Spatio-temporal Analysis of Public Sentiments towards COVID-19 in China: An Analysis of Posts from the Sina Weibo Microblogging Platform (Preprint)

2021 ◽  
Author(s):  
Yijun Wang ◽  
Wei Gao ◽  
Emeka Chukwusa
Keyword(s):  
Temporal Patterns ◽  
Moran’S I ◽  
Moran's I ◽  
Sina Weibo ◽  
Public Sentiment ◽  
The Social ◽  
Spatio Temporal ◽  
Sentiment Score ◽  
Chinese Public ◽  
Over Time

BACKGROUND The outbreak of COVID-19 has caused dismay worldwide. Analyzing how public sentiment changes over time and space is helpful for policymakers to understand and stabilize society during this difficult time, as well as for researchers to understand the social impact of the pandemic. OBJECTIVE To investigate the spatio-temporal patterns of public sentiments toward COVID-19 in China, by analyzing posts from the Sina Weibo microblogging platform, a social-media platform in China. METHODS We analyzed the spatio-temporal patterns of Chinese public sentiment from 57,706 COVID-19-related posts from January 1, 2020, to June 10, 2020. Posts were collected using web-crawler technology. A sentiment analysis based on Naïve Bayes was applied to assess the emotional polarity of individual posts. A sentiment score ranging from 0 (negative sentiment) to 1 (positive sentiment) was assigned to each post. The spatial variations of the sentiment scores were analyzed using global and local Moran’s I indicators of spatial autocorrelation. Spatio-temporal patterns were explored using the Mann-Kendall trend test. RESULTS Weibo posts from all provinces in China (n = 34) were analyzed. Monthly hot topics about COVID-19 changed from January to June. According to the daily sentiment score, Chinese public sentiment became increasingly positive, from 0.319 to 0.631, during this period. Findings from the spatial analysis showed a comparatively strong global autocorrelation between March (Moran’s I = 0.462) and April (Moran’s I = -0.269), especially in the western part of China. The sentiment scores in the central and eastern areas continuously increased. However, the sentiment score in the western area showed a trend of initially increasing and then decreasing. CONCLUSIONS Although national public sentiment became increasingly positive over time, the changing spatio-temporal patterns of public sentiment varied from region to region. This demonstrated the positive effect of the Chinese government's anti-COVID-19 measures on public sentiment during the pandemic. In addition, when facing public-health emergencies in the future, the health department should fully consider the social and economic differences between regions, when developing policies and strategies. This study also showed that Weibo is a good research channel for understanding Chinese public sentiment in the context of sudden infectious diseases, such as COVID-19.

Moran's I statistic-based nonparametric test with spatio-temporal observations

2018 ◽  
Vol 31 (1) ◽  
pp. 244-267 ◽  
Author(s):  
Y. Xiong ◽  
D. Bingham ◽  
W. J. Braun ◽  
X. J. Hu
Keyword(s):  
Nonparametric Test ◽  
Moran’S I ◽  
Moran's I ◽  
Spatio Temporal

scholarly journals Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Simon P. Kigozi ◽  
Ruth N. Kigozi ◽  
Catherine M. Sebuguzi ◽  
Jorge Cano ◽  
Damian Rutazaana ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Health Facility ◽  
Malaria Incidence ◽  
Temporal Patterns ◽  
Health Facilities ◽  
Moran’S I ◽  
Moran's I ◽  
Catchment Population ◽  
Targeted Interventions

Abstract Background As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period. Methods Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index. Results An estimated 38.8 million (95% Credible Interval [CI]: 37.9–40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9–21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7–9.4) to 36.6 (95% CI: 35.7–38.5) across the study period. Strong seasonality was observed, with June–July experiencing highest peaks and February–March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0–50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p < 0.001) and districts Moran’s I = 0.4 (p < 0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions. Conclusion Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

scholarly journals Clustering Analysis Influenza Disease to Identify Spatio-Temporal Spread Pattern in Thailand

2021 ◽  
pp. 81-89
Keyword(s):  
Population Density ◽  
Clustering Analysis ◽  
Statistical Significance ◽  
Winter Season ◽  
Moran’S I ◽  
P Value ◽  
Moran's I ◽  
Spread Pattern ◽  
Spatio Temporal ◽  
Influenza Outbreaks

