Correction: Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study.

[This corrects the article DOI: 10.2196/19034.].

Early Detection of Dengue Fever Outbreaks Using a Surveillance App (Mozzify): Cross-sectional Mixed Methods Usability Study.

Background: While early detection and effective control of epidemics depend on appropriate surveillance methods, the Philippines bases its dengue fever surveillance system on a passive surveillance method (notifications from barangay/village health centers, municipal or city health offices, hospitals, and clinics). There is no available mHealth (mobile health) app for dengue fever that includes all the appropriate surveillance methods in early detection of disease outbreaks in the country.

Objective: This study aimed to evaluate the usability of the Mozzify app in terms of objective quality (engagement, functionality, aesthetics, information) and app subjective and app-specific qualities and compare total app mean score ratings by sociodemographic profile and self and family dengue fever history to see what factors are associated with high app mean score rating among school-based young adult samples and health care professionals.

Fine-scale population genetic structure of dengue mosquito vector, Aedes aegypti, in Metropolitan Manila, Philippines.

Dengue is a highly endemic disease in Southeast Asia and is transmitted primarily by the mosquito, Aedes aegypti. The National Capital Region (NCR) of the Philippines, or Metropolitan Manila, is a highly urbanized area that is greatly affected by this arboviral disease. Urbanization has been shown to increase the dispersal of this mosquito vector.

Using Google Trends to Examine the Spatio-Temporal Incidence and Behavioral Patterns of Dengue Disease: A Case Study in Metropolitan Manila, Philippines.

Dengue is a major public health concern and an economic burden in the Philippines. Despite the country's improved dengue surveillance, it still suffers from various setbacks and needs to be complemented with alternative approaches. Previous studies have demonstrated the potential of Internet-based surveillance such as Google Dengue Trends (GDT) in supplementing current epidemiological methods for predicting future dengue outbreaks and patterns.

Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines.

Background: Several studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable.

Spatial analysis of wing geometry in dengue vector mosquito, Aedes aegypti (L.) (Diptera: Culicidae), populations in Metropolitan Manila, Philippines.

Background & Objectives: Aedes aegypti (L.) is an efficient vector for arboviral diseases such as dengue. The wings of Ae.

Ecdysis period and rate deviations of dengue mosquito vector, Aedes aegypti reared in different artificial water-holding containers.

Background & Objectives: Artificial water-holding containers (AWHCs) have been well-documented in many Aedes aegypti studies for dengue surveillance and developmental research. Hence, we investigated the role of different AHWCs on the development and ecdysis period of Ae. aegypti dengue vector, a container breeding mosquito.