Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models

Feroze, Navid

doi:10.1016/j.idm.2021.01.005

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…The BSTS model consists of three main components: the Kalman filter, spike-and-slab method, and Bayesian model averaging [20] , and it is well suited to discover causations with its counterfactual prediction and the observed data [20][21][22] . Based on the observed weekly number of NID cases during 2015-2019, we used the BSTS model to estimate the weekly counts of cases in weeks 4-53 of 2020 by accounting for seasonality and long-term trends, and the lower and upper 95% credible intervals (95% CI) were estimated.…”

Section: Discussionmentioning

confidence: 99%

Co-benefits of nonpharmaceutical intervention against COVID-19 on infectious diseases in China: A large population-based observational study

Xiao

Dai

et al. 2021

The Lancet Regional Health - Western Pacific

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Background : Nonpharmaceutical interventions (NPIs) are public health measures that aim to suppress the transmission of infectious diseases, including border restrictions, quarantine and isolation, community management, social distancing, face mask usage, and personal hygiene. This research aimed to assess the co-benefits of NPIs against COVID-19 on notifiable infectious diseases (NIDs) in Guangdong Province, China.Methods: Based on NID data from the Notifiable Infectious Diseases Surveillance System in Guangdong, we first compared the incidence of NIDs during the emergency response period (weeks 4-53 of 2020) with those in the same period of 2015-2019 and then compared that with the expected incidence during the synchronous period of 2020 for each city by using a Bayesian structural time series model. Findings: A total of 514,341 cases of 39 types of NIDs were reported in Guangdong during the emergency response period in 2020, which decreased by 50 •7% compared with the synchronous period during 2015-2019. It was estimated that the number of 39 NIDs during the emergency response in 2020 was 65 •6% (95% credible interval [CI]: 64 •0% -68 •2%) lower than expected, which means that 982,356 (95% CI: 913,443 -1,105,170) cases were averted. The largest reduction (82 •1%) was found for children aged 0-14 years. For different categories of NIDs, natural focal diseases and insect-borne infectious diseases had the greatest reduction (89 •4%), followed by respiratory infectious diseases (87 •4%), intestinal infectious diseases (59 •4%), and blood-borne and sexually transmitted infections (18 •2%). Dengue, influenza, and hand-foot-and-mouth disease were reduced by 99 •3%, 95 •1%, and 76 •2%, respectively. Larger reductions were found in the regions with developed economies and a higher number of COVID-19 cases.Interpretation: NPIs against COVID-19 may have a large co-benefit on the prevention of other infectious diseases in Guangdong, China, and the effects have heterogeneity in populations, diseases, time and space.

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Section: Discussionmentioning

confidence: 99%

Co-benefits of nonpharmaceutical intervention against COVID-19 on infectious diseases in China: A large population-based observational study

Xiao

Dai

et al. 2021

The Lancet Regional Health - Western Pacific

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“…3) Model diagnosis employs the ACF, PACF, and Ljung-Box Q test to assess the residual error of the model and determine if it is a random sequence (9). Navid Feroze (10)has described this method.…”

Section: Methodsmentioning

confidence: 99%

“…The forecast generated by the BSTS model is based on prior information and the likelihood function, which are combined to produce a posterior distribution (11). A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the posterior distribution, and the sampling results are then averaged to obtain the nal prediction (10,13). In contrast, ARIMA models typically predict based on past patterns of disease and previous prediction residuals (14).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model

