Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique

Parvin, Farhana; Ali, Sk Ajim; Hashmi, S. Najmul Islam; Ahmad, Ateeque

doi:10.1007/s41324-020-00375-1

Cited by 32 publications

(23 citation statements)

References 21 publications

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“…But at a certain distance the when zscore becomes peak it reveals distances where clustering are most prominent. 20 LISA statistics has been used to detect the hot-spot area across the districts considering the percentage of children who suffered from diarrhoea in the study area. 12 The LISA values consent the computation of its similarity with its neighbour districts and also test the level of significance (p values) for each locations.…”

Section: Spatial Analysismentioning

confidence: 99%

Prevalence of diarrhoea among under five children in India and its contextual determinants: A geo-spatial analysis

Ghosh

Chakraborty

Mog

2021

Clinical Epidemiology and Global Health

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Background: Diarrheal diseases which is one of the leading reason behind global mortality and morbidity is more threatening for infants and young children. Childhood diarrhoea is becoming increasingly prevalent disease in developing countries like India. Moreover, it is a major cause of malnutrition that contributes towards third major cause of under 5 mortalities. This has raised a serious concern in the domain of public health. Objectives: The present study attempts to identify the spatial prevalence and clustering of diarrhoea in India based on National Family and Heath Survey (NFHS-4, 2015-16) and also tries to identify the association of prevalence of diarrheal disease among under 5 children with socio-economic factors. Methods: The study applies spatial analysis software i.e ArcGIS 10.8 and GeoDa 1.18 including Moran's index and logistics regression to show the spatial prevalence and auto-correlation of diarrhoea among neighbourhood districts and their contextual determinants. Findings: The study finds a total of 71 districts belong to the hot spot area are mostly from central and eastern costal part of India. Analysis of socio-economic determinants show prevalence of diarrhoea among under 5 children is higher in rural areas, among children not staying in Pacca house, living with unimproved sanitation facilities, belonging to under privileged community (highest among OBC followed by SC/STs), children of younger mothers (<25 years) and "poor' households considering the wealth index. Conclusion:The study recommends to target hot-spot districts with high prevalence of diarrhoea and policy level interventions like improved sanitation to reduce the risk of childhood diarrhoea.

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Section: Spatial Analysismentioning

confidence: 99%

Prevalence of diarrhoea among under five children in India and its contextual determinants: A geo-spatial analysis

Ghosh

Chakraborty

Mog

2021

Clinical Epidemiology and Global Health

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“…24 For hot-spot analysis in GIS or Geo-Da a scale is important to specify the distance value. 25 The computation of spatial autocorrelation requires the construction on the bases of matrix known as spatial weight matrix (W). The study has made use of Moran's I (spatial autocorrelation) and Local Indicators of Spatial Autocorrelation (LISA) to measure the extent of autocorrelation among the neighbourhood districts in the country.…”

Section: Methodsmentioning

confidence: 99%

Prevalence and detecting spatial clustering of anaemia among children 6–59 months in the districts of India

Ghosh

Desai

2021

Clinical Epidemiology and Global Health

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Background: Among the developing countries, India was the largest contributor to child anaemia in the last decade. It has many harmful effects on children's lifestyle as it reduces learning capacity, attentiveness and intelligence. The study attempts to identify the spatial prevalence and detect the clustering of anaemia in India based on National Family Heath Survey-IV, 2015-16 and also tries to identify the determinants of anaemia among children (6-59 months). Methods: The study uses spatial analysis software ArcGIS 10.8 and GeoDa1.18 including Moran's index. Logistic regression model is applied by using STATA-14, to assess the effects of socio-economic and demographic, maternal and child characteristic predictors of prevalence of anaemia among children. Results:The anaemia prevalence is highest in Dadra & Nagar Haveli and lowest in Meghalaya. A total of 321 districts are having more than the national level average (58.65 %) of anaemia. Study also finds 110 districts as the hot-spot areas (Moran's I = 0.63) mostly from central, western, south-western, northern and eastern parts of India. Increase of mother's age, education, receipt of full ANC and richer wealth quantile reduce the risk of childhood anaemia. On the other hand, underweight mothers, children in the age group 12-23 months, underweight children and higher birth order increase the risk of childhood anaemia. Conclusion:The study suggests targeting the hot-spot districts with a high prevalence of anaemia and policy-level interventions addressing micronutrient deficiency like the Integrated Child Development Scheme, to reduce the risk of childhood anaemia.

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“…This is the framework for the COVID-19 pandemic, where the first studies were mostly conducted for mapping the incidence at country-wide scales. Some studies were carried out in countries such as Iran [58], Afghanistan [59], Italy [60], United States [61], or India [62] in order to determine what local drivers were behind the particular transmissibility of the virus across these countries. These studies not only allow understanding the virus mechanisms much better but also consider the particularities of each spatial unit, including the internal inequalities.…”

Section: Literature Reviewmentioning

confidence: 99%

Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

Carballada

Balsa-Barreiro

2021

IJGI

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The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects.

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Spatial prediction and mapping of the COVID-19 hotspot in India using geostatistical technique

Cited by 32 publications

References 21 publications

Prevalence of diarrhoea among under five children in India and its contextual determinants: A geo-spatial analysis

Prevalence of diarrhoea among under five children in India and its contextual determinants: A geo-spatial analysis

Prevalence and detecting spatial clustering of anaemia among children 6–59 months in the districts of India

Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS

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