On the public citation graph

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

https://doi.org/10.1007/s41324-020-00375-1
0 signed · 60 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9780470317013.ch8openalex-snapshot
Point Pattern Analysis
inferredcites →https://doi.org/10.1002/jmv.25776openalex-snapshot
Alert for non‐respiratory symptoms of coronavirus disease 2019 patients in epidemic period: A case report of familial cluster with three asymptomatic COVID‐19 patients
inferredcites →https://doi.org/10.1007/s40808-016-0230-7openalex-snapshot
Spatial evaluation of appropriate areas to collect runoff using Analytic Hierarchy Process (AHP) and Geographical Information System (GIS) (case study: the catchment “Kasef” in Bardaskan
inferredcites →https://doi.org/10.1007/s40808-019-00593-zopenalex-snapshot
Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India
inferredcites →https://doi.org/10.1007/s41324-018-0166-2openalex-snapshot
Locating Astragalus hypsogeton Bunge appropriate site using AHP and GIS
inferredcites →https://doi.org/10.1007/s41324-018-0187-xopenalex-snapshot
Using analytic hierarchy process with GIS for Dengue risk mapping in Kolkata Municipal Corporation, West Bengal, India
inferredcites →https://doi.org/10.1007/s41324-019-00242-8openalex-snapshot
Mapping of mosquito-borne diseases in Kolkata Municipal Corporation using GIS and AHP based decision making approach
inferredcites →https://doi.org/10.1007/s41324-020-00330-0openalex-snapshot
Accessibility and site suitability for healthcare services using GIS-based hybrid decision-making approach: a study in Murshidabad, India
inferredcites →https://doi.org/10.1007/s42489-020-00037-0openalex-snapshot
Temporal-Spatial Simulation of Landscape Variations Using Combined Model of Markov Chain and Automated Cell
inferredcites →https://doi.org/10.1016/j.geosus.2020.03.005openalex-snapshot
COVID-19: Challenges to GIS with Big Data
inferredcites →https://doi.org/10.1016/s0140-6736(20)30260-9openalex-snapshot
Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study
inferredcites →https://doi.org/10.1016/s0140-6736(20)30673-5openalex-snapshot
COVID-19: towards controlling of a pandemic
inferredcites →https://doi.org/10.1186/1471-2334-13-10openalex-snapshot
A risk factor analysis of healthcare-associated fungal infections in an intensive care unit: a retrospective cohort study
inferredcites →https://doi.org/10.1186/1471-2334-13-64openalex-snapshot
Spatial and temporal variation and hotspot detection of kala-azar disease in Vaishali district (Bihar), India
inferredcites →https://doi.org/10.1186/1471-2458-9-464openalex-snapshot
Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006
inferredcites →https://doi.org/10.1186/1476-072x-7-44openalex-snapshot
Spatial and demographic patterns of Cholera in Ashanti region - Ghana
inferredcites →https://doi.org/10.1186/s12942-020-00202-8openalex-snapshot
Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics
inferredcites →https://doi.org/10.1186/s12942-020-00221-5openalex-snapshot
Geostatistical COVID-19 infection risk maps for Portugal
inferredcites →https://doi.org/10.1186/s12942-020-00222-4openalex-snapshot
Modeling spatial access to cervical cancer screening services in Ondo State, Nigeria
inferredcites →https://doi.org/10.1371/journal.pone.0230405openalex-snapshot
Data-based analysis, modelling and forecasting of the COVID-19 outbreak
inferredcites →https://doi.org/10.4103/ijcm.ijcm_194_18openalex-snapshot
Challenges to healthcare in India - The five A's

