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Towards seasonal forecasting of malaria in India.

ABSTRACT: Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model.The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series.The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of "high", "above average" and "low" malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.

SUBMITTER: Lauderdale JM

PROVIDER: S-EPMC4251696 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Towards seasonal forecasting of malaria in India.

Lauderdale Jonathan M JM Caminade Cyril C Heath Andrew E AE Jones Anne E AE MacLeod David A DA Gouda Krushna C KC Murty Upadhyayula Suryanarayana US Goswami Prashant P Mutheneni Srinivasa R SR Morse Andrew P AP

Malaria journal 20140810

<h4>Background</h4>Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model.<h4>Methods</h4>The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological ...[more]

PMID: 25108445

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Project description:Introduction:Five states in India are reporting sporadic outbreaks of Kyasanur Forest Disease (KFD). Goa experienced an outbreak of KFD in 2015. It remains as an important differential diagnosis for tropical fever in the endemic regions. Few studies among neighboring two states (Karnataka and Kerala) have described the epidemiological characteristics of KFD. However, there is no study which describes the same among cases in the state of Goa. Hence, we planned to understand the epidemiology (time, place, and person distribution) of the disease including seasonal pattern with forecasting using zero-inflated negative binomial regression and time series models. We also explored geo-spatial clustering of KFD cases in Goa during 2015-2018 which would help design effective intervention to curb its transmission in Goa. Results:Blood samples of all suspected cases of KFD during 2015 to 2018 were tested using reverse transcriptase-polymerase chain reaction technique. Reports of these results were periodically shared with the state surveillance unit. Records of 448 confirmed cases of KFD available at the State Integrated Disease Surveillance Programme were analyzed. The mean (SD) age of the patients was 41.6 (14.9) years. Of 143 cases with documented travel history, 135 (94.4%) had history of travel to forest for cashew plucking. Two thirds of cases (66.3%) did not receive KFD vaccine prior to the disease. Case fatality rate of 0.9% was reported. Seasonal peaks were observed during January to April, and forecasting demonstrated a peak in cases in the subsequent year also during January-April persisting till May. Around 40 villages located along the Western Ghats had reported KFD, and affected villages continued to report cases in the subsequent years also. Case density-based geographic maps show clustering of cases around the index village. Conclusion:Most of the confirmed cases did not receive any vaccination. KFD cases in Goa followed a specific seasonal pattern, and clustering of cases occurred in selected villages located in North Goa. Most of the patients who had suffered from the disease had visited the forest for cashew plucking. Planning for public health interventions such as health education and vaccination campaigns should consider these epidemiological features.

Dataset Information

Towards seasonal forecasting of malaria in India.

Publications

Towards seasonal forecasting of malaria in India.

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