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Dengue imposes a heavy economic burden on families and society. We used surveillance data reported in 2019 to characterize the dengue epidemic in Zhejiang Province, China, which provided guidance for dengue prevention and control. Dengue epidemics mostly occurred in July to October. People aged 30-44 years, males, and commercial service workers were more likely to suffer from dengue. The epidemic areas were mainly in Hangzhou and Wenzhou. Meanwhile, we assessed the economic cost of dengue in the province from both family and organizational perspectives. The direct economic burden of dengue patients was estimated to be USD 405,038.25, and the indirect economic burden was USD 140,364.90, for a total economic burden of USD 543,213.00. The direct economic burden of dengue patients should be reduced by increasing the coverage and reimbursement of health insurance. Additionally, the total annual cost of dengue prevention and control for the government and organizational sectors was estimated to be USD 7075,654.83. Quantifying the dengue burden is critical for developing disease control strategies, allocating public health resources, and setting health policy priorities.

Background: Patients with cancer have a higher risk of coronavirus disease 2019 (COVID-19) than noncancer patients. The authors conducted a multicenter retrospective study to investigate the clinical manifestations and outcomes of patients with cancer who are diagnosed with COVID-19.Methods: The authors reviewed the medical records of hospitalized patients who were treated at 5 hospitals in Wuhan City, China, between January 5 and March 18, 2020. Clinical parameters relating to cancer history (type and treatment) and COVID-19 were collected. The primary outcome was overall survival (OS). Secondary analyses were the association between clinical factors and severe COVID-19 and OS.Results: A total of 107 patients with cancer were diagnosed with COVID-19, with a median age of 66 years (range, 37-98 years). Lung (21 patients; 19.6%), gastrointestinal (20 patients; 18.7%), and genitourinary (20 patients; 18.7%) cancers were the most common cancer diagnoses. A total of 37 patients (34.6%) were receiving active anticancer treatment when diagnosed with COVID-19, whereas 70 patients (65.4%) were on follow-up. Overall, 52.3% of patients (56 patients) developed severe COVID-19; this rate was found to be higher among patients receiving anticancer treatment than those on follow-up (64.9% vs 45.7%), which corresponded to an inferior OS in the former subgroup of patients (hazard ratio, 3.365; 95% CI, 1.455-7.782 [P = .005]). The detrimental effect of anticancer treatment on OS was found to be independent of exposure to systemic therapy (case fatality rate of 33.3% [systemic therapy] vs 43.8% [nonsystemic therapy]).Conclusions: The results of the current study demonstrated that >50.0% of infected patients with cancer are susceptible to severe COVID-19. This risk is aggravated by simultaneous anticancer treatment and portends for a worse survival, despite treatment for COVID-19. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has triggered a new response involving public health surveillance. The popularity of personal wearable devices creates a new opportunity for tracking and precaution of spread of such infectious diseases. In this study, we propose a framework, which is based on the heart rate and sleep data collected from wearable devices, to predict the epidemic trend of COVID-19 in different countries and cities. In addition to a physiological anomaly detection algorithm defined based on data from wearable devices, an online neural network prediction modelling methodology combining both detected physiological anomaly rate and historical COVID-19 infection rate is explored. Four models are trained separately according to geographical segmentation, i.e., North China, Central China, South China, and South-Central Europe. The anonymised sensor data from approximately 1.3 million wearable device users are used for model verification. Our experiment's results indicate that the prediction models can be utilized to alert to an outbreak of COVID-19 in advance, which suggests there is potential for a health surveillance system utilising wearable device data. [ABSTRACT FROM AUTHOR]