Spatial mapping of temporal risk to improve prevention measures: A case study of dengue epidemic in Lahore

Hafeez, Sidrah; Amin, Mochamad; Munir, B.A.

doi:10.1016/j.sste.2017.04.001

Cited by 17 publications

(17 citation statements)

References 11 publications

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“…It is of great significance for the scientific prevention and control of an epidemic. Geographic and environmental risk identification of infectious diseases has drawn great attention from various fields of researchers [8,27,31,41,[50][51][52]. Spatial statistics analysis has been used as a tool to identify geographic and environmental risks of epidemics such as SARS 2003 [25,27] and COVID-19 [30].…”

Section: Geographic Risk Identification Based On the Spatial Statistimentioning

confidence: 99%

Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

Xiong

Wang

Chen

et al. 2020

IJERPH

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The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people’s lives and socio-economic development. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman’s rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspots and cluster/outlier areas were observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation (p < 0.01) with the registered population (RGP), resident population (RSP), Baidu migration index (BMI), regional gross domestic production (GDP), and total retail sales of consumer goods (TRS), respectively, from 29 January to 18 February 2020 and had a negative and significant correlation (p < 0.05) with minimum elevation (MINE) from 2 February to 18 February 2020, but no association with the land area (LA), population density (PD), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) from 23 January to 18 February 2020. (4) At the county level, the number of CCC in Hubei province had a positive and extremely significant correlation (p < 0.01) with PD, RGP, RSP, GDP, and TRS, respectively, from 27 January to 18 February 2020, and was negatively associated with MINE, MAXE, MNE, and RAE, respectively, from 26 January to 18 February 2020, and negatively associated with LA from 30 January to 18 February 2020. It suggested that (1) the COVID-19 epidemics at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers.

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Section: Geographic Risk Identification Based On the Spatial Statistimentioning

confidence: 99%

Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

Xiong

Wang

Chen

et al. 2020

IJERPH

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“…The progression of an epidemic can be characterized and compared through measures of burden, intensity and transmission rate [13,14]. We can quantitatively differentiate between epidemics with sharp peaks in case incidence that quickly burn through the susceptible population and more sustained, low-level transmission [6,15,16].…”

Section: Introductionmentioning

confidence: 99%

Climate drives spatial variation in Zika epidemics in Latin America

2019

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Between 2015 and 2017, Zika virus spread rapidly through populations in the Americas with no prior exposure to the disease. Although climate is a known determinant of many Aedes -transmitted diseases, it is currently unclear whether climate was a major driver of the Zika epidemic and how climate might have differentially impacted outbreak intensity across locations within Latin America. Here, we estimated force of infection for Zika over time and across provinces in Latin America using a time-varying susceptible–infectious–recovered model. Climate factors explained less than 5% of the variation in weekly transmission intensity in a spatio-temporal model of force of infection by province over time, suggesting that week to week transmission within provinces may be too stochastic to predict. By contrast, climate and population factors were highly predictive of spatial variation in the presence and intensity of Zika transmission among provinces, with pseudo- R 2 values between 0.33 and 0.60. Temperature, temperature range, rainfall and population size were the most important predictors of where Zika transmission occurred, while rainfall, relative humidity and a nonlinear effect of temperature were the best predictors of Zika intensity and burden. Surprisingly, force of infection was greatest in locations with temperatures near 24°C, much lower than previous estimates from mechanistic models, potentially suggesting that existing vector control programmes and/or prior exposure to other mosquito-borne diseases may have limited transmission in locations most suitable for Aedes aegypti , the main vector of Zika, dengue and chikungunya viruses in Latin America.

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“…Various studies have investigated the geographical distribution of dengue outbreaks in countries where the disease is endemic. (Chang et al, 2009;Anno et al, 2015;Rotela et al, 2017;Hafeez et al, 2017). A few recent studies have also been conducted in Malaysia to evaluate the spatial pattern of the disease (Er et al, 2010;Hassan et al, 2012;Ling et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Geographical distribution and spatio-temporal patterns of hospitalization due to dengue infection at a leading specialist hospital in Malaysia

et al. 2018

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Increasing numbers of dengue infection worldwide have led to a rise in deaths due to complications caused by this disease. We present here a cross-sectional study of dengue patients who attended the Emergency and Trauma Department of Ampang Hospital, one of Malaysia's leading specialist hospitals. The objective was to search for potential clustering of severe dengue, in space and/or time, among the annual admissions with the secondary objective to describe the spatio-temporal pattern of all dengue cases admitted to this hospital. The dengue status of the patients was confirmed serologically with the geographic location of the patients determined by residency, but not more specific than the street level. A total of 1165 dengue patients were included in the analysis using SaTScan software. The mean age of these patients was 27.8 years, with a standard deviation of 14.2 years and an age range from 1 to 77 years, among whom 54 (4.6%) were cases of severe dengue. A cluster of general dengue cases was identified occurring from October to December in the study year of 2015 but the inclusion of severe dengue in that cluster was not statistically significant (P=0.862). The standardized incidence ratio was 1.51. General presence of dengue cases was, however, detected to be concentrated at the end of the year, which should be useful for hospital planning and management if this pattern holds.

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Spatial mapping of temporal risk to improve prevention measures: A case study of dengue epidemic in Lahore

Cited by 17 publications

References 11 publications

Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

Spatial Statistics and Influencing Factors of the COVID-19 Epidemic at Both Prefecture and County Levels in Hubei Province, China

Climate drives spatial variation in Zika epidemics in Latin America

Geographical distribution and spatio-temporal patterns of hospitalization due to dengue infection at a leading specialist hospital in Malaysia

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