A national surveillance of eosinophilic meningitis in Thailand. (November 2022)

• Record Type: Journal Article
• Authors: Aekphachaisawat, Noppadol; Sawanyawisuth, Kittisak; Khamsai, Sittichai; Boonsawat, Watchara; Tiamkao, Somsak; Limpawattana, Panita; Maleewong, Wanchai; Ngamjarus, Chetta
• Is Part Of: Parasite epidemiology and control. Volume 19(2022)
• Publication Date: 2022-11
• Subjects: Angiostrongylus cantonensis -- Disease control -- Snails -- Slugs -- Time series analysis; Parasitic diseases -- Epidemiology -- Periodicals; Parasitic diseases -- Prevention -- Periodicals; Parasitology -- Periodicals; Parasitic Diseases; Parasitic diseases -- Epidemiology; Parasitic diseases -- Prevention; Parasitology; Periodicals; Periodicals; 571.99905
• DOI: 10.1016/j.parepi.2022.e00272
• Languages: English
• ISSNs: 2405-6731

Abstract

Abstract: Introduction: Eosinophilic meningitis (EOM) is an emerging infectious disease worldwide. The most common cause of EOM is infection with Angiostrongylus cantonensis One possible method of monitoring and control of this infection is surveillance and prediction. There are limited data on national surveillance and predictive models on EOM. This study aimed to develop an online surveillance with a predictive model for EOM by using the national database. Methods: We retrospectively retrieved reported cases of EOM from all provinces in Thailand and quantified them by month and year. Data were retrieved from Ministry of Public Health database. We developed a website application to explore the EOM cases in Thailand including regions and provinces using box plots. The website also provided the Autoregressive Integrated Moving Average (ARIMA) models and Seasonal ARIMA (SARIMA) models for predicting the disease cases from nation, region, and province levels. The suitable models were considered by minimum Akaike Information Criterion (AIC). The appropriate SARIMA model was used to predict the number of EOM cases. Results: From 2003 to 2021, 3330 EOM cases were diagnosed and registered in the national database, with a peak in 2003 (median of 22 cases). We determined SARIMA(1, 1, 2)(2, 0, 0)[12] to be the most appropriate model, as it yielded the fitted values that were closest to the actual data. A predictive surveillance website was published on http://202.28.75.8/sample-apps/NationalEOM/ . Conclusions: We determined that web application can be used for monitoring and exploring the trend of EOM patients in Thailand. The predictive values matched the actual monthly numbers of EOM cases indicating a good fit of the predictive model.