Background During the COVID-19 pandemic, there is a heightened need to understand health information seeking behaviors to address disparities in knowledge and beliefs about the crisis. Objective This study assessed sociodemographic predictors of the use and trust of different COVID-19 information sources, as well as the association between information sources and knowledge and beliefs about the pandemic. Methods An online survey was conducted among US adults in two rounds during March and April 2020 using advertisement-based recruitment on social media. Participants were asked about their use of 11 different COVID-19 information sources as well as their most trusted source of information. The selection of COVID-related knowledge and belief questions was based on past empirical literature and salient concerns at the time of survey implementation. Results The sample consisted of 11,242 participants. When combined, traditional media sources (television, radio, podcasts, or newspapers) were the largest sources of COVID-19 information (91.2%). Among those using mainstream media sources for COVID-19 information (n=7811, 69.5%), popular outlets included CNN (24.0%), Fox News (19.3%), and other local or national networks (35.2%). The largest individual information source was government websites (87.6%). They were also the most trusted source of information (43.3%), although the odds of trusting government websites were lower among males (adjusted odds ratio [AOR] 0.58, 95% CI 0.53-0.63) and those aged 40-59 years and ≥60 years compared to those aged 18-39 years (AOR 0.83, 95% CI 0.74-0.92; AOR 0.62, 95% CI 0.54-0.71). Participants used an average of 6.1 sources (SD 2.3). Participants who were male, aged 40-59 years or ≥60 years; not working, unemployed, or retired; or Republican were likely to use fewer sources while those with children and higher educational attainment were likely to use more sources. Participants surveyed in April were markedly less likely to use (AOR 0.41, 95% CI 0.35-0.46) and trust (AOR 0.51, 95% CI 0.47-0.56) government sources. The association between information source and COVID-19 knowledge was mixed, while many COVID-19 beliefs were significantly predicted by information source; similar trends were observed with reliance on different types of mainstream media outlets. Conclusions COVID-19 information source was significantly determined by participant sociodemographic characteristics and was also associated with both knowledge and beliefs about the pandemic. Study findings can help inform COVID-19 health communication campaigns and highlight the impact of using a variety of different and trusted information sources.

IntroductionThe global spread and the increased frequency and magnitude of epidemic dengue in the last 50 years underscore the urgent need for effective tools for surveillance, prevention, and control. This review aims at providing a systematic overview of what predictors are critical and which spatial and spatio-temporal modeling approaches are useful in generating risk maps for dengue.MethodsA systematic search was undertaken, using the PubMed, Web of Science, WHOLIS, Centers for Disease Control and Prevention (CDC) and OvidSP databases for published citations, without language or time restrictions. A manual search of the titles and abstracts was carried out using predefined criteria, notably the inclusion of dengue cases. Data were extracted for pre-identified variables, including the type of predictors and the type of modeling approach used for risk mapping.ResultsA wide variety of both predictors and modeling approaches was used to create dengue risk maps. No specific patterns could be identified in the combination of predictors or models across studies. The most important and commonly used predictors for the category of demographic and socio-economic variables were age, gender, education, housing conditions and level of income. Among environmental variables, precipitation and air temperature were often significant predictors. Remote sensing provided a source of varied land cover data that could act as a proxy for other predictor categories. Descriptive maps showing dengue case hotspots were useful for identifying high-risk areas. Predictive maps based on more complex methodology facilitated advanced data analysis and visualization, but their applicability in public health contexts remains to be established.ConclusionsThe majority of available dengue risk maps was descriptive and based on retrospective data. Availability of resources, feasibility of acquisition, quality of data, alongside available technical expertise, determines the accuracy of dengue risk maps and their applicability to the field of public health. A large number of unknowns, including effective entomological predictors, genetic diversity of circulating viruses, population serological profile, and human mobility, continue to pose challenges and to limit the ability to produce accurate and effective risk maps, and fail to support the development of early warning systems.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-072X-13-50) contains supplementary material, which is available to authorized users.

