Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung, Indonesia

Jaya, I Gede Nyoman Mindra; Folmer, Henk

doi:10.1007/s10109-019-00311-4

Cited by 47 publications

(92 citation statements)

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“…For this reason, a modified method called Moran’s spatiotemporal autocorrelation statistic (MoransST) was proposed by Anderson and Ryan [ 69 ] to detect spatiotemporal patterns. Jaya et al in two recent studies used MoransST to explore spatiotemporal clusters of Dengue disease in Indonesia [ 24 , 31 ]. However, this methodology needs heavy mathematical calculations.…”

Section: Discussionmentioning

confidence: 99%

Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018

et al. 2021

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Background Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018. Methods This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran’s I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05. Results The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19–13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65–11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010–2014 and 2017–2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found. Conclusion The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008–2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

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Section: Discussionmentioning

confidence: 99%

Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018

et al. 2021

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“… 2 Work by Jaya and Folmer ( 2020 ) or O’Hara and Kotze ( 2010 ), among several others, make use of spatial statistical approaches as a means to model the spread of diseases. Spatial statistical frameworks (see, for example, Blangiardo and Cameletti 2015 ) typically treat spatial spillover processes rather differently than spatial econometric approaches.…”

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confidence: 99%

The spatial econometrics of the coronavirus pandemic

Krisztin

Piribauer

Wögerer

2020

Lett Spat Resour Sci

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In this paper we use spatial econometric specifications to model daily infection rates of COVID-19 across countries. Using recent advances in Bayesian spatial econometric techniques, we particularly focus on the time-dependent importance of alternative spatial linkage structures such as the number of flight connections, relationships in international trade, and common borders. The flexible model setup allows to study the intensity and type of spatial spillover structures over time. Our results show notable spatial spillover mechanisms in the early stages of the virus with international flight linkages as the main transmission channel. In later stages, our model shows a sharp drop in the intensity spatial spillovers due to national travel bans, indicating that travel restrictions led to a reduction of crosscountry spillovers.

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“…Linnenluecke et al, 2011), and the spread of diseases (e.g. Jaya et al, 2017and Jaya and Folmer 2020a, 2020b).…”

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confidence: 99%

Spatial economic aspects of climate change

Batabyal

Folmer

2020

Spatial Economic Analysis

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Our objective in this special issue is twofold. First, we emphasize the importance of comprehending that the global impacts of climate change notwithstanding, there are salient regionspecific impacts that vary across space. Second, given this observation, we show how rigorous modeling of the connections between climate change and (i) land use changes, (ii) forestry, (iii) infrastructure, and (iv) local labor markets sheds light on a variety of climate change induced spatial economic effects. Following this introductory paper, there are seven additional papers in this special issue. Each of these papers discusses a particular research question at the interface of what we call "climate change and space."

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Bayesian spatiotemporal mapping of relative dengue disease risk in Bandung, Indonesia

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Cited by 47 publications

References 63 publications

Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018

Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018

The spatial econometrics of the coronavirus pandemic

Spatial economic aspects of climate change

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