Forecasting Dengue Incidence Rate in Tamil Nadu Using ARIMA Time Series Model

S. Dhamodharavadhani, R. Rathipriya

Introduction

Mosquitoes are one of India's toxic insects. They have the ability to carry and spread disease to humans and this causes millions of deaths every year. In the year 2015, there 10,683 outbreaks of India Similarly, the worldwide incidence of dengue has risen 30-fold in the past 30 years, and an increasing number of countries are reporting their first outbreaks of the disease. The Aedesa Aegypti mosquito transmits Mosquito-Borne Disease (MBD) such as chikungunya, dengue, yellow fever, and zika virus to humans. Sustained and effective mosquito control efforts are necessary to avoid outbreaks of such diseases (Yong-Su Kwon 2015).

In India, the challenge MBD poses is serious because the increases in geographic distribution of vectors and MBD have the potential to affect 90% of the population. MBD is mostly an urban public health problem; however, outbreaks are being increasingly documented in rural areas too (Dhamodharavadhani and Rathipriya 2016).

Geographical Factors of Dengue:

  • 1. Water
  • 2. Housing
  • 3. Climate change
  • 4. Poverty
  • 5. Air Travel
  • 6. Health System

Climate change has had substantial consequences in the global distribution of MBD. Climate change often impacts dengue transmission, as mosquitoes grow faster in higher temperatures and bite more often. Researchers have developed a tool that can forecast the possibility of dengue outbreaks in different parts of India on the basis of environmental conditions; a development that can help to take preventive measures against deadly infection (Descloux 2012).

A climate-based model of dengue prediction will assist health authorities in assessing disease intensity in a country or region. On this basis, authorities may have systematic a plan for disease control well in advance and optimize the use of available resources for the same reason (Descloux 2012).

Dengue has been known in India since the 1940s, but the spread of the disease was previously very limited. The most important MBDs that affect humans are dengue viruses. According to the World Health Organization (WHO), dengue is divided into two: types 1 and type 2. Type 1 is a common form dengue that leads to dengue fever, Dengue Hemorrhagic Fever (DHF) is type 2. Four types of Dengue Hemorrhagic Fever exist: DHF1, DHF2, DHF3, and DHF4. Infection of dengue is one of the world's fastest spreading MBDs - a viral disease that accounts for nearly 50 million cases annually (Allard 1998).

  • • The rate of dengue virus transmission is dangerously high due to global warming, climate change, rapid urbanization, unsuitable sanitation, insufficient public health services, and migratory populations.
  • • Regions such as East Mediterranean, Latin America, South East Asia, and Western Pacific, and Africa are all susceptible to recurrent dengue fever outbreaks.

Figure 13.1 indicates that in 2017 the dengue cases had been the highest in a decade. In dengue cases, an increase of more than 300% occurred in 2009, and the total MBD-related death cases in 2017 was the highest in the last decade. According to data from the National Program for Vector Borne Disease Control (NVBDCP) and National Health Profile of 2018, dengue

FIGURE 13.1

Dengue cases and deaths in India.

cases rose to 188,401 in 2017 a than a 300% leap from less than 60,000 cases in 2009. It is more than a 250% jump compared to the 75,808 events in 2013 (Chiung Ching Ho 2015).

For example, Tamil Nadu has seen Dengue cases rise to 20,945 in 2017 and Puducherry's union territory has registered 4,507 dengue cases for the same year. This means that 2% of the entire population of Puducherry has been affected by dengue.

Other southern states like Kerala and Karnataka have been badly affected by this huge dengue outbreak. Kerala showed a dramatic rise from 7,439 cases of dengue in 2016 to 19,638 in 2017. In Karnataka, 16,209 people were affected by it which translates as 260% higher than in 2016 (Chandran and Azeez 2015).

As such, successful monitoring and prediction of the incidence rate of MBD is important in preventing disease spread. Passive, preventive, and reactive monitoring systems are used to monitor the MBD outbreaks and incidence rate in Tamilnadu (Dhamodharavadhani and Rathipriya 2020a). Nonetheless, these programs face problems such as preference for eradication over surveillance, difficulty in interpreting findings, and most importantly, lack of coordination between MBD eradication units and MBD monitoring units (Karnaboopathy and Venkatesan 2018).

As a result of the above factors, there is a compelling case for alternative forms of MBD tracking and forecasting. Therefore, this chapter proposed an approach based on ARIMA time-series model for forecasting MBD incidence rate using meteorological data.

 
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