Dengue

In Reunion Island, a French overseas territory located in the southwestern of Indian Ocean, the dengue virus circulation is sporadic. Since 2004, between 10 and 221 probable and confirmed autochthonous dengue fever cases have been reported annually. Since January 2018, the island has experienced a large epidemic of DENV serotype 2. As of 4 September 2018, 6,538 confirmed and probable autochthonous cases have been notified1. From the beginning of the epidemic, the regional office of National Public Health Agency (ANSP) in Indian Ocean enhanced the syndromic surveillance system in order to monitor the outbreak and to provide hospital morbidity data to public health authorities.

Objective: To describe the characteristics of ED vitis related to dengue fever and to show how the syndromic surveillance system can be flexible for the monitoring of this outbreak.

In recent years, mosquito-borne diseases such as Zika, chikungunya, and dengue have become particularly problematic due to global climate change. Rising temperatures and changes in precipitation are considered to be associated with habitat suitability of mosquito vectors and viruses. To address such cross-border infectious diseases, countries have come up with various strategies to control and manage mosquito-borne diseases. In line with this, international efforts have been made to minimize the burden of global infectious diseases. In 2014, Global Health Security Agenda (GHSA) has been launched in collaboration with the international organizations, member countries of GHSA, and non-governmental organizations in order to improve national and global capacities against global public health threat. In addition, various quarantine programs have been operated in and between countries borderlines and airports with cutting edge ICT technologies. These efforts could be made more effective when the authorities have reliable predicted future trends or events, utilize their capacities more efficiently and provide timely alerts to the public. However, very few studies have been conducted to deal with imported disease, while much attention has been paid to the endemic diseases. In this study, we aim to develop a prediction model for imported infectious disease by using the approach of ANN. We have chosen to model the imported cases of dengue in Korea, as the number of imported dengue cases is larger than other mosquito-borne diseases. Additionally, Japan, one of South Korea's neighboring countries, has recently experienced autochthonous dengue virus transmission, which has raised concerns about localization in Korea as well as in Japan.

Objective: We aim to develop a prediction model for the number of imported cases of infectious disease by using the recurrent neural network (RNN) with the Elman algorithm, a type of artificial neural network (ANN) algorithm. We have targeted to predict the number of imported dengue cases in South Korea as the number of dengue cases is greater than other mosquito-borne diseases.

With an estimated 500 million people infected each year, dengue ranks as one of the most significant mosquito-borne viral human diseases, and one of the most rapidly emerging vectorborne diseases. A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist.

Objective

We aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics.

Dengue fever is endemic in over 100 countries and there are an estimated 50 - 100 million cases annually. There is no vaccine for dengue fever yet, and the mortality rate of the severe form of the disease, dengue hemorrhagic fever, ranges from 10-20% but may be greater than 40% if dengue shock occurs. A predictive method for dengue fever would forecast when and where an outbreak will occur before its emergence. This is a challenging task and truly predictive models for emerging infectious diseases are still in their infancy.

Objective

This paper addresses the problem of predicting high incidence rates of dengue fever in Peru several weeks in advance.

Recent events have focused on the role of emerging and re-emerging diseases not only as a significant public health threat but also as a serious threat to the economy and security of nations. The lead time to detect and contain a novel emerging disease or events with public health importance has become much shorter, making developing countries particularly vulnerable to both natural and man-made threats. There is a need to develop disease surveillance systems flexible enough to adapt to the local existing infrastructure of developing countries but which will still be able to provide valid alerts and early detection of significant public health threats.

Objective

To determine system usefulness of the ESSENCE Desktop Edition in detecting increases in the number of dengue cases in the Philippines.

The Veterans Health Administration (VHA) is the VA organization responsible for providing healthcare to over 5 million patients annually at 153 medical centers and over 900 outpatient clinics across the United States and U.S. territories. The VA Subject Matter Expertise Center for Biological Events (SMEC-bio) aims to leverage data in the extensive VHA electronic health records system and other sources to provide decision support to leadership for emerging infectious disease threats. Initial SMEC-bio work to examine this capability suggested that the increased incidence of dengue disease in the VHA patient population in PR in 2010 may be related to increased rainfall (see reference). This present work analyzes dengue incidence in the PR VHA patient population over time to understand disease trends and contribute to a framework for predictive analysis. This paper describes trend analyses of dengue and dengue-like illness in VHA patient data in Puerto Rico (PR) with the goal of developing mechanisms for improved early warning and situational awareness of infectious disease threats.

Epidemic dynamics of dengue fever are driven by complex interactions between hosts, vectors and viruses that are influenced by environmental and climatic factors [1]. The development of new methods to identify such specific characteristics becomes crucial to better understand and control spatiotemporal transmission. We concentrated our efforts on applying sequential pattern mining [2] to an epidemiological and meteorological dataset to identify potential drivers of dengue fever outbreaks.

Objective

We used a data mining method based on sequential patterns extraction to identify local meteorological drivers of dengue fever epidemics in French Guiana.