The Impact of Mass Exodus on the Resurgence of COVID-19 Cases: Case Study of Regions in Indonesia

Aini, Wirdatul; Sukandar, Kamal Khairudin; Nuraini, Nuning; Handayani, Dewi

doi:10.3389/fams.2022.912150

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…This event typically occurs after the fasting month of Ramadan. Similar to China’s Lunar New Year migration, Mudik is recognized as a high-risk activity [ 85 , 86 ]. In an effort to curb the spread of SARS-CoV-2, the Indonesian government prohibited this traditional practice in both 2020 and 2021 [ 87 ].…”

Section: Background and Key Issues Of The Covid-19 Pandemic In Indonesiamentioning

confidence: 99%

From Archipelago to Pandemic Battleground: Unveiling Indonesia’s COVID-19 Crisis

Harapan,

Theodora

et al. 2023

J Epidemiol Glob Health

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The coronavirus disease 2019 (COVID-19) pandemic has posed unprecedented challenges to countries worldwide, including Indonesia. With its unique archipelagic geography consisting of more than 17,000 thousand islands, Indonesia faces unique complexities in managing the spread of the virus. Based on existing literature, this review article elaborates on key issues that have shaped Indonesia’s COVID-19 response. The article begins by examining the early stages of the COVID-19 pandemic in Indonesia, along with the implementation of various preventive measures and the impact of the virus on public health. This article examines how Indonesia’s socio-economic factors have generally influenced its healthcare system and further delves into the COVID-19 response strategies implemented by the Indonesian government and public health authorities as well as overall crisis preparedness. It discusses the actions taken to control the spread of the virus, including testing strategies and vaccination efforts. The difficulties encountered in implementing these measures are presented. In conclusion, this review article provides a comprehensive understanding of the COVID-19 crisis in Indonesia, covering facts on multiple dimensions ranging from the timeline of the pandemic to vaccination efforts, epidemiology, socio-economic implications, testing strategies, mobility patterns, public holidays, the impact of working from home, and the utilization of complementary and alternative medicine in addition to the standard of care for COVID-19. The insights gained from this article can complement future strategies for pandemic management and response in Indonesia and other countries facing similar challenges.

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Section: Background and Key Issues Of The Covid-19 Pandemic In Indonesiamentioning

confidence: 99%

From Archipelago to Pandemic Battleground: Unveiling Indonesia’s COVID-19 Crisis

Harapan,

Theodora

et al. 2023

J Epidemiol Glob Health

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“…Some studies model the spread of infection in both temporal and spatial aspects. For example, the spread of COVID-19 had been studied through temporal model 15 , 16 which incorporated human mobility 17 , limited testing capacity 18 and provide information for public policy making 19 – 21 . The spatio-temporal aspect in the spread of COVID-19 had also been modeled for cases in South America 22 , Bangladesh 23 , Iran 24 , Italy 25 , and Singapore 26 .…”

Section: Introductionmentioning

confidence: 99%

The epidemic forest reveals the spatial pattern of the spread of acute respiratory infections in Jakarta, Indonesia

Nasution,

Sitorus,

Sukandar

et al. 2024

Sci Rep

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Acute respiratory infection (ARI) is a communicable disease of the respiratory tract that implies impaired breathing. The infection can expand from one to the neighboring areas at a region-scale level through a human mobility network. Specific to this study, we leverage a record of ARI incidences in four periods of outbreaks for 42 regions in Jakarta to study its spatio-temporal spread using the concept of the epidemic forest. This framework generates a forest-like graph representing an explicit spread of disease that takes the onset time, spatio-temporal distance, and case prevalence into account. To support this framework, we use logistic curves to infer the onset time of the outbreak for each region. The result shows that regions with earlier onset dates tend to have a higher burden of cases, leading to the idea that the culprits of the disease spread are those with a high load of cases. To justify this, we generate the epidemic forest for the four periods of ARI outbreaks and identify the implied dominant trees (that with the most children cases). We find that the primary infected city of the dominant tree has a relatively higher burden of cases than other trees. In addition, we can investigate the timely ($$R_t$$ R t ) and spatial reproduction number ($$R_c$$ R c ) by directly evaluating them from the inferred graphs. We find that $$R_t$$ R t for dominant trees are significantly higher than non-dominant trees across all periods, with regions in western Jakarta tend to have higher values of $$R_c$$ R c . Lastly, we provide simulated-implied graphs by suppressing 50% load of cases of the primary infected city in the dominant tree that results in a reduced $$R_c$$ R c , suggesting a potential target of intervention to depress the overall ARI spread.

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“…As a case study, we applied the constructed models to the SARS-CoV-2 spread data that were collected in a university environment (Institut Teknologi Bandung College) in January 2022. The choice to use data from a university was made to ensure homogeneous socio-behavioral aspects for the whole society; no demographic is taken into account due to the homogeneity assumption [ 25 , 26 , 27 ]. The small scale of a university environment also ensures the involvement of pathogens in the air; the larger the scale of the observation, the smaller the effect of pathogens in the air.…”

Section: Introductionmentioning

confidence: 99%

Infectious Disease Modeling with Socio-Viral Behavioral Aspects—Lessons Learned from the Spread of SARS-CoV-2 in a University

et al. 2022

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When it comes to understanding the spread of COVID-19, recent studies have shown that pathogens can be transmitted in two ways: direct contact and airborne pathogens. While the former is strongly related to the distancing behavior of people in society, the latter are associated with the length of the period in which the airborne pathogens remain active. Considering those facts, we constructed a compartmental model with a time-dependent transmission rate that incorporates the two sources of infection. This paper provides an analytical and numerical study of the model that validates trivial insights related to disease spread in a responsive society. As a case study, we applied the model to the COVID-19 spread data from a university environment, namely, the Institut Teknologi Bandung, Indonesia, during its early reopening stage, with a constant number of students. The results show a significant fit between the rendered model and the recorded cases of infections. The extrapolated trajectories indicate the resurgence of cases as students’ interaction distance approaches its natural level. The assessment of several strategies is undertaken in this study in order to assist with the school reopening process.

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The Impact of Mass Exodus on the Resurgence of COVID-19 Cases: Case Study of Regions in Indonesia

Cited by 3 publications

References 19 publications

From Archipelago to Pandemic Battleground: Unveiling Indonesia’s COVID-19 Crisis

From Archipelago to Pandemic Battleground: Unveiling Indonesia’s COVID-19 Crisis

The epidemic forest reveals the spatial pattern of the spread of acute respiratory infections in Jakarta, Indonesia

Infectious Disease Modeling with Socio-Viral Behavioral Aspects—Lessons Learned from the Spread of SARS-CoV-2 in a University

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