RESUMEN
Objective: This study employs the Geographically and Temporally Weighted Regression (GTWR) model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks, emphasizing the spatial-temporal variability of these factors in border regions. Methods: We conducted a descriptive analysis of dengue fever's temporal-spatial distribution in Yunnan border areas. Utilizing annual data from 2013 to 2019, with each county in the Yunnan border serving as a spatial unit, we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region. Results: The GTWR model, proving more effective than Ordinary Least Squares (OLS) analysis, identified significant spatial and temporal heterogeneity in factors influencing dengue fever's spread along the Yunnan border. Notably, the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence, meteorological variables, and imported cases across different counties. Conclusion: In the Yunnan border areas, local dengue incidence is affected by temperature, humidity, precipitation, wind speed, and imported cases, with these factors' influence exhibiting notable spatial and temporal variation.
Asunto(s)
Dengue , Dengue/epidemiología , China/epidemiología , Humanos , Análisis Espacio-Temporal , Incidencia , Brotes de Enfermedades , Regresión EspacialRESUMEN
OBJECTIVE: To describe the experience of the China CDC in rebuilding reporting capacity and response to the Sichuan earthquake through use of mobile phones. METHODS: Software engineering and business modeling are used to design and develop a cell phone-based reporting system. The PDA-based system used by the Field Adapted Survey Toolkit (FAST) was deployed RESULTS: Approximately one week after deployment of the mobile phone-based reporting system, the cumulative reporting rate reached the same level (81%) as the same period in 2007. In the Sichuan provincial pilot investigation for infectious disease, 1339 records were collected using PDAs developed and deployed by FAST. CONCLUSION: The mobile-based system is recognized as a quick and effective response solution to this public health challenge. Our experience suggests that mobile-based data collection tools provide faster, cleaner, standardized, and shareable data for critical decision making. This system could be adapted as complementary to national infectious disease reporting systems after natural disaster occurrences.