Evaluation of risk factors associated with SARS-CoV-2 transmission

Luu, Mai; Alhady, Shamael Thabit Mohammed; Tran, Minh Duc Nguyen; Trường, Lê Văn; Qarawi, Ahmad Taysir Atieh; Venkatesh, U; Tiwari, Ranjit; Rocha, Ian Christopher N.; Minh, Le Huu Nhat; Ravikulan, R.; Dumre, Shyam Prakash; Giang, Hoang Thi Nam; Pavlenko, Dmytro; Ali, Fatmaelzahraa Yasser; Le, Bao-Tran Do; Karimzadeh, Sedighe; Bhandari, Parshal; Shah, Jaffer; Aziz, Jeza Muhamad Abdul; Huy, Nguyen Tien

doi:10.1080/03007995.2022.2125258

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…On the other hand, a series of social distancing interventions, including school closure, work-from-home-policy, and cancellation of mass gatherings, were implemented in Hong Kong [18], which may have a greater effect on reducing the potential of societal SSEs [19] and thus resulting in a relatively higher k. It was also concluded in [10] that rare superspreading events in community resulted from infectors from hospitals, healthcare facilities or schools, whereas some cases in hospitals, healthcare facilities or schools were caused by the transmission chains originated from community superspreading events, which may lead to a low dispersion parameter in the distribution of offspring from communities. The selection of threshold of superspreading events also vacillates the assessment of superspreading potential [20], as we defined the threshold of SSE for the COVID-19 as the 99-th percentile of the Poisson distribution of the basic reproduction number (R 0 ). Meanwhile, the super-aged society in Japan [10] can also be deemed as the underlying cause of the estimates in each setting.…”

Section: Discussionmentioning

confidence: 99%

Differences in the superspreading potentials of COVID-19 across contact settings

Zhao

Guo

et al. 2022

BMC Infect Dis

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Background Superspreading events (SSEs) played a critical role in fueling the COVID-19 outbreaks. Although it is well-known that COVID-19 epidemics exhibited substantial superspreading potential, little is known about the risk of observing SSEs in different contact settings. In this study, we aimed to assess the potential of superspreading in different contact settings in Japan. Method Transmission cluster data from Japan was collected between January and July 2020. Infector-infectee transmission pairs were constructed based on the contact tracing history. We fitted the data to negative binomial models to estimate the effective reproduction number (R) and dispersion parameter (k). Other epidemiological issues relating to the superspreading potential were also calculated. Results The overall estimated R and k are 0.561 (95% CrI: 0.496, 0.640) and 0.221 (95% CrI: 0.186, 0.262), respectively. The transmission in community, healthcare facilities and school manifest relatively higher superspreading potentials, compared to other contact settings. We inferred that 13.14% (95% CrI: 11.55%, 14.87%) of the most infectious cases generated 80% of the total transmission events. The probabilities of observing superspreading events for entire population and community, household, health care facilities, school, workplace contact settings are 1.75% (95% CrI: 1.57%, 1.99%), 0.49% (95% CrI: 0.22%, 1.18%), 0.07% (95% CrI: 0.06%, 0.08%), 0.67% (95% CrI: 0.31%, 1.21%), 0.33% (95% CrI: 0.13%, 0.94%), 0.32% (95% CrI: 0.21%, 0.60%), respectively. Conclusion The different potentials of superspreading in contact settings highlighted the need to continuously monitoring the transmissibility accompanied with the dispersion parameter, to timely identify high risk settings favoring the occurrence of SSEs.

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Section: Discussionmentioning

confidence: 99%

Differences in the superspreading potentials of COVID-19 across contact settings

Zhao

Guo

et al. 2022

BMC Infect Dis

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“…Madewell et al previously reported that (1) the household secondary infection rate in COVID-19 was 16.6%, which was higher than that of SARS-CoV-1, which was 7.5% and (2) secondary household transmission of COVID-19 from a spouse was about twice as common as transmissions from other family contacts [ 14 ]. Luu et al previously investigated the risk factors for SARS-CoV-2 transmission using a survey conducted across 66 countries and frequent close contact with colleagues, the habit of hugging when greeting is reported to be a risk for SARS-CoV-2 transmission [ 15 ]. Furthermore, studies have reported that close contact with symptomatic COVID-19 patients has a higher rate of secondary infection than with asymptomatic COVID-19 patients [ 2 , 14 ].…”

Section: Discussionmentioning

confidence: 99%

Simple Symptom-Based Prediction of COVID-19: A Single-Center Study of Outpatient Fever Clinic in Japan

Inaba¹,

Nakao²,

Ikeda³

et al. 2023

Cureus

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Introduction: Coronavirus disease 2019 (COVID-19) symptoms are not fully understood in non-hospitalized individuals in Japan, and COVID-19 differentiation by symptoms alone remained challenging. Therefore, this study aimed to examine COVID-19 prediction from symptoms using real-world data in an outpatient fever clinic.Methods: We compared the symptoms of COVID-19-positive and negative patients who visited the outpatient fever clinic at Imabari City Medical Association General Hospital and tested for COVID-19 from April 2021 to May 2022. This retrospective single-center study enrolled 2,693 consecutive patients.Results: COVID-19-positive patients had a higher frequency of close contact with COVID-19-infected patients compared with COVID-19-negative patients. Moreover, patients with COVID-19 had high-grade fever at the clinic compared with patients without COVID-19. Additionally, the most common symptom in patients with COVID-19 was sore throat (67.3%), followed by cough (62.0%), which was approximately twice as common in patients without COVID-19. COVID-19 was more frequently identified in patients having a fever (≥37.5℃) with a sore throat, a cough, or both. The positive COVID-19 rate reached approximately half (45%) when three symptoms were present.Conclusion: These results suggested that COVID-19 prediction by combinations of simple symptoms and close contact with COVID-19-infected patients might be useful and lead to recommendations for testing of COVID-19 in symptomatic individuals.

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“…where RSS is the residual sum of squares of the model. The stepwise AIC algorithm has been implemented in financial, medical, and epidemiological applications [55][56][57].…”

Section: Akaike Information Criterion (Aic)mentioning

confidence: 99%

Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

Untadi

et al. 2023

Axioms

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Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model’s forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applied to census and fire incident data from the South East Queensland region of Australia. A cross-validation procedure is used to assess the model’s accuracy, and comparative analyses are conducted using other elimination criteria such as p-value, Akaike’s information criterion (AIC), Bayesian information criterion (BIC), and predicted residual error sum of squares (PRESS). The results demonstrate that the RFPE criterion is a more accurate predictive model based on several goodness-of-fit measures. Overall, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modeling of building fires.

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Evaluation of risk factors associated with SARS-CoV-2 transmission

Cited by 9 publications

References 30 publications

Differences in the superspreading potentials of COVID-19 across contact settings

Differences in the superspreading potentials of COVID-19 across contact settings

Simple Symptom-Based Prediction of COVID-19: A Single-Center Study of Outpatient Fever Clinic in Japan

Modeling Socioeconomic Determinants of Building Fires through Backward Elimination by Robust Final Prediction Error Criterion

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