Li,

et al. 2024

Preprint

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Background There may be evidence that COVID-19 affects illness patterns. This study aimed to estimate epidemiological trends in China and to assess the effects of COVID-19 epidemic on the declines in hepatitis B (HB) case notifications. Methods The Bayesian structured time series (BSTS) method was used to investigate the causal effect of COVID-19 on the decline in HB cases based on the monthly incidence of HB from January 2013 to September 2022. To assess how well the BSTS algorithm performs predictions, we split the observations into various training and testing ranges. Results The incidence of HB in Henan was generally declining with periodicity and seasonality. The seasonal index in September and February was the smallest (0.91 and 0.93), and that in March was the largest (1.19). Due to the COVID-19 pandemic, the monthly average number of notifications of HB cases decreased by 38% (95% credible intervals [CI]: -44% ~ -31%) from January to March 2020, by 24% (95% CI: -29% ~ -17%) from January to June 2020, by 15% (95% CI: -19% ~ -9.2%) from January to December 2020, by 11% (95% CI: -15% ~ -6.7%) from January 2020 to June 2021, and by 11% (95% CI: -15% ~ -7.3%) from January 2020 to December 2021. From January 2020 to September 2022, it decreased by 12% (95% CI: -16% ~ -8.1%). From 2021 to 2022, the impact of COVID-19 on HB was attenuated. In both training and test sets, the average absolute percentage error (10.03%) generated by the BSTS model was smaller than that generated by the ARIMA model (14.4%). It was also found that the average absolute error, root mean square error, and root mean square percentage error generated by the BSTS model were smaller than ones generated by the ARIMA model. The trend of HB cases in Henan from October 2022 to December 2023 predicted by the BSTS model remained stable, with a total number of 81,650 cases (95% CI: 47,372 ~ 115,391). Conclusions After COVID-19 intervention, the incidence of HB in Henan decreased and exhibited clear seasonal and cyclical trends. The BSTS model outperformed the ARIMA model in predicting the HB incidence trend in Henan. This information may serve as a reference and provide technical assistance for developing strategies and actions to prevent and control HB. Take additional measures to accelerate the progress of eliminating HB.

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“…The model accounted for the exclusion of confounding effects arising from long-term trends and seasonal variations. The BSTS model consists of three main components: the Kalman lter, spike-and-slab method, and Bayesian model averaging [10] , and it is well suited for nding causal relationships [10][11][12] . The equations of BSTS are as follows: 1 2 Equation 1 is the observation equation, while Eq.…”

Section: Statisticsmentioning

confidence: 99%

Multilayer factors associated with excess all-cause mortality during the Omicron and non-Omicron waves of the COVID-19 pandemic: time series analysis in 29 countries

Zou,

Xiao,

Jin

et al. 2023

Preprint

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Background The COVID-19 pandemic has resulted in significant excess mortality globally. However, the differences in excess mortality between the Omicron and non-Omicron waves, as well as the contribution of local epidemiological characteristics, population immunity, and social factors to excess mortality, remain poorly understood. This study aims to solve the above problems. Methods Weekly all-cause death data and covariates from 29 countries for the period 2015–2022 were collected and used. The Bayesian Structured Time Series Model predicted expected weekly deaths, stratified by gender and age groups for the period 2020–2022. The quantile-based g-computation approach accounted for the effects of factors on the excess all-cause mortality rate. Sensitivity analyses were conducted using alternative Omicron proportion thresholds. Results From the first week of 2021 to the 30th week of 2022, the estimated cumulative number of excess deaths due to COVID-19 globally was nearly 1.39 million. The estimated weekly excess all-cause mortality rate in the 29 countries was approximately 2.17 per 100000 (95% CI: 1.47 to 2.86). Weekly excess mortality rates were significantly higher in both male and female groups and all age groups during the non-Omicron wave, except for those younger than 15 years (P < 0.001).Sensitivity analysis confirmed the stability of the results. Positive associations with excess mortality were found for the constituent ratio of non-Omicron in all variants, new cases per million, positive rate, cardiovascular death rate, people fully vaccinated per hundred, extreme poverty, hospital patients per million humans, people vaccinated per hundred, and stringency index. Conversely, other factors demonstrated negative associations with excess mortality from the first week of 2021 to the 30th week of 2022. Conclusion The COVID-19 Omicron wave exhibited fewer excess deaths than the non-Omicron wave. Weekly excess mortality rates were significantly higher in both male and female groups and all age groups during the non-Omicron wave, except for those younger than 15 years. Factors such as the constituent ratio of non-Omicron in all variants, new cases per million, positive rate, cardiovascular death rate, people fully vaccinated per hundred, extreme poverty and some social factors were positively associated with excess mortality, while other factors showed negative associations.

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Assessing the future progression of COVID-19 in Iran and its neighbors using Bayesian models

Cited by 8 publications

References 22 publications

Co-benefits of nonpharmaceutical intervention against COVID-19 on infectious diseases in China: A large population-based observational study

Co-benefits of nonpharmaceutical intervention against COVID-19 on infectious diseases in China: A large population-based observational study

Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model

Multilayer factors associated with excess all-cause mortality during the Omicron and non-Omicron waves of the COVID-19 pandemic: time series analysis in 29 countries

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