A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases.

BACKGROUND: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. METHODS: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. RESULTS: When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9). CONCLUSION: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

[Construction of a surveillance and early warning index system for schistosomiasis transmission risk along the middle and lower reaches of the Yangtze River basin].

OBJECTIVE: To establish a surveillance and early warning index system for schistosomiasis transmission risk along the middle and lower reaches of the Yangtze River basin, so as to provide insights into creation of a sensitive and highly efficient surveillance and early warning system for schistosomiasis. METHODS: National and international publications, documents, laws and regulations pertaining to schistosomiasis control were retrieved with keywords including schistosomiasis, surveillance, early warning and control interventions from 2008 to 2022, and a thematic panel discussion was held to preliminarily construct surveillance and early warning index system for schistosomiasis transmission risk along the middle and lower reaches of the Yangtze River basin. The index system was then comprehensively scored and screened using the Delphi method, and the weight of each index was determined using analytic hierarchy process and the modified proportional allocation method. In addition, the credibility of the Delphi method was evaluated using positive coefficient, authority coefficient, degree of concentration and degree of coordination of experts. RESULTS: Following two rounds of expert consultation, a surveillance and early warning index system for schistosomiasis transmission risk in endemic areas along the middle and lower reaches of the Yangtze River basin was preliminarily constructed, including 3 primary indicators, 9 secondary indicators and 41 tertiary indicators. The normalized weights of primary indicators epidemics, natural and social factors and comprehensive control were 0.639 8, 0.145 6 and 0.214 6, respectively, and among all secondary indicators, snail status (0.321 3) and schistosomiasis prevalence (0.318 5) had the highest combined weights, while social factors had the lowest combined weight (0.030 4). Of all tertiary indicators, human egg-positive rate (0.041 9), number of acute schistosomiasis cases (0.041 5), number of stool-positive bovine and sheep (0.041 1), and prevalence of Schistosoma japonicum in free-ranging livestock (0.041 1) had the highest combined weights. During two rounds of consultation, the positive coefficient of experts was both 100%, and the authority coefficient was both 0.9 and greater, while the coordination coefficients were 0.338 to 0.441 and 0.426 to 0.565 (χ2 = 22.875 to 216.524, both P values < 0.05). CONCLUSIONS: The established surveillance and early warning index system for schistosomiasis transmission risk along the middle and lower reaches of the Yangtze River basin is of great scientific values and authority, which may provide insights into construction of the sensitive and highly efficient surveillance and early warning system for schistosomiasis in the context of low prevalence and low intensity of infection in China.

Insights on Transmission, Spread, and Possible Endemization of Selected Arboviruses in Israel-Interim Results from Five-Year Surveillance.

Outbreaks of arthropod-borne (arbo) viruses that infect livestock impact the health and welfare of domestic and wild animals are often responsible for significant economic losses in livestock production. Surveillance and early warning systems effectively predict the emergence and re-emergence of arboviral disease. This paper presents the interim results of five years monitoring the exposure of sentinel naïve heifers and Culicoides biting midges (Diptera; Ceratopogonidae) to bovine ephemeral fever virus (BEFV), Simbu serogroup viruses, bluetongue viruses (BTV), and epizootic hemorrhagic disease viruses (EHDV). The data were collected from 11 dairy farms situated within eight different geographical regions in Israel. The results indicate that cattle in Israel are affected by all four viruses from the early summer onward. The investigated viruses exhibit unique site-specific profiles in both ruminants and vectors. The potential of several vectors to transmit these viruses and lack of cross-protection to re-infection with multiple serotypes (BTV and EHDV) or species (Simbu serogroup viruses) highlights some likely mechanisms that may play a role in these viruses' maintenance cycle and possible endemization in our region.

[Establishment of multi-point trigger and multi-channel surveillance mechanism for intelligent early warning of infectious diseases in China].

This paper reviews the limitations of current infectious disease surveillance and early warning system in China, analyzes the concepts and countermeasures of the establishment of an intelligent early warning platform of infectious diseases based on multi-point trigger mechanism and multi-channel surveillance mechanism and proposes the realization routes for the purpose of facilitating capacity building and improvement of surveillance and early warning of infectious diseases in China.