An epidemiological and spatiotemporal analysis to identify high risk areas of malaria in Visakhapatnam district of Andhra Pradesh, India, 1999–2015

Mopuri, Rajasekhar; Mutheneni, Srinivasa Rao; Kumaraswamy, Sriram; Kadiri, Madhusudhan Rao; Upadhyayula, Suryanarayana Murty; Naish, Suchithra

doi:10.1007/s41324-019-00267-z

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…A common feature of these states is the dominance of tribal populations, which are often less accessible, and lack adequate infrastructure and disease surveillance compared to other areas. Other studies have also observed tribal populations to be more vulnerable to the disease [ 36 ]. Ahmad et al [ 3 ] have provided evidence of malaria-affected regions in Jharkhand, which has a tribal-dominant population.…”

Section: Discussionmentioning

confidence: 99%

Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study

Jana

Gelband

et al. 2022

Malar J

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Background India has a substantial burden of malaria, concentrated in specific areas and population groups. Spatio-temporal modelling of deaths due to malaria in India is a critical tool for identifying high-risk groups for effective resource allocation and disease control policy-making, and subsequently for the country’s progress towards United Nations 2030 Sustainable Development Goals. Methods In this study, a spatio-temporal model with the objective of understanding the spatial distribution of malaria mortality rates and the rate of temporal decline, across the country, has been constructed. A spatio-temporal “random slope” model was used, with malaria risk depending on a spatial relative risk surface and a linear time effect with a spatially-varying coefficient. The models were adjusted for urban/rural status (residence of the deceased) and Normalized Difference Vegetation Index (NDVI), using 2004–13 data from the Million Death Study (MDS) (the most recent data available), with nationwide geographic coverage. Previous studies based on MDS had focused only on aggregated analyses. Results The rural population had twice the risk of death due to malaria compared to the urban population. Malaria mortality in some of the highest-risk regions, namely the states of Odisha and Jharkhand, are declining faster than other areas; however, the rate of decline was not uniformly correlated with the level of risk. The overall decline was faster after 2010. Conclusion The results suggest a need for increased attention in high-risk rural populations, which already face challenges like inadequate infrastructure, inaccessibility to health care facilities, awareness, and education around malaria mortality and prevalence. It also points to the urgent need to restart the MDS to document changes since 2013, to develop appropriate malaria control measures.

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Section: Discussionmentioning

confidence: 99%

Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study

Jana

Gelband

et al. 2022

Malar J

View full text Add to dashboard Cite

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“…Further investigations are done on the study of the identification of vulnerable area have included hotspot analysis [ 15 ], entropy-based hotspot detection [ 16 ], forest fire hotspot detection [ 17 ], risk assessment related to climate change [ 18 ], and identification of high-risk malaria areas [ 19 ]. Cheshmidari et al [ 20 ] used the Dolphi technique to prioritize the severity of floods in Iran's Ivar watershed.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal changes pattern in the hotspot's footprint: a case study of confirmed, recovered and deceased cases of Covid-19 in India

Tabarej

Minz

2022

Spat. Inf. Res.

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Hotspot detection and the analysis for the hotspot's footprint recently gained more attention due to the pandemic caused by the coronavirus. Different countries face the effect of the virus differently. In India, very little research has been done to find the virus transmission. The paper's main objective is to find changing pattern of the footprint of the hotspot. The confirmed, recovered, and deceased cases of the Covid-19 from April 2020 to Jan 2021 is chosen for the analysis. The study found a sudden change in the hotspot district and a similar change in the footprint from August. Change pattern of the hotspot's footprint will show that October is the most dangerous month for the first wave of the Corona. This type of study is helpful for the health department to understand the behavior of the virus during the pandemic. To find the presence of the clustering pattern in the dataset, we use Global Moran’s I. A value of Global Moran’s I greater than zero shows the clustering in the data set. Dataset is temporal, and for each type of case, the value Global Moran’s I > 0, shows the presence of clustering. Local Moran’s I find the location of cluster i.e., the hotspot. The dataset is granulated at the district level. A district with a high Local Moran’s I surrounded by a high Local Moran’s I value is considered the hotspot. Monte Carlo simulation with 999 simulations is taken to find the statistical significance. So, for the 99% significance level, the p-value is taken as 0.001. A hotspot that satisfies the p-value threshold is considered the statistically significant hotspot. The footprint of the hotspot is found from the coverage of the hotspot. Finally, a change vector is defined that finds the pattern of change in the time series of the hotspot’s footprint. Supplementary Information The online version contains supplementary material available at 10.1007/s41324-022-00443-8.

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Climate based malaria forecasting system for Andhra Pradesh, India

et al. 2020

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An epidemiological and spatiotemporal analysis to identify high risk areas of malaria in Visakhapatnam district of Andhra Pradesh, India, 1999–2015

Cited by 3 publications

References 29 publications

Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study

Spatio-temporal modelling of malaria mortality in India from 2004 to 2013 from the Million Death Study

Spatio-temporal changes pattern in the hotspot's footprint: a case study of confirmed, recovered and deceased cases of Covid-19 in India

Climate based malaria forecasting system for Andhra Pradesh, India

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