Early Spatiotemporal Patterns and Population Characteristics of the COVID-19 Pandemic in Southeast Asia

Zhu, Mingjian; Jirapat, Kleepbua; Guan, Zhou; Chew, Sien Ping; Tan, Joanna Weihui; Shen, Jian; Latthitham, Natthjija; Hu, Jianxiong; Law, Jia Xian; Li, Lanjuan

doi:10.3390/healthcare9091220

Cited by 10 publications

(9 citation statements)

References 45 publications

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“…For example, on the site of Johns Hopkins University dedicated to COVID-19 [ 1 ], data clearly shows this influence on the case fatality rate corresponding to the cumulative deaths recorded 5 months after the beginning of the outbreak (12 May 2020) vs. the median age of many countries in 2017 ( Figure 1 ). This first observation has been confirmed by many studies in different countries [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ] such as in France ( Figure 2 ), and we will confirm in this paper that the age of the patients suffering from COVID-19 is a good predictor of severity.…”

Section: Introductionsupporting

confidence: 85%

Age Dependent Epidemic Modeling of COVID-19 Outbreak in Kuwait, France, and Cameroon

et al. 2022

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Revisiting the classical model by Ross and Kermack-McKendrick, the Susceptible–Infectious–Recovered (SIR) model used to formalize the COVID-19 epidemic, requires improvements which will be the subject of this article. The heterogeneity in the age of the populations concerned leads to considering models in age groups with specific susceptibilities, which makes the prediction problem more difficult. Basically, there are three age groups of interest which are, respectively, 0–19 years, 20–64 years, and >64 years, but in this article, we only consider two (20–64 years and >64 years) age groups because the group 0–19 years is widely seen as being less infected by the virus since this age group had a low infection rate throughout the pandemic era of this study, especially the countries under consideration. In this article, we proposed a new mathematical age-dependent (Susceptible–Infectious–Goneanewsusceptible–Recovered (SIGR)) model for the COVID-19 outbreak and performed some mathematical analyses by showing the positivity, boundedness, stability, existence, and uniqueness of the solution. We performed numerical simulations of the model with parameters from Kuwait, France, and Cameroon. We discuss the role of these different parameters used in the model; namely, vaccination on the epidemic dynamics. We open a new perspective of improving an age-dependent model and its application to observed data and parameters.

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Section: Introductionsupporting

confidence: 85%

Age Dependent Epidemic Modeling of COVID-19 Outbreak in Kuwait, France, and Cameroon

et al. 2022

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“…Therefore, the government can prepare mitigation measures for handling Covid-19 by emphasising cumulative cases and death rate conditions over provinces in Indonesia. Furthermore, in terms of VLN, the proportion of age group seems associated with the number of positive and death cases, which are also demonstrated by Zhu et al (2021). By the time the study report was written, the COVID-19 cases were predominantly caused by variant B.1.466.2, B.1.470, B.1.1.398 and B.1.459 (CNN Indonesia, 2021).…”

Section: Discussionmentioning

confidence: 92%

A Year of Spatio-Temporal Clusters of COVID-19 in Indonesia

Jumadi

Fikriyah

Hadibasyir

et al. 2022

Quaestiones Geographicae

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Coronavirus disease-2019 (COVID-19) in Indonesia began to appear on March 2, 2020 and led to a number of fatalities. Spatial analysis is important to study the spatio-temporal trend of COVID-19 cases and fatalities to get a better understanding of the spread as well as to mitigate it. However, such a comprehensive study at national level is not to be seen in Indonesia with limited health infrastructure. This study aims to analyse the spatio-temporal distribution and clusters of COVID-19 in Indonesia for a year period. COVID-19 cases, as well as the fatalities as a consequence of this disease, were collected from the government through publicly shared data. A geographic information system (GIS) was used to manage and analyse the data on demographics, cases, and fatalities. The case fatality rate (CFR) was produced based on the number of cases and deaths per province weekly. The spatio-temporal data of both cases and fatalities were generated from the data. Finally, K-means clustering was employed to classify the cluster of Indonesia based on the proportion of vulnerable age groups, cases, and CFR. The results show that most of the provinces in Indonesia are affected by COVID-19, but the fatalities are not distributed evenly throughout the country. Based on the K-means clustering, two provinces are classified as moderate, namely the Province of East Kalimantan and North Kalimantan. The Province of Jakarta is classified as high, because the vulnerable age group there is highly correlated with the number of cases and deaths.

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“…Previous studies using space-time scan for COVID-19 discovered that Hubei province has higher risk than the other region in China (42) whereas a Korean study found higher risk in Daegu City than neighboring province (43). A retrospective spatial scan study in Southeast Asia during January 13, 2020 and March 16, 2020 further confirmed that Malaysia and Singapore were the most likely cluster between March 4-March 16, 2020 (RR = 72.07, LLR = 1910.08, p < 0.001) (44). The most likely cluster for the period 2 (January 25 to October 8, 2020) consisted of Negeri Sembilan, Putrajaya, Kuala Lumpur, and Selangor (10 districts) in this study correlates with a state-level spatial scan analysis study in Malaysia (45).…”

Section: Discussionmentioning

confidence: 96%

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

Cheong

Ghazali

Ibrahim

et al. 2022

Front. Public Health

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IntroductionThe unprecedented COVID-19 pandemic has greatly affected human health and socioeconomic backgrounds. This study examined the spatiotemporal spread pattern of the COVID-19 pandemic in Malaysia from the index case to 291,774 cases in 13 months, emphasizing on the spatial autocorrelation of the high-risk cluster events and the spatial scan clustering pattern of transmission.MethodologyWe obtained the confirmed cases and deaths of COVID-19 in Malaysia from the official GitHub repository of Malaysia's Ministry of Health from January 25, 2020 to February 24, 2021, 1 day before the national vaccination program was initiated. All analyses were based on the daily cumulated cases, which are derived from the sum of retrospective 7 days and the current day for smoothing purposes. We examined the daily global, local spatial autocorrelation and scan statistics of COVID-19 cases at district level using Moran's I and SaTScan™.ResultsAt the initial stage of the outbreak, Moran's I index > 0.5 (p < 0.05) was observed. Local Moran's I depicted the high-high cluster risk expanded from west to east of Malaysia. The cases surged exponentially after September 2020, with the high-high cluster in Sabah, from Kinabatangan on September 1 (cumulative cases = 9,354; Moran's I = 0.34; p < 0.05), to 11 districts on October 19 (cumulative cases = 21,363, Moran's I = 0.52, p < 0.05). The most likely cluster identified from space-time scanning was centered in Jasin, Melaka (RR = 11.93; p < 0.001) which encompassed 36 districts with a radius of 178.8 km, from November 24, 2020 to February 24, 2021, followed by the Sabah cluster.Discussion and ConclusionBoth analyses complemented each other in depicting underlying spatiotemporal clustering risk, giving detailed space-time spread information at district level. This daily analysis could be valuable insight into real-time reporting of transmission intensity, and alert for the public to avoid visiting the high-risk areas during the pandemic. The spatiotemporal transmission risk pattern could be used to monitor the spread of the pandemic.

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Early Spatiotemporal Patterns and Population Characteristics of the COVID-19 Pandemic in Southeast Asia

Cited by 10 publications

References 45 publications

Age Dependent Epidemic Modeling of COVID-19 Outbreak in Kuwait, France, and Cameroon

Age Dependent Epidemic Modeling of COVID-19 Outbreak in Kuwait, France, and Cameroon

A Year of Spatio-Temporal Clusters of COVID-19 in Indonesia

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

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