Influence of climate variables on dengue fever occurrence in the southern region of Thailand

Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models

Background

Influenza is a respiratory infection that poses a significant health burden worldwide. Environmental indicators, such as air pollutants and meteorological factors, play a role in the onset and propagation of influenza. Accurate predictions of influenza incidence and understanding the factors influencing it are crucial for public health interventions. Our study aims to investigate the impact of various environmental indicators on influenza incidence and apply the ARIMAX model to integrate these exogenous variables to enhance the accuracy of influenza incidence predictions.

Method

Descriptive statistics and time series analysis were employed to illustrate changes in influenza incidence, air pollutants, and meteorological indicators. Cross correlation function (CCF) was used to evaluate the correlation between environmental indicators and the influenza incidence. We used ARIMA and ARIMAX models to perform predictive analysis of influenza incidence.

Results

From January 2014 to September 2023, a total of 21,573 cases of influenza were reported in Fuzhou, with a noticeable year-by-year increase in incidence. The peak of influenza typically occurred around January each year. The results of CCF analysis showed that all 10 environmental indicators had a significant impact on the incidence of influenza. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model exhibited the best prediction performance, as indicated by the lowest AIC, AICc, and BIC values, which were 529.740, 530.360, and 542.910, respectively. The model achieved a fitting RMSE of 2.999 and a predicting RMSE of 12.033.

Conclusion

This study provides insights into the impact of environmental indicators on influenza incidence in Fuzhou. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model could provide a scientific basis for formulating influenza control policies and public health interventions. Timely prediction of influenza incidence is essential for effective epidemic control strategies and minimizing disease transmission risks.

Influence of insular conditions on wing phenotypic variation in two dominant mosquito vectors, Aedes albopictus and Armigeres subalbatus (Diptera: Culicidae), in the border archipelagos of Thailand

Insects geographically separated into island and mainland populations often exhibit phenotypic variations, a phenomenon known as insular conditions. These conditions can lead to rapid evolutionary changes that affect the morphological characteristics of mosquito vectors. Nevertheless, studies that specifically examine phenotype differences between island and mainland mosquito populations have been limited. In this study, wing variation in size and shape was investigated using the geometric morphometric (GM) technique in two dominant mosquito vectors, Aedes albopictus and Armigeres subalbatus, in the Ranong and Trat archipelagos of Thailand. Significant differences in average wing centroid size (CS) were found in 6 out of 15 population pairs for Ae. albopictus (p < 0.05) and in 5 population pairs for Ar. subalbatus (p < 0.05). After removing the allometric effect, canonical variate analyses (CVA) based on wing shape analysis revealed overlap across all populations for both Ae. albopictus and Ar. subalbatus. However, the statistical analysis indicated that Ar. subalbatus exhibited wing shape differences across all populations (p < 0.05), and most Ae. albopictus populations also displayed distinct wing shapes (p < 0.05), except for the populations from Chang Island and the mainland of Ranong, which showed no significant differences (p > 0.05). These findings enhance our understanding of mosquito adaptability in island regions and provide valuable data for the surveillance and monitoring of vector evolution.

Detecting the impacts of humidity, rainfall, temperature, and season on chikungunya, dengue and Zika viruses in Aedes albopictus mosquitoes from selected sites in Cebu city, Philippines

BACKGROUND

Aedes albopictus is the secondary vector for dengue virus (DENV) in the Philippines, and also harbors chikungunya (CHIKV) and Zika (ZIKV) viruses. This study aimed to determine the minimum infection rates (MIRs) of CHIKV, DENV serotypes, and ZIKV in Ae. albopictus collected from selected two-site categories by altitude (highland [H] and lowland [L] sites) in Cebu city, Philippines during the wet (WS) and dry seasons (DS) of 2021-2022, and to explore the relationships between these arboviral MIRs and the local weather.

METHODS

The viral RNA extracts in pooled and reared adult Ae. albopictus collected during the DS and WS from two-site categories were subjected to RT-PCR to amplify and detect gene loci specific for CHIKV, DENV-1 to DENV-4, and ZIKV and analyzed with the weather data.

RESULTS

The range of CHIKV MIRs was higher in the WS (13.61-107.38 infected individuals per 1,000 mosquitoes) than in the DS (13.22-44.12), but was similar between the two-site categories. Rainfall (RF) influenced the CHIKV MIR. The MIR ranges of both DENV-2 (WS: H = 0, L = 0; DS: H = 0-5.92; L = 0-2.6) and DENV-4 (WS: H = 0, L = 0-2.90; DS: H = 2.96-6.13, L = 0-15.63) differed by season but not between the two-site categories. Relative humidity (RH), RF, and temperature did not influence DENVs' MIRs. The MIR range of ZIKV was similar in both seasons (WS: 11.36-40.27; DS: 0-46.15) and two-site categories (H = 0-90.91, L = 0-55.56). RH and temperature influenced ZIKV MIR.

CONCLUSIONS

RF influenced CHIKV MIR in Ae. albopictus, whereas RH and temperature influenced that of ZIKV. Season influenced the MIRs of CHIKV and DENVs but not in ZIKV. Ae. albopictus were co-infected with CHIKV, DENVs, and ZIKV in both highland and lowland sites in Cebu city. Recommendations include all-year-round implementation of the Philippine Department of Health's  4S enhanced strategy and installation of water pipelines in rural highlands for vector and disease control. Our findings are relevant to protect public health in the tropics in this climate change.

Spatio-temporal dengue risk modelling in the south of Thailand: a Bayesian approach to dengue vulnerability

Background

More than half of the global population is predicted to be living in areas susceptible to dengue transmission with the vast majority in Asia. Dengue fever is of public health concern, particularly in the southern region of Thailand due to favourable environmental factors for its spread. The risk of dengue infection at the population level varies in time and space among sub-populations thus, it is important to study the risk of infection considering spatio-temporal variation.

Methods

This study presents a joint spatio-temporal epidemiological model in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation with the CARBayesST package of R software. For this purpose, monthly dengue records by district from 2002 to 2018 from the southern region of Thailand provided by the Ministry of Public Health of Thailand and eight environmental variables were used.

Results

Results show that an increasing level of temperature, number of rainy days and sea level pressure are associated with a higher occurrence of dengue fever and consequently higher incidence risk, while an increasing level of wind speed seems to suggest a protective factor. Likewise, we found that the elevated risks of dengue in the immediate future are in the districts of Phipun, Phrom Kili, Lan Saka, Phra Phrom and Chaloem Phakiat. The resulting estimates provide insights into the effects of covariate risk factors, spatio-temporal trends and dengue-related health inequalities at the district level in southern Thailand.

Conclusion

Possible implications are discussed considering some anthropogenic factors that could inhibit or enhance dengue occurrence. Risk maps indicated which districts are above and below baseline risk, allowing for the identification of local anomalies and high-risk boundaries. In the event of near future, the threat of elevated disease risk needs to be prevented and controlled considering the factors underlying the spread of mosquitoes in the Southeast Asian region.