A spatial clustering-based approach to design monitoring networks of infectious diseases: a case study of hand, foot, and mouth disease.

Background: Effective monitoring of infectious diseases is crucial for safeguarding public health. Compared to comprehensive nationwide surveillance, selecting representative sample cities to constitute the monitoring network for surveillance provides similar effectiveness at a lower cost. We developed Spatial Cluster Stratified Sampling (SCSS) to select sample cities for infectious diseases exhibiting spatial autocorrelation.

Methods: To improve monitoring efficiency for hand, foot, and mouth disease (HFMD), we used SCSS to design a monitoring network, which involved four main steps. First, we used Spatial Kluster Analysis by Tree Edge Removal (SKATER) to stratify the data. Second, we applied the cost-benefit balance to determine the optimal sample size. Third, we performed simple random sampling within each stratum to establish an initial monitoring network. Fourth, we used cyclic optimization to finalize the monitoring network. We evaluated the spatiotemporal representativeness using root mean square error (RMSE), Spearman's rank correlation, global Moran's I, local Getis-Ord G*, and Joinpoint Regression. We also compared the effectiveness of SCSS with K-means, traditional stratified sampling, and simple random sampling using RMSE.

Results: The optimal sample size was determined to be 103. Overall, the predicted values for each city significantly correlated with the true values (r = 0.81, P < 0.001). Both the predicted and true values showed positive spatial autocorrelation (Moran's I > 0, P < 0.05), and the sensitivity, specificity, and accuracy of the predicted local Getis-Ord G* values, evaluated against the true values as the gold standard, were 0.76, 0.91, and 0.87, respectively. The weekly predicted values for each city showed significant correlation with the true values (P < 0.05). The 95% confidence intervals (CI) for the predicted values of joinpoint locations, annual percent change (APC), and average annual percent change (AAPC) encompassed the true values, and the number of joinpoints matched the true values. Among the four methods compared, SCSS exhibited the lowest and most centralized RMSE.

Conclusions: SCSS proved to be more accurate and stable than traditional methods, which overlook spatial information. This method offers a valuable reference for future design of monitoring networks for infectious diseases exhibiting spatial autocorrelation, enabling more efficient and cost-effective surveillance.