessment and Planning for Scale (MAPS) toolkit. METHODS: This protocol follows the Guidance on Conducting and REporting DElphi Studies. Participants must have first-hand experience on the design, implementation or maintenance with COVID-19 digital surveillance systems. There will be no restrictions on the level of expertise or years of experience. The panel will consist of approximately 40 participants. We will use a modified Delphi process whereby rounds 1 and 2 will be hosted online by Qualtrics and round 3 will encompass a face-to-face workshop held in Malawi. Consensus will be defined as ≥70% of participants strongly disagree, disagree, or somewhat disagree, or strongly agree, agree, or somewhat agree. During round 3, the face-to-face workshop, participants will be asked to complete, the MAPS toolkit assessment on the digital tool on which they are experts. The MAPS toolkit will enable the panel members to assess the digital tools from a sustainable perspective from six distinct, yet complementary axes: (1) groundwork, (2) partnerships, (3) financial health, (4) technology and architecture, (5) operations, and (6) monitoring and evaluation. RESULTS: The ability of a country to collate, diagnose, monitor, and analyze data forms the cornerstone of an efficient surveillance system, allowing countries to plan and implement appropriate control actions. Malawi was one of the first countries in the African region to launch a government-led coordinated effort to harmonize and streamline the necessary COVID-19 digital health implementation through an integrated system architecture. CONCLUSIONS: We anticipate findings from this Delphi study will provide insights into how and why Malawi was successful in deploying digital surveillance systems. In addition, findings should produce recommendations and guidance for the rapid development, implementation, maintenance, and impact of digital surveillance tools during a health crisis. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58389.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".