The pandemics of influenza in Nonthaburi province was investigated by using autoregression and found the influenza spread pattern by autocorrelation (Moran's I). Population density, temperature, relative humidity, and rainfall are the factors used in the analysis. The influenza quantitative cross-section retrospective research design was employed from 2003-2010. Three seasons are classified as: hot, rainy, and winter season. The study found that influenza outbreaks in the rainy season was R2=0.45 and population density apparently affected the spread of influenza incidence with statistical significance coefficient (p-value <0.05). From the distribution pattern, the highest Moran's I values were related with the highest population density in 4 sub-districts: Suenyai, Taladkhwun, Bangkhen, and Bangkruay sub-district.

scholarly journals Power of Moran’s I Test for Spatial Dependence in Panel Data Models with Time Varying Spatial Weights Matrices

2015 ◽  
Vol 3 (5) ◽  
pp. 463-471 ◽  
Author(s):  
Bianling Ou ◽  
Xin Zhao ◽  
Mingxi Wang
Keyword(s):  
Panel Data ◽  
Spatial Dependence ◽  
Moran’S I ◽  
Panel Data Models ◽  
Time Varying ◽  
Moran's I ◽  
Spatial Weights ◽  
Spatial Weights Matrix ◽  
Time Invariant ◽  
Over Time

AbstractThe spatial weights matrix is usually specified to be time invariant. However, when it are constructed with economic/socioeconomic distance, trade /demographic/climatic characteristics, these characteristics might be changing over time, and then the spatial weights matrix substantially varies over time. This paper focuses on power of Moran’s I test for spatial dependence in panel data models with where spatial weights matrices can be time varying (TV-Moran). Compared with Moran’s I test with time invariant spatial weights matrices (TI-Moran), the empirical power of TV-Moran test for spatial dependence are evaluated. Our extensive Monte Carlo simulation results indicate that Moran’s I test with misspecified time invariant spatial weights matrices is questionable; Instead, TV-Moran test has shown superiority in higher power, especially for cases with negative spatial correlation parameters and the large time dimension.

scholarly journals Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

2020 ◽  
Author(s):  
Simon Kigozi ◽  
Ruth N Kigozi ◽  
Catherine M Sebuguzi ◽  
Jorge Cano ◽  
Damian Rutazaana ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Health Facility ◽  
Malaria Incidence ◽  
Temporal Patterns ◽  
Health Facilities ◽  
Moran’S I ◽  
Moran's I ◽  
Catchment Population ◽  
Targeted Interventions

Abstract Background. As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period.Methods. Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index.Results. An estimated 38.8 million (95% Credible Interval [CI]: 37.9 – 40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9 - 21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7 – 9.4) to 36.6 (95% CI: 35.7 – 38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0 – 50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p<0.001) and districts Moran’s I = 0.4 (p<0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions.Conclusion. Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

scholarly journals Using Moran’s I For Detection And Monitoring Of The Covid-19 Spreading Stage In Thailand During The Third Wave Of The Pandemic

2021 ◽  
Vol 14 (4) ◽  
pp. 155-167 ◽  
Author(s):  
Parichat Wetchayont ◽  
Katawut Waiyasusri
Keyword(s):  
Population Density ◽  
Early Stage ◽  
Adult Population ◽  
Spatial Association ◽  
Incidence Rates ◽  
Moran’S I ◽  
Moran's I ◽  
Spatial Spread ◽  
Spatio Temporal ◽  
The Difference

Spatial distribution and spreading patterns of COVID-19 in Thailand were investigated in this study for the 1 April – 23 July 2021 period by analyzing COVID-19 incidence’s spatial autocorrelation and clustering patterns in connection to population density, adult population, mean income, hospital beds, doctors and nurses. Clustering analysis indicated that Bangkok is a significant hotspot for incidence rates, whereas other cities across the region have been less affected. Bivariate Moran’s I showed a low relationship between COVID-19 incidences and the number of adults (Moran’s I = 0.1023- 0.1985), whereas a strong positive relationship was found between COVID-19 incidences and population density (Moran’s I = 0.2776-0.6022). Moreover, the difference Moran’s I value in each parameter demonstrated the transmission level of infectious COVID-19, particularly in the Early (first phase) and Spreading stages (second and third phases). Spatial association in the early stage of the COVID-19 outbreak in Thailand was measured in this study, which is described as a spatio-temporal pattern. The results showed that all of the models indicate a significant positive spatial association of COVID-19 infections from around 10 April 2021. To avoid an exponential spread over Thailand, it was important to detect the spatial spread in the early stages. Finally, these findings could be used to create monitoring tools and policy prevention planning in future.

