The lockdown and vaccination distribution in Thailand's COVID-19 epidemic: A model study.

Background: Several countries used varied degrees of social isolation measures in response to the COVID-19 outbreak. In 2021, the lockdown in Thailand began on July 20 and lasted for the following six weeks. The lockdown has extremely detrimental effects on the economy and society, even though it may reduce the number of COVID-19 instances. Our goals are to assess the impact of the lockdown policy, the commencement time of lockdown, and the vaccination rate on the number of COVID-19 cases in Thailand in 2021. Methods: We modeled the dynamics of COVID-19 in Thailand throughout 2021 using the SEIR model. The Google Mobility Index, vaccine distribution rate, and lockdown were added to the model. The Google Mobility Index represents the movement of individuals during a pandemic and shows how people react to lockdown. The model also examines the effect of vaccination rate on the incidence of COVID-19. Results: The modeling approach demonstrates that a 6-week lockdown decreases the incidence number of COVID-19 by approximately 15.49-18.17%, depending on the timing of the lockdown compared to a non-lockdown scenario. An increasing vaccination rate potentially reduce the incidence number of COVID-19 by 5.12-18.35% without launching a lockdown. Conclusion: Lockdowns can be an effective method to slow down the spread of COVID-19 when the vaccination program is not fully functional. When the vaccines are easily accessible on a large scale, the lockdown may terminated.

The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017).

BACKGROUND: In Thailand, dengue fever is one of the most well-known public health problems. The objective of this study was to examine the epidemiology of dengue and determine the seasonal pattern of dengue and its associate to climate factors in Bangkok, Thailand, from 2003 to 2017. METHODS: The dengue cases in Bangkok were collected monthly during the study period. The time-series data were extracted into the trend, seasonal, and random components using the seasonal decomposition procedure based on loess. The Spearman correlation analysis and artificial neuron network (ANN) were used to determine the association between climate variables (humidity, temperature, and rainfall) and dengue cases in Bangkok. RESULTS: The seasonal-decomposition procedure showed that the seasonal component was weaker than the trend component for dengue cases during the study period. The Spearman correlation analysis showed that rainfall and humidity played a role in dengue transmission with correlation efficiency equal to 0.396 and 0.388, respectively. ANN showed that precipitation was the most crucial factor. The time series multivariate Poisson regression model revealed that increasing 1% of rainfall corresponded to an increase of 3.3% in the dengue cases in Bangkok. There were three models employed to forecast the dengue case, multivariate Poisson regression, ANN, and ARIMA. Each model displayed different accuracy, and multivariate Poisson regression was the most accurate approach in this study. CONCLUSION: This work demonstrates the significance of weather in dengue transmission in Bangkok and compares the accuracy of the different mathematical approaches to predict the dengue case. A single model may insufficient to forecast precisely a dengue outbreak, and climate factor may not only indicator of dengue transmissibility.

Estimation of dengue infection for travelers in Thailand.

BACKGROUND: Dengue infection among travelers has become one of the most public health concerns in present days. The importation of dengue virus can initiate an outbreak in non-endemic regions. Thailand is one of the countries topping the list of highest dengue infections in travelers. METHOD: This study estimates the risk of dengue infection among travelers during their visit in Thailand by using a mathematical model with seasonal variations. RESULTS: The risk of dengue infection in high dengue season is 2.50-4.07 times that on low dengue season depending on the locations. The average daily risk of dengue infections of Thailand per 100,000 travelers is 2.14 and 7.03 for low and high dengue season, respectively. The highest rate of infection is Rayong and the lowest rate is Sing Buri. Several popular tourist provinces are high dengue endemic areas. CONCLUSIONS: This study provides useful information on dengue infection among travelers. The main factors are the time of arrival in the year, the duration of stay of the travelers and the locations where the travelers spend most of their time.

Retraction: The seasonal reproduction number of dengue fever: impacts of climate on transmission.

[This retracts the article DOI: 10.7717/peerj.1069.].

The seasonal reproduction number of dengue fever: impacts of climate on transmission.

Background. Dengue fever is a mosquito-borne viral disease and a regular epidemic in Thailand. The peak of the dengue epidemic period is around June to August during the rainy season. It is believed that climate is an important factor for dengue transmission. Method. A mathematical model for vector-host infectious disease was used to calculate the impacts of climate to the transmission of dengue virus. In this study, the data of climate and dengue fever cases were derived from Chiang Mai during 2004-2014, Thailand. The value of seasonal reproduction number was calculated to evaluate the potential, severity and persistence of dengue infection. Results. The mosquito population was increasing exponentially from the start of the rainy season in early May and reached its the peak in late June. The simulations suggest that the greatest potential for the dengue transmission occurs when the temperature is 28.9 °C. The seasonal reproduction numbers were larger than one from late March to end of August and reaching the peak in June. The highest incidences occurred in August due to the delay of transmission humans-mosquito-humans. Increasing mean temperature by 1 °C, the number of incidences increases 28.1%. However, a very high or very low temperature reduces the number of infection. Discussion and Conclusion. The results show that the dengue infection depends on the seasonal variation of the climate. The rainfall provides places for the mosquitoes to lay eggs and develop to the adult stage. The temperature plays an important role in the life cycle and behavior of the mosquitoes. A very high or very low temperature reduces the risk of the dengue infection.

The estimation of imported dengue virus from Thailand.

BACKGROUND: Dengue fever is one of the important causes of illness among travelers returning from Thailand. The risk of infection depends on the length of stay, activities, and arrival time. Due to globalization, there is a concern that infected travelers may carry dengue virus (DENV) to their country of residence and cause an outbreak. METHODS: To estimate the infective person-days of travelers returning from Thailand, we developed a model with the following parameters: the probability of travelers being infected, number of arrivals, length of stay of travelers, incubation period, and duration of the infective period. The data used in this study were the dengue incidences in Thailand during 2004-2013 and foreign traveler arrivals in 2013. RESULTS: We estimated the highest infective person-days for each country group. The highest value was from June to August during the rainy season in Thailand for all groups. Infective person-days ranged from 87 to 112 per 100,000 travelers each year. CONCLUSION: Our results provided a fundamental step toward estimation of the risk of the secondary transmission of DENV in non-epidemic countries via travelers, which can serve as an early warning of a dengue outbreak. The highest infective person-day is associated with the rainy season in Thailand. The increasing number of overseas travelers may increase the risk of global transmission of the DENV. Better understanding of the virus transmission dynamics will enable further quantitative predictions of epidemic risk.