METHODS: Descriptive analyses were used to explore the temporal, spatial, and demographic distribution of dengue fever.

RESULTS: Of the 73,761 dengue cases reported in mainland China during 2004-2018, 93.7% indigenous and 65.9% imported cases occurred in Guangdong and Yunnan, respectively. A total of 55,970 and 5938 indigenous cases occurred in 108 Guangdong and 8 Yunnan counties, respectively during 2004-2018. Whereas 1146 and 3050 imported cases occurred in 84 Guangdong and 72 Yunnan counties, respectively during 2004-2018. Guangdong had a much higher average yearly indigenous incidence rate (3.65 (1/100000) vs 0.86 (1/100000)), but a much lower average yearly imported incidence rate (0.07 (1/100000) vs 0.44(1/100000)) compared with Yunnan in 2004-2018. Furthermore, dengue fever occurred more widely in space and more frequently in time in Guangdong. Guangdong and Yunnan had similar seasonal characteristics for dengue fever, but Guangdong had a longer peak period. Most dengue cases were clustered in the south-western border of Yunnan and the Pearl River Delta region in Guangdong. Most of the imported cases (93.9%) in Guangdong and Yunnan were from 9 Southeast Asian countries. Thailand, Cambodia, and Malaysia imported mainly into Guangdong while Myanmar and Laos imported into Yunnan. There was a strong male predominance among imported cases and an almost equal gender distribution among indigenous cases. Most dengue cases occurred in individuals aged 21-50 years, accounting for 57.3% (Guangdong) vs. 62.8% (Yunnan) of indigenous and 83.2% (Guangdong) vs. 62.6% (Yunnan) of imported cases. The associated major occupations (house worker or unemployed, retiree, and businessman, for indigenous cases; and businessman, for imported cases), were similar. However, farmers accounted for a larger proportion of dengue cases in Yunnan.

STUDY DESIGN AND STATISTICAL ANALYSIS: Overall, 5058 respondents from Thailand, Malaysia, the UK, Italy and Slovenia completed the self-administered survey between May and June 2020. Poststratification weighting was applied, and associations between categorical variables assessed. Frequency counts and percentages were used to summarise categorical data. Associations between categorical variables were assessed using Pearson's χ2 test. Data were analysed in Stata 15.0 RESULTS: Among the five countries, Thai respondents reported having been most, and Slovenian respondents least, affected economically. The following factors were associated with greater negative economic impacts: being 18-24 years or 65 years or older; lower education levels; larger households; having children under 18 in the household and and having flexible/no income. Regarding social impact, respondents expressed most concern about their social life, physical health, mental health and well-being.There were large differences between countries in terms of voluntary behavioural change, and in compliance and agreement with COVID-19 restrictions. Overall, self-reported compliance was higher among respondents who self-reported a high understanding of COVID-19. UK respondents felt able to cope the longest and Thai respondents the shortest with only going out for essential needs or work. Many respondents reported seeing news perceived to be fake, the proportion varying between countries, with education level and self-reported levels of understanding of COVID-19.

CONCLUSIONS: Our data showed that COVID-19 and public health measures have uneven economic and social impacts on people from different countries and social groups. Understanding the factors associated with these impacts can help to inform future public health interventions and mitigate their negative consequences.