scholarly journals Stage 1 registered report: spatiotemporal patterns of the COVID-19 epidemic in Mexico at the municipality level

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10622
Author(s):  
Jean-François Mas
Keyword(s):  
Spatial Autocorrelation ◽  
Temporal Patterns ◽  
Spatiotemporal Patterns ◽  
Spatial Scan ◽  
Spatio Temporal ◽  
Stage 1 ◽  
Over Time

In this stage 1 registered report, we propose an analysis of the spatio-temporal patterns of the COVID-19 epidemic in Mexico using the georeferenced confirmed cases aggregated at the municipality level. We will compute weekly Moran index to assess spatial autocorrelation over time and identify clusters of the disease using the “flexibly shaped spatial scan” approach. Finally, different distance models will be compared to select the best suited to predict inter-municipality contagion. This study will help us understand the spread of the epidemic over the Mexican territory and give insights to model and predict the epidemic behavior.

scholarly journals Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

2020 ◽  
Author(s):  
Simon Kigozi ◽  
Ruth N Kigozi ◽  
Catherine M Sebuguzi ◽  
Jorge Cano ◽  
Damian Rutazaana ◽  
...  
Keyword(s):  
High Risk ◽  
Incidence Rate ◽  
Health Facility ◽  
Malaria Incidence ◽  
Temporal Patterns ◽  
Health Facilities ◽  
Moran’S I ◽  
Moran's I ◽  
Catchment Population ◽  
Targeted Interventions

Abstract Background. As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda over a recent 5-year period.Methods. Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019 was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index.Results. An estimated 38.8 million (95% Credible Interval [CI]: 37.9 – 40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9 - 21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7 – 9.4) to 36.6 (95% CI: 35.7 – 38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0 – 50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran’s I = 0.3 (p<0.001) and districts Moran’s I = 0.4 (p<0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central – Busoga regions.Conclusion. Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

scholarly journals Moran’s I quantifies spatio-temporal pattern formation in neural imaging data

2017 ◽  
Vol 33 (19) ◽  
pp. 3072-3079 ◽  
Author(s):  
Christoph Schmal ◽  
Jihwan Myung ◽  
Hanspeter Herzel ◽  
Grigory Bordyugov
Keyword(s):  
Pattern Formation ◽  
Temporal Pattern ◽  
Moran’S I ◽  
Imaging Data ◽  
Moran's I ◽  
Neural Imaging ◽  
Spatio Temporal

scholarly journals Spatial distribution of coffee crop productivity in Minas Gerais over time

2020 ◽  
Vol 12 ◽  
pp. 1-10
Author(s):  
Renan Serenini ◽  
Patrícia de Siqueira Ramos ◽  
Lincoln Frias
Keyword(s):  
Spatial Distribution ◽  
Spatial Dependence ◽  
Crop Productivity ◽  
Minas Gerais ◽  
The State ◽  
Moran’S I ◽  
Coffee Production ◽  
Moran's I ◽  
High Productivity ◽  
Over Time

Brazil is the world's largest coffee producer and the state of Minas Gerais is responsible for half of the Brazilian production. However, productivity is unevenly distributed throughout the state. Therefore, the purpose of this study is to analyze the spatial distribution of coffee productivity in Minas Gerais from 2002 to 2017, a valuable information to identify regions where coffee production may be more promising in the future. This paper investigates the existence of spatial dependence of productivity between regions (using Moran's I), its dynamics throughout the period and the presence of clusters of high and low productivity (using local Moran's I). The results show that the spatial dependence of productivity was stronger from 2002 to 2009 than between 2010 and 2017. Some regions with small coffee areas but high productivity have stopped producing the crop whereas some of those with large areas but low productivity increased their productivity levels. Therefore, there is a tendency of homogenization of productivity in Minas Gerais, with values close to 30 bags per hectare.

Sign in / Sign up

Export Citation Format

Share Document