Evidence map›Paper›PMID 41790614›Full record

ArticlePLOS global public health2026

Performance evaluation of an operational dengue forecasting system (D-MOSS) in Vietnam.

Amy Marie Campbell, Felipe Colón-González, Do Kien Quoc, Nguyen Hai Tuan, Nguyen Thanh Dong, Tran Thi Trang, Lokman Hakim Bin Sulaiman, Shew Fung Wong, Barbara Hofmann, Gina Tsarouchi and 3 more

Abstract read
In one paragraph

Article in PLOS global public health, 2026. 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

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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

13 authors.

Amy Marie CampbellLondon School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0003-4111-8286
Felipe Colón-GonzálezWellcome Trust, London, United Kingdom.ORCID https://orcid.org/0000-0002-9671-3405
Do Kien QuocPasteur Institute, Ho Chi Minh City, Vietnam.
Nguyen Hai TuanNational Institute of Hygiene and Epidemiology, Hanoi, Vietnam.ORCID https://orcid.org/0000-0003-3791-5215
Nguyen Thanh DongPasteur Institute, Nha Trang, Vietnam.
Tran Thi TrangTay Nguyen Institute of Hygiene and Epidemiology, Dak Lak, Vietnam.ORCID https://orcid.org/0009-0002-2690-238X
Lokman Hakim Bin SulaimanIMU University, Kuala Lumpur, Malaysia.
Shew Fung WongIMU University, Kuala Lumpur, Malaysia.ORCID https://orcid.org/0000-0001-8194-2144
Barbara HofmannHR Wallingford, Oxfordshire, United Kingdom.ORCID https://orcid.org/0000-0002-6611-6626
Gina TsarouchiHR Wallingford, Oxfordshire, United Kingdom.
Quillon HarphamHR Wallingford, Oxfordshire, United Kingdom.ORCID https://orcid.org/0000-0003-4252-8244
Vu Sinh NamNational Institute of Hygiene and Epidemiology, Hanoi, Vietnam.
Oliver BradyLondon School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID https://orcid.org/0000-0002-3235-2129

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

D-MOSS (Dengue forecasting Model Satellite-based System) was launched operationally in Vietnam in June 2019, providing near-real time dengue forecasts across all 63 provinces. Very few dengue forecasting systems have prospectively evaluated the performance of dengue forecasting under real-world operational conditions. This study comprehensively assesses D-MOSS dengue forecasting performance since operationalisation through both statistical accuracy (absolute dengue incidence, trajectory of incidence, timing of peaks), and operational utility (predictions for specific decision-making scenarios). The D-MOSS dengue forecasts in Vietnam outperformed null model baselines across almost all performance metrics.. While lead times of one month reported the highest accuracy, there was no steep linear decline in accuracy as lead times increased up to six months, and the greatest value-added over seasonal average baseline models was found for later lead times at four to six months. Higher value-added differences were observed for the second half of the year, but the unusually-early June dengue outbreak in 2022 provided a notable challenge. Spatially, larger errors were found in central and southern provinces, that report higher dengue incidence, alongside contrastingly greater value-added over null baseline models, particularly at shorter lead times. Four contextualised operational utility scenarios were tested through probabilistic classification of outbreak threshold exceedance, with accuracy ranging across probability cut-offs but peaking at 0.83 - 0.94 across scenarios.The value of waiting for the next month's forecast and utilising different outbreak thresholds was assessed, with heterogeneous results and the distribution of false alarms or missed outbreaks clustering spatially. The results of both the global performance analysis and utility assessments continue to highlight the strong predictive ability of D-MOSS in an operational setting. Lessons should be taken from the higher-than-expected performance over long-term horizons to improve our ability to forecast further into the future, amidst the key insights these results provide into the improvement of operational dengue forecasting for key decision-making situations in Vietnam and beyond.

Identifiers

PMID41790614
PMCPMC12965583

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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.