The other side of Big Data – Dengue Fever Prediction Models

The recent scrutiny of Mark Zuckerberg and Facebook brought big data into the limelight for all the wrong reasons. When used appropriately—and with important protections for privacy—big data can be positive and it can play a major role in healthcare. This is especially true in predicting the spread of diseases.

One example is the viral disease dengue fever that spreads through mosquito bites and turns epidemic very quickly. With a 30-fold increase in global incidence over the past five decades, the World Health Organization (WHO) has named dengue fever as the most critical mosquito-borne viral disease. Every year, an estimated 390 million dengue cases are reported. Of these, around 500,000 develop into dengue hemorrhagic fever, which causes up to 25,000 deaths each year. The global estimated annual cost of dengue stands at US$8 billion, which is higher than other major infectious diseases.

Modern means of transportation and climate change have transformed the dynamics of the spread of infectious diseases like dengue and malaria. They have become more prevalent, resistant and widespread: dengue fever is present in more than 100 countries and malaria still causes one million deaths annually.

The dengue burden is likely to increase in the future owing to trends such as increased urbanization, scarce water supplies and, possibly, environmental change. The 30 countries with the highest rates of dengue include Brazil, Malaysia, Philippines, India, Honduras and Columbia. Last July, Sri Lanka faced an unprecedented outbreak of dengue and today, in 2018, Australia is facing a 20-year-high of dengue cases.

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