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4172101openalex-extracted
Modelling Geographically Varying Association of Covid-19 with the Risk Factors Over the Multiwave Using Multi-Scalar Data
inferred← citeshttps://doi.org/10.1002/spe.3144openalex-snapshot
STOPPAGE: Spatio‐temporal data driven cloud‐fog‐edge computing framework for pandemic monitoring and management
inferred← citeshttps://doi.org/10.1007/978-3-031-10031-4_26openalex-snapshot
Use of Remote Sensing and GIS Techniques for Adaptation and Mitigation of COVID-19 Pandemic
inferred← citeshttps://doi.org/10.1007/978-3-031-17439-1_28openalex-snapshot
Preliminary Spatiotemporal Analysis of Mortality in Northern Italy During COVID-19 First Wave
inferred← citeshttps://doi.org/10.1007/978-3-031-67624-6_1openalex-snapshot
Development of Health and Medical Geography: A Systematic Review
inferred← citeshttps://doi.org/10.1007/978-3-032-00457-4_30openalex-snapshot
Exploring the Geographical Tapestry of Non-Communicable Disease-Induced Morbidity in the Elderly: A Cross-Sectional Inquiry in West Bengal, India
inferred← citeshttps://doi.org/10.1007/978-981-97-9532-1_26openalex-snapshot
Assessing the Spatiotemporal Trends and Clustering of the Brick Kiln Industry and Its Impact on Agricultural Land Degradation in West Bengal Via a GIS and Statistical Technique-Based Approach
inferred← citeshttps://doi.org/10.1007/s10668-022-02727-3openalex-snapshot
A district-level vulnerability assessment of next COVID-19 variant (Omicron BA.2) in Uttarakhand using quantitative SWOT analysis
inferred← citeshttps://doi.org/10.1007/s12524-026-02582-zopenalex-snapshot
Mapping Landslide Susceptibility Along the Darjeeling Himalayan Railway: A Comparative Analysis of LightGBM and TOPSIS Methods
inferred← citeshttps://doi.org/10.1007/s13171-023-00312-yopenalex-snapshot
COVID-19 Hotspot Mapping and Prediction in Aizawl District of Mizoram: a Hotspot and SEIR Model-Based Analysis
inferred← citeshttps://doi.org/10.1007/s41324-021-00408-3openalex-snapshot
CoBiD-net: a tailored deep learning ensemble model for time series forecasting of covid-19
inferred← citeshttps://doi.org/10.1007/s41324-022-00443-8openalex-snapshot
Spatio-temporal changes pattern in the hotspot's footprint: a case study of confirmed, recovered and deceased cases of Covid-19 in India
inferred← citeshttps://doi.org/10.1007/s41324-022-00483-0openalex-snapshot
Post-lockdown spatiotemporal pattern of COVID clustering in North 24 Parganas, West Bengal, India
inferred← citeshttps://doi.org/10.1007/s41324-022-00501-1openalex-snapshot
Spatial pattern analysis on incidence of dengue hemorrhagic fever (DHF) in the Leuwigajah, West Java, Indonesia
inferred← citeshttps://doi.org/10.1016/j.cegh.2021.100813openalex-snapshot
Prevalence of diarrhoea among under five children in India and its contextual determinants: A geo-spatial analysis
inferred← citeshttps://doi.org/10.1016/j.cegh.2021.100845openalex-snapshot
Prevalence and detecting spatial clustering of anaemia among children 6–59 months in the districts of India
inferred← citeshttps://doi.org/10.1016/j.geogeo.2025.100418openalex-snapshot
Geospatial analysis of soil salinity dynamics: Exploring topographic and vegetation influences in coastal Bangladesh
inferred← citeshttps://doi.org/10.1016/j.gsd.2023.100946openalex-snapshot
Evaluation of iron contamination in groundwater with its associated health risk and potentially suitable depth analysis in Kushtia Sadar Upazila of Bangladesh
inferred← citeshttps://doi.org/10.1016/j.heliyon.2021.e08419openalex-snapshot
Spatial priority for COVID-19 vaccine rollout against limited supply
inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e25810openalex-snapshot
Conditioning factors in the spreading of Covid-19 – Does geography matter?
inferred← citeshttps://doi.org/10.1016/j.resglo.2021.100053openalex-snapshot
Impact assessment of lockdown amid COVID-19 pandemic on tourism industry of Kashmir Valley, India
inferred← citeshttps://doi.org/10.1016/j.rsma.2024.103949openalex-snapshot
An evidence-based study of spatiotemporal trends and hotspots of sea surge events in Kerala, India
inferred← citeshttps://doi.org/10.1016/j.scs.2022.104158openalex-snapshot
What have we learned about socioeconomic inequalities in the spread of COVID-19? A systematic review
inferred← citeshttps://doi.org/10.1080/23744235.2022.2129778openalex-snapshot
Mapping of space–time patterns of infectious disease using spatial statistical models: a case study of COVID-19 in India
inferred← citeshttps://doi.org/10.1109/icrito51393.2021.9596226openalex-snapshot
Analysis of AI-driven tools and Impact of Covid-19 on Age: A Case Study for India
inferred← citeshttps://doi.org/10.1111/1365-2664.14775openalex-snapshot
Light quality and spatial variability influences on seedling regeneration in Hawaiian lowland wet forests
inferred← citeshttps://doi.org/10.1111/cag.70054openalex-snapshot
Density and disease: A systematic review of COVID‐19 transmission and policy across scales and time
inferred← citeshttps://doi.org/10.11113/jagst.v4n2.90openalex-snapshot
Hotspot Analysis of COVID-19 using Spatial Statistic in Selangor Malaysia
inferred← citeshttps://doi.org/10.1186/s12887-023-04073-3openalex-snapshot
Identifying spatial clustering of diarrhoea among children under 5 years across 707 districts in India: a cross sectional study
inferred← citeshttps://doi.org/10.3390/ijerph19073840openalex-snapshot
SARS-CoV-2 Transmission Risk Model in an Urban Area of Mexico, Based on GIS Analysis and Viral Load
inferred← citeshttps://doi.org/10.3390/ijerph19148267openalex-snapshot
Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review
inferred← citeshttps://doi.org/10.3390/ijgi10090602openalex-snapshot
Geospatial Analysis and Mapping Strategies for Fine-Grained and Detailed COVID-19 Data with GIS
inferred← citeshttps://doi.org/10.3390/ijgi12020045openalex-snapshot
Geospatial Modeling of Health, Socioeconomic, Demographic, and Environmental Factors with COVID-19 Incidence Rate in Arkansas, US
inferred← citeshttps://doi.org/10.36548/jitdw.2022.2.002openalex-snapshot
Getis-Ord (Gi*) based Farmer Suicide Hotspot Detection
inferred← citeshttps://doi.org/10.4018/979-8-3373-6162-8.ch011openalex-snapshot
Modelling Tourism Demand in Uncertain Times
inferred← citeshttps://doi.org/10.4018/ijagr.297517openalex-snapshot
Identifying Spatio-Temporal Clustering of the COVID-19 Patterns Using Spatial Statistics
inferred← citeshttps://doi.org/10.4236/jgis.2023.151007openalex-snapshot
Geospatial Coronavirus Vulnerability Regression Modelling for Malawi Based on Cumulative Spatial Data from April 2020 to May 2021
inferred← citeshttps://doi.org/10.5993/ajhb.47.4.5openalex-snapshot
Spatial Statistical Analysis and Risk Factor Identification of COVID-19 in China
inferred← citeshttps://doi.org/10.7163/gpol.0209openalex-snapshot
Temporal variation of spatial autocorrelation of COVID-19 cases identified in Poland during the year from the beginning of the pandemic
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.