ArticleTropical medicine and health2026
Leveraging machine learning for accurate forecasting of pulmonary tuberculosis epidemics in a coastal city in China.
Article in Tropical medicine and health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Trends and spatial distribution of pulmonary tuberculosis in China: a surveillance study.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundWhile pulmonary tuberculosis (PTB) remains a leading notifiable cause of death in China, city-level monthly forecasts with sufficient resolution to guide vaccine, drug and bed logistics are scarce, and no head-to-head comparison of classical time-series versus machine-learning strategies under identical epidemiological conditions has been published.
methodsUsing 168 monthly PTB case reports from Fuzhou (January 2009-December 2022) and an 24-month prospective validation set (2023-2024), we developed, tuned and independently tested three forecasting frameworks: seasonal ARIMA with automatic order selection, Facebook Prophet with multiplicative seasonality and change-point detection, and extreme-gradient-boosting (XGBoost) fed with 1-12 month lagged incidence, calendar and linear-trend covariates. Hyper-parameters were optimized by grid search and early stopping; accuracy was quantified with MSE, RMSE and MAE, while residual diagnostics, stationarity and white-noise tests assessed model adequacy.
resultsAll algorithms fitted the training data closely (RMSE 25.11, 25.31 and 0.0258 cases; MAE ≤ 22 cases). However, on unseen data XGBoost achieved substantially lower prediction errors (RMSE 9.80; MAE 2.93; MSE 96.10) than ARIMA (60.43; 50.28; 3651.86) or Prophet (64.74; 54.49; 4191.86), correctly anticipating the observed 5.7% annual decline and progressively narrowing spring-summer double peaks. Prophet slightly over-estimated seasonal amplitude, whereas ARIMA accumulated trend extrapolation bias; XGBoost residuals remained approximately white noise.
conclusionsFor cities with nonlinear waning epidemics and seasonally contracting amplitude, machine-learning-based XGBoost offers superior extrapolation robustness over traditional ARIMA or Prophet approaches, providing an evidence-based tool for monthly PTB early-warning, precise resource pre-positioning and targeted control in comparable high-density, coastal urban settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.