Spatiotemporal analysis of imported and local dengue virus and cases in a metropolis in Southwestern China, 2013-2022.

Dengue fever is a viral illness, mainly transmitted by Aedes aegypti and Aedes albopictus. With climate change and urbanisation, more urbanised areas are becoming suitable for the survival and reproduction of dengue vector, consequently are becoming suitable for dengue transmission in China. Chongqing, a metropolis in southwestern China, has recently been hit by imported and local dengue fever, experiencing its first local outbreak in 2019. However, the genetic evolution dynamics of dengue viruses and the spatiotemporal patterns of imported and local dengue cases have not yet been elucidated. Hence, this study implemented phylogenetic analyses using genomic data of dengue viruses in 2019 and 2023 and a spatiotemporal analysis of dengue cases collected from 2013 to 2022. We sequenced a total of 15 nucleotide sequences of E genes. The dengue viruses formed separate clusters and were genetically related to those from Guangdong Province, China, and countries in Southeast Asia, including Laos, Thailand, Myanmar and Cambodia. Chongqing experienced a dengue outbreak in 2019 when 168 imported and 1,243 local cases were reported, mainly in September and October. Few cases were reported in 2013-2018, and only six were imported from 2020 to 2022 due to the COVID-19 lockdowns. Our findings suggest that dengue prevention in Chongqing should focus on domestic and overseas population mobility, especially in the Yubei and Wanzhou districts, where airports and railway stations are located, and the period between August and October when dengue outbreaks occur in endemic regions. Moreover, continuous vector monitoring should be implemented, especially during August-October, which would be useful for controlling the Aedes mosquitoes. This study is significant for defining Chongqing's appropriate dengue prevention and control strategies.

Economic burden of dengue fever in China: A retrospective research study.

BACKGROUND: Dengue fever has been a significant public health challenge in China. This will be particularly important in the context of global warming, frequent international travels, and urbanization with increasing city size and population movement. In order to design relevant prevention and control strategies and allocate health resources reasonably, this study evaluated the economic burden of dengue fever in China in 2019. METHODS: The economic burden of dengue fever patients was calculated from both family and the organisation perspectives. A survey was conducted among 1,027 dengue fever patients in Zhejiang, Chongqing, and Yunnan Provinces. Treatment expenses, lost working days, and insurance reimbursement expenses information were collected to estimate the total economic burden of dengue fever patients in 2019. The expenditures related to dengue fever prevention and control from government, Center for Disease Control and Prevention (CDC), communities and subdistrict offices of 30 counties (or districts) in Zhejiang Province and Chongqing City were also collected. RESULTS: The direct, indirect and total economic burden for dengue fever patients in 2019 in the three Provinces were about 36,927,380.00 Chinese Yuan (CNY), 10,579,572.00 CNY and 46,805,064.00 CNY, respectively. The costs for prevention and control of dengue fever for the counties (or districts) without cases, counties (or districts) with imported cases, and counties (or districts) with local cases are 205,800.00 CNY, 731,180.00 CNY and 6,934,378.00 CNY, respectively. The total investment of dengue fever prevention and control in the 30 counties in China in 2019 was approximately 3,166,660,240.00 CNY. CONCLUSION: The economic burden of dengue fever patients is relatively high, and medical insurance coverage should be increased to lighten patients' direct medical economic burden. At the same time, the results suggests that China should increase funding for primary health service institutions to prevent dengue fever transmission.

Association between meteorological factors and the prevalence dynamics of Japanese encephalitis.

Japanese encephalitis (JE) is an acute infectious disease caused by the Japanese encephalitis virus (JEV) and is transmitted by mosquitoes. Meteorological conditions are known to play a pivotal role in the spread of JEV. In this study, a zero-inflated generalised additive model and a long short-term memory model were used to assess the relationship between the meteorological factors and population density of Culex tritaeniorhynchus as well as the incidence of JE and to predict the prevalence dynamics of JE, respectively. The incidence of JE in the previous month, the mean air temperature and the average of relative humidity had positive effects on the outbreak risk and intensity. Meanwhile, the density of all mosquito species in livestock sheds (DMSL) only affected the outbreak risk. Moreover, the region-specific prediction model of JE was developed in Chongqing by used the Long Short-Term Memory Neural Network. Our study contributes to a better understanding of the JE dynamics and helps the local government establish precise prevention and control measures.

Forecast of Dengue Cases in 20 Chinese Cities Based on the Deep Learning Method.

Dengue fever (DF) is one of the most rapidly spreading diseases in the world, and accurate forecasts of dengue in a timely manner might help local government implement effective control measures. To obtain the accurate forecasting of DF cases, it is crucial to model the long-term dependency in time series data, which is difficult for a typical machine learning method. This study aimed to develop a timely accurate forecasting model of dengue based on long short-term memory (LSTM) recurrent neural networks while only considering monthly dengue cases and climate factors. The performance of LSTM models was compared with the other previously published models when predicting DF cases one month into the future. Our results showed that the LSTM model reduced the average the root mean squared error (RMSE) of the predictions by 12.99% to 24.91% and reduced the average RMSE of the predictions in the outbreak period by 15.09% to 26.82% as compared with other candidate models. The LSTM model achieved superior performance in predicting dengue cases as compared with other previously published forecasting models. Moreover, transfer learning (TL) can improve the generalization ability of the model in areas with fewer dengue incidences. The findings provide a more precise forecasting dengue model and could be used for other dengue-like infectious diseases.