Dataset Information

A Cluster of Dengue Cases in Travelers: A Clinical Series from Thailand.

ABSTRACT: Dengue is an overlooked tropical disease for which billions of people are at risk. The disease, caused by a Flavivirus with four distinct serotypes, is transmitted primarily by urban Aedes mosquito species. The infection leads to a spectrum of clinical manifestations, with the majority being asymptomatic. Primary dengue fever and, to a greater extent, a subsequent infection with a different serotype is associated with increased severity. Increased global travel and recreational tourism expose individuals naïve to the dengue viruses, the most common arboviral infections among travelers. We describe a cluster of possible primary acute dengue infections in a group of 12 individuals who presented to Bangkok Hospital for Tropical Diseases in 2017. Infection was confirmed by dengue NS1 antigen and multiplex real-time RT-PCR. Nine individuals required hospitalization, and four developed dengue warning signs. Leukocytes, neutrophils, and platelets declined towards defervescence and were negatively correlated with day of illness. Six clinical isolates were identified as dengue serotype-1, with 100% nucleotide identity suggesting that these patients were infected with the same virus.

SUBMITTER: Imad HA

PROVIDER: S-EPMC8396219 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundWith the challenges that dengue fever (DF) presents to healthcare systems and societies, public health officials must determine where best to allocate scarce resources and restricted budgets. Constrained optimization (CO) helps to address some of the acknowledged limitations of conventional health economic analyses and has typically been used to identify the optimal allocation of resources across interventions subject to a variety of constraints.MethodsA dynamic transmission model was developed to predict the number of dengue cases in Thailand at steady state. A CO was then applied to identify the optimal combination of interventions (release of Wolbachia-infected mosquitoes and paediatric vaccination) within the constraints of a fixed budget, set no higher than cost estimates of the current vector control programme, to minimize the number of dengue cases and disability-adjusted life years (DALYs) lost. Epidemiological, cost, and effectiveness data were informed by national data and the research literature. The time horizon was 10 years. Scenario analyses examined different disease management and intervention costs, budget constraints, vaccine efficacy, and optimization time horizon.ResultsUnder base-case budget constraints, the optimal coverage of the two interventions to minimize dengue incidence was predicted to be nearly equal (Wolbachia 50%; paediatric vaccination 49%) with corresponding coverages under lower bound (Wolbachia 54%; paediatric vaccination 10%) and upper bound (Wolbachia 67%; paediatric vaccination 100%) budget ceilings. Scenario analyses indicated that the most impactful situations related to the costs of Wolbachia and paediatric vaccination with decreases/ increases in costs of interventions demonstrating a direct correlation with coverage (increases/ decreases) of the respective control strategies under examination.ConclusionsDetermining the best investment strategy for dengue control requires the identification of the optimal mix of interventions to implement in order to maximize public health outcomes, often under fixed budget constraints. A CO model was developed with the objective of minimizing dengue cases (and DALYs lost) over a 10-year time horizon, within the constraints of the estimated budgets for vector control in the absence of vaccination and Wolbachia. The model provides a tool for developing estimates of optimal coverage of combined dengue control strategies that minimize dengue burden at the lowest budget.

Project description:BackgroundDisease incidence data are needed to guide decision-making for public health interventions. Although dengue is a reportable disease in Thailand and Cambodia, the degree that reported incidence underrecognizes true disease burden is unknown. We utilized dengue incidence calculated from laboratory-confirmed outpatient and inpatient cases in prospective cohort studies to estimate the magnitude of dengue underrecognition and to establish more accurate disease burden estimates for these countries.Methods and findingsCohort studies were conducted among children aged <15 years by members of a dengue field site consortium over at least 2 dengue seasons. Age-group specific multiplication factors (MFs) were computed by comparing data from three cohort studies to national surveillance data in the same province and year. In Thailand, 14,627 person-years of prospective cohort data were obtained in two provinces and 14,493 person-years from one province in Cambodia. Average annual incidence of laboratory-confirmed dengue was 23/1,000 and 25/1,000 in Thailand, and 41/1,000 in Cambodia. Calculated MFs in these provinces varied by age-group and year (range 0.4-29). Average age-group specific MFs were then applied to country-level reporting data and indicated that in Thailand a median 229,886 (range 210,612-331,236) dengue cases occurred annually during 2003-2007 and a median 111,178 (range 80,452-357,135) cases occurred in Cambodia in children <15 years of age. Average underrecognition of total and inpatient dengue cases was 8.7 and 2.6-fold in Thailand, and 9.1 and 1.4-fold in Cambodia, respectively. During the high-incidence year 2007, >95,000 children in Thailand and >58,000 children in Cambodia were estimated to be hospitalized due to dengue.ConclusionCalculating MFs by comparing prospective cohort study data to locally-reported national surveillance data is one approach to more accurately assess disease burden. These data indicate that although dengue is regularly reported in many countries, national surveillance data significantly underrecognize the true burden of disease.

Dataset Information

A Cluster of Dengue Cases in Travelers: A Clinical Series from Thailand.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets