Assessing the Spatiotemporal Spread Pattern of the COVID-19 Pandemic in Malaysia

GIS-based assessment of spatial and temporal disparities of urban health index in Shenzhen, China

Purpose

To explore the inter-regional health index at the city level to contribute to the reduction of health inequalities.

Methods

Employed the health determinant model to select indicators for the urban health index of Shenzhen City. Utilized principal component analysis, the weights of these indicators are determined to construct the said health index. Subsequently, the global Moran's index and local Moran's index are utilized to investigate the geographical spatial distribution of the urban health index across various administrative districts within Shenzhen.

Results

The level of urban health index in Shenzhen exhibits spatial clustering and demonstrates a positive spatial correlation (2017, Moran's I = 0.237; 2019, Moran's I = 0.226; 2021, Moran's I = 0.217). However, it is noted that this clustering displays a relatively low probability (90% confidence interval). Over the period from 2017 to 2019, this spatial clustering gradually diminishes, suggesting a narrowing of health inequality within economically developed urban areas.

Conclusion

Our study reveals the urban health index in a relatively high-income (Shenzhen) in a developing country. Certain spatially correlated areas in Shenzhen present opportunities for the government to address health disparities through regional connectivity.

Spatio-temporal clustering analysis of COVID-19 cases in Johor

At the end of the year 2019, a virus named SARS-CoV-2 induced the coronavirus disease, which is very contagious and quickly spread around the world. This new infectious disease is called COVID-19. Numerous areas, such as the economy, social services, education, and healthcare system, have suffered grave consequences from the invasion of this deadly virus. Thus, a thorough understanding of the spread of COVID-19 is required in order to deal with this outbreak before it becomes an infectious disaster. In this research, the daily reported COVID-19 cases in 92 sub-districts in Johor state, Malaysia, as well as the population size associated to each sub-district, are used to study the propagation of COVID-19 disease across space and time in Johor. The time frame of this research is about 190 days, which started from August 5, 2021, until February 10, 2022. The clustering technique known as spatio-temporal clustering, which considers the spatio-temporal metric was adapted to determine the hot-spot areas of the COVID-19 disease in Johor at the sub-district level. The results indicated that COVID-19 disease does spike in the dynamic populated sub-districts such as the state's economic centre (Bandar Johor Bahru), and during the festive season. These findings empirically prove that the transmission rate of COVID-19 is directly proportional to human mobility and the presence of holidays. On the other hand, the result of this study will help the authority in charge in stopping and preventing COVID-19 from spreading and become worsen at the national level.

The transmission network and spatial-temporal distributions of COVID-19: A case study in Lanzhou, China

Public emergencies exert substantial adverse effects on the socioeconomic development of cities. Investigating the transmission characteristics of COVID-19 can lead to evidence-based strategies for future pandemic intervention and prevention. Drawing upon primary COVID-19 data collected at both the street level and from individuals with confirmed cases in Lanzhou, China, our study examined the spatial-temporal distribution of the pandemic at a detailed level. First, we constructed transmission networks based on social relationships and spatial behavior to elucidate the actual natural transmission chain of COVID-19. We then analyze key information regarding pandemic spread, such as superspreaders, superspreading places, and peak hours. Furthermore, we constructed a space-time path model to deduce the spatial transmission trajectory of the pandemic while validating it with real activity trajectory data from confirmed cases. Finally, we investigate the impacts of pandemic prevention and control policies. The progression of the pandemic exhibits distinct stages and spatial clustering characteristics. People with complex social relationships and daily life trajectories and places with high pedestrian flow and commercial activity venues are prone to becoming superspreaders and superspreading places. The transmission path of the pandemic showed a pattern of short-distance and adjacent transmission, with most areas not affected. Early-stage control measures effectively disrupt transmission hotspots and impede the spatiotemporal trajectory of pandemic propagation, thereby enhancing the efficacy of prevention and control efforts. These findings elucidate the characteristics and transmission processes underlying pandemics, facilitating targeted and adaptable policy formulation to shape sustainable and resilient cities.

Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia

This research studies the evolution of COVID-19 crude incident rates, effective reproduction number R(t) and their relationship with incidence spatial autocorrelation patterns in the 19 months following the disease outbreak in Catalonia (Spain). A cross-sectional ecological panel design based on n = 371 health-care geographical units is used. Five general outbreaks are described, systematically preceded by generalized values of R(t) > 1 in the two previous weeks. No clear regularities concerning possible initial focus appear when comparing waves. As for autocorrelation, we identify a wave's baseline pattern in which global Moran's I increases rapidly in the first weeks of the outbreak to descend later. However, some waves significantly depart from the baseline. In the simulations, both baseline pattern and departures can be reproduced when measures aimed at reducing mobility and virus transmissibility are introduced. Spatial autocorrelation is inherently contingent on the outbreak phase and is also substantially modified by external interventions affecting human behavior.

Potential Epidemic Vulnerability and Socioepidemiological Profile of SARS-CoV2 in the Brazilian Northeast Region

Background: COVID-19 is a significant public health problem that can have a negative impact, especially in vulnerable regions. Objective: This study aimed to provide evidence that could positively influence coping with COVID-19 based on the relationship between the potential epidemic vulnerability index (PEVI) and socioepidemiological variables. This could be used as a decision-making tool for the planning of preventive initiatives in regions with relevant vulnerability indices for the spread of SARS-CoV-2. Methodology: We performed a cross-sectional study, with the analysis of the population characteristics of COVID-19 cases associated with neighborhoods’ PEVIs in the conurbation region of Crajubar, northeastern Brazil, through the mapping of socioeconomic–demographic factors and spatial autocorrelation. Results: The PEVI distribution indicated low vulnerability in areas with high real estate and commercial value; as communities moved away from these areas, the vulnerability levels increased. As for the number of cases, three of the five neighborhoods with a high–high autocorrelation, and some other neighborhoods showed a bivariate spatial correlation with a low–low PEVI but also high–low with indicators that make up the PEVI, representing areas that could be protected by public health measures to prevent increases in COVID-19 cases. Conclusions: The impact of the PEVI revealed areas that could be targeted by public policies to decrease the occurrence of COVID-19.