Prevention and Control of the Dengue Fever

One of the most important mosquito-borne viral disease currently is the dengue fever. Dengue Fever affects close to 100 million people a year which is what makes it one of the worlds most important vector-borne diseases. The transmission area for dengue fever expands due to there being many indirect and direct factors such as increased travel, global warming, and urban sprawl. Some of the preventive measures for the disease are having mosquito control programs in place. However, due to disease having a complex nature, increased importation risk, and lack of efficient prophylactic measures being in place, being able to have successful control and elimination of the disease is not realistic at this time.

By using the information known on the risk factors, epidemiological models are used to try to predict future outbreaks. The authors of the journal article are looking at the variety and depth of the available models for surveillance of dengue fever and then use them to have early warning tools.

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Modelling helps focus on the different transmission patterns shown between the different components such as human, animal, or vector, along with including the parameters that affect each of the pathways such as environmental, geographical, or climiatic.

Article Review

This review will examine the research methodology, the limitation of the study, and the study .findings. The findings are analyzed by using the evidence form other studies that are similar and then the recommendations are provided to help identify any limitations.

Review of Methodology

The authors of the article did a comprehensive literature review by using multiple databases and many key words that are related to dengue fever.

A study was included if there was a use of one or more epidemiological model reported. At the beginning of the research, the studies that were chosen based on a review of the title and abstract of the study. To be able to analyze the dengue fever models, the authors had to review the background information along with the methods that were used in the output generation. The comparison of efficacy between the models are complex due to the differences in all the biological factors, spatio-temporal parameters, geographical scales, and the mathematical equations that were used in the more recent models.

Findings Review

Even though most of the studies used a combination of epidemiological tools, there were three main areas of focus that were identified. Those areas are: mapping tools, mathematical models, and then combination of the two1. The purpose of mapping and the models are aimed at dengue reporting and surveillance. In addition, they are usually based on the risk factors even though the recent studies have been introducing the disease forecasting as the main objective.

There are many different models that can produce prediction equations for the transmission of dengue fever. When looking at the use of the outputs on the models studied, there are two main objectives that were identified. Those objectives are the use of dengue fever models as a retrospective and validating method, and the other is the early warning tools used to predict the potential epidemics1. The interventions that are based on the early warning signs that are aimed at preventing the transmission of dengue fever, require there to be significant financial resources available along with human input. Therefore, it is more desirable to target areas that are at a higher risk for dengue fever. It is important in developing countries for there to be a role of resource-limited settings in the development of timely prediction tools. The type of model that was used was based on the different type of data that was collected and the different variables, along with the differences in the outbreaks, there is not universal model that can be used for analysis or prediction.

The data of the study was normally consisted of serologic and environmental variables. Recently, however, the socio-environmental changes have been very important in the determinants in the transmission of dengue fever. Including the spatial data allows for there to be identification of the patterns for occurrence and the ability to identify the high risk areas of the disease. Many of previous studies over the past decade have implemented logistic or multiple regression models to help find the risk factors. However, a draw back from those models were that they were not able to account for autocorrelation in time data, which limits the ability to have accurate predicting capabilities.

Recommendations

Having prompt detection and the appropriate clinical management, can help reduce the mortality rates from severe cases of dengue fever. In addition, effective vector control is important for prevention and control of the disease. Using the different models as a way of prediction or as a part of the surveillance systems of early warning signs, are done for other vectors such as malaria, bluetongue virus, and rift valley fever. Surveillance and reporting of cases is very important to be able to gauge the global situation as indicated by WHO Global Strategy for Dengue Prevention and Control. Having access to more accurate data helps be able to better prioritize the research, health policy and the financial resources needed to reduce this poorly-controlled disease.

At this time, there is not any antiviral therapy or a vaccination for dengue fever. This leaves only early detection and symptomatic treatment with fluid resuscitation as the main management for the severe cases. Due to there being limited therapeutic strategies, it is important for there to be effective vector control methods in place which are promoted by the WHO. Having surveillance and proper reporting is highly important for the control of dengue. In addition, there needs to be more accurate quantification of the impact of dengue globally, which will allow for improvements in political, financial, and the prioritization for research, along with be able to make more accurate decision making regarding enhanced modeling.