Dengue fever is a mosquito-borne viral disease estimated to cause about 230 million infections worldwide every year, of which 25,000 are fatal. Global incidence has risen rapidly in recent decades with some 3.6 billion people, over half of the world's population, now at risk, mainly in urban centres of the tropics and subtropics. Demographic and societal changes, in particular urbanization, globalization, and increased international travel, are major contributors to the rise in incidence and geographic expansion of dengue infections. Major research gaps continue to hamper the control of dengue. The European Commission launched a call under the 7th Framework Programme with the title of ‘Comprehensive control of Dengue fever under changing climatic conditions’. Fourteen partners from several countries in Europe, Asia, and South America formed a consortium named ‘DengueTools’ to respond to the call to achieve better diagnosis, surveillance, prevention, and predictive models and improve our understanding of the spread of dengue to previously uninfected regions (including Europe) in the context of globalization and climate change. The consortium comprises 12 work packages to address a set of research questions in three areas: Research area 1: Develop a comprehensive early warning and surveillance system that has predictive capability for epidemic dengue and benefits from novel tools for laboratory diagnosis and vector monitoring. Research area 2: Develop novel strategies to prevent dengue in children. Research area 3: Understand and predict the risk of global spread of dengue, in particular the risk of introduction and establishment in Europe, within the context of parameters of vectorial capacity, global mobility, and climate change. In this paper, we report on the rationale and specific study objectives of ‘DengueTools’. DengueTools is funded under the Health theme of the Seventh Framework Programme of the European Community, Grant Agreement Number: 282589 Dengue Tools.

Dengue is currently listed as a “neglected tropical disease” (NTD). But is dengue still an NTD or not? Classifying dengue as an NTD may carry advantages, but is it justified? This review considers the criteria for the definition of an NTD, the current diverse lists of NTDs by different stakeholders, and the commonalities and differences of dengue with other NTDs. We also review the current research gaps and research activities and the adequacy of funding for dengue research and development (R&D) (2003–2013). NTD definitions have been developed to a higher precision since the early 2000s, with the following main features: NTDs are characterised as a) poverty related, b) endemic to the tropics and subtropics, c) lacking public health attention, d) having poor research funding and shortcomings in R&D, e) usually associated with high morbidity but low mortality, and f) often having no specific treatment available. Dengue meets most of these criteria, but not all. Although dengue predominantly affects resource-limited countries, it does not necessarily only target the poor and marginalised in those countries. Dengue increasingly attracts public health attention, and in some affected countries it is now a high profile disease. Research funding for dengue has increased exponentially in the past two decades, in particular in the area of dengue vaccine development. However, despite advances in dengue research, dengue epidemics are increasing in frequency and magnitude, and dengue is expanding to new areas. Specific treatment and a highly effective vaccine remain elusive. Major research gaps exist in the area of integrated surveillance and vector control. Hence, although dengue differs from many of the NTDs, it still meets important criteria commonly used for NTDs. The current need for increased R&D spending, shared by dengue and other NTDs, is perhaps the key reason why dengue should continue to be considered an NTD.

G lobal incidence of dengue has increased 7-fold, from 8.3 million cases in 1990 to >58.4 million in 2013 (1). Currently, ≈390 million new infections occur annually in 128 dengue-endemic countries (2). Worldwide, ≈14,000-20,000 dengue-related deaths occur each year (1,2). In dengue-endemic countries, Aedes (Stegomyia) aegypti and Ae. (Stegomyia) albopictus mosquito vectors transmit the disease. Sri Lanka, a tropical island in the Indian Ocean (population 21 million) (3), has reported dengue cases since the 1960s; seasonal epidemics predominantly affect areas that have annual rainfall >2,500 mm (4). However, until 1988, the more severe form of dengue virus (DENV) infection, dengue hemorrhagic fever, was reported only sporadically (5,6). During 1991-2008, dengue epidemics occurred once every few years on the background of endemic transmission (6). A disproportionate epidemic occurred in 2009 comprising 35,008 suspected cases (incidence 170 cases/100,000 population) and 346 deaths (case-fatality rate 1%) (7). During 2010-2016, dengue became a major public health problem; cases increased steadily (from 28,473 in 2011 to 55,150 in 2016) throughout the country but disproportionately affected the most populated Western province (7). In 2017, a total of 186,101 suspected cases and 440 dengue-related deaths were reported to the Central Epidemiology Unit of the Ministry of Health, Sri Lanka (7). This number is the highest number of suspected cases reported in a single calendar year in Sri Lanka since dengue was designated a notifiable disease in 1996. We compared the temporal, epidemiologic, virologic, entomologic, and climatic characteristics of the 2017 dengue epidemic with those of the epidemics during the preceding 5 years (2012-2016). Methods Data Sources Epidemiology We obtained epidemiologic data from the integrated national communicable disease surveillance system, which captures symptomatic dengue patients classified according to a standard case definition based on the 1997-2011 World Health Organization classification (8). Etiologic screening was conducted with NS1 antigen testing or dengue antibody assays. However, given the limited diagnostic test availability, especially in remote areas of the country, many cases were clinically diagnosed using the surveillance case

