Statistical modeling for identifying chikungunya high-risk areas of two large-scale outbreaks in Thailand's southernmost provinces.

Chikungunya fever (CHIKF) has re-emerged in the southernmost Thailand and presents a significant threat to public health. The problem areas can be identified using appropriate statistical models. This study aimed to determine the geographic epidemic patterns and high-risk locations. Data on CHIKF's case characteristics, including age, gender, and residence sub-district, were obtained from the Office of Disease Prevention and Control of Thailand from 2008 to 2020. A logistic model was applied to detect illness occurrences. After removing records with no cases, a log-linear regression model was used to determine the incidence rate. The results revealed that two large-scale infections occurred in the southernmost provinces of Thailand between 2008 and 2010, and again between 2018 and 2020, indicating a 10-year epidemic cycle. The CHIKF occurrence in the first and second outbreaks was 28.4% and 15.5%, respectively. In both outbreaks of occurrence CHIKF, adolescents and working-age groups were the most infected groups but the high incidence rate of CHIKF was elderly groups. The first outbreak had a high occurrence and incidence rate in 39 sub-districts, the majority of which were in Narathiwat province, whilst the second outbreak was identified in 15 sub-districts, the majority of which were in Pattani province. In conclusion, the CHIKF outbreak areas can be identified and addressed by combining logistic and log-linear models in a two-step process. The findings of this study can serve as a guide for developing a surveillance strategy or an earlier plan to manage or prevent the CHIKF outbreak.

Modelling malaria in southernmost provinces of Thailand: a two-step process for analysis of highly right-skewed data with a large proportion of zeros.

BACKGROUND: Malaria remains a serious health problem in the southern border provinces of Thailand. The issue areas can be identified using an appropriate statistical model. This study aimed to investigate malaria for its spatial occurrence and incidence rate in the southernmost provinces of Thailand. METHODS: The Thai Office of Disease Prevention and Control, Ministry of Public Health, provided total hospital admissions of malaria cases from 2008 to 2020, which were classified by age, gender, and sub-district of residence. Sixty-two sub-districts were excluded since they had no malaria cases. A logistic model was used to identify spatial occurrence patterns of malaria, and a log-linear regression model was employed to model the incidence rate after eliminating records with zero cases. RESULTS: The overall occurrence rate was 9.8% and the overall median incidence rate was 4.3 cases per 1,000 population. Malaria occurence peaked at young adults aged 20-29, and subsequently fell with age for both sexes, whereas incidence rate increased with age for both sexes. Malaria occurrence and incidence rates fluctuated; they appeared to be on the decline. The area with the highest malaria occurrence and incidence rate was remarkably similar to the area with the highest number of malaria cases, which were mostly in Yala province's sub-districts bordering Malaysia. CONCLUSIONS: Malaria is a serious problem in forest-covered border areas. The correct policies and strategies should be concentrated in these areas, in order to address this condition.