The study included 3,971,083 reported cases and 661 deaths across all 31 provincial-level administrative regions. Investigators used Joinpoint regression to assess long-term trends, time-series decomposition to quantify seasonality, and global Moran’s I and Local Indicators of Spatial Association, or LISA, to assess spatial autocorrelation and local clusters. They also evaluated six forecasting models for each province: a neural network, exponential smoothing with state-space models, seasonal autoregressive integrated moving-average models, a hybrid model, a Bayesian structural time-series model and Prophet. Model performance was compared through a composite standardised index.

Incidence declined throughout the study period, and the estimated rate of decline accelerated after 2017. Joinpoint analysis estimated an annual percentage change of minus 11.47 for 2004–2017 and minus 18.85 for 2017–2020. The timing of seasonal peaks remained relatively stable from year to year, although the amplitude of those peaks decreased. These results distinguish a continuing reduction in the overall burden from a largely unchanged annual seasonal pattern.

Spatial clustering was statistically significant during 2004–2019 and weakened to its lowest level in 2020, although the significance of the 2020 result depended on the spatial-weight specification used. Beijing and Tianjin had the highest incidence of bacillary dysentery, while southwestern provinces had a higher burden of amoebic dysentery. The analysis also found that children aged zero to one year accounted for the largest single-year age-band share, at 13.1% of cases. Because the study used aggregated surveillance data, it could not examine individual exposures or separate the effects of specific interventions, changes in care-seeking and reporting, and other influences on observed incidence.

For the pandemic-period comparison, province-specific forecasting models were trained on data from 2004–2019 and used to generate pre-pandemic baselines for 2020. Observed national incidence was then compared with the combined provincial projections. In six provinces, reported incidence fell below the lower bound of the corresponding 95% prediction interval, indicating that the reduction exceeded the range expected from the pre-existing trend and seasonal models. The overall deviation coincided temporally with COVID-19 control measures, but the observational design supports an association rather than a causal conclusion. The results show that the national decrease in 2020 was accompanied by persistent seasonality, geographic clustering and provincial disparities, with the strongest reductions in some regions and less uniform departures from baseline elsewhere.

Subject of Research: Spatiotemporal surveillance and forecasting of dysentery incidence in mainland China, 2004–2020

Article Title: Dysentery in Mainland China, 2004–2020: spatiotemporal dynamics and changes during the COVID-19 pandemic period

Article References: Ma, H., Ke, D., Xu, M., Zhou, X., Lei, Z., Huang, B., Liu, Q., & Lei, Z. (2026). Dysentery in Mainland China, 2004–2020: spatiotemporal dynamics and changes during the COVID-19 pandemic period. BMC Public Health. https://doi.org/10.1186/s12889-026-29811-x

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29811-x

Keywords: dysentery, China, disease surveillance, COVID-19 measures, time-series forecasting, spatial clustering, seasonality, bacillary dysentery, amoebic dysentery, Mainland, spatiotemporal, dynamics