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

Nuraini, Nuning; Sukandar, Kamal Khairudin; Tahu, Maria Yulita Trida; Giri-Rachman, Ernawati Arifin; Barlian, Anggraini; Suhardi, Sri Harjati; Pasaribu, Udjianna S.; Yuliar, Sonny; Mudhakir, Diky; Ariesyady, Herto Dwi; Rosleine, Dian; Sofyan, Iyan; Martokusumo, Widjaja

doi:10.3390/tropicalmed7100289

Cited by 2 publications

(3 citation statements)

References 43 publications

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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

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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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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

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our knowledge has expanded on the pathogenesis and treatment of COVID-19 and experience gained within different countries, and this is reflected in the enormous number of scientific papers generated, including those in this Special Issue. There have been 24 papers published upon peer review acceptance in this Special Issue, including 17 research papers [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], 2 review papers [ 20 , 21 ], 1 opinion piece [ 22 ], 1 commentary [ 23 ], and 3 systematic reviews [ 24 , 25 , 26 ]. Each paper in this Special Issue contributes to our understanding of COVID-19.…”

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confidence: 99%

“…The model was applied 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 [ 6 ]. The fifth of these analyzed the COVID-19 contact tracing dataset from 15 July to 31 December 2021 using multiple logistic regression analyses, considering exposure details, demographics, and vaccination history.…”

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confidence: 99%