A national surveillance of eosinophilic meningitis in Thailand

Noppadol Aekphachaisawat; Kittisak Sawanyawisuth; Sittichai Khamsai; Watchara Boonsawat; Somsak Tiamkao; Panita Limpawattana; Wanchai Maleewong; Chetta Ngamjarus

doi:10.1016/j.parepi.2022.e00272

A national surveillance of eosinophilic meningitis in Thailand

Parasite Epidemiol Control. 2022 Sep 8:19:e00272. doi: 10.1016/j.parepi.2022.e00272. eCollection 2022 Nov.

Authors

Noppadol Aekphachaisawat¹, Kittisak Sawanyawisuth², Sittichai Khamsai², Watchara Boonsawat², Somsak Tiamkao², Panita Limpawattana², Wanchai Maleewong³, Chetta Ngamjarus⁴

Affiliations

¹ Central Library, Silpakorn University, Bangkok, Thailand.
² Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
³ Department of Parasitology, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
⁴ Department of Epidemiology and Biostatistics, Faculty of Public Health, Khon Kaen University, Khon Kaen, Thailand.

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.

Keywords: Angiostrongylus cantonensis; Disease control; Slugs; Snails; Time series analysis.