Objectives
This study examined the social and behavioral factors influencing community leaders’ participation in community-based surveillance (CBS) of infectious diseases. We hypothesized that knowledge, attitudes, and perceptions significantly predict the behavioral likelihood of engaging in CBS activities. Methods: A cross-sectional study was conducted among 470 schoolteachers in Kelantan, Malaysia, from March to June 2024. Participants were selected using multistage sampling, with random selection of schools followed by convenience sampling of teachers. Data were collected using the validated KAP-CBS-ID questionnaire, which was based on the Theory of Reasoned Action and Health Belief Model. Structural equation modeling was used to analyze relationships among the study variables with the maximum likelihood robust estimator. Results: The final model demonstrated good fit (comparative fit index, 0.921; standardized root mean square residual, 0.071; root mean square error of approximation, 0.040 [90% confidence interval, 0.036–0.044]). Ten significant path relationships were identified, explaining 46.1% of the variance in behavioral likelihood. Knowledge of infectious diseases (β=0.419, p<0.001), subjective norms (β=0.235, p=0.038), and negative attitudes (β=–0.432, p<0.001) significantly influenced intention. Behavioral likelihood was directly predicted by intention (β =0.347, p<0.001), perceived susceptibility (β=0.310, p<0.001), perceived benefits (β=0.198, p=0.001), and perceived barriers (β=–0.132, p=0.008). Self-efficacy showed significant indirect effects through perceived benefits (β=0.785, p<0.001) and perceived susceptibility (β=0.765, p<0.001). Conclusion: Knowledge and psychosocial factors significantly inf luence community engagement in CBS of infectious diseases. Public health interventions should address these interconnected determinants to strengthen early disease detection and reporting systems.

Objectives
This study aimed to validate the psychometric properties of the recently developed knowledge, attitudes, and perceptions questionnaire for community-based surveillance of infectious diseases (KAP-CBS-ID questionnaire), using confirmatory factor analysis (CFA) and item response theory (IRT).
Methods
A cross-sectional study using multistage sampling recruited 470 schoolteachers from Kelantan, Malaysia. The self-administered KAP-CBS-ID questionnaire consists of 3 domains: knowledge (31 items), attitudes (23 items), and perceptions (21-items). Two-parameter logistic (2-PL) IRT analysis and CFA were performed to validate the knowledge section. For attitudes and perceptions sections, CFA proceeded using a 4-factor model to evaluate both model fit and construct validity.
Results
Two-PL IRT analysis of the knowledge section resulted in elimination of 14 items due to inadequate discrimination or difficulty parameters. The 3-factor CFA model demonstrated good fit indices for knowledge (root mean square error of approximation [RMSEA], 0.028; comparative fit index [CFI], 0.945; Tucker-Lewis index [TLI], 0.941) without any modifications. The attitudes section required re-specification, ultimately yielding 21 items across 4 factors with acceptable fit indices (standardized root mean square residual [SRMR], 0.067; RMSEA, 0.055; CFI, 0.937; TLI, 0.927). Similarly, the perceptions section was refined to 17 items across 4 factors, showing good model fit (SRMR, 0.055; RMSEA, 0.059; CFI, 0.962; TLI, 0.954). Factor loadings ranged from 0.33 to 0.98, while Raykov’s rho reliability estimates ranged from 0.71 to 0.93. Factor determinacy exceeded 80% for all factors.
Conclusion
The KAP-CBS-ID is a valid and reliable instrument for assessing community representatives’ knowledge, attitudes, and perceptions regarding community-based surveillance of infectious diseases.

Objectives
Nontyphoidal Salmonella (NTS) is a major cause of foodborne illness in Malaysia. Time-series modeling can characterize temporal patterns, although structural disruptions, nonlinear dynamics, and regional variability may affect model performance. This study aimed to characterize NTS seasonality in Malaysia, assess temporal stability, and explore model-based approaches for surveillance.
Methods
Monthly laboratory-confirmed human NTS cases from national laboratory surveillance databases for 2018–2024 were analyzed using time-series models, including autoregressive integrated moving-average (ARIMA) models with Fourier terms and an exploratory hybrid ARIMA–long short-term memory (LSTM) model. Interrupted time-series analysis was used to assess structural stability. Anomalies were identified using 95% prediction intervals, and stratified modeling assessed regional heterogeneity.
Results
NTS cases showed recurring annual seasonal patterns of moderate strength, with temporal variation over the study period. A significant decline occurred in March 2020 (β=−62.24 cases, p=0.001), followed by gradual recovery (β=2.71 cases/month, p<0.001), suggesting a marked coronavirus disease 2019 pandemic-related disruption and a subsequent positive post-intervention trend. The ARIMA(0,1,1)–Fourier (K=1) model outperformed conventional ARIMA, benchmark, and exponential smoothing models (root mean squared error, 42.9 vs. 51.37–108.79). The hybrid ARIMA–LSTM model provided modest additional improvement, particularly during high-case periods. Model-based anomaly detection identified deviations from expected patterns that coincided with documented outbreak-associated months. Regional heterogeneity in temporal dynamics suggested the need for surveillance strategies tailored to local epidemiological patterns.
Conclusion
Parsimonious models captured most temporal patterns in NTS cases, with only modest gains from more complex approaches. Context-specific modeling strategies, model-based anomaly detection, and tiered surveillance frameworks may support more proactive NTS surveillance in Malaysia.