Abstract:
Dengue fever is an important infectious disease in Pakistan with increasingly frequent epidemics. Since 2010, Pakistan has been facing a severe epidemic of dengue fever that has infected almost 16,580 people from all over the country with 257 deaths in Lahore and nearly 5000 cases and 60 deaths reported from the rest of the country [1]. Not only in Pakistan but throughout the tropical and subtropical regions of the world, particularly Asia-Pacific region, Dengue has become a major public-health concern caused by mosquitoes which thrive in areas with standing water, including puddles, water tanks, containers and old tires. Lack of reliable sanitation and regular garbage collection contribute to the spread of the dengue mosquitoes. One such example was the 2011 dengue breakout during which, more than 21,204 people were diagnosed with dengue fever [2]. In 2019, about 1,585 cases have been stated solely from Karachi due to stagnant water which resulted from the poor sanitation system after record breaking rainfall this year [19]. The twin cities also suffered from the epidemic, resulting in almost 200 patients in August 2019 [20].
In the developing countries like Pakistan, there are many cases of overcrowded hospitals which are under-staffed and have a low doctor to patient ratio which makes diagnostic screening a big challenge, especially in the case of an epidemic breakout like dengue. To account for the human suffering caused by the epidemics of dengue, there is a need to develop a system that provides a cost-effective and rapid solution for diagnostic screening of dengue fever. To achieve this, clinical and laboratory parameters of the subjects are acquired and fed in to an Artificial Intelligence powered system which provides diagnostic screening of dengue disease through machine learning based classification algorithms. The system can be re-trained on any epidemic data like the recent COVID-19 for screening purposes.
