Ms. Shamaila Fatima will present her APS as per the detail:
Date: 28 February 2023
Time: 1900 - 2000 hrs.
Venue: CTARA Conference Room No.2
Topic: Climate-based participatory predictive model for early warning of Vector borne disease outbreak
Guide: Prof Pennan Chinnasamy
RPC members: Prof Satish B. Agnihotri and Prof Sarthak GauravAbstract: By the end of the 21st century, the global average temperature is projected to rise by 2.5°C to 2.9°C or higher. This brings into issue the long-term existence and balance of the environment and necessitates implementing new policies to provide a sustainable environment. Less frequently considered in this regard is the effect of climate changes on the survival of earth's natural living species and the resulting burden on humankind. A significant aspect to recognize and manage here is how climatic conditions will impact the abundance and distribution of disease-causing vectors. Vector-borne diseases are among the most common diseases worldwide. It affects around 700,000 individuals each year and is responsible for more than 17% of all infectious diseases. Vector-borne diseases are endemic in over one hundred countries and impact over 50% of the global population. It is estimated that most low-income nations are adversely affected by at least 5 vector-borne diseases, which are among the leading causes of death in children under 5. India has the highest malaria incidence rate in Southeast Asia and the third-highest dengue incidence rate worldwide. The seasonal and geographic distribution of vector populations and disease transmission is influenced by climatic conditions such as temperature, precipitation and humidity. Thus, there is a need to monitor these changes, understand the casualties and predict the disease outbreak through modern technologies to help health practitioners, policymakers, and the local people to mitigate. With this background and the need to improve such understanding, the main aim of the current study is to develop an early warning system to predict the vector-borne disease outbreak using climate data and machine learning algorithms. The detailed literature review reveals that diseases transmitted by vectors lack a conceptual framework, despite the best efforts of experts. Although some research has been carried out on vector-borne diseases in India, it is limited. There have been researches done to forecast vector-borne disease outbreaks; however, there is no common platform that may be an early warning system. Therefore, developing a standardized vector-borne disease prediction model and presenting it as an early warning system through a web-based dashboard is essential. A 2-way Citizen oriented Information system could help in the flow of information between health experts and local people to better tackle the vector-borne disease on the ground.