Dengue is the major public health burden in Sri Lanka. Kalutara is one of the highly affected districts. Understanding the drivers of dengue is vital in controlling and preventing the disease spread. This study focuses on quantifying the influence of weather variability on dengue incidence over 10 Medical Officer of Health (MOH) divisions of Kalutara district. Weekly weather variables and data on dengue notifications, measured at 10 MOH divisions in Kalutara from 2009 to 2013, were retrieved and analysed. Distributed lag non-linear model and hierarchical-analysis was used to estimate division specific and overall relationships between weather and dengue. We incorporated lag times up to 12 weeks and evaluated models based on the Akaike Information Criterion. Consistent exposure-response patterns between different geographical locations were observed for rainfall, showing increasing relative risk of dengue with increasing rainfall from 50 mm per week. The strongest association with dengue risk centred around 6 to 10 weeks following rainfalls of more than 300 mm per week. With increasing temperature, the overall relative risk of dengue increased steadily starting from a lag of 4 weeks. We found similarly a strong link between the Oceanic Niño Index to weather patterns in the district in Sri Lanka and to dengue at a longer latency time confirming these relationships. Part of the influences of rainfall and temperature can be seen as mediator in the causal pathway of the Ocean Niño Index, which may allow a longer lead time for early warning signals. Our findings describe a strong association between weather, El Niño-Southern Oscillation and dengue in Sri Lanka.

BackgroundViral diseases transmitted via Aedes mosquitoes are on the rise, such as Zika, dengue, and chikungunya. Novel tools to mitigate Aedes mosquitoes-transmitted diseases are urgently needed. We tested whether commercially insecticide-impregnated school uniforms can reduce dengue incidence in school children.MethodsWe designed a cluster-randomised controlled trial in Thailand. The primary endpoint was laboratory-confirmed dengue infections. Secondary endpoints were school absenteeism; and impregnated uniforms’ 1-hour knock-down and 24 hour mosquito mortality as measured by standardised WHOPES bioassay cone tests at baseline and after repeated washing. Furthermore, entomological assessments inside classrooms and in outside areas of schools were conducted.ResultsWe enrolled 1,811 pupils aged 6–17 from 5 intervention and 5 control schools. Paired serum samples were obtained from 1,655 pupils. In the control schools, 24/641 (3.7%) and in the intervention schools 33/1,014 (3.3%) students had evidence of new dengue infections during one school term (5 months). There was no significant difference in proportions of students having incident dengue infections between the intervention and control schools, with adjustment for clustering by school. WHOPES cone tests showed a 100% knock down and mortality of Aedes aegypti mosquitoes exposed to impregnated clothing at baseline and up to 4 washes, but this efficacy rapidly declined to below 20% after 20 washes, corresponding to a weekly reduction in knock-down and mosquito mortality by 4.7% and 4.4% respectively. Results of the entomological assessments showed that the mean number of Aedes aegypti mosquitoes caught inside the classrooms of the intervention schools was significantly reduced in the month following the introduction of the impregnated uniforms, compared to those collected in classrooms of the control schools (p = 0.04)ConclusionsEntomological assessments showed that the intervention had some impact on the number of Aedes mosquitoes inside treatment schools immediately after impregnation and before insecticidal activity declined. However, there was no serological evidence of protection against dengue infections over the five months school term, best explained by the rapid washing-out of permethrin after 4 washes. If rapid washing-out of permethrin could be overcome by novel technological approaches, insecticide-treated clothes might become a potentially cost-effective and scalable intervention to protect against diseases transmitted by Aedes mosquitoes such as dengue, Zika, and chikungunya.Trial RegistrationClinicalTrials.gov NCT01563640

With regard to fully harvesting the potential of big data, public health lags behind other fields. To determine this potential, we applied big data (air passenger volume from international areas with active chikungunya transmission, Twitter data, and vectorial capacity estimates of Aedes albopictus mosquitoes) to the 2017 chikungunya outbreaks in Europe to assess the risks for virus transmission, virus importation, and short-range dispersion from the outbreak foci. We found that indicators based on voluminous and velocious data can help identify virus dispersion from outbreak foci and that vector abundance and vectorial capacity estimates can provide information on local climate suitability for mosquitoborne outbreaks. In contrast, more established indicators based on Wikipedia and Google Trends search strings were less timely. We found that a combination of novel and disparate datasets can be used in real time to prevent and control emerging and reemerging infectious diseases.