Association between Climatic Conditions, Population Density and COVID-19 in Indonesia

Azizah, R.; Martini, Santi; Sulistyorini, Lilis; Mahmudah, Pawitra; Nagari, S. S.; Fitrahanjani, Cendana; Ramadhani, Fitriana; Latif, Muhammad; Budijanto, Didik

doi:10.17576/jsm-2021-5003-28

Cited by 3 publications

(2 citation statements)

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“…We can conclude that the higher the percentage of the high-risk population in an area, the higher the percentage of deaths due to COVID-19 will increase. However, the percentage of the population at high risk is not necessarily associated with an increase in cumulative incidence A study in Indonesia states that a higher population density is associated with a higher number of COVID-19 cases (R. Azizah et al, 2021). One study said that countries with high density would have higher Covid-19 cases, too (Sy et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

What We Learned From The Three Waves of The Covid-19 Pandemic In Indonesia

Dewi,

Wulandari

2023

J. Sci. n.a

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Background: The COVID-19 pandemic, which has hit the entire world, has so far experienced three peaks of rising cases, and Indonesia is no exception. However, no research discusses the factors that play a role in the three peaks of this wave, especially in Indonesia. Objective: To find out which of the factors affect the increase in the cumulative incidence and rate of deaths due to COVID-19 in the first, second and third waves in Indonesia. Methods: This research is analytic observational, using secondary data. The data is processed using crosstabulation. Results and Conclusion: Mask use compliance factor, regardless of the correct use of masks, did not have much impact on reducing the cumulative incidence and the percentage of deaths due to COVID-19 in all three waves. Likewise, the increase in social distancing compliance factors, regardless of mobility restrictions and population density, did not affect the decrease in cumulative incidence and the percentage of deaths due to COVID-19. The percentage of the population at high risk was shown to be positively correlated with the percentage of deaths but not with the cumulative incidence. While the population density factor has a relationship with the cumulative incidence and percentage of deaths due to COVID-19 in almost all waves

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Section: Resultsmentioning

confidence: 99%

What We Learned From The Three Waves of The Covid-19 Pandemic In Indonesia

Dewi,

Wulandari

2023

J. Sci. n.a

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“…Even the latest research on Coronavirus Disease 2019 (COVID-19) still uses these data from the BPS or the Ministry of Health. Wirawan et al [5] and Azizah et al [16] looked for the correlation of population density with the number of COVID-19 cases. This is the condition of large-scale medical research in Indonesia lately.…”

Section: Previous Workmentioning

confidence: 99%

Settlement Mapping for Population Density Modelling in Disease Risk Spatial Analysis

Mutasodirin

Rizqullah

Setiyoko

et al. 2021

2021 6th International Workshop on Big Data and Information Security (IWBIS)

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In disease risk spatial analysis, many researchers especially in Indonesia are still modelling population density as the ratio of total population to administrative area extent. This model oversimplifies the problem, because it covers large uninhabited areas, while the model should focus on inhabited areas. This study uses settlement mapping against satellite imagery to focus the model and calculate settlement area extent. As far as our search goes, we did not find any specific studies comparing the use of settlement mapping with administrative area to model population density in computing its correlation to a disease case rate. This study investigates the comparison of both models using data on Tuberculosis (TB) case rate in Central and East Java Indonesia. Our study shows that using administrative area density the Spearman's ρ was considered as "Fair" (0.566, p < 0.01) and using settlement density was "Moderately Strong" (0.673, p < 0.01). The difference is significant according to Hotelling's t test. By this result we are encouraging researchers to use settlement mapping to improve population density modelling in disease risk spatial analysis. Resources used by and resulting from this work are publicly available at https://github.com/mirzaalimm/PopulationDensityVsDisease.

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Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis

Zheng

Zhang

Shi

et al. 2022

Big Data Min. Anal.

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The unprecedented coronavirus disease 2019 (COVID-19) pandemic is still raging (in year 2021) in many countries worldwide. Various response strategies to study the characteristics and distributions of the virus in various regions of the world have been developed to assist in the prevention and control of this epidemic. Descriptive statistics and regression analysis on COVID-19 data from different countries were conducted in this study to compare and evaluate various regression models. Results showed that the extreme random forest regression (ERFR) model had the best performance, and factors such as population density, ozone, median age, life expectancy, and Human Development Index (HDI) were relatively influential on the spread and diffusion of COVID-19 in the ERFR model.In addition, the epidemic clustering characteristics were analyzed through the spectral clustering algorithm. The visualization results of spectral clustering showed that the geographical distribution of global COVID-19 pandemic spread formation was highly clustered, and its clustering characteristics and influencing factors also exhibited some consistency in distribution. This study aims to deepen the understanding of the international community regarding the global COVID-19 pandemic to develop measures for countries worldwide to mitigate potential large-scale outbreaks and improve the ability to respond to such public health emergencies.

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Association between Climatic Conditions, Population Density and COVID-19 in Indonesia

Cited by 3 publications

References 0 publications

What We Learned From The Three Waves of The Covid-19 Pandemic In Indonesia

What We Learned From The Three Waves of The Covid-19 Pandemic In Indonesia

Settlement Mapping for Population Density Modelling in Disease Risk Spatial Analysis

Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis

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