Application of time series methods for dengue cases in North India (Chandigarh)

Shashvat, Kumar; Basu, Rikmantra; Bhondekar, Amol P.

doi:10.1007/s10389-019-01136-7

Cited by 9 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Poison regression model has been used in another (K. M. Rahman et al, 2020). Auto Regressive Integrated Moving Average (ARIMA) model was used in two articles (Shashvat, Basu, & Bhondekar, 2021) (Zahirul Islam et al, 2018). Time series analysis was used in two articles (Goto et al, 2013;Salim & Syairaji, 2020).…”

Section: Analytical Approachesmentioning

confidence: 99%

How Do Climate Variables Affect the Transmission of Dengue Infection in South and Southeast Asia: A Scoping Review

Fernando

R.M

2023

Asian J. Interdicip. Res.

View full text Add to dashboard Cite

Introduction: Dengue is an arboviral infection, transmitted from person to person via mosquito vectors. Dengue transmission is affected by rainfall, temperature, humidity and wind speed. These climate variables are changing as a result of anthropogenic climate change, and it is expected that this will affect the incidence of dengue. This scoping review explored the impacts on dengue transmission of variation in these climate variables in 12 Asian countries located in South- and South-East Asia regions. Methods: The protocol developed by the Joanna Briggs Institute was followed for this scoping review. Articles were found using the PubMed, Scopus, and Embase databases, as well as grey literature. Studies conducted in India, Bangladesh, Bhutan, the Democratic Republic of Korea, Indonesia, Maldives, Myanmar, Nepal, Thailand, Sri Lanka, and Timor Leste and published in English between 2002 and 2022 were included. An extensive literature search was conducted in February and March, 2022. A modified checklist designed for assessing the quality of ecological studies was used to assess the quality. Following quality assessment, ninety articles were included in the review. Results: The initial search yielded 1912 articles. After the duplicates were removed, 983 were identified as potentially relevant. Following the exclusion of 729 articles by two reviewers, 254 articles were subjected to full text screening. Thereafter, 90 articles were selected for the review that strictly met the inclusion criteria. Although the results were mixed, we found that dengue transmission decreases in temperature extremes. Dengue transmission increases with heavy rainfall, with a 1- 3-month lag. Humidity was also found to be related to dengue transmission. Other factors found to be associated with dengue transmission include the hours of sunshine and wind speed. Conclusion: There is a complex relationship between changing climate parameters and dengue incidence in Asia. This reflects the complex ecology of vector-borne diseases and suggests that the impact of changes in climate variables on dengue transmission may vary according to local climate and other factors.

show abstract

Section: Analytical Approachesmentioning

confidence: 99%

How Do Climate Variables Affect the Transmission of Dengue Infection in South and Southeast Asia: A Scoping Review

Fernando

R.M

2023

Asian J. Interdicip. Res.

View full text Add to dashboard Cite

show abstract

“…Time-series forecasting during epidemics has been regarded as an essential tool in the past for containing the spread of contagious diseases like ebola, influenza, etc. [10][11][12][13][14][15][16]. Timing plays a critical role in an epidemic, and from the very beginning, an exceptional level of monitoring is required to curb the spread.…”

Section: Fig2: Total Confirmed Cases Of Covid-19 Worldwide From Jan 22 To May 15 2020[1]mentioning

confidence: 99%

“…We analysed the ACF and PACF plots of the differenced data for all the three datasets (fig. [6][7][8][9][10][11], and proposed a series of candidate models for fitting (table. 3-a, 3-b, 3-c).…”

Section: Selecting the Best-fit Arima Modelmentioning

confidence: 99%

Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India

Swaraj

Verma

Kaur

et al. 2021

Journal of Biomedical Informatics

View full text Add to dashboard Cite

Background Time-series forecasting has a critical role during pandemics as it provides essential information that can lead to abstaining from the spread of the disease. The novel coronavirus disease, COVID-19, is spreading rapidly all over the world. The countries with dense populations, in particular, such as India, await imminent risk in tackling the epidemic. Different forecasting models are being used to predict future cases of COVID-19. The predicament for most of them is that they are not able to capture both the linear and nonlinear features of the data solely. Methods We propose an ensemble model integrating an autoregressive integrated moving average model (ARIMA) and a nonlinear autoregressive neural network (NAR). ARIMA models are used to extract the linear correlations and the NAR neural network for modeling the residuals of ARIMA containing nonlinear components of the data. Comparison: Single ARIMA model, ARIMA-NAR model and few other existing models which have been applied on the COVID-19 data in different countries are compared based on performance evaluation parameters. Result The hybrid combination displayed significant reduction in RMSE(16.23%), MAE(37.89%) and MAPE (39.53%) values when compared with single ARIMA model for daily observed cases. Similar results with reduced error percentages were found for daily reported deaths and cases of recovery as well. RMSE value of our hybrid model was lesser in comparison to other models used for forecasting COVID-19 in different countries. Conclusion Results suggested the effectiveness of the new hybrid model over a single ARIMA model in capturing the linear as well as nonlinear patterns of the COVID-19 data.

show abstract

“…Previous studies have attempted to understand the relationship between climate and the presence of dengue in India by means of statistical and mathematical models. These techniques have included Poisson regression (30), Naïve Bayes and multivariable regression (31), Bayesian models such as spatial autoregressive (SAR) models or conditional autoregressive (CAR) models (Mudele et al, 2021), autoregressive integrated moving average (ARIMA) models (32), generalized linear models (33), Extreme Gradient Boosting and rule-based classification (34), and other types of mathematical modeling (35). The above methods have focused on multiple parameters influencing dengue and on the discovery of the climate variables that most influence dengue disease dynamics (36).…”

Section: Introductionmentioning

confidence: 99%

Applications of artificial intelligence in predicting dengue outbreaks in the face of climate change: a case study along coastal India

Tallam¹,

Quang²

2023

Preprint

View full text Add to dashboard Cite

The climate crisis will have an increasingly profound effect on the global distribution and burden of infectious diseases. Climate-sensitive diseases can serve as critical case studies for assessing public health priorities in the face of epidemics. Preliminary results denote that machine learning-based predictive modeling measures can be successfully applied to understanding environmental disease transmission dynamics. Ultimately, machine learning models can be trained to detect climate-sensitive diseases early, diseases which might represent serious threats to human health, food safety, and economies. We explore how machine learning can serve as a tool for better understanding climate-sensitive diseases, taking dengue dynamics along the Godavari River of coastal India as our case study. We hypothesize that a climate-driven predictive model with controlled calibration can help us understand several of the most critical relationships and climate characteristics of climate-sensitive disease dynamics.

show abstract

Application of time series methods for dengue cases in North India (Chandigarh)

Cited by 9 publications

References 7 publications

How Do Climate Variables Affect the Transmission of Dengue Infection in South and Southeast Asia: A Scoping Review

How Do Climate Variables Affect the Transmission of Dengue Infection in South and Southeast Asia: A Scoping Review

Implementation of stacking based ARIMA model for prediction of Covid-19 cases in India

Applications of artificial intelligence in predicting dengue outbreaks in the face of climate change: a case study along coastal India

Contact Info

Product

Resources

About