Forecasting Malaria Cases Using Climatic Factors in Delhi, India: A Time Series Analysis

Kumar, Varun; Mangal, Abha; Panesar, Sanjeet; Yadav, Geeta; Talwar, Richa; Raut, Deepak; Singh, Sapna

doi:10.1155/2014/482851

Cited by 50 publications

(41 citation statements)

References 9 publications

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“…Although the two models developed in this study produced prediction intervals having widths of some hundred cases, the regression model was the one able to anticipate accurately the peak of the occurrence. ARIMA model was also used for malaria forecasting in South Africa [17], Zambia [19], Burundi [20] and India [21] with comparable results.…”

Section: Discussionmentioning

confidence: 99%

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique

2017

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BackgroundMozambique was recently ranked fifth in the African continent for the number of cases of malaria. In Chimoio municipality cases of malaria are increasing annually, contrary to the decreasing trend in Africa. As malaria transmission is influenced to a large extent by climatic conditions, modelling this relationship can provide useful insights for designing precision health measures for malaria control. There is a scarcity of information on the association between climatic variability and malaria transmission risk in Mozambique in general, and in Chimoio in particular. Therefore, the aim of this study is to model the association between climatic variables and malaria cases on a weekly basis, to help policy makers find adequate measures for malaria control and eradication.MethodsTime series analysis was conducted using data on weekly climatic variables and weekly malaria cases (counts) in Chimoio municipality, from 2006 to 2014. All data were analysed using SPSS-20, R 3.3.2 and BioEstat 5.0. Cross-correlation analysis, linear processes, namely ARIMA models and regression modelling, were used to develop the final model.ResultsBetween 2006 and 2014, 490,561 cases of malaria were recorded in Chimoio. Both malaria and climatic data exhibit weekly and yearly systematic fluctuations. Cross-correlation analysis showed that mean temperature and precipitation present significantly lagged correlations with malaria cases. An ARIMA model (2,1,0) (2,1,1)52, and a regression model for a Box-Cox transformed number of malaria cases with lags 1, 2 and 3 of weekly malaria cases and lags 6 and 7 of weekly mean temperature and lags 12 of precipitation were fitted. Although, both produced similar widths for prediction intervals, the last was able to anticipate malaria outbreak more accurately.ConclusionThe Chimoio climate seems ideal for malaria occurrence. Malaria occurrence peaks during January to March in Chimoio. As the lag effect between climatic events and malaria occurrence is important for the prediction of malaria cases, this can be used for designing public precision health measures. The model can be used for planning specific measures for Chimoio municipality. Prospective and multidisciplinary research involving researchers from different fields is welcomed to improve the effect of climatic factors and other factors in malaria cases.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-017-2205-6) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique

2017

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“…The fundamental concept behind this emanated from the fact that a causal relationship exists among the climatic factors [3]. Some recent studies [4,5] combined meteorological variables together with malaria incidence data and established time series models for predicting malaria incidence. Regression and correlation analysis modelling was applied and using meteorological variables the trend of malaria incidence was determined [6].…”

Section: Introductionmentioning

confidence: 99%

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

et al. 2017

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Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third world countries, which have very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in third world countries, due to poverty and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. In this study, we have deployed an intelligent malaria outbreak early warning system, which is a mobile application that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application; this paper also makes a significant contribution by identifying hidden ecological factors of malaria.

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“…[16][17][18][19][20] These authors ensured that the time series processes attained stationarity in the homogenous sense (stationary in its level) and variance, which are indispensable conditions of a SARIMA model. This was done by carrying out the first differencing and the seasonal differencing, which results in a stationary time series by removing trends and seasonal effects.…”

Section: Discussionmentioning

confidence: 99%

A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa

2018

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Background. South Africa (SA) in general, and KwaZulu-Natal (KZN) Province in particular, have stepped up efforts to eliminate malaria. To strengthen malaria control in KZN, a relevant malaria forecasting model is important. Objectives. To develop a forecasting model to predict malaria cases in KZN using the Seasonal Autoregressive Integrated Moving Average (SARIMA) time series approach. Methods. The study was carried out retrospectively using a clinically confirmed monthly malaria case dataset that was split into two. The first dataset (January 2005 -December 2013) was used to construct a SARIMA model by adopting the Box-Jenkins approach, while the second dataset (January -December 2014) was used to validate the forecast generated from the best-fit model. Results. Three plausible models were identified, and the SARIMA (0,1,1)(0,1,1) 12 model was selected as the best-fit model. This model was used to forecast malaria cases during 2014, and it was observed to fit closely with malaria cases reported in 2014. Conclusions. The SARIMA (0,1,1)(0,1,1) 12 model could serve as a useful tool for modelling and forecasting monthly malaria cases in KZN. It could therefore play a key role in shaping malaria control and elimination efforts in the province.

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Forecasting Malaria Cases Using Climatic Factors in Delhi, India: A Time Series Analysis

Cited by 50 publications

References 9 publications

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

A Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa

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